Building complete and accurate customer profiles is no easy task, but it has a lot of value. If yours is like most companies, customer data is all over the place, full of errors and duplicates and not accessible to everyday marketers. Fortunately, predictive technology, including fuzzy matching, can help – at least some – to clean up your data mess and to connect online and offline data to resolve customer identities across the digital and physical divide. Just getting all customer data in one place has enormous value, and making customer profiles accessible to customer-facing personnel throughout the organization is a great first step to start to deliver better experiences to each and every customer.
It is our strong belief that the best way for any business to optimize enterprise value is to optimize the customer lifetime value of each and every customer. Customers are the unit of value for any company and therefore customer lifetime value is the most important metric in marketing. If you maximize the lifetime value, or profitability, of each and every customer, you also maximize the profitability and valuation of your company as a whole. The best way to optimize lifetime value for all customers is to manage your customers as if they were a stock portfolio. You take different actions and send different messages for customers who are brand-new than for those who have been doing business with you for a while. You will need to adjust your thinking and budget for unprofitable, medium-value, and high-value customers.
When asked to allocate marketing budgets, most marketers immediately think about acquisition spending and about allocating budget to the best performing channels and products. However, the predictive marketing way to allocate spending is based on allocating dollars to the right people, rather than to the right products or channels. Most companies are focused on acquisition, whereas they could achieve growth more cost-effectively by focusing more of their time and budget on retention and reactivation of customers. Marketers should learn to allocate budgets based on their goals to acquire, retain, and reactivate customers and to find products and channels that deliver the highest value customers.
We will look at the predictive technique of clustering and how it is different from classical customer segmentation. Clustering is a powerful tool in order to discover personas or communities in your customer base. Specifically, in this chapter we look at product-based, brand-based, and behavior-based clusters as examples. Clustering can be used to gain insight into differences in customers' needs, behaviors, demographics, attitudes, and preferences regarding marketing interactions, products, and service usage. Using these clusters, you can also start to differentiate and optimize both marketing actions and product strategy for different groups of customers.
In this chapter we look at the customer life cycle in more detail, from acquisition, to growth, and to retention and see how your engagement strategy should evolve with each and every customer during the life cycle. The basic principle of optimizing customer lifetime value is the same for all stages of the life cycle and can be summarized in three words: give to get. Customers are much more likely to buy from you if they trust you. The best way to gain trust is to deliver an experience of value. So to get customer value, give customer value.
Not all customers have equal lifetime value. Any business will have high-value customers, medium-value customers, and low lifetime value customers. There is an opportunity to create enterprise value by crafting marketing strategies that are differentiated based on the value of the customer. This practice to segment and target by customer lifetime value is called value-based marketing. Spend more money to appreciate and retain high-value customers. Upsell to medium-value customers in order to migrate these customers to higher value segments. Finally, reduce your costs to service low-value or unprofitable customers.
Likelihood to buy models is what most people think about when you use the word predictive analytics. With these models you can predict the likelihood of a certain type of future behavior of a customer. In this chapter we look at programs based on likelihood to buy predictions spanning both consumer and business marketing. We see how in business marketing predictive lead scoring or customer scoring can optimize the time of your sales and customer success teams. We also show you how consumer marketers can optimize their discount strategy and the frequency of their emails based on propensity models.
Another popular predictive technique is personalized recommendations. In this chapter we provide marketers a primer on recommendations and we teach you about different types of recommendations. We explore recommendations made at the time of purchase versus those made as a follow-up to a purchase, and recommendations that are tied to specific products versus those that are tied to specific customer profiles. We also discuss what can go wrong when making personalized recommendations, and we highlight the need for merchandising rules, omni-channel orchestration, and giving customers control when making personal recommendations.
In this chapter we cover three specific predictive marketing strategies that can help you acquire more, and better, customers: using personas to design better acquisition campaigns, using remarketing to increase conversion and using look alike targeting. When it comes to remarketing, you should be able to differentiate between customers who are likely to come back, and send them a simple reminder, versus those who are unlikely to come back and may need an additional incentive. This is true for abandoned cart, browse, and search campaigns. Using lookalike targeting features of Facebook and other advertising platforms, you can find more customers who look just like your existing customers, for example, new customers just like your best customers.
The secret to retaining a customer is to start trying to keep the customer the day you acquire her. The initial transaction is just the beginning of a long relationship that needs to be nurtured and developed. Engagement with customers should not stop when you convert a prospect into a buyer. In this chapter we cover a number of specific predictive marketing strategies to help grow customer value: postpurchase campaigns, replenishment campaigns, repeat purchase programs, new product introductions, and customer appreciation campaigns. We will also discuss loyalty programs and omni-channel marketing in the age of predictive analytics.
We recommend you focus on dollar value retention. If you don't, you could be retaining customers, but losing money anyway. Also, when measuring customer retention it is important to realize that not all churn is created equal. Losing an unprofitable customer is not nearly as bad as losing one of your best customers. Also, it is a lot easier, cheaper, and more effective to try and prevent a customer from leaving than it is to reactivate that customer after she has already stopped shopping with you. In this chapter we look at different churn management programs, from untargeted, applying equally to all your customers, to targeted, and we will cover proactive retention management