Data is one of your brand’s biggest resources, as it informs every decision you make. It helps you find your best-performing marketing channels, it shows you how your brand stacks up to the rest of your industry - and it can also guide the structure of your Shopify loyalty program.
Customer loyalty analysis helps you lower churn and nurture your existing customer base for more repeat purchases. In a best-case scenario, they’ll turn into a referral channel that grows your top and bottom lines.
We’ll walk you through the most important customer loyalty analytics to focus on, including:
- Customer retention rate
- Customer lifetime value (LTV)
- Net promoter score (NPS)
- Repeat purchase rate
- Churn rate
- And many, many more.
You’ll also learn how to analyze your brand’s customer loyalty and make changes based on data. Not taking advantage of data is one of the primary reasons why loyalty programs fail - don’t fall victim to the same pitfalls. Learn about customer loyalty data analytics below.
What is customer loyalty analytics?
Customer loyalty analytics uses data to measure and understand how effectively your business retains customers and fosters long-term engagement.
Knowing who your repeat buyers are and building out brand loyalty segmentation isn’t enough. You need to uncover the why behind it so you can use those trends and predictive behaviors to boost loyalty further. Essentially, a customer loyalty analysis answers questions like:
- What motivates repeat purchases?
- Where do customers drop off in their journey?
- Which rewards or experiences move the needle furthest?
This empowers you to refine customer loyalty strategies, enhance the customer experience, and build a stronger connection with each person who supports your brand. Let’s take a closer look at how you can use customer loyalty data analytics to your advantage below.
The importance of customer loyalty analysis
You might already be starting to see the power of analyzing customer loyalty. The short answer is it helps you retain more customers and drive more profitable sales. But, there’s more to it than initially meets the eye.
Optimizing marketing and personalization
It may not be fair, but today’s consumer expects brands to know what they want even before they do. This sounds impossible, but with a wealth of customer data, it’s easier than you might think. You can use insights to deliver personalized experiences that resonate.
Understanding purchasing patterns, preferences, and behaviors, empowers you to craft tailored campaigns that feel less like marketing and more like a one-on-one conversation.
For instance, you can use predictive analytics to deliver personalized product recommendations or exclusive deals based on a customer’s past activity. This will have far better results than sending a generic email to your entire customer base.
The same can be said for ad targeting. In this sense, customer loyalty analytics help you make the most of your marketing budget, generating a higher ROI.
Identifying at-risk customers
Every brand faces the risk of losing customers. Churn is a part of doing business, but you can use data to attempt to stop it before it actually happens.
There are certain patterns associated with “at-risk” customers, like decreased engagement, fewer purchases, or declining participation in loyalty programs. Keeping a vigilant eye on these metrics means you can try to re-engage the customer with a targeted win-back campaign.
Improving customer experience
Examining feedback, behavior, and touchpoints, helps you uncover friction in the customer journey. From there, you can reduce friction and create a better experience. This leads to easier acquisition and better retention.
Are customers abandoning their carts because the checkout process is clunky? Are they leaving negative reviews due to unclear return policies? Data-driven insights give you a roadmap for smoothing out these pain points.
Happy customers are loyal customers, so working to deliver a world-class experience is well worth the time and resource investment!
Driving retention and revenue
Your retention rate - a data point we’ll break down for you below - directly impacts your bottom line. It’s far more profitable to retain existing customers than acquire new ones, as we discuss in our guide on customer retention stats. That’s because repeat buyers spend more over time and are more likely to explore other products or services you offer.
Analyzing loyalty trends helps you pinpoint what keeps customers coming back, whether it’s a specific product, experience, or reward. In turn, you can do a better job allocating resources, maximizing ROI while building a steady stream of predictable revenue.
Most powerful customer loyalty data analytics to track
What is the best way to get started with something as complex as customer loyalty analysis? It’s not as daunting as you might think. You only need to focus on a few customer loyalty data analytics:
Customer retention rate
This measures the percentage of customers who stick with your brand over a given period. It answers a fundamental question: how good are you at keeping customers engaged? Calculating this metric is simple:
Retention Rate = [(Customers at End of Period - New Customers Acquired) / Customers at Start of Period] × 100
So if you had 1,000 customers, gain 200 new ones, and end with 1,050, that’s an 85% retention rate.
Customer lifetime value (LTV)
LTV reveals the total revenue a customer is expected to generate during their relationship with your brand. It helps you understand whether your efforts to retain and upsell customers are paying off. It also helps you figure out how much you can spend to acquire a customer.
A customer may only spend $50 on their first order, but if they spend $500 over the next 2-3 years, you have much more wriggle room in your cost-per-acquisition (CAC).
Calculation: LTV = Average Purchase Value × Purchase Frequency × Customer Lifespan. It’s that easy. So if a customer spends $50 per purchase, makes 4 purchases per year, and stays for 5 years, their LTV is $1,000.
Net promoter score (NPS)
NPS measures how likely your customers are to recommend your brand to others. It’s a powerful way to gauge how big of an advocate your most loyal customers are. A better NPS means you generate organic growth through referrals and word-of-mouth.
Calculating this metric is a bit more involved as it requires surveying customers by asking, “On a scale of 0 to 10, how likely are you to recommend us?”. From there, you simply subtract the percentage of detractors (0–6) from promoters (9–10).
So if 60% are promoters, 20% are detractors, and the rest are neutral, your NPS is 40.
Repeat purchase rate
This metric tracks the percentage of your customers who make more than one purchase, indicating whether your retention strategies are working or not.
Finding repeat purchase rate is fairly straightforward. Just divide repeat customers by total customers and multiply by 100. So if 300 out of 1,000 customers make multiple purchases, you’d have a repeat purchase rate of 30%.
