Today, more data is available than ever before, and yet not all businesses are set to take advantage of its fullest potential. According to a McKinsey's DataMatics 2013 poll, responsive businesses that heavily rely on customer analytics, are more than twice as likely to produce above-average profits. They outperform their colleagues across the whole customer lifecycle. They are nine times more likely to have better customer loyalty and outperform less analytical peers on new customer acquisition by an astounding 23 times.
Using advanced segmentation and lifecycle management models, loyalty programs can play a significant part in the entire data value chain by allowing the gathering of member personal data, analyzing the data on loyalty platforms, and employing the insights gained to encourage deeper member engagement. This enables managers of loyalty programs to find out when, why, and how members interact with the program.
Compared to their predecessors, loyalty program owners today are incredibly fortunate. Over the past three decades, loyalty program management has shifted from simply running a loyalty program to companies wanting to harness the “invisible” power of such programs through the insights they provide into the behavior of their customers, the ability to create better relationships & informed decision-making for targeted marketing campaigns. Today, the focus has shifted again to the design & management of high-value customers through segmentation and quality customer experience across various channels & touch points.
Businesses can access customer data insights in a variety of ways. With the technology available today, brands can easily access real-time reporting dashboards & segment insights using a loyalty program software. This is especially useful for smaller DTC brands, who don’t have to call in fancy analysts & data scientists to analyze their data.
This suggests that reporting modules provided by modern loyalty program providers should be adaptable to diverse teams' demands and organizational needs, by utilizing technology that combines capabilities for automated marketing, machine learning/AI, and dashboard reporting. For example, being able to search by specified periods and country/region/online across different campaigns, segments or tiers, and member behaviors or get a quick glimpse into your most critical metrics right off the bat.
Data is most important when measuring and monitoring the effectiveness of the loyalty program. It also serves as a baseline for the overall improvements to the loyalty program. After implementing a loyalty program, brands should be well equipped to collect data right away, analyze and use customer data where more accurate marketing & business decisions could be made. There’s an array of metrics available to loyalty program managers including both qualitative and quantitative data.
"Data-oriented businesses are nine times more likely to have better customer loyalty and outperform less analytical peers on new customer acquisition by an astounding 23 times.” — Mckinsey, Datamatics poll 2013.
Customer-based indicators of business performance have been adopted because of marketing's shifting focus on fostering connections with customers. Among them, several loyalty metrics have grown in popularity over time in marketing and business practices.
Loyalty programs play a key role in the overall data value chain, facilitating the collection of zero-party data (Consumer-owned data that they voluntarily and deliberately share with a brand they respect) & first-party data (Data that belongs to the brand and is collected through consumer interactions).
For a long time now, cookies have been the primary way websites tracked their users' interactions. However, back in 2020 Google announced that it will no longer support cookie tracking starting in 2022, while they are already blocked in Safari & Firefox (albeit they’re still testing the system that will replace cookies).
The intrinsic value that customers’ data can offer to loyalty programs is the ability to gain valuable consumer insights that can drive revenue growth and make better-informed decisions across multiple business functions. Let’s take a look at some ways Loyalty program members’ data can bring value to online DTC businesses :
A 2017 study by Salesforce, showed that personalized product recommendations drive just 7% of visits but 26% of revenue. Data collection & analysis has the potential to unlock hyper-personalized recommendations & digital marketing campaigns. This includes delivering personalized messaging & segment-based targeting depending on buyers’ unique needs and behavior. This is an important part of running a successful loyalty program, as not all rewards are created equal, where you’re more likely to drive conversions & engagement by offering relevant products & rewards to your customers.
Linking your advertising platforms and your email marketing software to your loyalty program data could help you deliver a highly relevant dynamic re-targeting, based on individual behavior patterns across multiple channels, with lower remarketing costs. For example, using custom audiences, you can remind your members about a product they may have been considering or a high-margin product left on the cart, accompanied by an offer of a points multiplier if they buy from you in the next 48h.
Through the use of loyalty program data, DTC businesses can adapt their customer support to achieve the best results by learning more about the customer, his interactions & level of engagement with the loyalty program, and the brand. For example, if they are a premium tier member showing a higher than usual churn risk score, this could entail the use of apps that encourage the customer support representative to suggest a satisfactory incentive to the member based on his previous interactions with the brand.
Linking your advertising platforms and your email marketing software to your loyalty program data could help you deliver a highly relevant dynamic re-targeting, based on individual behavior patterns across multiple channels, with lower remarketing costs.
