Formation Rethinks How to Measure Customer Loyalty

Practically every retailer, restaurant and major brand today has its own customer loyalty program. The popularity of these programs has skyrocketed, as organizations seek to give customers reasons to return and make more purchases, increasing customer lifetime value (LTV).

Understanding customer LTV is critical, since it ties into the larger business objectives of more sales and more revenue. Customer LTV is a common marketing metric that estimates the amount of money a customer will spend with your brand over time. Customer loyalty is a critical component of LTV because those who return to shop your brand purchase 31% more on average than new customers. Over time, loyal customers are estimated to be worth 10x more than their first purchase.

Getting Beyond Clicks and Likes to Track Customer Metrics

Done right, improving customer loyalty can be worthwhile. Studies show that just a 5% increase in loyalty can increase the average profit per customer from between 25% and 100%.

But how can you tell if a customer loyalty program is successful and putting your organization on the path to achieve these kinds of results? Marketers typically rely on customer engagement metrics, such as clicks, impressions, likes and goal completions, to qualify the success of their offers. However, this isn't the most effective way to determine if their offers are truly impacting the bottom line.

You want to measure customer loyalty program success by setting goals that are aligned to larger business objectives. Sure, clicks and impressions can help determine customer engagement on specific campaigns. But they are often disconnected and don’t, or can’t, show the impact they have on sales and revenue.

Formation Delivers Insights into Net Incremental Revenue and Funnel Metrics

We take a two-pronged approach to measuring offer performance. Formation allows you to track funnel metrics - such as registrations (opt-ins), progressions and completions - to get insights into customer engagement.

But we realize this is not enough to understand the impact of marketing on those important, larger business goals. We also look at Net Incremental Revenue (NIR). NIR quantifies the change in net revenue earned by the company after running a promotional campaign, and also takes into consideration the cost associated with the offer.

Using NIR as a yardstick helps tie marketing to the larger organizational goals and objectives, the most common of which is increased revenue. NIR is an important yardstick because it provides a clear picture of revenue minus expenses, helps the C-Suite understand the impact of a program, and proves marketing return on investment.

Here's a closer look at how we measure NIR:

Total Net Revenue = Qualifying Revenue – Incentive Costs

• Incentive Costs = Dollar value of Rewards granted
• Incentive Costs: 100 Reward Points = $1

Net Revenue per Customer = Total Net Revenue ÷ Number of Customers

• This is calculated for test and control separately.

NIR per Customer = Net Revenue per Customer from Test – Net Revenue per Customer from Control

Total NIR = NIR per Customer x # Test Customers

Efficiency = Total NIR ÷ Incentive Costs

So, let's look at a grocery store loyalty program as an example. To get started, you’ll determine four key components of audience selection: :

• Eligible Population, or which households are globally eligible to receive Formation offers. You should look at all digitally engaged households provided by the grocery store,
• Shuffle Frequency, or how often are households re-sampled/shuffled between test and control groups. You'll want to have long-term test and control assignments for the duration of the test period. We suggest shuffling the groups once every 3-6 months depending on offer cadence.
• Test Group Size/Proportion, or how many households will receive offers in each deployment. We decide this by performing power analyses to determine the sample size necessary to measure significant lift.
• Sampling Strata, or the important subgroups/dimensions to randomize within to ensure balance. We typically stratify on customer segment or on a combination of customer segment and other customer attributes (e.g. geography, spend quartiles, etc).

As this example shows, the success of a customer loyalty program isn't simply engaging with an app or competing in contests. What matters most is whether that program can turn engagement into revenue.

The best way to do this is to personalize offers to meet all your customers' expectations. Our artificial intelligence (AI) and machine learning (ML) solution enables you to do 1:1 personalization at scale, something not possible with traditional martech tools. By going beyond segmentation to true personalization, our customers are able to increase engagement by 2x and net incremental revenue by 3x.

Learn to measure your customer loyalty program effectively. Contact us for a demo today.