The best way to solidify your company’s standing in the hearts and minds of clients is to offer them customized options that anticipate their needs with pinpoint accuracy.
The more companies can tailor their promotions to individual preferences, the more customers will come to experience their interactions with the company as elements of a relationship rather than interchangeable, transactional encounters. Elevating your company’s engagement with its clients is pivotal because it transforms your business into an entity customers care about, as opposed to a resource that can be replaced by any competitor.
Data-driven predictive modeling using customer loyalty analytics can shed some light on the needs of your loyalty member base—and even give advanced warning if they’re planning on migrating to a competitor.
Let’s explore some of the novel and innovative ways in which predictive modeling can be used to make your loyalty program more effective.
1. Targeted recommendations
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Nothing drums up customer engagement like a loyalty program that anticipates a customer’s needs and delivers on them.
Healthy customer engagement levels are truly invaluable. In one study, actively engaged clients showed a purchasing frequency of almost 90% more than their unengaged counterparts—and buy a staggering 60% more on average person transaction.
To get the best understanding of what loyalty program enrollees need, your accounting department should integrate historical purchasing information alongside real-time buying data. For example, in the hospitality industry, recommending nearby places to customers who used a hotel’s website to book a stay could both increase returns for the company and value to the customer.
2. Personalized rewards
A rewards program will not increase customer engagement if the rewards it offers aren’t of interest to members. However, it’s difficult to arrive at a uniform rewards structure that appeals to everyone equally, and in capturing the interest of some, others might slip away.
The solution is to offer personalized promotions informed by member analytics that distill customer data trails into roadmaps to increased engagement levels.
3. Promoting at the right time to the right people
Knowing when to promote—and to whom to promote—is indispensable for increasing the ROI of your loyalty program. For example, if you run a restaurant, you might want to promote your all-you-can-eat buffet. However, promotions for this might be better received around the middle of the year than at, say, the beginning of it, when many customers embark on diets as part of their New Year’s resolutions.
Similarly, clients who have consistently bought smaller-sized meals and low-calorie alternatives will probably be less receptive than clients with a less-discerning diet.
Predictive modeling can allow your company to focus its resources on the right people at the right time, increasing the likelihood of your campaign attaining the right results.
The bottom line
Customer loyalty analytics are an invaluable tool for increasing the value of your loyalty program to your customers and to your company’s bottom line. Well-honed, robust predictive models can help you customize promotions, reach out to the right customers, target recommendations, and more. Make sure analytics is a part of your customer engagement strategy for 2019.