Driving Customer Loyalty Strategy Through Data Analytics

With the ease of online shopping, the pop-up ads sharing news of sales and low prices, and the help (or harm) of online reviews, many companies are feeling pressure from their competitors. The challenge of gaining return customers is at the forefront of the battle for survival and many organizations are turning to loyalty programs as a way of incentivizing their products and services. There are a number of different loyalty programs out there, however, the most commonly used ones measure the amount of customer engagements (number of visits, money spent, products purchased, services used, etc.) and allow customers to accumulate points for being a loyal customer. As an Information Architect, I spend my days helping clients to understand analytics and insights resulting from these programs. Let’s take a closer look at some examples and their affects.

Customer Loyalty Reward Types

Not all customers are profitable; similarly, not all rewards lead to loyalty. When measuring the success of loyalty programs, it is important to take a hard look at various rewards and measure which ones lead to the best level of engagement.

Some loyalty programs have simple rewards. For example, Kroger, the grocery chain, provides instant rewards to customers in the form of a discount for being a Kroger Plus customer. There is no process of accumulating points and redeeming them.

On the more complex end of the loyalty spectrum are the organizations that utilize programs in which customers accumulate points that they are then able to redeem for different types of rewards. You may be familiar with some of these programs. One example is the program in place for American Express customers. These users accumulate points simply by using their American Express credit card. The points they earn can be used for travel expenses (flight, hotel, etc.), gift cards, merchandise, or the customer can choose to receive cash back. Similarly, airlines such as Delta and Southwest, give you a host of redemption options to choose from just for choosing their airline. Another example of this type of loyalty rewards program is Dunkin Donuts offer of free beverages for the customers that buy their products most often.

Evaluating Rewards Using Data Analytics Techniques

Assuming your company offers a variety of redemption options, the most accurate way to measure the effectiveness of the different options is by conducting a pre-post analysis using matched pair design, a special case of the randomized block design statistical experimentation technique. More simply, individuals redeeming their rewards are divided into pairs of similar customers. Well, what does that mean? The way you define “similar” is based on your specific definition. Generally speaking, you will match customers using attributes such as age, gender, loyalty class (basic, premium, etc.), tenure, market/location or previous revenue spend. When creating these matched pairs, you will ensure that while the customers are similar on all other attributes, one of them has redeemed the reward you are evaluating while the other has not. This essentially gives you a control group of customers. The next step is to measure the customer’s behavior before and after the redemption activity. The measurement can be revenue, number of visits, number of trips/miles, etc. If you have succeeded in creating these pairs, you should find that the pre behavior of the control and target groups will be similar. You will then be able to observe the behavior post redemption and measure the lift the reward/redemption generates.

Potential Pitfalls

Although this form of evaluation does give you measurable results, there are many factors that you need to be aware of. For example, you could introduce sampling bias if you start by choosing the wrong group of customers. Many large corporations have tens of millions of customers in their loyalty program and only the active ones are maintained in a readily available database. This makes sense for different types of analysis as it improves the speed and performance of queries. However, if you start with this set of active customers, it could introduce sampling bias as you will probably miss customers that moved toward other vendors/loyalty programs after using a certain reward. In this instance, my recommendation would be to use a random sample from all customers, both active and inactive.

Other factors to keep in mind are the enrollment date of customers and the period for the analysis. You want to avoid customers that have only been members for a partial time period. The recommendation here is to choose customers that became enrolled before the date of the analysis and ensure that specific pairs of customers have similar tenure, as tenure can influence the level of engagement in certain industries.

In most cases customers do not only engage with one type of reward. In order to ensure that the analysis is specific to the reward being evaluated, its important to try to choose customers that exclusively participated in the given reward.

Linking ROI to Various Customer Loyalty Rewards

The result of your analysis will give you the metrics you are looking for relating to the effectiveness of each reward and the amount of lift it is generating. You can conduct a bit more analysis to compute the incremental revenue as well.  For example, if you know the dollars spent per month by a group of customers you can calculate the average revenue in the post-reward period for the control group customers and compare that value to the target group customers to calculate the percentage age increase due to the redemption. If the number of customers is known, you can now compute the total incremental revenue.

There are many online resources that explain how to compute lift or incremental revenue, but it’s important to understand your unique situation and business problem, discuss the approach with the stakeholders to whom the results will be presented, and take the approach that best fits your situation.

Conducting an analysis will reveal the types of rewards that will likely be most profitable and what makes sense for your company and for your customers. Once you have made that determination, you may want to begin to do more broad market research into the different reward types to see if you can identify any trends. You may also choose to create a customer profile to determine the most profitable types of rewards and to understand the types of customers that take advantage of these rewards. Your findings will help to inform your customer acquisition strategy.

You Know Who Will Respond…Now What?

As with any analytics project, understanding what the numbers are telling you is not always easy. It is entirely feasible to come up with varying results based on underlying assumptions. Once you have put all of the work into understanding who is participating in your organization’s loyalty program and how that is affecting their decisions as a customer, be sure to find a partner to help you fully realize the potential of the program you have in place and to keep your customers coming back for more.

AmirCharaniaInformation Architect

Amir Charania is an Information Architect at Daugherty Business Solutions - Atlanta. With 15+years experience in the information technology industry including technology consulting, product management and business intelligence architecture/development, Amir has been an integral part of delivering end-to-end large-scale solutions to a number of Fortune 500 companies. He has gained extensive experience in every aspect of data analytics, customer loyalty programs, machine learning and data warehouse solutions crafting.Amir earned bachelor’s degrees in computer science and engineering from the University of Mumbai before moving to Atlanta to complete the MBA program at the Georgia Tech College of Management. He currently lives in the Northlake area of Atlanta with his wife and two children.