Data gives DTC brands the capacity to create in-depth insights about the Loyalty program members' identities, behaviors, interactions & responses to various marketing campaigns and offers, consumption trends over time, life stages, and segmentation, amongst other things.
The requirement is that reporting modules should offer enough flexibility to display real-time and period-over-period measurements. These allow businesses to monitor the overall performance & the effectiveness of loyalty campaigns. As a result, these insights can later communicate relevant offers to the right members at the right time in order to encourage desirable behaviors, safeguard profit margins, and produce new member insights. KPIs such as engagement rate, return on ad spend (ROAS), churn rate and reasons, RFM (recency, frequency, monetary value) tracking, repeat purchase ratio, share of wallet, member lifetime value, cost per acquisition, status tier progression are most commonly used when analyzing a loyalty programs’ performance.
By utilizing loyalty programs’ insights, DTC brands can better understand how customers are using their products & services, what they like and disapprove of, as well as any new features (and ideally pay more for). This data can also play an important role in inventory management, especially for businesses that sell a wide range of products (like retailers).
Data is also essential for the creation of new products because it enables sophisticated modeling that predicts the effects such innovations may have on operations and profits.
When running a loyalty program, members’ Lifecycle management is an important part of its success. Loyalty program data can inform businesses on how their members change the way they interact with the brand (lifecycle journey), how their situation develops, and how it can relate to the business over time.
By forecasting which products would appeal to the members at particular periods and introducing those items with messages and offers, these valuable insights may retain loyalty.
Loyalty programs play a key role in the overall data value chain, facilitating the collection of zero-party data (Consumer-owned data that they voluntarily and deliberately share with a brand they trust) & first-party data (Data that belongs to the brand and is collected through consumer interactions).
Consultant Peter Drucker once said “you can’t manage what you can’t measure”, and we couldn’t agree more. There are a variety of metrics available around loyalty programs. The challenge is to find the “metrics that matter'' from the giant pool of measures. As an online DTC business, finding your Key performance indicators for your loyalty program will allow you to filter out the noise and assess the success of your loyalty program as well as predict future customer value strategies. We cannot measure everything, and we shouldn’t anyway. Finding the right balance as a business lies in focusing on those KPIs that truly impact your business, creating compound effects that translate positively on your growth & revenue.
Here we will discuss the most commonly used key loyalty metrics that online DTC brands can use to measure the success of their loyalty programs.
“Not everything that counts can be counted, and not everything that can be counted counts.”
Businesses that continually acquire new customers but struggle to keep them are less likely to experience profitable bottom lines results. When a retained customer leaves, the revenue stream from that customer is lost. Along with losing sales, the company also forfeits the advantages of keeping clients, such as cheaper service and marketing costs. A new customer that purchases less frequently and in smaller amounts needs more assistance and is less likely to refer to new customers. Replacing the loyal customer is going to come at a higher acquisition cost. Thus, a business with a poor retention rate must therefore incur additional costs in order to generate the same level of revenue. At its core, a loyalty programs’ mission is to boost the retention rate, a measure commonly used as a typical indicator of a brand's “health”, and a proxy for future revenue.
When businesses focus solely on acquisition, they run the risk of gaining less quality customers that require more effort to retain. Thus, the expected returns from these customers may diminish as they become costly to retain. Ideally, businesses should aim to grow sustainably by retaining & acquiring the right customers. The most useful & widely used measure of this is customer lifetime value. It is the total revenue expected to be generated by a customer during their lifetime.
For DTC businesses, one of the most common conceptualizations of a loyal customer is a customer that keeps coming back & purchasing in higher frequency. Understanding this metric gives a sense of how engaged they are with a brand & measures the effects of the loyalty program on customers’ behavior. For example, F&B and apparel brands are more inclined to consider this measure as an important indicator of loyalty.
Aside from purchase frequency, another central KPI to look for when measuring the effectiveness of loyalty programs is the average value of orders or spending. It measures the average amount of money that a customer spends when they purchase from your store. When this value increases, it is a good indicator that a brand is retaining higher value customers.
A practical method for assessing consumer usage and loyalty patterns is recency, frequency, and monetary value analysis. Recency refers to the most recent service contact or transaction. Frequency measures how frequently these customer/company events happen, whereas monetary value examines how much a client commits to spending, investing, or committing to a certain brands’ products or services. This method is also highly relevant for tier-based loyalty programs.
