Decoding Customer Lifetime Value and Designing Buyer Behavior

Decoding Customer Lifetime Value and Designing Buyer Behavior

feature

The following is a special guest contribution from David Williams of RJMetrics

I’m immediately skeptical of blog posts that start off with grandiose statements.

I’d prefer the writer would recognize that I’ve read the title, and I’m reading on because it addresses a topic that I’m interested in learning more about. It doesn’t need to sell me in the first few lines about, ‘why this topic will change my business forever,’ or ‘how this information is the single most important piece of information separating my business from success and failure.’

With that being said, I’m going to start off this post with a grandiose statement that I wholeheartedly believe to be true: Customer Lifetime Value (CLV) is the most important metric for understanding your business.

To be fair, I’m not the first to make this statement, and you (hopefully) may already agree. But, there are a lot of smart people who recognize that customer lifetime value is important, yet admit that they fail to calculate it regularly.

This could be because CLV can seem intimidating and a bit tricky to calculate. But, there are a number of great resources out there that provide simple ways to help you calculate, as well as great posts that provide in depth context on why CLV is so important. But, even companies that know how to calculate CLV, and know that it’s important, are admittedly failing to use the metric to its full potential.

This is good news.

Because it means that the ecommerce companies who are using CLV to understand their buyer behavior are ahead of the curve, and have a competitive advantage over those who don’t.

In this article, I’d like to share recent research we did at RJMetrics that illustrates why using CLV to understand buyer behavior is now more important than ever, as well as share practical ways ecommerce companies can use this information to prioritize and guide their strategy around cultivating loyal customers.  

How the Top Ecommerce Companies are Setting Themselves Apart

Research from our Ecommerce Growth Benchmark revealed that there is a tier of ecommerce companies that stand out from everyone else.

Top Ecommerce Companies vs Everyone Else - Customer Lifetime Value

After six months in business this top quartile of companies are bringing in over $600k in monthly revenue and they are also acquiring new customers at a rate over 3.5x their competition.

New Customer Acquisition Of Top Ecommerce Companies - Customer Lifetime Value

Yet, what’s most interesting is on top of acquiring customers faster, these companies are also excelling at retaining customers early on, with 20% of their revenue coming from repeat purchases in their very first months.

New vs Recurring Revenue Of Top Ecommerce Companies - Customer Lifetime Value

And even more importantly, when you compare their rates of new vs. repeat revenue to companies in the bottom three quartiles (seen below), you notice that by the end of the three-year period, the top quartile companies see the majority of their revenue coming from repeat purchases.

Average Ecommerce Companies New Vs Recurring Revenue - Customer Lifetime Value

This reveals that the fastest growing ecommerce companies are separating themselves from the competition, by mastering both acquisition and retention.

In other words, the classic online retail growth strategy of casting a wide-net in the hope of capturing as much customer demand as possible, and then putting all your focus on converting that demand immediately, is no longer a viable path to long term success. Instead, the top companies are setting themselves apart by retaining customers and encouraging repeat purchases almost immediately.

We found evidence from our Ecommerce Buyer Behavior benchmark also supported this when we looked at the customer lifetime value of all four quartiles.

Average Number of Orders & CLV of Average And Top Ecommerce Companies - Customer Lifetime Value

The top quartile companies proved to have a CLV 79% higher than the other three quartiles. This was a result of their combined higher number of orders, as well as an overall higher average order value, which supported the evidence of their ability to balance acquisition and retention.

The advantage will go to the retailers who are thinking up different ways to turn new customers into loyal customers.

So, how do they do this? Well, it’s not easy.

The Challenges of Earning Customer Loyalty

Our research found that the majority of customers seem to be more interested in filling a need, versus building a long-term relationship with the online retailer.

Single vs Repeat Purchases - Customer Lifetime Value

This chart shows that only 32% of customers actually order a second time over the course of the first year. This accentuates the ability of the top quartile companies to generate over 50% of their revenue from repeat purchases. It’s clearly not easy.

