Personalization Personalized marketing, also known as one-to-one marketing or individual marketing, involves tailoring marketing efforts to meet the specific needs, preferences, and behaviors of individual customers. Unlike traditional marketing approaches that target broad demographics or segments, personalized marketing treats each customer as a unique entity and seeks to create personalized experiences for them. This may involve collecting and analyzing data on individual customers to understand their purchase history, preferences, and interactions with the brand, and then using this information to deliver highly targeted and relevant marketing messages, offers, and recommendations. Personalized marketing aims to enhance customer satisfaction, and retention by providing customers with personalized experiences that align with their individual needs and preferences. From “one-size-fits-all” to “one-to-one” approach Personalization is not a new concept at all. In fact, historically, it was the standard. Before mass production and the industrial revolution, goods were often tailor-made to suit the individual needs and preferences of each customer. Craftsmen, like tailors, shoemakers, and furniture makers, created unique, personalized products.  It was only with the advent of mass production techniques that standardized products became the norm, sacrificing personalization for efficiency and scale. From the industrial revolution until some decades ago, mass production was the focus of manufacturing and management. Main advantage is efficiency, through scale and repetition. It was very helpful to down costs and prices and make products affordable for more and more consumers1. This is related to production orientation and leads to produce and market a single or a few versions of the product, also called “one-size-fits-all” approach.  As markets evolved and competition increased, companies changed their focus to improve product quality and to increase selling and promotion efforts. All this reflects a productcentered first-make-then-sell or inside-out philosophy2. Companies focusing on product quality are said to suffer “marketing myopia” or to follow a product orientation. Companies focusing on selling efforts are said to follow a selling orientation.  The realization that consumers do not always want cheaper or higher quality products, and that bombarding consumers with promotions and advertising may ineffective and inefficient, 1“Ford’s innovation lay in his use of mass production to manufacture automobiles. He revolutionized industrial work by perfecting the assembly line, which enabled him to lower the Model T’s price from $850 in 1908 to $300 in 1924, making car ownership a real possibility for a large share of the population”. According to (1920s Consumption (Article) | 1920s America, s. f.). Henry Ford apparently also said “Every time I reduce the charge for our car by one dollar, I get a thousand new buyers,” 2(Kotler & Armstrong, 2017, p. 36) 4 Apellidos del autor/Título del libro made companies to evolve. It is more intelligent to offer consumers products that fit their preferences. The idea was to start by understanding the needs and wants of consumers and to create and market products that deliver satisfaction to them. Since consumers are heterogeneous a single product will not satisfy the needs of all the consumers equally. Then, companies must divide the market into homogeneous groups of consumers (segments) and offer each group a different product or version of the product.  In a continuous effort to adapt offerings to consumers preferences, it is natural to increase the granularity of these offerings. This increase in granularity has a limit: more granular marketing is more costly, since creating and marketing a higher number of product versions and communications imply higher costs than a small number of them.  In the last decades, favored by digitalization, this cost has decreased dramatically. Now, with advanced technology, we're seeing a resurgence of personalized goods and services, blending the historical norm with modern capabilities. Personalization in marketing refers to the practice of tailoring marketing content, offers, and experiences to individual consumers based on their preferences, behaviors, and previous interactions with the brand. This approach aims to make the marketing effort more relevant and effective by addressing the specific needs, interests, or desires of each customer. Here are some key aspects of personalization in marketing: 1. Data-Driven Insights: Personalization relies heavily on data collected from various sources such as customer interactions, purchase history, online behavior, and demographic information. This data helps in understanding individual preferences and predicting future behaviors. 2. Targeted Communication: By leveraging data insights, marketers can create customized messages that resonate with each individual. This could be through personalized emails, tailored website content, or specific product recommendations. 3. Improved Customer Experience: Personalization aims to enhance the customer experience by making interactions with the brand more relevant and engaging. This can lead to increased customer satisfaction, loyalty, and retention. 4. Increased Efficiency: Personalized marketing can be more efficient than broadspectrum campaigns because it targets individuals who are more likely to respond positively, thus optimizing marketing resources and potentially increasing return on investment (ROI). 5. Use of Technology: Advanced technologies like artificial intelligence (AI), machine learning, and automation tools are often used to implement personalization at a scale. These technologies can analyze large sets of data quickly to identify patterns and automate personalized interactions. Overall, personalization in marketing is about moving away from a "one-size-fits-all" approach to a more segmented and individualized or “one-to-one” strategy, enhancing both the effectiveness of marketing campaigns and the overall customer journey (Peppers & Rogers, 1993). Personalization is a continous clycle that includes (1) learning consumer 5 Apellidos del autor/Título del libro preferences, (2) adapting offerings to consumers, and (3) evaluating the effectiveness of the personalization3 Personalization and mass customization Personalization and mass customization are not exactly the same. Personalization involves tailoring the content, products, services, or experiences to individual customers based on data collected about their preferences, behaviors, and past interactions. Mass customization, on the other hand, refers to the process of producing goods or services that allows customers to select specific options within a standard framework to create a product that meets their personal preferences. Personalization ir more data-driven, more focused on marketing communications and consumer experience. Mass customization is more focused on design, flexible manufacturing, and product, and requires more consumer involvement to select features. An example of personalization is an online retailer using customer's previous browsing and purchasing data to show them a personalized set of products on the homepage during their next visit. An example of mass customization is a car manufacturer offering customers the option to choose various features like color, interior materials, and additional technology packages when ordering a car. Figure 1 shows graphically that who is the most active part in the purchase process (the firm or the consumer) distinguishes personalization and mass customization4 Figure 1. Personalization and mass customization However, personalization and mass customization share several common goals and characteristics that aim to enhance the customer experience and meet individual preferences more effectively. Here are the key commonalities between these two approaches: 1. Customer-Centric Approach, Enhanced Perceived Value and Customer Satisfaction 3(Wedel and Kannan 2016) 4(Arora et al., 2008) 6 Apellidos del autor/Título del libro By focusing on individual customer needs and preferences, both personalization and mass customization strategies not only increase the perceived value of products and services but also significantly enhance customer satisfaction. This targeted approach makes customers feel uniquely valued and more connected to the brand, which can lead to greater customer loyalty and retention. When customers see that a product or service is tailored specifically for them, whether through personalized marketing efforts or customizable product options, they tend to perceive a higher value in these offerings. This enhanced perception can support premium pricing strategies and reinforce the brand's image. Ultimately, this synergy between increased perceived value and customer satisfaction is crucial for standing out in competitive markets and fostering long-term customer relationships. 2. Utilization of Technology and Data-Driven Decision Making Both personalization and mass customization strategies rely heavily on advanced technology and data-driven decision making to cater effectively to individual customer preferences. Personalization utilizes data analytics, machine learning, and artificial intelligence to analyze customer behaviors and tailor marketing efforts accordingly. Mass customization, on the other hand, employs technologies such as modular design and flexible manufacturing systems, enabling the efficient production of customized products. The success of these strategies is based on the collection and analysis of extensive customer data. For personalization, understanding customer interactions, preferences, browsing history, and purchase history is critical to dynamically adjust content and offers. In mass customization, detailed feedback and preferences related to product features and design choices are essential for determining the customization options offered. This integration of technology and data not only enhances the capability to deliver personalized experiences and products but also enables businesses to continuously improve their offerings based on realtime feedback and evolving customer needs. 3. Dynamic Interaction Both approaches require an ongoing dialogue with the customer. Personalization involves dynamically adjusting content and offers based on continual learning from customer interactions, while mass customization involves interacting with customers to specify their product preferences during the buying process. 4. Competitive Differentiation Both personalization and mass customization serve as key differentiators in competitive markets. They allow companies to stand out by offering something unique to the customer, which is not merely a standard product or service but something that has been tailored to the customer's individual needs or desires. 5. Scalability Challenges Implementing both strategies at a scale presents challenges. For personalization, scaling up requires sophisticated technology systems to manage vast amounts of data and deliver realtime customization. For mass customization, scaling up often involves rethinking traditional production processes to accommodate variability without incurring excessive costs. 7 Apellidos del autor/Título del libro By focusing on individual needs and leveraging technology to meet these needs efficiently, both personalization and mass customization represent a shift towards more adaptable, customer-focused business practices. While their applications and methods may differ, their core philosophy is the same: enhance the customer experience by acknowledging and catering to individual preferences. Personalization and digital markets The relationship between personalization and digital markets is symbiotic, with each concept enhancing and complementing the other. Here's how they are interconnected: • Data-driven Personalization: Digital markets provide vast amounts of data through online interactions, transactions, and behaviors. This data fuels personalized marketing efforts by providing insights into individual preferences, interests, and purchase histories. Digital platforms enable the collection, analysis, and utilization of this data to deliver tailored experiences to customers. • Dynamic Content Delivery: Digital channels such as websites, email, social media, and mobile apps allow for the dynamic delivery of personalized content to users. Through techniques such as dynamic website content, personalized email campaigns, and targeted social media ads, businesses can customize their messaging and offerings to match the interests and preferences of individual customers. • Automation and Scale: Digital technologies enable marketers to automate personalized marketing efforts at scale. Marketing automation platforms allow businesses to set up workflows and triggers based on customer behaviors, ensuring timely and relevant communications with minimal manual intervention. This scalability enables businesses to deliver personalized experiences to a large audience efficiently. Overall, personalization and digital markets are closely intertwined, with digital technologies providing the infrastructure and capabilities necessary to implement personalized marketing strategies effectively. As digital markets continue to evolve, businesses that prioritize personalization will be better positioned to meet the expectations of today's digitally savvy consumers and drive success in the digital era5. 5See this video The Economist (Director). (2021, marzo 18). The future of shopping: What’s in store? https://www.youtube.com/watch?v=ad-GuV6YIMI  (The Economist, 2021) 8 Apellidos del autor/Título del libro Example: how Spotify personalizes customer experience Spotify utilizes a variety of techniques to personalize targeting and service for its users. Here are some key strategies: 1. User Data Analysis: Spotify collects extensive data on user preferences, listening habits, and interactions with the platform. This includes data on genres, artists, playlists, listening history, skip rates, and device usage. By analyzing this data, Spotify gains insights into individual user preferences and behavior patterns. 