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Core Concepts of Predictive Marketing: Predictive Marketing Use Cases

Omer Artun continues his series on predictive marketing with predictive marketing use cases, including most profitable channel and lifetime value.

We believe that anyone can learn to do predictive marketing with the right foundation. In our series, “Core Concepts of Predictive Marketing”, Acquia’s Chief Science Officer, Omer Artun shares excerpts from his book: “Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data.” 

This series will be a guide to everything you need to know about relationship marketing and predictive analytics in marketing. Dive in to learn how to activate your customer data and tap into unlimited opportunity.   

Recently, we covered how predictive marketing can be used to understand customer equity and overall customer lifetime value by putting customer data directly into the hands of marketers. Predictive marketing is much more than just providing recommendations. The most common use cases of predictive marketing are:

  • Improve precision of targeting and acquisition efforts. With predictive marketing, it is possible to know which channels produce the most profitable customers and optimize marketing spending based on this knowledge. Armed with better information about behavioral buying personas, marketers can also design more effective acquisition campaigns that hyper-target a specific microsegment and increase conversions by four times or more.

 

  • Use personalized experiences to increase lifetime value. Predictive marketing can predict future customer preferences and interactions (such as a customer’s likelihood to buy). Armed with this information, marketers can improve personalization, relevancy and timing of customer interactions. It is these experiences that will keep customers coming back and maximize customer lifetime value. If you can maximize the lifetime value of each of your customers, you will automatically maximize the value of your entire customer portfolio and the value of your company as a whole.

 

  • Understand customer retention and loyalty. Predicting when, why and which customers will return or leave is a big challenge for many organizations. Predictive marketing can help flag customers who are at risk of leaving so that marketers can take proactive steps to retain these customers. Predictive analytics can also generate insights about loyalty-inducing behaviors that maximize customer lifetime value.

 

  • Optimizing customer engagement. Predicting who will respond to an email promotion, what would it take to convert a browser into a buyer, what discount is needed to incentivize the customer to complete the transaction are all methods of increasing customer engagement in real time or near-real time that maximizes marketing effectiveness.

 

 Figure 1 gives examples of questions that predictive analytics can answer for marketers.

Figure 1: Ten Examples of Predictive Marketing

10 Questions to Answer

How Predictive Marketing Can Help

Who will your best customers be?

Predict which prospects or customers have the highest lifetime value, taking into account revenues, but also the cost to acquire and service these accounts. Use this information to spend time and money on high potential customers early on.

How can you find more new customers like your existing best customers?

Predict which prospects are most like your existing high-value customers using look-alike targeting (B2C) or specialized lead generation vendors (B2B).

Find personas in your data to use to acquire more customers like this?

Predict the customer clusters that most distinguish buying personas with respect to brands, products, content and behaviors in your customer base. Then develop creative, content, products and services to attract more buyers like this.

Which marketing channels are most profitable?

Predict which channels attract the customers with the highest lifetime value, including all future purchases. Use this information to influence keyword bidding strategies and channel investments.

Which prospects (non buyers) are most likely to buy?

Determine who is most likely to buy so you can give the right incentive (in B2C) or prioritize your sales personnel’s time with the right prospects (in B2B).

Which existing (or past) customers are most likely to buy?

Product incentive (or discount) is needed to convince a one-time buyer to become a repeat customer. Prioritize the time of account managers to focus on likely upsell candidates.

Which existing customers are least likely to buy?

Predict which customers are likely to leave and target them proactively with a “please come back” incentive, a personalized recommendation or by having the customer success manager make a call.

What customers might be interested in a specific new product?

Predict which customers might be interested in overstock items or a new product release so you can focus your sales and marketing efforts on these businesses or consumers.

What other products or content might this customer be interested in?

Predict what product or content recommendations to make to a particular customer in order to win, upsell or re-engage this customer.

What is my share of wallet with a specific customer?

Predict in what markets or customer groups you have high value potential to focus future customer acquisition strategies.

Armed with information ranging from likelihood to buy, predicted lifetime value and future product preferences, brands can better serve their prospects and their customers by delivering personalized experiences. Applying machine learning and predictive analytics to marketing takes the guesswork out of knowing your customer preferences so you can leave the right and strongest impression on your customers. 


Our next installment of our Core Concepts in Predictive Marketing series will go into detail about the different steps involved in designing and executing a predictive marketing technique with your customer data.

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