6 Easy Ways to Start AI Marketing
Tags: Personalization, Best Practices, Artificial Intelligence, Customer Segmentation, Digital Marketing

Artificial intelligence (AI) is no longer the next big thing, but a mainstream tech being adopted in digital marketing now. From startups to large organizations, more and more firms are opting for AI-powered digital marketing tools to enhance campaign strategies and decision making. According to a recent report, 51 percent of marketers are already using AI, while 27 percent of them are planning to incorporate it within their digital marketing strategy.

For those not yet investing, there are several barriers holding them back. According to a survey by McKinsey, the two main reasons are poor digitalization and a lack of skilled people to implement it. In other cases, it is simply a lack of knowledge about what AI can actually do.

The reality is, there are plenty of potential ways you can use AI in digital marketing — but which ones should you use? Here are six top use cases that are feasible to implement and add long term value from the start.

1. Optimizing Ad Spend

For advertising to make sense, you need to be making more money than you spend. Therefore, when optimizing your campaigns, it is important you know exactly what a customer is worth to you, so you are not paying more to acquire them than their worth.

Instead of figuring this out manually through a process of trial and error for say 10–20 segments, AI techniques such as deep learning (DL) can help you analyze users’ massive behaviors for segmentation. It can then accurately predict conversion rate (CVR) and return on advertising spend (ROAS) for over 1 million segments so you can identify and target the most profitable ones.

AI can also help you learn user intention and interest, which help you create campaigns with relevant products to these profitable segments, boosting your ROAS further. According to Appier data, AI product recommendations can generate 10–20 percent better CVR.

2. Enabling True Cross-Screen Targeting

With many consumers owning multiple devices these days, being able to effectively target them has become trickier. Should you target a user on their desktop, tablet, or smartphone? When is the best time to target them? What are they using each device for?

AI can answer questions such as these and help you target ‘cross-screen’ with much greater precision. By monitoring and understanding a customer’s typical purchase journey across different devices, AI can help you develop a single customer view to link the same user the multiple devices he or she owns. You can then use this insight to target the right people at the right time on the right device, and adapt your campaigns to suit these different touchpoints.

3. Improving App User Lifetime Value

With one in four people abandoning apps after just one use, it is never easy to keep your app users hooked.

While success used to be determined by how often your app was downloaded and installed, it is now determined by a much more valuable metric: continued engagement and user retention. After all, only the users who fulfill in-app events such as subscriptions and purchases will continue to spend.

Instead of relying on guesswork and human experience to find your most valuable app users, DL can not only minimize fraudulent installs for a better start, but also analyze user patterns in your app, such as clicks and in-app events, along with their browsing behavior on external websites based on third-party data, to figure out which types of users are most likely to stay and convert. You can then focus your efforts on these people.

5. Expanding Share of Wallet

As the Pareto Principle suggests that 80 percent of your company sales come from 20 percent of your customers, it is crucial to remarket to your existing customers.

Marketers can use machine learning (ML) to increase share of wallet by creating more personalized product recommendations, which, in turn, encourages existing customers to buy more. Rather than base your personalized recommendations on pre-defined rules, ML can consolidate historical first- and third-party customer data to discover their interests and preferences beyond what they have shown on your own channels.

These insights enable you to make hyper-personalized recommendations, mapped to customers’ real interests. The result? You could see your revenue increase by 6 to 10 percent.

5. Enhancing Customer Experiences

To have sticky customers, you need to be delivering not just great customer service, but exceptional customer service. Delivering this is not easy, but it becomes much more achievable with the help of AI.

No one likes waiting. Widely adopted chatbots allow brands to respond to commonly asked customer questions in timely fashion through live chat experiences. In addition, they are available 24/7, meaning customers can get answers from your company at any time.

AI predictive analysis can also help you personalize product recommendations and make relevant suggestions during customer communication. By analyzing past and real-time data from online chat, email, phone calls, CRM and social media using natural language processing, you will not only know what your audience is interested in, but also their sentiment, such as “satisfied”, “frustrated”, “excited” or others.

6. Predicting & Preventing Customer Churn

Customer churn is an ongoing challenge for brands. Even if your monthly churn is just 3 percent, compounded over a year, these lost customers add up. Plus, acquiring a new customer can cost five times more than retaining an existing one.

Traditionally, tackling churn involves taking a retroactive approach. You make tweaks and changes then look back and work out whether they were effective.

Instead of doing this back work, marketers can use ML to predict what types of people are most likely to churn, and what makes them tick. You can then put measures in place to prevent it. For example, setting up an alert for specific churn indicators, tailor campaigns based on dormant customers’ current interests, or tweaking campaign creatives resonant with customer preferences.

From optimizing your ad spend to expanding your share of wallet and preventing customer churn, by using AI to turn your customer data into actionable insights, you can create a much more effective marketing strategy and gain a competitive edge. Which ones will you invest in?