Improved Customer Loyalty Starts with Analytics
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Improved Customer Loyalty Starts with Analytics

Improved Customer Loyalty Starts with Analytics

Customer loyalty is a competitive game - and it goes beyond traditional programs. Consumers belong to more loyalty programs now than ever before, but the number of programs they actively use is in decline. That's because, according to research cited by the New York Times, plain-vanilla discount and freebie programs are no longer enough to keep customers interested.

Merchants need to think about getting all customers to increase frequency of purchasing and to spend more per visit. To create successful programs and offers that target both new customer conversion and long-term retention of loyalty members, franchises need to make smarter use of their analytics to develop offers that will engage their target markets.

Create customer experiences that build loyalty

Simply having products in stock, competing on price, and offering industry-standard service isn't enough anymore. If it were, department stores would be thriving instead of relentlessly declining. Today's customers expect a great experience when they shop, and loyalty programs can deliver just that if they're properly focused.

Historically, big-box stores have had a significant advantage over their smaller counterparts when it comes to knowing their customers and having the resources to measure, test, and refine their campaigns. However, in recent years, tools such as social media analytics and Google's website analytics have started to level the playing field by making it easier for SMBs to access, analyze, and use data. Transaction-related analytics add another level of detail and usefulness to the data landscape. Most important, it doesn't take a data scientist to get the benefits. A few basic insights and a little bit of common sense go a long way.

For example, a restaurant in New York that specializes in gourmet meatball dishes determined that one of their locations had a higher-than-average proportion of customers who lived within walking distance. Using their analytics tools, they discovered that these customers have a median age of 29, do not have children, are financially affluent, and likely to order alcohol when dining out. To better appeal to these customers, the restaurant implemented a happy hour promotion at that location and found that their best customers are purchasing more and visiting more frequently since the campaign began.

What experiences can you create with your customer insights? More important, how can you ensure that the offers you make are effective? What should you offer, and to which customers? When should you make your offer? These are all questions that can be answered with customer insights.

Loyalty metrics

To measure loyalty, you must first decide on a metric. This metric should be some weighting of time, frequency, and dollars spent. This metric can be applied to each customer segment and used as a base to understand the degree to which a customer segment is "loyal" to you, and as a measuring stick to compare and benchmark how that varies by time, location, and activity. In the short term, according to Richard Boire's often-cited "Data Mining for Customer Loyalty" article, you can get a snapshot of your most loyal customers with the RFM method, which rates past purchase behavior according to Recency of purchases, Frequency of purchases during a particular time, and the Monetary value of all those purchases. Simply put, the more a customer segment spends with you per visit and the more frequently they visit your business, the better. Your goal is to increase the sum of the two without having an offsetting effect. This is commonly referred to as "lift."

Measuring success

The success of an offer should be measured based on its ability to lift net sales above what you were otherwise going to achieve without having made the offer, whether that offer is a happy hour promotion, a traditional card-based rewards program, or any other offer or incentive. In other words, find out what the incremental benefit is beyond what you would have otherwise received had you not run the offer or promotion. This measure should consider a baseline amount and include the cost of the offer itself. Do your offers generate lift, and if so, how much and for how long? You can track the response among your top RFM customers for each loyalty offer you make. recommends testing different offers among the same type of customers and testing the same offer in different customer groups such as loyalty program members versus non-members. This information can reveal your most effective offers and most responsive customers.

Measuring lift is much more than just measuring a single metric like frequency, average sale amount, or total sales. It is the net change in relationship of those metrics that matters. For example, increasing a single metric such as traffic through a promotional Groupon offer at the expense of a lower average sale amount is counterproductive. Yes, you increased traffic, but you did not achieve lift.

Your goal should be to influence the behavior of all your customer segments across all your locations and to attract new customers who are likely to spend more, more frequently, such that you are able to get lift across your entire business.

Changing customer behavior

There is no "one size fits all" method of influencing the behavior of customers. Each customer segment within each market is unique, so the typical, generic discount rewards program is unlikely to change the behavior of your customers as a whole. In fact, providing incentives this way may be costing you money. Our research has shown that the people who participate in these types of "loyalty" programs are likely your most frequent customers already, and that you often end up rewarding them for behavior they are likely to engage in anyway.

For example, my wife takes our two boys to a local, low-cost hair salon with a punch card loyalty program that offers a free cut for every 10 visits. She takes them there because it is proximally convenient, reasonably priced, and they do a consistently good job. When they go, they get two regular haircuts for a total of $24. So, with their reward plan we save $12 every five months or so. But here's the reality: the salon is wasting that $12. The truth is, unless they significantly raise their prices or start providing bad service, we are going to continue going there the same number of times and spend the same amount. So, in any given five-month period, they have not incentivized our family to come more often or to spend more. The only thing their "loyalty" program accomplished was reducing their total sales by 10%. That could be a big expense if a significant percentage of their customers is doing the same thing.

What they should be doing instead is gathering data that helps them understand more about our family. Can they get my wife to do an occasional trim, color, or blow dry? How can they convince me to join my boys in getting a haircut there? Can they sell us any styling gel? The point is, each customer segment is different and the goal is to influence behavior. The punch card loyalty program is not accomplishing that goal.

In practice

By breaking both your loyalty and non-loyalty program customers into segments, you can understand their unique attributes, preferences, and habits. Then you can craft the right offer at the right time, which should result in higher redemption metrics with lower offer costs. You can also use your data to see where customers engage with your loyalty offers. Social media posts, promotional emails, offers on your website, and other channels are all potential touchpoints.

Careful analysis can show which campaign touchpoints deliver the best return on investment, based on the key performance indicators you select for each channel. Attribution also can help you track and refine your overall loyalty approach over time and show you how tests and refinements perform in your most effective channels.

Build more efficient loyalty offers

The ultimate goal of smart data use is to develop loyalty offers that are more cost-effective, have higher redemption rates, and generate more lift and higher average purchase amounts per customer than loyalty offers developed without analytical insights. The more highly targeted and effective your loyalty offers are, the more likely customers are to keep actively participating in your program, spending their money with you, and recommending you to their friends, family, and social networks.

Charles Hogan, Tranzlogic co-founder and CEO, has over 20 years of experience in the fintech, data analytics, retail services, and payment processing industries. Follow him at twitter@Tranzlogic or call 888-855-0855.

Published: July 26th, 2016

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