2. Personalized Recommendations: Spotify uses sophisticated algorithms and machine learning techniques to generate personalized recommendations for each user. These recommendations appear in various forms, including personalized playlists, as well as suggested songs, albums, and artists tailored to the user's tastes. 3. Customized Playlists and Mixes: Spotify allows users to create their own playlists and mixes based on their preferences. Users can also follow curated playlists created by Spotify's editorial team or other users.  4. Targeted Advertising: Spotify leverages its user data to deliver targeted advertising to listeners. Advertisers can target users based on factors such as age, gender, location, music preferences, and listening behavior. This allows advertisers to reach specific audience segments with relevant messages and offers. 5. Localized Content: Spotify offers localized content and recommendations tailored to the user's geographical location and language preferences. This includes localized playlists, charts, and recommendations featuring popular music from the user's region. 6. Personalized User Experience: Spotify personalizes the user experience across its platform, including the mobile app, desktop app, and web player. This may include personalized homepages, recommendations on the browse page, and customized search results based on the user's preferences and listening history. Overall, Spotify's personalized targeting and service strategies enhance the user experience by delivering relevant content, recommendations, and advertising tailored to each individual listener's tastes and preferences. This personalization helps increase user engagement, retention, and satisfaction on the platform. Spotify also targets free users with the goal of converting them into premium subscribers. Here's how Spotify achieves this: 1. Limited Features: Free users have access to Spotify's vast music library but with certain limitations, such as ad interruptions, limited skips, and no offline listening. Spotify strategically showcases the benefits of premium features, such as ad-free listening, unlimited skips, and offline mode, to encourage free users to upgrade to a premium subscription. 9 Apellidos del autor/Título del libro 2. Promotional Offers: Spotify periodically offers promotional discounts and trials for its premium subscription to free users. These offers are often targeted based on user behavior and preferences, such as offering a discounted subscription to users who frequently listen to specific genres or artists. 3. Email Marketing: Spotify utilizes email marketing campaigns to communicate with free users and promote the benefits of premium subscription. These emails may include personalized recommendations, special offers, and success stories from premium subscribers to encourage free users to upgrade. 4. In-App Messaging: Spotify employs in-app messaging and notifications to educate free users about the advantages of premium subscription and to prompt them to upgrade. These messages may appear when a free user reaches their skip limit or encounters ads during playback. 5. Targeted Advertising: Spotify leverages its ad-supported model to deliver targeted advertising messages to free users. These ads may promote the benefits of premium subscription, highlight exclusive features, or offer limited-time discounts to encourage conversion. 6. Social Proof: Spotify showcases social proof by highlighting the benefits and popularity of premium subscription among existing subscribers. This includes featuring testimonials, reviews, and success stories from premium users to build credibility and trust with free users. By personalizing targeting efforts through these strategies, Spotify aims to convert free users into premium subscribers by demonstrating the value proposition of the premium subscription and providing incentives for users to upgrade. Real time personalization: adaptive personalization  Adaptive personalization involves dynamic, real-time personalization. A prime example of this is Real-time Bidding (RTB) Auctions on Ad-Exchange Platforms, where online display ad impressions are bought and sold. This process includes tracking consumers’ browsing behavior across websites, encompassing purchases, searches, and clickstream data. During these auctions, ads compete based on various criteria, and a winning ad is selected. These auctions are fully automated and completed in less than one-tenth of a second. Once an ad wins, it is instantly displayed on the publisher’s site. The response of consumers to these ads is recorded and used to inform strategies for subsequent auctions. 10 Apellidos del autor/Título del libro How websites track customer on the web and collect data Websites use two main tools to track customers on the web: cookies and pixels. Here's a breakdown of how they work: Cookies Cookies are small data files that a website stores on your device (computer, phone, etc.). They act like little reminder tags that help the website remember your activity. Cookies can store information like what pages you visited, items you looked at, and even things you added to your shopping cart. This information is usually anonymous, meaning it's tied to your device, not you personally. However, it can be used to build a profile of your browsing habits. Cookies can be very useful for both you and the website. For example, cookies can keep you logged in to websites, remember your preferences, and even personalize your browsing experience. Figure 2 and Figure 3 show examples of how websites ask a new user to set the cookies. Figure 3 also shows how websites exchange information for price: if the user allows cookies, she can access the website content for free. Otherwise, the user must pay to access the content. Also, under the European GDPR (General Data Protection Regulation) the user has the right to know who can access the information tracked by the cookies.  Pixels Pixels (also called tracking pixels or web beacons) are tiny, invisible images embedded in a website or email. When you visit a webpage with a pixel, your device tries to load the image, which sends a signal back to the company that placed the pixel. This signal can include information like the fact that you visited the webpage, along with the date and time of your visit. Pixels are often used to track the effectiveness of marketing campaigns. For instance, a company might place a pixel on a social media ad. If you click on the ad and visit the company's website, the pixel will fire, letting the company know that their ad campaign is working. Here's a table summarizing the key differences between cookies and pixels: Feature Cookies Pixels What it is Small data file stored on your device Tiny, invisible image embedded in a webpage or email 11 Apellidos del autor/Título del libro How it works What information is collected Usefulness for websites Stores information about your browsing activity Typically browsing history and preferences Personalizes browsing experience, remembers preferences Sends a signal back to a company's server when loaded Usually just confirms a visit and basic details Tracks marketing campaigns, measures ad effectiveness Both cookies and pixels can raise privacy concerns. Users may not be aware that they're being tracked, and some may feel uncomfortable with companies collecting data about their browsing habits. It's important to note that regulations like GDPR (General Data Protection Regulation) require websites to be transparent about their use of cookies and pixels. Websites should also give users options to control or opt-out of tracking. How apps track customers and collect data Tracking customers on websites and apps involves similar principles but may differ in implementation and capabilities due to the nature of the platforms. Here are some key differences: 1. Access to Device Features: Apps have access to device-specific features such as GPS, camera, microphone, and contacts, which websites typically do not. This allows apps to gather more diverse and detailed data, including location-based information, photos, and audio recordings. 2. Persistent Presence: Apps remain installed on a user's device, providing a persistent presence and the ability to track user activity even when offline. Websites, on the other hand, require an active internet connection and do not have a persistent presence on the user's device. 3. User Identification: Apps often require users to create accounts or sign in, allowing for more accurate and persistent user identification. Websites may use cookies or session identifiers to track users, but these methods are less reliable and may be affected by browser settings or privacy measures. 4. Push Notifications: Apps can send push notifications to users, enabling direct engagement and re-engagement opportunities. Websites typically do not have this capability and rely on email or other channels for communication. 5. App Store Regulations: Apps must adhere to guidelines and regulations set by app stores (e.g., Apple App Store, Google Play Store), which may impose restrictions on tracking methods and data collection practices. Websites are not subject to the same app store regulations. 12 Apellidos del autor/Título del libro 6. App SDKs: Apps often integrate third-party software development kits (SDKs) for analytics, advertising, and other purposes. These SDKs provide more comprehensive tracking capabilities compared to website analytics tools and may offer additional features tailored to mobile platforms. Overall, while both websites and apps can track customer behavior and preferences, apps have certain advantages such as access to device features, persistent presence, and direct engagement capabilities. However, both platforms must adhere to privacy regulations and obtain user consent for tracking activities. Figure 2. Cookies setting 13 Apellidos del autor/Título del libro Figure 3. Cookies setting permitting to access free content if allowing cookies. The image also shows the number of partners with whom the website shares the cookies information  14 Apellidos del autor/Título del libro Economics of personalization Figure 4 shows a graphical representation of the economics of personalization. The demand function for seats in a flight is Q=390−0,1p, where Q is quantity and p is price, indicating that as price increases, demand decreases6. There is a variable cost of €100 per seat. What is the price that maximizes contribution, where contribution is (p−variable cost)×Q? The first image outlines the case of uniform pricing. It shows the demand function Q at different prices. If the airline has chosen the optimal uniform price of $2000 for each seat7, this results in selling 190 seats (the intersection of price with the demand curve). The area of the rectangle represents the total contribution to profit (revenue minus variable costs), which is $361.000. This image also discusses the lost contribution that results from uniform pricing. It points out two things: • "Passed-up contribution": Potential customers exist who are willing to pay more than the $100 variable cost but less than $2000. • "Money left on the table": Some customers who paid $2000 were actually willing to pay more. The second image introduces price discrimination with two prices. The airline recognizes that there are two types of customers in the market with different willingness to pay (WTP): Segment A (leisure travelers) with a lower WTP and Segment B (business travelers) with a higher WTP. The airline then offers economy class seats for leisure travelers at price pL, and first-class seats for business travelers at price pH. By charging two different prices, the airline increases its total contribution to $41.330, which is 34% higher than the $361.000 from uniform pricing8.  The third image shows an increase in the granularity, closer to what would be personalizing prices to each consumer. As granularity increases, contribution also increases.  Overall, these graphs teach a fundamental lesson in personalization by price discrimination: By tailoring prices to the willingness to pay of different customer segments, a business can 6Example taken from (Kopalle et al., 2023) 7Contribution=p(390−0.1p)−100(390−0.1p). To find the price that maximizes the contribution (which is equal to profit in this scenario because there are only variable costs), we'll use the derivative of the profit function and set it to zero. d(contribution)/dp=400−0,2p=0  implies that p=$2.000. Now, using this price, we find the demand Q=390−0,1=390−0,1×2 000=190. Finally, we calculate the contribution at this price Contribution=p(390−0.1p)−100(390−0.1p)= $361.000. 