Churn rate
Churn refers to customers that no longer interact with your brand. It’s unavoidable, but the goal is to minimize it as much as you can. Churn rate doesn’t tell the whole story, though. You need to figure out where customers are dropping off - and why - through deeper churn analysis.
Measuring churn is pretty simple, too. You just divide the customers lost in a given period by the total customers at the start of a period before multiplying by 100. So, if you start with 1,000 customers and lose 50, your churn rate is 5%.
Average order value (AOV)
AOV is the average amount customers spend per transaction. The more they spend per order, the more you can spend on marketing while remaining profitable.
Exclusive offers such as buy 2, get 1 free, free shipping over a certain threshold, or “earn more points” campaigns are a great way to encourage customers to add more to their carts.
Assessing AOV can be done on a per-customer basis or across your sales entirely. Either way, you simply divide total revenue by the number of ordres. So if you earn $10,000 across 200 ordres, that’s a $50 AOV.
Redemption rates in loyalty programs
If you already have a loyalty program in place you can take a look at how often customers redeem the rewards or points they’ve earned.
Low redemption rates might indicate that your rewards aren’t compelling enough. In contrast, high rates suggest your program is engaging and well-aligned with customer expectations.
Again, the calculation here is simple. Divide rewards redeemed by the total rewards issued and multiply it by 100. So if you issue 10,000 reward points and 4,000 are redeemed, that’s a 40% redemption rate.
Engagement metrics
There are a number of engagement metrics you want to keep a close eye on as part of your customer loyalty data analytics efforts, too. These include email open rate, social media interactions, click-through rate, bounce rate, and conversion rate - among many others.
These metrics are all different but answer the same question more or less - how connected do your customers feel to your brand? If engagement is low, you can safely assume the same for loyalty in general.
Incorporating customer loyalty analysis into your business
Understanding all the different customer loyalty data analytics isn’t enough. You need a more streamlined customer loyalty analysis process to make the most of all these insights have to offer.
This involves setting up the right tools, collecting the most relevant data, and refining your loyalty programs and marketing efforts accordingly. Here’s a quick guide to getting started with customer loyalty analytics.
Setting up the right tools
You don’t need to manually calculate the various customer loyalty data analytics we covered earlier. Rivo offers a ton of insights within our loyalty solution, too. The metrics we track for you are more specific to loyalty, so you can track program participation, redemption rates, and more.
Make sure you’re pulling information from other channels as well - like your email marketing software and social media platforms to get the full picture of your customer journey.
Collecting and centralizing data
The quality of your data can make or break your customer loyalty analysis. You don’t want data living in a silo. Rather, you want data collection to come from a multitude of touch points and all be stored in one place.
You can start by pulling in data from your website, in-store purchases, mobile apps, and customer support channels. Make sure you’re cleaning the data on a regular basis to keep it accurate and actionable. This means deleting duplicate entries, outdated information, etc.
The better job you do organizing your database the easier it will be to segment customers based on key data points - be it purchase history, engagement, or loyalty behaviors.
Refining loyalty programs with analytics
After centralizing your data you can start using it to refine your loyalty program. Here are some tips to get you started on the right foot:
- Identify Top Performers: Pinpoint which rewards or incentives generate the most engagement and focus resources there. Similarly, you should have a list of “VIP” customers that are your most loyal customers.
- Adjust Rewards Structures: If redemption rates are low, it could be that rewards aren’t enticing - or feasible. Reassess the perceived value of your rewards and make them more lucrative and attainable.
- Personalize Experiences: Use segmentation to create tailored offers aligned to customer preferences. This will make a massive difference in improving engagement and satisfaction.
Remember that Rivo makes it easy to build on-brand loyalty landing pages and structure your rewards based on customer preferences. Schedule a demo today to see what’s possible.
Leveraging predictive analytics
Data analysis is about understanding the past so you can better predict the future. In this sense, you’ll need to start making informed decisions that impact retention and growth as you dive deeper into customer loyalty analytics.
For instance, you can use historical trends to predict when customers are likely to leave and proactively address their needs with a win-back campaign.
You can also predict which first-time buyers are most likely to become repeat customers and prioritize efforts to nurture them. It’s also important to pinpoint the best times to send offers or launch campaigns based on customer activity patterns so you’re setting yourself up for success.
Integrating insights into campaigns
Loyalty data can also be used to streamline marketing campaigns. This involves personalizing outreach with tailored email campaigns, retargeting ads, and social content to specific customer segments based on past behavior.
You should also use this information to roll out campaigns that adjust incentives based on how close a customer is to their next milestone or purchase.
Another way you can leverage insights from customer loyalty data analytics is by reactivating dormant customers based on their unique reasons for disengaging - be it price, overwhelming communication, poor customer service, or anything else for that matter.
Measuring success and iterating
We want to make it clear that loyalty analysis isn’t something you set up and forget about after making a couple of changes. This is going to be an ongoing process as you strive to get better and better over time.
Create processes for regularly monitoring loyalty metrics like NPS, retention rate, and AOV to gauge your program’s impact over time. You should also get in the habit of setting up A/B tests on loyalty campaigns to refine what works best for your audience.
Most importantly, gather feedback from your customers and use it to create better offers and experiences that are more in line with what they want.
Closing thoughts on customer loyalty analytics
We hope this guide has answered all of your questions surrounding customer loyalty data analytics. This isn’t just about tracking numbers, it’s understanding what drives your customers to stay, engage, and advocate for your brand.
You can turn data into growth with the right tools and strategies. So, take the next step today.
Whether you want to set up a Shopify referral program, loyalty program, or both, Rivo is trusted by hundreds of leading DTC brands. Schedule a demo to learn more, or compare our offerings to the alternatives below:
