How to Use Data to Expand the Lifetime Value of a Customer
CLTV – a.k.a. customer lifetime value – is a measure of the amount of revenue or profit generated by a customer over the course of a relationship. For marketers in the retail industry, few if any KPIs pack as much punch.
But for many organizations, getting an accurate read on CLTV is difficult because customer data is spread out across multiple data sources and customer touchpoints. These include not only points of sale (in-store, online, phone, mobile apps, IoT, etc.) but also social media and any other way a company interacts with its customers.
Better Customer Visibility with a Customer Data Platform
To bring these data sources together, many companies are turning to Customer Data Platforms (CDP). These platforms provide a single view of the customer relationship across channels and interaction points – which makes it easier to calculate CLTV with greater accuracy.
Calculating CLTV is important – but only insofar as it leads you toward improving it. Here again, a CDP plays a critical role by enabling holistic customer visibility required for successful marketing campaigns through one-to-one personalization.
Personalization is important because it’s what customers want. According to The State of Marketing, a recent report from Salesforce Research, 52% of consumers are likely to switch brands if a company doesn’t personalize communications to them. For B2B scenarios, the number climbs to 65%.
Why do customers want more personalization? Because in an increasingly digital economy, these customers are empowered to go wherever they like and buy from whomever they wish. The result is that the field of competition among retailers has shifted. Price, quality, and availability are still important; but increasingly, customers measure you by the customer experience you deliver.
Understanding Customers – and Acting on It
A key way to improve the customer experience is to tailor products, services, and interactions to customer preferences. To personalize, in other words.
But to personalize, you need control over and visibility into customer data. With a single, consistent, and accurate view of the customer, you can track interactions across channels. This helps you understand who your customers are, wherever they appear.
This is only a first step. Companies like FitBit – providers of fitness tracking apps – know how to use 360-degree customer data to increase loyalty through precision segmentation and retargeting. With comprehensive customer data, these companies can more readily show correlations between customer behavior and the likelihood to buy or remain loyal. FitBit recently launched a new brand campaign that features user’s stories to show that the devices are much more than a step counter. The campaign aims to increase brand awareness and engagement and drive user acquisition by reinforcing the global impact Fitbit has had.
Food logging, for example, is a key indicator of loyalty. One fitness tracker found that “users who logged their food intake within the first week of using the app were more than four times as likely to be retained than those who did not log their intake.”
Based on insights like this, you can better segment your customer base. Customers who are likely to be more loyal become your segment for potential add-on services. Other customers for whom you detect potential disengagement can be retargeted with special discounted promotional offers designed to keep them on board.
The Many Faces of Personalization
This is only one example. Companies are figuring out a variety of ways to personalize the customer experience. Take, for instance, Shiseido, a global top 10 beauty brand based in Japan. Shiseido uses a CDP to analyze historical purchase data (remarkably, the company has been tracking purchase data for over 80 of their 150 years of operation), demographic data, and recent customer behavior together. This has helped the company generate and send tailored communications that increase personalization on their loyalty app. The result: a 20% in-store revenue increase per loyalty program member and 38% growth in net income, year over year.
Another example is Wish, a startup challenging Amazon’s dominance in e-commerce. Wish has put the emphasis on personalization like few in the industry, using a unified view of customer data to build and run an AI-powered marketplace. Wish uses this marketplace to instantaneously connect millions of shoppers and merchants with products offered up based on personal preferences, history, and other data.
Companies are also using CDPs for analysis, insight, and the ability to predict what customers want, before they may know it themselves. A single view of customer data, with the help of machine learning algorithms, is what lies behind the power of recommendation engines. For Amazon and Netflix, these engines help create personalized experiences that keep customers attached – to a virtually unrivaled degree.
In these and many other ways, a holistic view of customer data is what’s helping companies deliver highly personalized customer experiences that keep customers loyal and engaged like never before – which is how data insights can be used to increase the lifetime value of your customers.
As a marketer, utilizing data to provide insights can not only help draw in new customers, but also retain current ones. Customers are empowered like never before with a plethora of options, convenience and price points. Companies need to deliver personalized customer experiences to be able to compete with the large companies that have set the standard.