8For simplification, we assume the same demand function for both types of customers, and the same variable cost for both types of seats. To find the prices that maximize contribution in the case of two prices, is like maximizing the area of the two rectangles in the figure. Contribution =(pH −100)sH + (pL −100)(sL- sH), where sH =390−0,1pH  and sL=390−0,1pL. Taking the derivative of the contribution function with respect pH and pL and setting each equal to zero we get 390–0,2pH+0,1pL = 0 and 0,1pH–0,2pL+10=0. Solving the two equations simultaneously we get pH≈$2633,33 and pL≈$1366,67. At these prices the demand for economy and first-class seats is sL=sH=127, and the total contribution $481.330 15 Apellidos del autor/Título del libro significantly increase its total revenue and profits compared to a single uniform pricing strategy. Figure 4. Graphical representation of the economics of personalization. Source: (Kopalle et al., 2023) 16 Apellidos del autor/Título del libro The perils of personalization A recent survey revealed significant privacy concerns among consumers, with over 75% feeling uncomfortable about the amount of information online advertisers have about them. Despite these concerns, many consumers still take minimal measures to protect their personal data. This is known as the privacy paradox. The privacy paradox occurs when individuals express concerns about online privacy but still engage in behaviors that compromise it. This happens due to trade-offs between convenience and privacy, lack of awareness about data practices, complex privacy policies, social norms and peer pressure, and trust in technology. Personalization, despite its many benefits, has attracted significant criticism on various fronts. Here’s a summary of the main critiques: 1. Privacy Concerns: • Personalization often requires the collection, analysis, and storage of vast amounts of personal data. Critics argue that this infringes on individual privacy, as personal details about habits, preferences, and behaviors are continuously tracked and analyzed, sometimes without clear consent or awareness of the individual. 2. Data Security Risks: • With the accumulation of personal data comes the increased risk of data breaches. Sensitive information can be exposed or misused if not properly secured, leading to potential harm such as identity theft or financial fraud. 3. Manipulation and Exploitation: • Personalization can be used to manipulate consumer choices and exploit vulnerabilities. For example, dynamic pricing might charge individuals more based on their buying habits or current demand, while personalized marketing could exploit psychological weaknesses or biases to promote overconsumption or unhealthy behaviors. 4. Loss of Serendipity and Homogenization: • By filtering content and experiences to align closely with known preferences, personalization can limit exposure to new ideas or diverse perspectives. This can lead to a homogenization of experiences where individuals are not challenged or exposed to content that could broaden their horizons, potentially reinforcing echo chambers, especially in social media contexts. 5. Ethical and Social Justice Concerns: • There are ethical concerns about how personal data is used, who it benefits, and whether personalization processes might reinforce existing social inequalities. For example, personalized advertising might target vulnerable populations with predatory lending ads, or algorithmic biases could result in discriminatory practices in personalized pricing or services. 17 Apellidos del autor/Título del libro 6. Impact on Autonomy and Authenticity: • Personalization algorithms can shape individuals’ choices without them being fully aware, subtly directing their actions and decisions. This raises questions about the erosion of personal autonomy and the authenticity of choices made in highly personalized digital environments. 7. Economic Implications: • There is a concern that personalization can lead to market segmentation that may not always be beneficial for consumers. It might enable companies to extract maximum profits while offering less value, or create barriers to market entry for new competitors, thereby reducing overall market competition. 8. Regulatory Challenges: • The global nature of digital platforms and the varying degrees of personalization they offer pose significant challenges for regulation. Ensuring consistent and effective oversight to protect consumers across different jurisdictions is complex and often lagging behind technological advances. These critiques suggest that while personalization can enhance consumer experiences and operational efficiency, it also brings significant challenges that need careful management to avoid undermining individual rights, social equity, and consumer welfare. References Some references for personalization and privacy: (Chellappa & Sin, 2005; Frischmann & Desai, 2018; McKinsey, 2021b; Rust et al., 2002; SAS Customer Intelligence solutions, 2015) Some references for personalization: (Salesforce, 2024) 18 Apellidos del autor/Título del libro Customer Relationship Management Customer Relationship Management (CRM) is the process of managing information, even in real time, about individual customers and their detailed interactions with the company to maximize profits. CRM refers to both a business strategy and software technology designed to help businesses manage their interactions with current and potential customers. The goal of CRM is to improve customer relationships to grow the business. In the last decades, there has been a shift from product orientation to customer orientation. Similarly, there has been a transition from a transactional focus to a relationship focus. Consequently, customers are now perceived as intangible assets that generate cash flow in both present and future periods. However, there are some differences between companies that are truly customer-centric vs merely customer oriented. The first ones have the ability to track individual customers over time and seek to calculate forward-looking metrics at a granular level. These companies see products as “means to an end” that is to grow the value of their customers (new and current ones).  Table 1. Some changes in the last decades from transaction to relationship • Transaction → Learning relationship • Mass Marketing → Individual marketing • Transaction Value → Lifetime value • Conquest Marketing → Retention marketing • Product Life Cycle → Customer Life Cycle • Customer Satisfaction → Customer Loyalty • Share of the market → Share of the customer • Product differentiation → Customer differentiation 19 Apellidos del autor/Título del libro Figure 5. Share of the customer vs share of the market CRM (Customer Relationship Management) and personalization CRM serves as a central repository for customer data, enabling marketers to tailor their communications and offerings based on the detailed insights it provides. 1. Data Aggregation: CRM systems collect detailed customer data from various touchpoints, giving a holistic view of customer preferences and behaviors. 2. Segmentation: Marketers use CRM to segment customers into different groups based on demographics, purchase history, and behaviors, allowing for more targeted marketing. 3. Tailored Communication: With CRM data, marketers can customize their messages to address the individual needs and interests of each customer, increasing relevance and engagement. 4. Behavior Tracking: CRM tools can track customer interactions across channels, helping marketers understand the customer journey and identify opportunities for personalized engagements. 5. Predictive Analysis: Advanced CRMs use AI and machine learning to predict future customer behaviors, guiding personalized marketing strategies. In essence, CRM enables businesses to deliver the right message to the right customer at the right time. CRM as a business strategy 20 Apellidos del autor/Título del libro In CRM as a business strategy, each customer is viewed as an asset that has a value, contributing to a portfolio of assets comprised of customer segments. The value of this customer portfolio, also known as customer equity, is determined by summing the value of each customer. The objective is maximizing the customer equity, or value of all customers. This total number of customers includes both current and new customers. To increase the number of new customers, customer selection and acquisition strategies are implemented. Maintaining and growing the existing customer base relies on effective customer retention methods. Additionally, increasing the value of current customers involves implementing strategies for customer growth (Blattberg & Deighton, 1996; Kumar & Reinartz, 2012). The four classic activities of CRM The four classic activities supported by CRM (Customer Relationship Management) systems are customer selection, acquisition, retention, and growth. These foundational activities help businesses identify potential customers, effectively engage, and convert them, maintain their satisfaction and loyalty, and ultimately increase their value over time. Figure 6. The four classic marketing activities of customer relationship management. (Chaffey, Dave & Ellis-Chadwick, Fiona, 2022; Leeflang et al., 2015) Select customers: the right prospects for a marketing strategy or action. CRM distinguishes between leads, prospects, and customers, according to their stage in the sales funnel9.  • Lead: An individual or business showing initial interest in your product, like attending an event, subscribing to your email list, a new LinkedIn connection, a follower who engages with you on social media, or people who visited your 9The sales funnel is a visual representation of the customer journey from initial awareness of a product or service to the final purchase decision. It's called a "funnel" because it narrows at each stage, representing the decrease in the number of potential customers as they progress through its stages. 21 Apellidos del autor/Título del libro website. They have not been qualified yet as a potential customer because there is no further information about them. • Prospect: A lead may qualify as a prospect based on criteria such as need and budget, indicating a higher potential to purchase. • Customer: Someone who has actually purchased your product or service, completing the sales process. Effective sales and marketing strategies focus on generating a steady stream of leads, qualifying those leads into prospects, and then converting those prospects into customers. CRM identifies the most suitable prospects for a marketing strategy or campaign. The ideal prospect criteria are targeting prospects who offer maximum expected profitability, as a combination of response likelihood and expected customer value.  Acquire customers: use marketing tools to acquire the selected customers Once a lead has been qualified as a prospect, she has to be targeted to move her through the sales funnel to the purchase stage and convert her to a new customer. Successful customer acquisition not only attracts new customers but also lays the groundwork for long-term relationships. Decisions to take include:  • Budget to spend • Communication channel (mass media, personal selling, direct marketing – mail, internet, induced wom, etc.) and message • Incentive given to the first purchase (price promotional offer, free sample, freemium product, etc.) The amount of money spent on acquiring new customers can be very high. For example, an online broker may incur a variety of customer acquisition costs as they compete to attract new clients in a crowded and competitive market. During the period from March 2001 to March 2002, E*Trade, an online broker, allocated approximately $274,3 million towards marketing efforts to acquire new customers. These efforts were successful in attracting roughly 701.555 new customers. Consequently, the acquisition cost per customer can be estimated to be around $390.  Here are several types of acquisition costs it might spend: 1. Advertising: This includes costs for digital ads (like Google AdWords, social media ads), television, radio, print media, and outdoor advertising such as billboards. The average cost per click (CPC) for advertising on Google Ads and Facebook varies significantly depending on the industry and the specific keywords or target audience. In Google Ads, the average CPC across various industries is about $2.96, but this can range widely, from as low as $0.58 for Google Shopping ads to over $4.22 for more competitive keywords10. In Facebook Ads, the average CPC is approximately $0.83, with variations across different industries and types of ads. Some reports indicate it 10 Kraus, E., & Emnace, H. (2023, January 31). How Much It Costs to Advertise on Google. Fit Small Business; Fit Small Business. https://fitsmallbusiness.com/google-ads-cost/  22 Apellidos del autor/Título del libro can be as low as $0.44, while others suggest averages closer to $1.6 or even $3.89 for highly competitive industries like finance11 . 2. Promotions and Bonuses: Offering financial incentives, such as cash bonuses for opening a new account or for referring other customers, reduced commissions, or free trades for a certain period. 3. Content Marketing: Creating and distributing valuable content to attract and engage potential clients, such as financial education articles, investment webinars, and market analysis videos. 4. Affiliate Marketing: Paying commissions to affiliate partners who refer new clients through various affiliate programs. 5. Search Engine Optimization (SEO): Investing in SEO to improve the visibility of the broker's website in search engine results, which helps in attracting organic traffic. 6. Email Marketing: Sending targeted email campaigns to potential customers who have signed up to receive more information. 7. Sponsorships: Sponsoring events, webinars, or podcasts that target potential customers interested in investing or trading. 8. Technology and Platform Development: Costs associated with developing a userfriendly website and mobile app, which are essential for providing customers with access to online trading tools and resources. 9. Customer Service and Support: Establishing a robust customer service system to assist potential clients during the onboarding process, which includes call centers, chat support, and educational resources. 10. Partnerships and Collaborations: Collaborating with financial influencers, bloggers, or advisors who can influence their followers or clients to consider the broker's services. 11. Market Research: Conducting research to understand market trends, customer preferences, and behavior which helps in creating targeted marketing strategies. 