Customer loyalty programs are a popular way to boost retention and engagement. But how well do they work? Well, how many customer loyalty clubs do you belong to? How many of these companies do you do business with regularly? And how many customer loyalty cards do you have for direct competitors (e.g. two supermarkets, hotel chains, etc.)? Exactly. Customer loyalty programs can be improved.
The question is, how can loyalty programs be improved? If you start with the data, things become clear. In this post, we’ll look at two ways data analytics can help you transform your customer loyalty program into something that’s motivating to your customers and profitable to your company.
How Data Analytics Can Transform Your Loyalty Program
For a long time, customer loyalty programs looked at one thing and only one thing: purchase history. Customers were rewarded when they hit a certain spending threshold; some companies added a time-related dimension (e.g. for customers that hit the threshold quickly) or an anniversary-type bonus (e.g. “You’ve been a member of our loyalty club for 10 years! Get an extra 10% off your next order!”). This is a perfectly acceptable start, but it only considers one or two data points. And it does nothing towards personalization, which we know is an important draw for today’s customers.
We can do better than this. Why not use all the customer data your company has acquired to build a better loyalty program? There are two ways to do this:
1. Designing a More Motivating Program
First, build a richer picture of the customers you are rewarding. For this, you’ll need:
- Demographic data. This is the starting point of all personalization.
- Purchase history. If you know a person buys organic food, lots of baby products, or an inordinate number of cookies, pay attention: these preferences can help you determine which reward would be most meaningful to that individual.
- Behavioral data. When and where do they make a purchase? Is there seasonality or other recurring themes? Do they mention your brand on social media or have they referred friends to your company? Also, pay attention to how they feel about your company and whether there are pain points (like a confusing website checkout process) that they mention. This information won’t directly impact your reward program, but it will help you understand your customer.
With this data, you can craft rewards and tiers that are more engaging for your audience. For example, you can use this data to create targeted promotions and rewards for each customer segment. You can time such rewards for maximum effect – e.g. when you know customers are likely to be shopping, you could offer them a special discount. Or if you know they often vacation during August, you could provide perks for hotel stays during that month.
2. Analyzing Loyalty Program Results
Next, data analytics can help you measure the effectiveness of you royalty programs. There are many ways you can do this: using matching pairs of customers (one who redeemed a reward, reached a level, etc. and one who has similar characteristics but did not) to determine why some customers acted and others didn’t. Purchase history data can determine the best type of reward for each customer. You can also learn which rewards work in tandem with other factors like seasonality, types of discounts, or even other rewards.
As with all data analytics, you’ll want to choose your datasets carefully for these studies. Be especially sure not to include partial data (e.g. customers who have been in the loyalty program only one month can skew the results of a quarterly study) and data for customers that have used multiple rewards at the same time.
Why You Should Regularly Analyze Your Customer Loyalty Program
Essentially, data analytics provides greater clarity into the mechanics of your customer loyalty program. Knowing what works (and what doesn’t) with each group of customers is essential in maximizing customer satisfaction as well as customer loyalty – which means more business comes your way.
Related Absolutdata products and services: AI & Data Sciences, Customer Analytics, Marketing Analytics, NAVIK MarketingAI
