The 360 Degree Customer

By: Mark Horbal, CIO, SVP-IT, EVP Corporate Vision and Innovation, SpringBIG

Mark Horbal, CIO, SVP-IT, EVP Corporate Vision and Innovation, SpringBIG

In the simplest terms, the fundamental objective of any retail enterprise is to sell as many products as possible. Each sale generates revenue and adds to the topline. There are other considerations, of course, such as product and other costs, which eventually result in the bottom line–profit.

"These insights, acquired through analytics should translate to campaigns that are more accurate, effective and have a lower cost"

Two of the many KPIs (Key Performance Indicators) that retailers use to analyze their business are of particular value in driving retail success. These are the customers’ RFM and LTV. A customer’s RFM (Recency / Frequency / Monetary) metric attempts to shed light on her value to the enterprise in terms of when she was in the store last, how frequently she visits the retailer’s stores and how much she spends. While the first two components are related, they shed additional light on trends. On the other hand, the LTV (Lifetime Value) metric represents the total dollar value of all purchases that a customer makes during his relationship with the retailer.

The fundamental premise is that the two direct ways in which a retail business can grow is by increasing the number of customers and increasing the value of its customers, as measured by RFM and LTV.

Know Your Customer

In order to accomplish this, a retailer must understand his/her customers. 360-degree customers are those about whom the retailer knows a lot about. So what kind of information do we need? Most importantly, a retailer needs to associate purchase transactions with a customer. Understanding what she has bought, when and for how much, allows them to compute the RFM and LTV metrics. Secondly, knowing how to communicate with the customer is critically important. To do this the retailer needs email and ideally mobile phone number, since SMS is becoming an important channel. Understanding the customer’s in-store and e-commerce behavior, including purchases, product browsing and abandoned shopping cart contents provides additional insight.

Sources of Information

Transactional information is relatively easy to collect from a combination of POS, e-commerce and possibly loyalty program registrations, but there are other means as well that can be used to create a more complete picture of the customer. For example, third party data appends (data augmentation) allow retailers to purchase data from a number of companies based on, for example, the customer’s phone number; asking a customer to provide information while registering for a contest, or discount; interfaces with social media, including referrals, web login with social media credentials and others; data collected during product returns or exchanges; private credit cards issued by the retailer automated matching of data collected during different customer interactions with the retailer.

Information Drives Highly Targeted Campaigns

In the past, the customer’s identity was typically acquired by registration in a loyalty program or the retailer’s credit card. It served a dual purpose of promoting loyalty by rewarding purchases and enabling the tracking of members’ purchase activity. The latter allowed the comparison of purchasing behavior of loyalty members vs. other customers.

Today, the retailer needs much more–understanding customer behavior is the fundamental goal. While transactional data collected from POS and e-commerce can be attributed in the conventional manner, it can be augmented with associated data from social media, web analytics, browsing behavior, product interests, campaign responses and augmentation from third party sources.

Once collected and aggregated, typically in data warehouses, the data serves to provide analytic insight via KPIs and reports. It is also used for segmentation or clustering customers into groups with similar attributes, frequently, but not always associated with their RFM or LTV values. Its main function, however, is to identify actionable strategies. For instance, personalized campaigns targeting specific customer segments deemed receptive to a particular offer, delivered via multiple channels, including email, SMS, mobile apps and social media and possibly reflecting each customer’s channel preference; engagement strategies, which deepen the customers’ engagement with the brand; conversion strategies, which leverage data to move customers to the retailer’s more profitable segment (for example, converting “lunch customers” to dinner increases profits); acquisition strategies which identify the characteristics of the customers comprising the retailer’s most profitable segments and executing campaigns to acquire or sign-up more such customers, frequently leveraging 3rd party data sources in the process; retention strategies aimed at retention of customer’s at risk of abandoning the brand.

In the recent past, the ability to deliver individualized offers entered the realm of real-time. Many retailers have now successfully deployed the ability to deliver offers in real-time, based on browsing behavior or geo-triggers. This was made possible by introduction of in-store beacons and mobile apps that integrate with mobile device GPS to deliver geo-relevant offers.

Secondary Uses

While driving more sales and acquiring more customers is the primary goal of database marketing, the acquired data has many other important applications–namely, Analytics. With proper tools and expertise, the collected data can be used to provide critically important insights. For example, continuous evaluation and redefinition of KPIs, customer segments and LTV computation; ex post facto evaluation of campaign effectiveness and ROI; A/B testing of strategies and offers: How did customers targeted with an offer perform vs. the control group? How did two groups with different offer values perform against each other?; channel effectiveness and analysis of offer propagation via social media are additional examples;

These insights, acquired through analytics should translate to campaigns that are more accurate, effective and have a lower cost.

Some Practical Considerations:

• Don’t overlook opportunities to collect data about your customers. Aside from the actual purchase transaction, there are many other opportunities to do this.
• Even if the customer’s identity is unknown but you are able to attribute purchases to him/her, this information is useful and provides insight.
• Disconnected information collected from multiple sources may be connectable later, resulting in more complete picture of your customers. Opportunities to connect and aggregate data are present at each interaction that a customer has with the retailer.
• Don’t be afraid of information that is unstructured, examples being social media activity, web browsing, e-commerce interactions and others. There are now a number of technology solutions that provide the ability to ingest massive amounts of such data.
• Protect the information that you collect and assure its privacy. Possession of PII (Personally Identifiable Information) comes with great responsibility. Combining data from multiple transactions and customer-retailer interactions, while useful, should be done with extreme caution because of the risk of erroneously combining multiple customer identities.
• Finally, for many retailers, it is frequently difficult to build an infrastructure that supports both store operations while simultaneously providing database marketing and analytics. Consequently, most retailers rely on companies specializing in the latter to provide such services.