12. Legal and Compliance Costs: Ensuring all marketing materials and promotions comply with financial regulations, which can involve legal reviews and compliance procedures. These costs contribute to the total cost of acquiring each new customer and are considered investments in growing the customer base and market share. Retain customers In addition to enhancing customer satisfaction, retention can be boosted by  11 Irvine, M. (2023, November 28). Facebook Ad Benchmarks for YOUR Industry | WordStream. WordStream. https://www.wordstream.com/blog/ws/2019/11/12/facebook-ad-benchmarks  23 Apellidos del autor/Título del libro • Investing in loyalty programs such as loyalty cards, rewards schemes, point cards, advantage cards, or club cards. These programs incentivize repeat business and strengthen customer loyalty.  • Enhancing customer service, including fast response times and effective issue resolution • Offering personalized experiences:, using customer data to tailor experiences, offers, and communications can make customers feel valued and less likely to leave. • Implementing customer feedback, regularly collecting and analyzing customer feedback to continually improve the product and service offerings. • Using predictive analytics, using data analytics to identify at-risk customers before they churn. This can involve analyzing usage patterns to spot customers who may be losing interest or facing issues. • Analyzing competitors, regularly assessing the competitive landscape to ensure your offerings remain compelling compared to alternatives available in the market. Grow customers There are to ways to increase the customer expenditures: increase the share of wallet or increase the expenditure in the product category keeping the share of wallet. The second is more difficult than the first. Share of wallet refers to the amount of a customer's total spending that is captured by a particular business. It's a metric used to understand a company's customer loyalty and spending distribution compared to competitors. Essentially, it's about how much of a customer's total expenditures in a category are devoted to purchases from a specific company or brand. Improving share of wallet is a key goal for businesses because increasing the proportion of customer spending can be more cost-effective than acquiring new customers. It can be measured in value or in volume: 𝑉𝒂𝒍𝒖𝒆 𝑆𝑂𝑊 (%) = 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 𝑜𝑛 𝑐𝑜𝑚𝑝𝑎𝑛𝑦 (€) 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 𝑜𝑛 𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 (€) 𝑉𝒐𝒍𝒖𝒎𝒆 𝑆𝑂𝑊 (%)= # 𝑢𝑛𝑖𝑡𝑠 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑑 𝑡𝑜 𝑐𝑜𝑚𝑝𝑎𝑛𝑦 # 𝑢𝑛𝑖𝑡𝑠 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑑 𝑜𝑓 𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 For example, a gas retailer can calculate the share of wallet of a customer by dividing the number of times a customer fills up her car's gas tank at its pump in one month by the total number of times the customer refills her tank throughout the month. For instance, if a customer refills her tank four times a month, with three of those refills at the gas retailer, the retailer will have a 75% share of the wallet. It may be difficult to estimate the expenditure of a customer on a category, moreover because sometimes defining a category is subjective. For example, if Coca-Cola wants to estimate the share of the wallet of some customers, should it only include regular cola-flavored soft drinks or also fruits-flavored? A broader way to define where the increase the share of wallet is to look at complementors. Complementors are products your customers use or buy jointly with your product. In the mid80s, Disney discovered that the average visitor to its park spent thousands of dollars on their 24 Apellidos del autor/Título del libro entire trip, yet Disney only captured a small portion of this expenditure through ticket sales. In order to increase their share of wallet (SOW), Disney made the strategic decision to construct hotels and restaurants within their parks and began collaborating with travel agencies to offer comprehensive vacation packages. In general there are two activities to increase customer expenditures (engagement) with the firm: cross- and up-selling: • Cross-selling involves offering complementary products or services to customers in addition to what they are already purchasing. Example: If a customer is buying a smartphone, cross-selling might involve suggesting they also buy a phone case or headphones that work well with that phone. In banks, customers typically start with checking accounts, and later are offered new products such as debit cards. • Up-selling encourages customers to purchase a more expensive, upgraded, or premium version of the chosen item or service. Example: If a customer is considering a basic model of a product, upselling would involve suggesting a higher-end model of the same product that offers more features or better performance. Other examples include the following: once the customer has a debit card, offer him a credit card; suggesting a premium brand of alcohol when a brand is not specified by a customer (such as if a customer simply requests a "rum and Coke"); selling an extended service contract for an appliance; selling a luxury finishing on a vehicle; suggesting a customer to purchase more RAM or a larger hard drive when servicing his or her computer; suggesting a brand of watch that the customer hasn't previously heard of as an alternative to the one being considered; suggesting a customer purchase a more extensive car wash package; asking the customer to super size a meal at a fast-food restaurant. Cross- and up-selling can generate higher sales in the current period and in future periods. Besides, it can increase satisfaction (by providing more value, ideally showing the customer that you understand their needs) and retention (it has been shown that customers owning more products from the firms have higher retention rate due to higher switching costs). Third, a higher number of interactions means the firm can have a deeper knowledge of the customer. A key question is what products should the firm cross-sell, to which customers and at what time. A model that can be used for this is a recommendation system. Recommendation systems are a type of information filtering system designed to predict and present items (products, services, content) that a user may be interested in, based on various inputs. These systems are widely used in e-commerce, streaming services, and content platforms to enhance user experience and increase sales. Recommendation systems use a combination of user data (such as past behavior, preferences, and demographic information) and item data (attributes or content of the items) to generate suggestions. They typically employ algorithms that find patterns and connections between users and items, and suggest products likely to be of interest. A classic example of a recommendation system in action is Netflix's content recommendation engine. This system is designed to suggest TV shows, movies, and documentaries to users based on a variety of inputs and complex algorithms. Market basket analysis (Blattberg et al., 2008, p. 339, 515 and 498), is a data analysis technique that might be considered as a recommendation system. It has been used primarily by retailers to discover purchasing patterns by examining the combinations of items that 25 Apellidos del autor/Título del libro frequently co-occur in transactions. It's most commonly implemented through association rule mining, where the relationships among items are uncovered. One of the most famous findings from market basket analysis is the association between diapers and beer: a retail grocery chain used market basket analysis and discovered that diapers and beer are often purchased together during certain times, particularly on weekends. Various models can be used for targeting, though the key challenge lies in possessing the necessary data. Typically, marketing efforts aim to move the targeted customer forward through the purchase funnel or to grow them. • Recency, Frequency, and Monetary value (RFM) models. RFM models analyze customer behavior using three dimensions: Recency (how recently a customer made a purchase), Frequency (how often they purchase), and Monetary value (how much they spend). This approach helps businesses identify their most valuable customers for targeted marketing strategies. • Scoring models. Scoring models score potential customers by evaluating key data points such as demographic information (age, location, income), engagement metrics (website visits, email opens), behavioral data (purchase history, content downloads, webinar sign-ups), lead source (origin of the lead, like organic search or referral), and customer feedback (survey results and feedback scores). These elements are combined using a weighted system to rank leads and optimize sales efforts. A model that can be used for scoring is the choice model, a statistical method used to predict consumer decisions among two or more options (for example responding to a marketing campaign or not, or purchasing brand A, B or C). • Customer Lifetime Value (CLV) models. CLV models are used to estimate the total profit a customer is expected to bring over their relationship with the company. Understanding and predicting CLV helps businesses in making informed decisions about how much money to invest in acquiring new customers and retaining existing ones. CLV models use historical data to forecast future transactions and project future profits from a customer. CRM as a software technology CRM, as a software technology is a collection of tools for businesses aiming to maintain strong customer relationships, increase sales, and improve service delivery across various industries. Here’s a breakdown of the key aspects of CRM as a software technology: 1. Data Collection and Management: CRM systems collect data from various customer interactions, including sales transactions, phone calls, emails, and social media activity. This data is organized to provide comprehensive customer profiles and interaction histories. 26 Apellidos del autor/Título del libro 2. Customer Insights: By analyzing the collected data, CRM tools provide insights into customer behavior, preferences, and needs. This helps businesses tailor their marketing and sales strategies to individual customers or segments. 3. Enhanced Communication: CRM tools help ensure that communication with customers is consistent and personalized across all channels, which can improve customer satisfaction and loyalty. 4. Automation of Tasks: Many routine tasks such as sending follow-up emails, updating sales opportunities, and managing marketing campaigns can be automated with CRM software, increasing efficiency, and allowing staff to focus on more complex tasks. 5. Reporting and Analytics: CRM systems offer detailed reports and analytics on sales trends, customer engagement, campaign effectiveness, and more, enabling data-driven decision-making. Some of the main CRM software platforms include Salesforce, Microsoft Dynamics 365, Oracle CRM, SAP CRM, HubSpot, and Zoho CRM, some targeting larger or smaller companies, and some being more user-friendly than others. Technology facilitates the creation and dissemination of information across users. However, it's important to note that while technology serves as an enabler, the success of customer relationship management (CRM) heavily relies on the involvement of people and the design of the organization. Typical data used in CRM Data has been called “the oil” of the modern economy. The routine capture of digital information through online and mobile applications produces vast data streams on how consumers feel, behave, and interact around products and services as well as how they respond to marketing efforts” (Wedel & Kannan, 2016). Data is needed to personalize marketing and automate marketing processes.  The typical data used in CRM includes:  • Customer Characteristics: Collect data such as name, address, phone number, age, family status, income, and cultural background. • Marketing History: Track details of marketing interactions like calls, mailings, pricing, discounts, rewards, customer-initiated contacts, and loyalty card usage. • Response Data: o Direct Response: Record specifics of customer reactions including what was purchased, when, where, the amount, whether a purchase was made, the sales volume, the channel used, and the payment method. 27 Apellidos del autor/Título del libro o Calculated Metrics: Analyze revenues and costs per customer, profit, sales recency (time between purchases), purchase frequency, customer satisfaction, opinions, and complaints. 28 Apellidos del autor/Título del libro Some companies are more able to gater from customers than others. Mail-order Catalog Sellers: Companies like L.L.Bean or La Redoute track customer orders, preferences, and responses to catalog offers. Subscription-Based Business Models: Companies such as Netflix and Spotify monitor ongoing customer behavior and usage to tailor their offerings. Financial Services: Firms like Banco Santander, ING, and American Express gather detailed transactional and behavioral data through banking and financial activities. Electronic Commerce: E-commerce platforms like Amazon and eBay meticulously track customer browsing and purchasing patterns. Digital companies in general: Tech giants such as Google and Facebook have sophisticated systems that allow for comprehensive data collection across various customer touchpoints. 29 Apellidos del autor/Título del libro Individual targeting In modern marketing, companies face the challenge of optimizing their resources. These resources, often limited, must be strategically invested in customers who are viewed not just as buyers, but as valuable assets. The starting point of marketing strategy, therefore, lies in targeting—selecting those customers who are most likely to maximize the return on investment (ROI). The goal is to direct efforts toward those with the highest expected profitability within the bounds of resource availability. Individual targeting has three main benefits:  • Effectiveness: By focusing on specific customers, marketing strategy and actions can be tailored to resonate more deeply with an audience, leading to higher likelihood of purchase, increased customer satisfaction and retention. For example, it can help to better product development: understanding the specific needs and preferences of a target market can guide product development processes, ensuring that new products or updates align closely with customer expectations and market demand. • Efficient Use of Resources: Targeting helps companies allocate their marketing resources more effectively by focusing on the most promising individual customers that are likely to yield the best returns, rather than spreading efforts thinly across all potential customers. For example, an e-commerce company optimized its digital advertising spend by allocating a limited budget to target only customers with a high probability of purchasing, improving the overall return on advertising. • Competitive Advantage: A well-executed targeting strategy can provide a competitive edge by differentiating a brand’s offerings in ways that are uniquely appealing to its target audience. This specificity can make it harder for competitors to attract the same consumers with a one-size-fits-all approach. For example, a gourmet pet food company can gain a competitive advantage by using targeted marketing to cater specifically to pet owners looking for organic and allergen-free options, differentiating itself in a crowded market and capturing a substantial share of the niche market. Targeting individual consumers Personalization typically occurs through two key steps: selecting individual customers and personalizing the offering to them. Let's explore each step in more detail: Selecting individual customers involves identifying customers on an individual level based on their unique characteristics, behaviors, and preferences. This step relies heavily on data 30 Apellidos del autor/Título del libro collection and analysis to understand each customer's needs, interests, and past interactions with the brand. Here's how individual targeting works: • Data Collection: Digital marketing channels provide vast opportunities for collecting data on individual customers, including website visits, social media interactions, email engagement, purchase history, and more. This data is collected through various tracking mechanisms such as cookies, pixels, and customer relationship management (CRM) systems. • Data Analysis: Once data is collected, marketers use analytics tools and algorithms to analyze and interpret it. This analysis helps identify patterns, trends, and insights about individual customer behavior, preferences, and demographics. • Customer selection: Based on the insights gained from data analysis, marketers can select individual customers for a marketing action. Selection criteria may include factors such as demographics, psychographics, purchase history, browsing behavior, or engagement level. Personalizing offerings  Once customers are selected, the next step is to develop a personalized marketing action that addresses the specific needs and preferences of each. This involves personalizing various elements of the marketing mix (product, price, place, and promotion) to create a tailored experience for customers. Here's how personalized marketing mix works: • Product: Businesses may personalize their products or services to meet the unique needs and preferences of different segments. This could involve offering different product versions, features, or bundles tailored to specific customers. • Price: Pricing strategies can be personalized based on individual customer characteristics, behaviors, and willingness to pay. This may include offering personalized discounts, promotions, or pricing plans to different customers. • Placement: The distribution channels and touchpoints through which products or services are offered can be personalized to match the preferences and habits of different customers. This may involve optimizing digital channels such as websites, mobile apps, social media platforms, and email marketing to deliver a seamless and personalized customer experience. • Promotion: Marketing communications and promotional efforts can be personalized to resonate with the interests, preferences, and needs of different customers. This may include personalized email campaigns, targeted social media ads, customized content, and personalized recommendations based on individual customer data. By implementing individual targeting and personalized marketing mix, businesses can create more relevant, engaging, and effective digital marketing campaigns that drive higher levels of customer engagement, satisfaction, and loyalty.  31 Apellidos del autor/Título del libro Levels of granularity The concept of personalization in marketing emphasizes leveraging big data to optimize the marketing mix. The term "granularity" refers to the level of detail used to differentiate members within a customer base. Big data allows firms to determine the most effective level of granularity for various components of their marketing strategies. The choice of granularity often depends on balancing economies of scale with the return on investment (ROI).  For instance, Mercedes-Benz utilizes big data effectively. The company develops a global brand image while also tailoring its product and brand advertising to specific customer segments. Furthermore, Mercedes-Benz personalizes its sales efforts, pricing, and promotions down to the individual level through the car dealers and internet advertising. It even personalizes the in-car experience, adjusting aspects such as air-conditioning, display information, and music to suit individual preferences. Another real-world example of a company that uses marketing at different levels of granularity is Coca-Cola. Coca-Cola employs marketing that spans global to personal scales. Globally, uses broad messaging themes like happiness and unity, which are universally appealing. Campaigns like “Open Happiness” were launched worldwide, showcasing diverse groups of people enjoying Coca-Cola products, emphasizing the brand's association with joy and togetherness. Nationally, it taps into local passions, like supporting Spain's female football team. Locally, Coca-Cola sponsors events and releases special editions that reflect community pride. Individually, it uses data-driven ads and personalized campaigns like "Share a Coke" to connect directly with consumers. Figure 7. Coca-Cola uses different levels of granularity. It sponsors the Spanish female football national team targeting the whole market of Spain, and launches the “Share a Coke” campaign targeting individual consumers  32 Apellidos del autor/Título del libro The two components of expected profitability Targeting consists of selecting those customers who are most likely to maximize the return on investment (ROI). The goal is to direct efforts toward those with the highest expected profitability within the bounds of resource availability. The equation that comes into play here is the Expected Profitability of targeting a customer, which equals the customer value multiplied by the probability of her responding positively. This calculation guides the decisions on whom to target. 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑝𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡𝑎𝑟𝑔𝑒𝑡𝑖𝑛𝑔 𝑎 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑓𝑜𝑟 𝑎 𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔 𝑎𝑐𝑡𝑖𝑜𝑛 = 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑣𝑎𝑙𝑢𝑒 𝑥 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔  Example: The BookBinders Club Take, for example, the BookBinders Company, which sells books directly to consumers, this is, operates through direct marketing without any intermediaries. This approach allows them to have a clear view of their customer base, evidenced by their detailed database containing historical purchase volumes and margins per euro. BookBinders is considering a promotional campaign offering new titles to a limited number of customers on its database. The question the company faces now is which customers to target for this promotional campaign. To take this decision BookBinders would like to know what the expected profitability is of reaching out to each customer in their database with this offering. BookBinders has a database about thousands of customers with information like Table 2: 1. CustomerID: A unique identifier assigned to each customer, which is typically used to track and manage customer information within a company's database. 2. Purchase Volume: The historical amount of money (in dollars) that the customer has spent on purchases over a certain period. 3. Margin per $: The profit margin per dollar spent, representing how much profit the company makes for each dollar sold to that customer. Why may this margin vary by customer? This reflects the cost efficiency of sales to that customer or the premium pricing of products they tend to buy. For instance, customer 7 tend to buy only low margin books, and this is reflected in low profitability. With no additional information, BookBinders may estimate the potential profitability of its customers multiplying the historical purchase volume times the margin per dollar fir each customer (Table 3).  But some consumers will be more likely to make purchases than others. This occurs due to variations in individual preferences, needs, and circumstances among consumers. Factors such as product relevance, pricing, promotional offers, and past purchasing behavior can influence their likelihood of making a purchase.  33 Apellidos del autor/Título del libro Suppose the company creates a model to predict the probability of each customer making a purchase following a direct marketing campaign. This model takes into account various factors like demographics, past interactions with the company, and response to previous marketing efforts. Now, to determine the expected profitability of these customers after incorporating this new information (the purchase probability), the company would multiply each customer's estimated probability of purchasing by the potential revenue from their purchase. Then, they would subtract the estimated costs associated with reaching and serving that customer. For the shake of simplicity, suppose the cost of reaching each of these customers is $3,50, and the same for all. This calculation allows the company to prioritize resources and tailor marketing efforts to target those customers with the highest expected profitability based on their purchase probability. By focusing on customers more likely to purchase, the company can optimize its marketing strategies and improve overall profitability. In summary: (1) Profitability without considering purchase probability: €442,88 (€407,88 when marketing costs are factored in). (2) Profitability considering purchase probability: €2,21. (3) Profitability considering purchase probability and targeting only profitable customers: €9,29. The intriguing aspect here is that scenario (1) is improbable since it overlooks crucial information. By reaching out to all customers, the actual expected profitability reduces to just €2,21. However, by focusing solely on the most profitable customers (those with positive expected profitability here), the anticipated profit rises to €9,29. Table 2. Database of BookBinders Co. CustomerID Purchase volume Margin per $ 1      31,00    0,70 2    143,00    0,60 3      54,00    0,67 4      88,00    0,62 5      20,00    0,58 6      60,00    0,47 7      77,00    0,38 8      39,00    0,66 9    184,00    0,56 34 Apellidos del autor/Título del libro 35 10      72,00    0,65  Table 3. Bookbider's expected profitability by customer Customer Purchase volume $ (A) Margin per $ (B) Profitability $ (C=AxB) 1      31,00    0,70 21,70 2    143,00    0,60 85,80 3      54,00    0,67 36,18 4      88,00    0,62 54,56 5      20,00    0,58 11,60 6      60,00    0,47 28,20 7      77,00    0,38 29,26 8      39,00    0,66 25,74 9    184,00    0,56 103,04 10      72,00    0,65 46,80 Total     Table 4. BookBinders' customers expected profitability. Custo mer Purcha se probab ility Purchas e value (A) Margi n per $ (B) Profitabilit y $ (C=AxB) Expected prof. considerin g purchase probabilit y Cost of mark eting New Expected Profitabilit y – discountin g the cost (contacting all customers) ↓ Id (contacti ng only profitabl e customer s) 5 60%      20.00    0.58 21,70 6,96 3.5 3.46 3.46 1 30%      31.00    0.7 85,80 6,51 3.5 3.01 3.01 6 22%      60.00    0.47 36,18 6,20 3.5 2.70 2.70 Apellidos del autor/Título del libro 54,56 3,62 3.5 0.12 0.12 3 10%      54.00    0.67 8 13%      39.00    0.66 11,60 3,35 3.5 7 11%      77.00    0.38 28,20 3,22 -0.15 3.5 4 5%      88.00    0.62 29,26 2,73 3.5 -0.28 10 -0.77 4%      72.00    0.65 25,74 1,87 3.5 2 2%    143.00    0.6 103,04 1,72 -1.63 3.5 9 1%    184.00    0.56 46,80 1,03 3.5 -1.78 Total -2.47 442,88 37,20 2.20 9.29 36 Apellidos del autor/Título del libro Customer value The two faces of value In seller and buyer relationships, there are two types of value involved: value to the customer, and value to the company.  Value to the customer The first is the perceived benefits that a customer receives from a product or relative to the cost of acquiring or using it. Essentially, it's what the customer gains from the product or service compared to what they give up in terms of money, time, effort, or other resources. The concept of customer value is highly subjective and can vary from one individual to another. It's influenced by factors such as personal preferences, needs, expectations, and experiences. For example, one customer may value a product for its quality and durability, while another may prioritize affordability or convenience. Customer value is not solely determined by the features or attributes of a product. It also encompasses the overall experience associated with acquiring, using, and disposing of the offering. This includes factors such as customer service, brand reputation, ease of purchase, and post-purchase support. Businesses aim to deliver customer value by understanding their target customers' needs and preferences and designing products or services that meet or exceed those expectations. By creating value for customers, businesses can build strong relationships, foster customer loyalty, and ultimately drive long-term success. Value to the company Also called customer value, is the value that a customer brings to a company, generated from her purchases in the long run. It encompasses both the monetary transactions between the customer and the company and the intangible aspects of the relationship, such as brand loyalty, advocacy to other consumers, and feedback to the company.  From a financial perspective, customer value is often measured by the lifetime value of a customer, which calculates the total revenue that a customer is expected to generate over their entire relationship with the company, taking into account factors such as repeat purchases, upsells, and referrals. This metric helps companies understand the financial impact of acquiring and retaining customers. How customer value is distributed across customers When companies analyze how their profit is distributed across their customers, many find that it follows the Pareto Principle. This rule of thumb suggests that around 80% of outcomes 37 Apellidos del autor/Título del libro result from 20% of the causes, that in this case means most of the profits come from a minority of clients.  Companies may use the A-B-C customer classification to understand customers based on their profitability. It may lead to following hypothetical distribution: • "A" customers are on the top tier, making up about 5% of the client base but generating 70% of profits. • "B" customers comprise about 25% of clients and account for 25% of profits. • "C" customers, the largest group at 70%, contribute only 5% to the profits. Below you can see a graphical representation of a hypothetical ABC distribution.  Figure 8. Graphical representation of profit distribution across ABC groups of customers A study from a Japanese bank found that "A" and "B" customers together generated 150% of total profits, implying that the "C" group represented a net loss when considering the full spectrum of servicing costs. This analysis helps businesses focus their efforts on different groups12.  There are several reasons why this may happen:  1. Purchase Volume: Some customers buy more frequently or in larger quantities. 2. Profitability: Certain customers may buy higher-margin items. 3. Cost to Serve: Some customers require less support or use more cost-effective channels. 4. Long duration: Long-term customers end buying more along time Two of the main metrics used to estimate customer value are the Recency, Frequency, and Monetary value (RFM) metric and the Customer Lifetime Value (CLV) metric 12 (McKinsey, 2021a) 38 Apellidos del autor/Título del libro The matrix of value to customer and value to company Examining the interplay between the value a company provides to its customers and the value customers bring to the company can be a highly insightful exercise. The image depicts a matrix categorizing customers based on two dimensions: the value they bring to the company and the value they receive from the company. Here's the breakdown: 1. Stars: High value to the company and perceive high value from the company. They are both profitable for the company and satisfied with what they receive, making them ideal customers. 2. Vulnerables: High value to the company but perceive low value from the company. They might be spending a lot with the company but could easily switch to a competitor if they perceive better value elsewhere. To enhance the perceived value to customers, businesses can offer more services or adjust pricing. For instance, engaging in positive discrimination by providing preferential customer service, such as reducing the wait time for customer-call attention from 2 minutes to 15 seconds, can significantly increase customer satisfaction and perceived value. This strategy can lead to improved customer loyalty and a higher retention rate. 3. Lost Causes: Low value to the company and perceive low value from the company. When customers neither receive much value from a company's products or services nor provide much value to the company, they are not typically a priority unless they contribute to economies of scale by reducing production costs. If this is not the case, it might be more strategic for a company to either reduce investments in these customers or stop serving them altogether. This approach was evidenced by a crosssectional study of U.S. banks in the 90s, which found that only about 30% of customers were profitable in the long term. 4. Free Riders: Low value to the company but perceive high value from the company. They might be taking advantage of free services or items on promotions without contributing significantly to profits. These customers are called cherry-pickers. The strategy to manage these customers includes offering lower levels of service to them or guiding them towards more profitable products that offer better value to both the customer and the company. This matrix helps businesses understand customer segments and formulate strategies for enhancing value, improving satisfaction, and optimizing resource allocation. Table 5. Matrix of value to customer and value to company Value to customer (perceived value) Low High High Vulnerables Stars 39 Apellidos del autor/Título del libro Lost causes Free riders Value to company (customer value) Low Metrics for customer value Here are several methods commonly used to measure customer value: 1. RFM Analysis: RFM (Recency, Frequency, Monetary) analysis segments customers based on their recent purchasing behavior, frequency of purchases, and monetary value. By grouping customers into segments such as high-value, medium-value, and low-value, companies can tailor their marketing and retention strategies to maximize the value of each segment. 2. Customer Lifetime Value (CLV): CLV calculates the total value a customer is expected to generate for the company over the entire duration of their relationship. It considers factors such as purchase frequency, average order value, customer retention rate, and acquisition cost. CLV helps companies understand the long-term profitability of acquiring and retaining customers. It is on of the most complete metrics.  3. Average Revenue Per User (ARPU): ARPU calculates the average revenue generated by each customer. It provides insights into the spending behavior of customers and helps identify opportunities to increase revenue per customer. It is a typical measure is digital companies and subscription-based services. Figure 9 shows the ARPU of Meta (before Facebook) across regions. It is easier to compute than CLV because it only needs to divide the total revenue from a group of customers by the number of customers.  4. Customer Satisfaction and Loyalty Metrics: Metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES) measure customer satisfaction and loyalty. Satisfied and loyal customers are more likely to contribute positively to customer value through repeat purchases, referrals, and advocacy. 5. Customer Retention Rate: Customer retention rate measures the percentage of customers who continue to do business with the company over time. Higher retention rates indicate stronger customer loyalty and lifetime value. 6. Churn Rate: Churn rate measures the percentage of customers who stop doing business with the company over a specific period. Monitoring churn rate helps identify potential issues with customer satisfaction and retention, which can impact customer value. It is the inverse of retention rate.  7. Customer Acquisition Cost (CAC): CAC calculates the cost incurred by the company to acquire a new customer. It is included in CLV and helps determine the 40 Apellidos del autor/Título del libro return on investment (ROI) of customer acquisition efforts and assess the long-term value of acquired customers. 8. Customer Referral Rate: Referral rate tracks the percentage of new customers acquired through referrals from existing customers. Encouraging referrals can help expand the customer base and increase overall customer value. It is a measure of the intangible value of current customers.  By using a combination of these methods, companies can gain a comprehensive understanding of customer value and make informed decisions to maximize customer relationships and drive business growth. Figure 9. Facebook ARPU across regions 41 Apellidos del autor/Título del libro RFM analysis RFM analysis is one of the first and most popular targeting models in database13 and direct marketing, as used by catalog-order, charity donations, digital companies, etc. RFM is a marketing analysis tool used to identify and quantify the value of a customer to a business based on three specific factors: Recency, Frequency, and Monetary value. The acronym RFM stands for: • Recency: How recently a customer has made a purchase. This metric is based on the premise that customers who have purchased recently are more likely to respond positively to new offers than those who have not purchased in a long time. • Frequency: How often a customer makes a purchase within a given time frame. A higher frequency indicates a higher level of customer engagement and loyalty. • Monetary Value: The total amount of money a customer has spent during their relationship with the company. This metric helps businesses identify their most valuable customers in terms of revenue contribution. In digital services a proxy of monetary value can be time spent with the service, for example a website or a mobile app.  Although RFM analysis itself is more descriptive and segmentative in nature, it provides valuable inputs for predictive analytics. The RFM scores can be used as features in machine learning models to predict future customer behaviors, such as the likelihood of making a purchase, the expected monetary value of future purchases, or the probability of churn. By incorporating RFM data into broader predictive models, businesses can anticipate future trends and adjust their strategies accordingly. How RFM Analysis Works RFM analysis involves segmenting customers into various groups based on the three dimensions mentioned above. Typically, each dimension is assigned a score, often on a scale of 1 to 5 (with 5 being the highest). Customers are then scored based on their transaction history data: Recency: Customers who made a purchase very recently get the highest score, while those who purchased a long time ago get the lowest score. 13 Database marketing is a form of direct marketing that uses databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. This approach focuses on leveraging data analytics to create more targeted and effective marketing strategies. See Annex for more details.  42 Apellidos del autor/Título del libro Frequency: Customers who purchase more frequently get a higher score. Monetary: Customers who spend more money get a higher score. The final RFM score for each customer is often represented as a concatenated string of these three numbers (e.g., 5-5-5 for a customer who scores the highest in all three categories). Examples of Targeting Strategies Using RFM Analysis 1. Differentiating Campaign Messages: • High RFM Scores: Customers with high scores across all RFM categories (like 5-5-5 or 4-4-4) are your most valuable and loyal customers. Target them with exclusive offers, loyalty programs, and premium services that acknowledge their value to your business. • High Recency, Low Frequency and Monetary: These customers are recent but not frequent shoppers and haven't spent much yet. Target them with incentives to make another purchase or to join a membership that encourages frequent buying. • High Frequency, Low Recency: Customers who used to shop frequently but haven’t made recent purchases might be slipping away. Re-engagement campaigns using personalized emails or retargeting ads14 can remind them of what they're missing. • Low on all RFM metrics: possibly at risk of churning or already disengaged. They could be targeted with "Reactivation Campaigns" offering very tempting deals, or feedback surveys to understand their disengagement, possibly paired with a re-engagement incentive. In general, we can say: • 155 customers need a reminder,  • 551 customers need to be upsold (make them to spend more),  • 515 customers need a sticky recurring relationship (make them to buy more often). 2. Creating Customer Loyalty Programs: 14 Retargeting ads, also known as remarketing, is a way to target people with ads who have already interacted with your business in some way. This could be someone who visited your website, looked at a specific product, or abandoned their cart without purchasing. 43 Apellidos del autor/Título del libro • RFM can help in designing tiered loyalty programs where customers with higher RFM scores get more benefits. This not only rewards the best customers but also motivates others to improve their RFM scores by interacting more with the brand. 3. Launching New Products: • When introducing new products, companies can target customers with high Monetary and Frequency scores as they are more likely to try new offerings and spend more. 4. Preventing Churn: • Customers with a once-high but rapidly declining RFM score might be at risk of churning. Targeted intervention through special offers, surveys to inquire about their changing needs, or personalized communications can help retain these customers. 5. Optimizing Marketing Spend: • RFM helps in identifying less profitable or low-engagement segments where marketing spend can be reduced. Conversely, more resources can be allocated to high-value segments identified by RFM, enhancing ROI. How to calculate the RFM scores A requirement is to gather Customer Transaction Data: collect data on each customer’s purchasing history. You need at least the following information: • Customer ID • Order Date • Purchase Amount Method 1 The typical method starts sorting customers based on recency, from highest to lowest (more recent purchases at the top). Customers are then split into quintiles and given the first quintile a score of 5, and so on. The, customers are sorted and scored by frequency and money/time (both from highest to lowest). Finally, all customers are ranked concatenating R, F and M scores.  Step 1: Calculate Recency, Frequency, and Monetary Values for Each Customer • Recency (R): Measure how many days ago each customer made their last purchase. The most recent purchase gets the lowest score. For example, if it’s currently October 1, and a customer last purchased on September 30, their Recency is 1 day. 44 Apellidos del autor/Título del libro • Frequency (F): Count how many purchases each customer has made during a specific period (e.g., over the last year). • Monetary (M): Calculate the total amount each customer has spent during that period; it also can be the average amount per purchase spent in a period. Step 2: Rank Customers for Each RFM Metric • Rank customers based on each metric. You can either use quintiles (dividing the ranked customers into five equal groups), quartiles, deciles, etc., depending on how detailed you want the segmentation to be. • Assign a score from 1 to 5 (if using quintiles) for each RFM metric. The best performing customers (e.g., most recent, most frequent, highest spenders) receive the highest score (5), and the least performing receive the lowest score (1). Step 3: Assign RFM Scores • Combine the individual R, F, and M scores into a single RFM score. This is often done by concatenating the scores into a three-digit number (e.g., 451, where 4 is the Recency score, 5 is the Frequency score, and 1 is the Monetary score). Example Suppose you have three customers with the following transaction data: • Customer A: Last purchased 10 days ago, has 15 transactions, total spend of $1500. • Customer B: Last purchased 20 days ago, has 5 transactions, total spend of $500. • Customer C: Last purchased 5 days ago, has 10 transactions, total spend of $1200. After ranking these customers within your data set and assuming quintiles for simplicity in this example: • Recency: C(5), A(4), B(2) – fewer days ago is better. • Frequency: A(5), C(4), B(2) – more transactions are better. • Monetary: A(5), C(4), B(1) – higher spend is better. RFM Scores would then be: • Customer A: 545 • Customer B: 221 • Customer C: 454 These scores help identify which customers need attention and which should be targeted for retention, up-selling, or re-engagement campaigns. 45 Apellidos del autor/Título del libro Method 2 Another method gives different points or different weights to R, F and M/T and assigns a score to each customer as the sum the points for each customer. Then, customers are ranked by descending score. The company now can target customers by score.  For example:   • Recency: Last purchase in the last 3 months: 25 points; 4 to 12 months: 20 points; Did not purchase in the last 12 months: 0 points  • Frequency (based on total purchases as recorded in the database): Has purchased a total of less than 10 books: 10 points; 11 to 20 books: 20 points; 21 o more books: 50 points  • Monetary: Has purchased a total of less than $50: 10 points; between $51 and $150: 20 points; $151 or more: 50 points Method 3 A third method involves the following: If we possess historical data on customer responses to past marketing activities, we can correlate these responses with R, F, and M codes. The procedure begins by splitting the historical data into two samples: • Calibration (also referred to as training or trial) is used to estimate the probability of response and its relationship with RFM scores. • Validation (also known as hold-out, testing, or prediction). The method then calculates the probability of response as a function of the three RFM codes using the calibration sample. This model is subsequently validated with the validation sample. Utilizing this model, the company can target specific groups for a promotional campaign. For example, a catalog retailer with 400.000 customers, sending a mail with a promotion to all customers would maximize sales but not profits, as many customers might not respond. It would be more profitable to send the mail only to customers with a higher likelihood of responding. The company could conduct a pilot study with a sample size of 40.000 customers, sending them a $150 discount coupon. The response rate observed was 808 customers (2,02%), but some responded more than others: response rates for R codes 5, 4, 3, 2, and 1 were 4,50%, 2,80%, 1,50%, 1,05%, and 0,25%; for F codes were 2,45%, 2,22%, 2,08%, 1,67%, and 1,68%; for M codes were 2,35%, 2,05%, 1,95%, 1,90%, and 1,85% respectively (Source: Kumar & Reinartz, 2006). 46 Apellidos del autor/Título del libro Some limitations and extensions Coding involves transforming continuous data into discrete data, which inevitably results in information loss. Additionally, RFM scoring is somewhat arbitrary. Another limitation is that the assumed relationship between R, F, or M and response may differ from reality, necessitating case-specific analysis. For instance, the relationship could be either positive or exhibit a U-shape due to the inventory effect.  Beyond the methods previously discussed, there are further extensions to consider: • RFM as Classical Linear Regression: In this model, the dependent variable is customer response (0 or 1), and the independent variables are the customers' original recency, frequency, and monetary values before coding. This model requires the estimation of only four parameters. With this analysis we can estimate the real relationship between positive response and recency, frequency and money spent.  • RFM as Logistic Regression: This is similar to classical regression but better accommodates the discrete nature of the dependent variable. We will now demonstrate a few examples of how to compute RFM scores. Example: Empowering Change15 Empowering Change, a Non-Governmental Organization (NGO), aims to raise funds for its impactful initiatives. They have a vast database containing historical transactions from thousands of donors. However, targeting every donor might not be the most efficient strategy. To optimize their fundraising efforts, Empowering Change wants to leverage the RFM method to segment their donors based on their donor value. A subsample of the database is given in Table 1. Reference date for recency: 31/12/2006 To calculate the RFM score for each donor we need transaction data which should include the following: • a unique customer id • date of transaction/order • transaction/order amount Table 6. Transaction data donor_id last_name first_name donation_date donation_amount 15 Example adapted from Rsquared Academy. (2024). rfm | Rsquared Academy. Rfm.rsquaredacademy.com. https://rfm.rsquaredacademy.com/  47 Apellidos del autor/Título del libro 48 01 Barton Aileen 04/01/2004 25 01 Barton Aileen 07/03/2004 147 01 Barton Aileen 11/06/2004 190 01 Barton Aileen 15/06/2004 87 01 Barton Aileen 17/07/2004 46 01 Barton Aileen 25/07/2004 34 01 Barton Aileen 10/05/2005 37 01 Barton Aileen 03/07/2006 81 01 Barton Aileen 08/10/2006 116 02 Bruen Aiyanna 15/05/2005 21 02 Bruen Aiyanna 01/07/2005 32 02 Bruen Aiyanna 11/04/2006 33 02 Bruen Aiyanna 29/04/2006 71 03 Hermann Ailene 02/01/2004 41 03 Hermann Ailene 22/02/2004 68 03 Hermann Ailene 11/07/2004 75 03 Hermann Ailene 23/09/2004 138 03 Hermann Ailene 01/08/2005 50 03 Hermann Ailene 20/10/2005 47 03 Hermann Ailene 15/12/2005 122 03 Hermann Ailene 25/03/2006 158 04 Lesch Aden 16/08/2004 100 04 Lesch Aden 14/11/2004 109 04 Lesch Aden 17/04/2005 149 04 Lesch Aden 20/06/2006 47 05 Murphy Aden 26/04/2005 29 05 Murphy Aden 13/05/2005 113 05 Murphy Aden 27/05/2005 65 05 Murphy Aden 18/07/2005 97 Apellidos del autor/Título del libro 49 05 Murphy Aden 22/09/2005 121 05 Murphy Aden 21/11/2005 159 05 Murphy Aden 24/09/2006 12 06 O'Keefe Agness 01/04/2004 111 06 O'Keefe Agness 17/08/2004 101 06 O'Keefe Agness 21/04/2005 46 06 O'Keefe Agness 06/06/2005 44 06 O'Keefe Agness 07/06/2005 43 06 O'Keefe Agness 24/08/2005 167 06 O'Keefe Agness 17/02/2006 86 06 O'Keefe Agness 22/04/2006 75 06 O'Keefe Agness 02/10/2006 170 07 O'Reilly Abbey 16/07/2005 47 07 O'Reilly Abbey 04/09/2005 152 07 O'Reilly Abbey 03/11/2005 41 07 O'Reilly Abbey 24/12/2005 145 07 O'Reilly Abbey 06/01/2006 67 07 O'Reilly Abbey 09/06/2006 20 08 Senger Add 08/06/2005 114 08 Senger Add 12/11/2005 208 08 Senger Add 13/08/2006 18 09 Senger Admiral 17/02/2005 141 09 Senger Admiral 25/06/2005 115 09 Senger Admiral 31/08/2005 35 09 Senger Admiral 15/09/2005 79 09 Senger Admiral 21/08/2006 78 10 Walsh Akeelah 16/06/2004 132 10 Walsh Akeelah 16/08/2004 45 10 Walsh Akeelah 21/11/2004 208 Apellidos del autor/Título del libro 50 10 Walsh Akeelah 18/12/2004 150 10 Walsh Akeelah 18/09/2005 32 10 Walsh Akeelah 15/01/2006 171 10 Walsh Akeelah 24/07/2006 41  The segmentation starts with recency, then frequency, and finally monetary value. We start sorting customers based on recency, i.e. days after last purchase, in increasing order (most recent purchasers at the top). Customers are then split into quintiles (five equal groups) and given the top 20% a recency score of 5, the next 20% a score of 4, and the rest 3, 2 and 1. Then, customers are sorted and scored for frequency – from the most to least frequent, coding the top 20% as 5, and so on. The process continues for money or time as well. Finally, all customers are ranked by concatenating R, F, and M values. This example shows that RFM analysis can be useful even if the database is small of only 15 transactions whereas it would be more powerful when the database grows.  RFM analysis assigns scores to each customer based on her past behavior. Using the quintile system explained above, 125 different scores (5x5x5) can be assigned. A customer score can range from 555 (highest), to 111 (lowest). Usually the best customers are in quintile 5 for each factor (555), meaning she has purchased most recently, most frequently and has spent the most money or time.  Table 7. Donor data c_id R_days R  c_id F_times F  c_id M_money M 01 84 5  01 9 5  06 843 5 06 90 5  06 9 5  10 779 5 05 98 4  03 8 4  01 763 4 09 132 4  05 7 4  03 699 4 08 140 3  10 7 3  05 596 3 10 160 3  07 6 3  07 472 3 04 194 2  09 5 2  09 448 2 07 205 2  04 4 2  04 405 2 02 246 1  02 4 1  08 340 1 03 281 1  08 3 1  02 157 1 Apellidos del autor/Título del libro Table 8. RFM scores c_id RFM 06 555 01 554 05 433 09 422 10 345 08 311 07 233 04 222 03 144 02 111 Depending on the objective of the targeting strategy, different donors should be targeted. For example, donor 03 which has RFM score 144, has made a high number of donations with high monetary values but not for a long time. Something might have gone wrong with this donor, for example, she has switched to another NGO or has stopped donating. At this situation, marketers can contact this donor and get feedback about how to do it better. Moreover, it is possible to plan a customer reactivation program to get her attention.  Donors 06 and 01 could be targeted to raise the highest amount of money needed for an emergency.  Exercises MusicSpot Software RFM can be done, among others, with Excel, SPSS Direct Marketing, most CRM commercial software, R packages or in R from the scratch 51 Apellidos del autor/Título del libro References • Birant, D. (2011). Data Mining Using RFM Analysis, in the book KnowledgeOriented  •   Hardie, B. (2023) Creating an RFM Summary Using Excel. https://brucehardie.com/notes/022/  • Hughes, A. (2016) Making Your Database Pay Off Using Recency Frequency and Monetary Analysis, http://www.dbmarketing.com/2010/03/making-your-databasepay-off-using-recency-frequency-and-monetary-analysis/ (accessed 25/1/2017) Customer lifetime value Customer Lifetime Value (CLV) is a metric that represents the total value a customer brings to a business over the entire duration of their relationship. It takes into account all the revenue generated from a customer's purchases, subscriptions, or interactions with the business, minus the costs associated with acquiring, serving, and retaining that customer. CLV helps businesses understand the long-term profitability of their customer base and informs strategic decisions related to marketing, customer acquisition, and retention efforts. By calculating and optimizing CLV, businesses can focus their resources on acquiring and retaining high-value customers, ultimately driving sustainable growth and profitability. How to estimate the value of a customer to the company Case Study: The Dry Cleaner Customer16 As a regional manager of a national chain of dry cleaners, a decision must be made regarding a customer who seeks 300 Euros compensation for a lost suit. The suit was later found, but the customer had already replaced it due to an important appointment.  In order to decide if to compensate the customer, the manager can consider what is the value of this customer to the store, or in other words, how much the store will lose if the customer leaves or churns.  