How Customer-Centric Marketers Use Machine Learning
By: Atif M.
Tags: Artificial Intelligence, Marketing, Machine Learning, Startup, Data Science

How Customer-Centric Marketers Use Machine Learning

Leveraging ML in your marketing strategy is no longer a luxury — rather, it’s a necessity

Photo by Austin Distel on Unsplash

Marketing is an important aspect of any successful company. In simple words, marketing refers to the business’s activities related to buying and selling a product or service.

Customer Centricity

Customer centricity is putting your customer first and at the core of your business to provide the best possible experience and build long-term relationships.

An organization with a customer-centric mindset has customer experience as a core value that helps in the decision-making process for leadership, reflects the beliefs that employees support, and provides clarity to current and potential customers.

Customer-Centric Marketing

Customer-centric marketing is an approach that prioritizes customers’ needs and interests in all decisions related to advertising, selling, and promoting products and services.

For successful customer-centric marketing, you need a deep understanding of the question: why do your customers need your product or service? The goal is not business growth alone; it is growth driven by convincing customers to believe that whatever your company provides can improve some aspect of their work or life.

Machine Learning — A Subset of Artificial Intelligence

Artificial Intelligence (AI) — is a computer science field focused on making machines seem like they possess human intelligence. It is called “artificial” because humans create it, and it does not exist naturally.

Machine Learning (ML) is a subset of AI. ML algorithms are computer-implementable instructions that take a dataset as input. They find out patterns within the dataset that were previously undiscovered.

Supervised ML is when these patterns lead to some labels on the data, and your ML model learns how to map each unique pattern to the most appropriate label. This type of ML is used for predicting labels for unlabeled data.

Unsupervised ML is when your model presents the discovered patterns to the users to provide insights.

ML models improve their performance over time as they encounter more and more data. They improve by experience, just like a human would when indulging in a new activity through careful observation and self-correction.

Machine Learning For Customer-Centric Marketing

As competition gets tougher, consumers now have more choices of businesses to engage with, making ML evaluative to efficiently reaching and engaging your customers.

All business owners have noticed the growing hype around AI in marketing. AI applications in marketing include chatbots, content creation, programmatic advertising and a lot more.

With so many AI applications in marketing, you should never lose sight of what is essential in implementing effective and optimized marketing strategy i.e. deep and clear understanding of your customers.

Once your ML model is well trained, it can quickly categorize any new data inputs and predict likely outcomes. You can generate deeper customer insights and make better behavioral predictions.

These can be related to your prospects’ and customers’ probability of converting on certain campaigns, increasing their frequency of purchase, churn or lapse, or something more specific.

Leveraging ML in your marketing strategy is no longer a luxury. Rather, it has become a necessity.

Let’s take a look at ML-driven insights marketers are using to come up with best customer-centric marketing strategies.

Behavioral Insights and Predictions

Which behavioral patterns of your customers led them to take a particular action in the past?

Did the customers who ended their relationship with you developed behavioral patterns significantly different from those who remained loyal to you?

It is not easy for a human mind to figure out and study all these patterns. The mathematics behind all this is very complex.

Therefore, your ML model takes care of this task. It can detect which piece of information about a customer has put how much weight on their decision to take a particular action, such as churning.

It is important to anticipate your customer’s actions before they take them, especially when the action is irreversible. A lost customer is an irreversible loss. You should never have to begin facing the loss and then start thinking about reducing and preventing it.

Supervised ML algorithms can discover predictive patterns hidden deep in your customer data.

You can use them to find out which prospects are most likely to become your customers by training your model on the data of previous prospects who were successfully converted into customers and the prospects who were lost.

Once your model is trained, your model can take potential customers’ data as input and predict the extent to which a new lead best “looks like” leads who were successfully converted into customers in the past.

Similarly, you can use supervised ML to predict if a customer is likely to churn. You can train your model on data of those customers who are already lost and those who are still active.

Your model can then take data of your current customers as input and figure out if any customer’s data shows patterns similar to those in data of lost customers.

The anticipation of your customers’ actions would allow you to come up with the most effective marketing campaigns according to the point they are at in their customer journey.

Persona-Based Insights and Predictions

Although predicting your customer’s next move is very helpful in reaching the right people at the right time, it is not where the road to truly optimized marketing ends.

To interact effectively with your customers, you need to know them as real people. You would then be able to provide hyper-personalized experiences that evoke emotional responses.

If you can show your audience that you understand well their reasons to interact with you, whether they are an early prospect or a loyal customer, the relationship and trust between you and your customers grow stronger.

Market Segmentation

Customer segmentation is to divide your customers into segments. A customer from one segment is significantly different from a customer of another segment, based on the data used to carry out the segmentation process.

Unsupervised ML comes into play here. Clustering algorithms, such as K-Means, are part of unsupervised ML, used to discover hidden patterns within an unlabeled dataset and group data-points that are significantly similar.

The figure above visualizes a very simple example of cluster formation using ML. The dataset included two pieces of information (attributes) about each customer.

Based on the values of these attributes, each customer occupies their unique position within the two-dimensional representation above. It is two dimensional because there are two attributes.

The algorithm’s task is to figure out how to group these data-points into a number explicitly provided by the users of the algorithm.

The resulting groups, or clusters, could form the foundation of unbiased, truly data-driven personas. As you move forward and collect more data, rerunning the clustering algorithms may reveal new groups amongst your customers that emerge.

Therefore, you would be able to update your knowledge and refresh your messaging, creative, and other personalization efforts to always stay relevant as your customer base evolves.

Let us tell you an interesting example that would clear how clustering can allow customer-centricity in marketing. Burrow, a disruptive direct-to-consumer furniture brand, employs ML-driven personas to identify what color couches their audience segments see in targeted ads.

They discovered that older customers, living in single-family homes, with children were more inclined to buy dark-colored couches.

The younger customers, living in apartments, having few or no children were likely to buy light-colored couches.

These insights allowed Burrow to push creative that reflected these attributes to the audiences that possessed them.

Conclusion — Use Of Machine Learning is Saving Millions For Companies

Although many complex AI applications would need more years or even decades to develop fully, the democratization of ML is allowing marketing teams to generate predictive customer insights with no need of spending millions on expensive consultants or hire large data science teams.

It is time for you to explore this option.