Online retailers have adopted RFM analysis with unexpected results, following the example of direct marketers who are prominent users of this measure. For example, new clients cost apparel e-tailers 20 to 40 percent more than their brick-and-mortar counterparts, whereas retained online customers spend more than twice as much in the first 24 to 30 months of their relationships as they do in the first six months.
To further illustrate, let’s imagine a customer who spends $200 on products she needs from an online beauty store while on vacation, while not having that brand in their country of origin. As the beauty store considers this transaction “high value”, they place this customer in their “favorite” customers list, enroll them into their loyalty program and start sending them costly emails, SMS & other marketing assets with no follow-up transactions. If they applied the RFM approach, they would quickly find that this customer doesn’t adhere to any of the 3 RFM dimensions.
“Loyalty measures still have ground to gain when one considers that only 50% of companies calculate retention rates and less than one in three companies employs customer lifetime value, RFM or share of wallet.”
NPS is an indicator used to measure intent to repurchase customer satisfaction & a company's ability to grow. It first gained traction due to its simplicity and explanatory power. It is also more suitable to measure future behavior, as it shows the probability of a customer to recommend products or services of a company to friends and family. Although it is not an answer to all metrics, businesses could use NPS as a proxy for customer experience measures.
Your loyalty program's active engagement rate can also be used to gauge its effectiveness. This makes it easier for businesses like online DTC brands to see how many consumers are accruing and using points in the loyalty program, which can provide ideas for increasing that proportion. At the same time, it helps determine a loyalty program's success scenarios. To calculate it, divide the number of customers who are actively engaged in your loyalty program by your total number of customers.
As mentioned earlier, technology available today should provide a flexible reporting analytics dashboard that would make your KPIs easily accessible. Some Loyalty programs software like Loyaly can provide you with this metric automatically right in your Dashboard.
Loyalys’ analytics metrics
Customers showing more satisfaction don’t prove the monetary value of a loyalty program. As a business, you never want to go in blind before launching your loyalty program. Businesses should at the very least have some visibility as to the target or desired results based on existing and historical customer data. This allows brands to make better informed decisions if the projected improvement in customer retention justifies launching the program, and how much investment they need to run and commercialize it.
Loyalty programs don’t come in without a cost (discounts, rewards, customer service..) but the cost should be quickly surpassed by the incremental revenue from retained & loyal customers. One way to compare yields is by measuring the return on investment of your loyalty program.
There are many ways you can calculate your ROI, depending on the loyalty program's design framework. If you’re not sure where to start, one simple way to get started is by figuring out :
There are more advanced and better ways to measure your loyalty programs ROI with more precision, such as the Lifecycle management model developed by author Philipp Shelper, which accounts for incremental revenue generated from new customer acquisition and existing customer retention.
Ra = average revenue per customer
Mn = new customers acquired as a result of the program
Ri = Average increase in revenue by existing customer participating
in the program
Mm = Number of existing customers participating in the program
(members)
Mr = Number of existing customers forecast to churn who stay be-
cause of the program
D = Dollar amount of incentives and rewards used
A = Administrative costs of program
However, you can always start small, then experiment with different methods until you find what works best for your business.
The percentage of rewards redeemed by those who have enrolled in your particular loyalty program, expressed as a percentage of all rewards redeemed by all users during the relevant period (e.g., 1 month). For example, if you have 1 million rewards points available and 100,000 people enrolled in your loyalty program during the relevant period, then 10% of all rewards redeemed would be 100,000 points (10% of 1 million). The best way to increase your loyalty program’s redemption rate is by offering more rewards that are relevant, valuable and desirable to your customers. This doesn’t mean you should offer a higher number of points for purchases–it means you should offer rewards that people want and will use.
If you’re looking for more detailed methods to calculate the KPIs mentioned above, this article is for you: https://www.loyaly.com/blog/retention/what-is-customer-retention-analytics-and-how-to-measure-metrics
As a business, you never want to go in blind before launching your loyalty program. Businesses should at the very least have some visibility as to the target or desired results based on existing and historical customer data.
Loyalty programs are a great way to keep your customers engaged, and a few KPIs can help you determine the success of your loyalty program. By looking at a few key metrics, you can determine how well your loyalty program is working. Loyalty programs are a great way to keep customers engaged, and a few KPIs can help you determine the success of your loyalty program. Using the right loyalty program analytics can help you determine if your loyalty program is successful. Use these key metrics to gauge the success of your loyalty program.
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