16 Taken form (Bruno, 2011) 52 Apellidos del autor/Título del libro 53 The contribution margin, which is the price minus variable costs and is computed with cost accounting, needs to be considered in the decision-making process. Let’s imagine the customer spends 25 Euros weekly, and the gross profit margin of the store is 20%. In one year of 50 weeks that makes 250 Euros per year. But the customer could be coming to (patronizing) our store for years. Let's assume an annual discount rate of 10%. We can compute the net present value of the profits, or Customer Lifetime Value (CLV)17 as 𝐶𝐿𝑉=∑ 250 (1+0,10)𝑡 =⋯=250 0,10=2.500 euros ∞ 𝑡=1  Lifetime duration Infinite duration with retention rate The length of time a customer continues to patronize a business can vary. Customers may stop doing business with a company for several reasons. These include dissatisfaction, where the customer is unhappy with the service or product; a competitor offering a more attractive deal; changes in customer preferences; or circumstances such as moving to a new city. To predict customer behavior, we introduce the concept of a retention rate, denoted as r. This rate represents the likelihood that a customer who is active in year t will still be active in year t+1. It’s like each year, the customer flips a coin. If it lands on “continue being a customer,” which happens with probability r, she stays. If it lands on the other side, indicating “leave this company” (also called churn) which has a probability of 1-r, they stop being a customer. The formula is 𝐶𝐿𝑉=𝑚 𝑟 1+𝑖 +𝑚( 𝑟 1+𝑖 )2 +𝑚( 𝑟 1+𝑖 )3 +𝑚( 𝑟 1+𝑖 )4 ⋯ =𝑚 𝑟 1+𝑖 [1+ 𝑟 1+𝑖 +( 𝑟 1+𝑖 )2 +( 𝑟 1+𝑖 )3 +...] =𝑚 𝑟 1+𝑖 [ 1 1− 𝑟 1+𝑖 ]=⋯=𝑚 𝑟 1+𝑖−𝑟 And in this case 𝐶𝐿𝑉=𝑚 𝑟 1+𝑖−𝑟 =€250 0.8 1+0.1−0.8 =€667. We are assuming here that the retention rate is constant over time, something that is not necessarily true. Finite duration Some buyers have a fixed duration relationship with a seller. For example, in a house rental contract, the duration is finite. Imagine that the store knows that a customer typically resides in the area for 12 years and that most of the customers remain loyal in the dry-cleaners business. In this case the value of the customer would be  17 See (Fader & Hardie, 2012): Fader, P. S., & Hardie, B. G. S. (2012). Reconciling and Clarifying CLV Formulas. https://brucehardie.com/notes/024/reconciling_clv_formulas.pdf for more details.  Apellidos del autor/Título del libro 54 𝐶𝐿𝑉=∑ 250 (1+0,10)𝑡 =⋯=250×1−(1+0,10)−12 0,10 =1.703,42 € 12 𝑡=1 Finite duration with retention rate Both situations can be combined in this formula 𝐶𝐿𝑉=𝑚 𝑟 1+𝑖 +𝑚( 𝑟 1+𝑖 )2 +𝑚( 𝑟 1+𝑖 )3 +...+𝑚( 𝑟 1+𝑖 )𝑇 =𝑚 𝑟 1+𝑖 [1+ 𝑟 1+𝑖 +( 𝑟 1+𝑖 )2 +( 𝑟 1+𝑖 )3 +...+( 𝑟 1+𝑖 )𝑇 ]=⋯ =𝑚( 𝑟 1+𝑖−𝑟)[1−( 𝑟 1+𝑖 )𝑛 ]=𝑚×𝑀×[1−( 𝑟 1+𝑖 )𝑇 ] That gives the following result 𝐶𝐿𝑉=€652,07 As a summary we have the following general formulas and results for our example: Table 9. Formulas for CLV Infinite duration 𝑚 𝑖 2.500€ Infinite duration with retention rate r 𝑚 𝑟 1+𝑖−𝑟 667€ Finite duration T years 𝑚×1−(1+𝑖)−𝑇 𝑖 1.703,42€ Finite duration T with retention rate r 𝑚×[1−( 𝑟 1+𝑖 )𝑇 ] 652,07€  The importance of retention To demonstrate the importance of retention we show two examples. The margin multiplier In a scenario where a company anticipates an infinite duration of customer with a probability of churning, which is the most common situation, the margin multiplier (M) becomes a crucial factor in the computation of the customer lifetime value. The margin multiplier depends on two key elements: 1. The retention rate, which for the majority of companies fluctuates between 60% and 90%. This rate is indicative of the percentage of customers that the company expects to keep from year to year. Apellidos del autor/Título del libro 2. The discount rate, which is determined by the company’s cost of capital. The cost of capital is a function of the risk associated with the business and the structure of its liabilities. 𝐶𝐿𝑉 = 𝑚 𝑟 1 +𝑖−𝑟 Increases (faster) with retention rate =𝑚 × M Decreases with discount rate Lifetime needed for positive CLV The relationship between customer lifetime value (CLV) and retention can be further illustrated through the example of a credit card. Let's consider an acquisition cost of 70€, which encompasses expenses such as advertising, sales force commissions, risk analysis, card manufacturing, and postal fees. Initially, in year 1, the CLV is negative (-40), considering the acquisition costs. However, as the duration of the customer relationship extends, the CLV (after acquisition cost) improves, turning slightly less negative in year 2 (-10) and becoming positive by year 3 (20). This transition illustrates the critical role of customer retention; as the relationship extends, the initial investment in acquisition is gradually offset by the ongoing profits, eventually leading to a positive CLV. Overall, the table emphasizes the importance of customer retention and the increasing benefits of longer customer relationships. Table 10. CLV depends on duration Duration (years) 1 2 3 55 Apellidos del autor/Título del libro 1.000 1.000 1.000 Average balance per year and customer Contribution margin % CLV without considering acquisition cost CLV considering AC 3% 30 -40 3% 60 -10 3% 90 20 tracks the evolution of market shares over time for the only two competitors in a market, labeled A and B. Starting with equal sales (or share) of 50 units each at Time 0, the table uses a retention and acquisition model to predict sales in subsequent times. Competitor A retains 95% of its sales from the previous time period, while Competitor B retains 80%. This implies that Competitor A acquires 20% of the sales from the previous period of B and B acquires only 5% from A. As we progress from Time 1 to Time n, the table shows calculations indicating that Competitor A's market share is growing while Competitor B's is shrinking. By Time 3, A has grown to 67,3 units whereas B has decreased to 32,6 units. The pattern suggests that if this trend continues, Competitor A will have 80 units of share by Time n, while Competitor B will decrease to 20 units, reflecting a significant shift in market dominance over time, having reached an equilibrium at this point. . The mathematical operations shown are the applications of the retention and acquisition rates to the sales figures from the previous time periods. Mathematically, the long run market share as a function of retention rate is  𝑚𝑠𝐴 = 1−𝑟𝑂 2−𝑟𝐴−𝑟𝑂 =  1−0,8 2−0,95−0,8 =0,8 (80%) Where 𝑟𝐴 is bank A retention rate and 𝑟0 other banks retention rate (average). If rA reduced by 10% (from 0,95 to 0,85) bank A market share will drop 27,5% (from 80 to 58%) Making the analysis more realistic Acquisition cost As seen in the Acquire customers: use marketing tools to acquire the selected customers section, acquisition cost per customer can be substantial. In this case the CLV formula simply becomes 𝐶𝐿𝑉 = 𝑚 𝑟 1+𝑖−𝑟 −𝐴𝐶 An example is another online broker, Ameritrade, established in 1971, that in 2002 had 6 million customers, more than 5000 employees, and revenues of $2.5 billion. In its early years Ameritrade made significant marketing investments in customer acquisition, like offering zero transaction fees and extensive advertising. As of March 2002, the cost of acquiring each customer was reported at $203, with the contribution margin per customer and year being 56 Apellidos del autor/Título del libro $201. The firm enjoyed a high average customer retention rate of 95%. With an assumed discount rate of 12%, the margin multiplier came out to be 5.59. As a result, the Customer Lifetime Value (CLV) was calculated at $1,126, which is substantially higher than the acquisition cost of $203. Contribution margin increases over time18 When the contribution margin grows at a rate g the formula becomes  𝑪𝑳𝑽 =𝒎 𝒓 𝟏+𝒊−𝒓(𝟏+𝒈) More assumptions can be relaxed to make models closer to reality. The retention rate, which reflects the likelihood of customers staying with a business over time, is expected to increase. This can vary across different customer segments, which may also have varying levels of contribution margins and retention rates, as well as rates of change. Additionally, if customers purchase a variety of products or services, their contribution margins may be considered collectively across all transactions.  Other topics in customer value19 Valuing companies using the CLV framework How do you determine the value of a company that lacks a historical track record, experiences negative cash flows, and possesses no physical assets? This question is exemplified by the case of Facebook and other internet companies. Facebook, upon its initial public offering (IPO) on May 18, 2012, was valued at $104 billion, a figure that some analysts believed to be overstated, estimating its true value to be no more than $10 billion. However, through a customer lifetime value (CLV) analysis, it was suggested that the value could be derived from multiplying its 850 million users by the $8.1 CLV per user, totaling $6.9 billion. This was discussed by Bruno in his article "What does a $100bn valuation for 18 (Gupta & Lehmann, 2005) • 19 Some additional references: Online CLV calculator (from Harvard Business School Publishing) – http:///hbsp.hardvard.edu/multimedia/flashtools/cltv  • Gupta, S., & Lehmann, D. R. (2005). Managing customers as investments. Wharton School Pub. • Gupta, S., Lehmann, D., & Ames Stuart, J. (2004). Valuing customers. Journal of Marketing Research, 7-18. • Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of Interactive Marketing, 17(1), 924. • Meta, A. at. (2022, septiembre 7). Analytics and Product-Market Fit. Medium. https://medium.com/@AnalyticsAtMeta/analytics-and-product-market-fit-11efaea403cd  • Stanford Online (Director). (2017, mayo 3). How to Get Users and Grow - Alex Schultz, VP of Growth at Facebook - Stanford CS183F: Startup School. https://www.youtube.com/watch?v=URiIsrdplbo  57 Apellidos del autor/Título del libro Facebook mean?"20 (2012). The discrepancy between the estimated and actual valuation prompts the question: why did investors pay such a premium? The answer lies in their anticipation of growth in both the number of users and the CLV. This anticipation was validated over time, as seen in Facebook's average revenue per user (ARPU), which increased from $1.38 in 2012 to $10.14 in Q4 of 2020, alongside a surge in the user base from 850 million in 2012 to 2.797 billion in Q4 of 2020. A customer may add other types of value to a company, beyond its immediate revenue contribution.  Indirect value  The indirect value or value from word-of-mouth (WOM) refers to the benefits that arise from customers recommending or endorsing a company's products or services to others. This can be from just writing positive reviews to becoming a brand ambassador. While this may not directly result in direct monetary transactions, it can significantly impact the company's reputation, brand image, and customer acquisition efforts. Positive word-of-mouth recommendations can lead to increased trust and credibility among potential customers, resulting in higher conversion rates and expanded market reach21.  According to (Villanueva et al., 2008), customers obtained after traditional marketing actions typically focus on "trial" purchases, resulting in short-term effects for the firm. Conversely, customers acquired through word-of-mouth (WOM) tend to prioritize repeat purchases, leading to long-term effects. In their research they also found that each customer obtained through marketing efforts is projected to bring approximately 1.77 new customers over their lifetime. In contrast, each customer acquired through WOM is expected to bring in 3.64 new customers. Moreover, WOM customers exhibit greater longevity with the company, thus making a more substantial long-term contribution to the firm's value. Other types of value Some customers provide feedback that can help improve products or services, enhance brand reputation, and drive future growth.  Customers may provide market insights, networking opportunities, or increased bargaining power with suppliers. While these benefits may not directly translate into financial gains, they can still have a significant impact on the overall success and competitiveness of a company. In particular, electronic word of mouth (eWOM), for example in the form of online consumer reviews, contains insightful information that can be used to understand customers (J. Berger et al., 2020). Strategic value, such as market influence: some customers may have an influential role within their communities or industries. Their endorsements can influence the opinions and behaviors of others, shaping market trends and preferences. 20 Bruno (2012) What does a $100bn valuation for Facebook mean?, https://knowledge.insead.edu/marketing/what-does-100bn-valuation-facebook-mean     21 See (Trusov et al., 2009; Villanueva et al., 2008) 58 Apellidos del autor/Título del libro Risk Mitigation happens when diversifying the customer base can help mitigate risks associated with market fluctuations, economic downturns, or competitive threats. A diversified customer portfolio provides stability and resilience to the firm. Customer acquisition in new products Predicting customer acquisition for new products involves forecasting how quickly and extensively consumers will adopt and purchase the product. This prediction often relies on diffusion models, which describe the process by which innovations spread throughout a population. These models, such as the Bass diffusion model and others22, consider factors like market size, advertising efforts, and word-of-mouth influence to estimate the rate of adoption over time. Product adoption theory further informs this prediction by examining the characteristics and behaviors of potential adopters. Innovators and early adopters are typically the first to embrace new products, followed by the early and late majority, and finally, the laggards. Understanding these segments helps marketers tailor their strategies to target each group effectively and accelerate adoption. By combining insights from diffusion models and product adoption theory, businesses can develop more accurate forecasts of customer acquisition for new products, enabling them to allocate resources efficiently and maximize market penetration. Exercises • What’s a customer worth for a supermarket • MusicBumBum • Bancard(1) • Bancard(2) • Voztel • Case Royal Bank of Canada 22 (Bass, 1969; Mahajan et al., 1990; Rogers, 2003) 5。can you summarize all this document for exam purpose summarize the most important point