But, now take a look at what happens when you are able to get that customer to repurchase:

Repeat Purchase Probability - Customer Lifetime Value

The likelihood of a customer to make a second purchase is near 30%, but after they make their second purchase, the likelihood of a third purchase jumps past 50%. Once a retailer gets a customer to make that second purchase the chance of an additional purchase continues to increase.

This means that there is only a small group of customers who are making these repeat purchases and engaging in this long-term customer-company relationship. But, take a look at what we found when we analyzed how much this small group of customers is worth:

Top 1% of Customers Worth 18x More Than "Average" Customer - Customer Lifetime Value

After one year, the top 10 percent of customers on average are worth 6 times the industry average,and the top 1 percent are worth 18 times more. Now, consider the ability to identify the marketing campaigns that are attracting these top customers.

Using Customer Lifetime Value to Understand Buyer Behavior

For the research in our Ecommerce Buyer Behavior benchmark we decided to look at buyer behavior specifically through the lens of customer lifetime value, in order to capture how customer behavior changes over time.

Now, if you’re familiar with CLV, or you’ve read the post I mentioned earlier, you’ll know that there are multiple methods for calculating CLV. For our research, we kept it simple and calculated CLV based on the sum of all purchases a customer makes in a given time period.

When we calculated average CLV over 30, 90, and 365 day intervals across all companies in our data set, we found that an average customer spends $154 over the course of their first year, and more importantly, they spend 69% of their first year’s spend within the first 30 days.

average CLV over 30, 90, and 365 day intervals - Customer Lifetime Value

This is big. Industry research has long indicated the importance of the first 90 days in getting a customer to purchase again. Our research found that for online retailers, the window is even tighter, and the first month is the most important period of the customer-company relationship.

This is valuable information for an ecommerce marketing team, emphasizing the need to put extra focus on the first month of the customer relationship is helpful in guiding strategy towards encouraging that ever valuable second purchase. But how can they identify a customer that may fall into that top 1 percent?

The Magic of Month One

Here’s what we found when we mapped average 365-day CLV across a full year:

Customer Lifetime Value Over A Full Year - Customer Lifetime Value

Once again we see that the majority of a customer’s 365-day CLV is realized within the first 30 days, but we now also see that grows to 79% by three years. Now, take a look at how this plays out for the top quartile customers:

On day one, the top customers are spending over three times the amount of the next closest quartile - Customer Lifetime Value

On day one, the top customers are spending over three times the amount of the next closest quartile, but even more importantly they continue to spend more over time.

Ultimately, the top quartile customers spend over four times the amount of the next closest quartile over the course of a full year. Notice how flat the bottom quartiles’ customer CLV stays? This indicates almost zero repeat purchases.

What this reveals is that a retailer who calculates CLV and uses their data in this way, won’t need to wait 365 days before knowing which marketing campaigns, products, or promotions are bringing in the best customers. The most aggressive buying behavior is happening in the first 30 days, and understanding that behavior provides strong predictive power. In other words, by identifying the customers that are outspending their peers within the first month, retailers can anticipate those customers will have a high likelihood of becoming loyal customers.

Conclusion - Finding The Competitive Edge

The ecommerce landscape is becoming increasingly competitive, but the top companies are charting a clear path to breakout success. Our Buyer Behavior Benchmark revealed very real challenges that retailers are up against.

Fundamentally, the top companies are succeeding because they are simultaneously mastering acquisition and retention. The acquisition game is changing and the focus on retention is increasingly becoming the most important aspect to a retailer's growth strategy.

Our research also revealed how retailers can calculate their customer lifetime value, and use their own data to hone in on the customers who are spending more in their first month and purchasing more than two times. With this knowledge, retailers can build a clear strategy around turning first-time buyers into loyal customers.



About The Author

David Williams is the content lead at RJMetrics & believes that stories are “equipment for living.” We encounter something we don’t know about, we explore and absorb it, and by merging it with what we already know, our knowledge grows. This is why David loves telling stories with, about, and around data - it’s his way of trying to make sense of a chaotic universe.