Over the last 20 years, probably the most significant development to impact marketing is the role collected data now plays. Whether in the form of data analytics, statistical modeling or advanced formulas created within Excel, marketers’ reliance on numbers and estimations is reshaping how key marketing decisions are made. Of course, the use of quantitative techniques has long been part of the marketing research process, such as information obtained through surveys or collected through sales data. But now, with advances in computer, mobile and Internet technologies, as well as the creation of specialized customer management software, advanced web analytics and other software, the type of data being gathered has dramatically expanded well beyond what used to be obtained with traditional marketing research.
The information now collected is being used in myriad of ways including managing online advertising, selecting new locations for retail stores, and setting optimal price points. Data is also being used to predict whether a customer can be classified as a “good” customer.
As we note in our Managing Customers tutorial, marketers do not treat all customers equally, as some offer more value than others. One commonly employed way to determine whether someone can be classified as a “good” customer is to estimate how profitable he or she will be over his or her lifetime experience with the marketer. Estimating customer lifetime value (CLV) requires significant information about individual customer’s purchasing habits and how they interact with the organization. For instance, a retailer can easily track purchases when customers use a shopper card. With this information, they can see what is purchased, how often purchases are made and what methods are used to make the purchase. By plugging this and other data into software that estimates CLV, a marketer can obtain a prediction on whether one customer offers more value than another. For example, a customer whose purchasing patterns do not seem to be overly influenced by price may hold more value than a customer who waits until price discounts are available before making purchases.
While CLV has been a key part of marketing strategy for many years, some have questioned how useful this is in a digital age. As discussed in this CRM Magazine story, one advocate for changing the reliance on CLV comes from Georgia State University marketing professor V. Kumar. He suggests customer value is a multi-dimensional measure and should not be based on profit alone as customers can positively affect a company in other ways. For instance, a customer’s positive comments on social media can prove beneficial.
Unfortunately, tracking customers across many contact points is not going to be easy. Thus, fully assessing what customers offer and the value the provide may be challenging. However, the fact there is now discussion of changing how customer value is determined is a positive sign and marketers should at least give this serious thought.
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