2015
Benchmark
Report Series

Ecommerce Buyer Behavior

Key Findings

We looked at the data of 176 ecommerce retailers and 18 million customers to uncover insights on how ecommerce customers behave. Here are a few of the highlights:

Average Spend

A new ecommerce customer is worth $154 in their first year.

Spend in the First 30 Days

69% of a customer’s first year spend comes within their first 30 days as a customer.

Value of Top Customers

The top 1% of customers are worth 18 times more than the average customer.

Customer Lifetime Value by Segment

Housewares/Home and Food/Drug are the only industry segments in this report that have a customer lifetime value above the industry average.

Repeat Purchase Behavior

Only 32% of customers place a second order in their first year as a customer.

Customer Spend and Company Performance

The fastest growing ecommerce companies have a customer lifetime value 79% higher than their peers.

Executive Summary

Kevin Hillstrom

President, MineThatData

For twenty years, marketing strategy focused on getting a customer to purchase something today. The entire ecosystem of online retail is designed to capture customer demand and convert the demand immediately before somebody else gets the chance to capture the order.

This is an entirely appropriate marketing approach when the goal is to acquire as many customers as possible. However, the days of easy customer acquisition are ending. Customer acquisition costs via Google, Facebook, and others are increasing. Meanwhile, the Baby Boomer generation is retiring, and Millennials are focusing on newer business models that will reveal themselves via mobile devices over the next five years. These trends conspire to shrink the available pool of newly acquired buyers.

As a result, marketers will find it increasingly difficult to acquire new customers. Marketers will respond by increasing their focus on retaining customers and maximizing customer lifetime value (CLV) in an effort to generate as much profit as possible from existing customers.

In this report, the folks at RJMetrics have studied the topic of customer lifetime value, and have identified fundamental truths that will require marketers to think carefully about the best way to grow customers to loyal status. There are two critical findings in this study, findings that I routinely observe in my projects as well.

  1. First, the report identified a somewhat low percentage of customers who purchase for a second time within a twelve month period of time. This finding is absolutely true for more than eighty percent of brands, and runs contrary to the industry narrative of easily harvesting unlimited profit from loyal customers. In reality, the majority of customers who purchase for a first time are in the process of filling a need, and are not interested in a long-term engaging relationship with a brand.

    The ramifications of this finding by RJMetrics cannot be understated. Smart marketers will use this research to identify the customers most likely to return, and will work hard to encourage a second purchase as soon as possible. Fortunately, it is not difficult to identify the customers most likely to return after a first purchase. Marketers simply need to conduct the research necessary to identify these customers.

  2. Second, the report accurately identified a very short window of opportunity for marketers. In my experience, spanning nearly thirty years of analytics work for many of the largest brands in commerce, the first ninety days after a first purchase represent the timeframe when a customer is most likely to purchase again. RJMetrics identified a slightly different, and more valuable, timeframe for conversion from a first to a second purchase.

    During this short window, customers purchase additional items to complement a first order, or are in the process of returning and/or exchanging merchandise. Marketers must work hard to please the customer during this window of time in order to maximize the long-term value of a customer. I routinely observe that the brands with the best long-term value are the brands that best manage the repurchase window identified by RJMetrics.

I am really pleased with the nuggets of gold identified by RJMetrics. Their data scientist researched millions of customers, correctly measuring the dynamics that surround a customer as s/he migrates from a first purchase to loyal status. This study shows how very, very difficult it is to grow a loyal customer base. I expect marketers to take advantage of the information in the study via enhanced marketing programs that attempt to quickly push a first-time buyer to second purchase status. Companies that correctly interpret the information in the study will have a competitive advantage over the brands who continue to prioritize conversion rates over customer relationships.

The Most Important Metric

There are many different lenses online retailers could use to explore customer behavior -- web analytics data would reveal how customers behave on your website, cart abandonment data would reveal behavior around the checkout process. In this report we’re going to explore buyer behavior through a very specific lens -- customer lifetime value (CLV).

CLV is the most important metric for understanding your business. It reveals not just how customers behave in a specific campaign or on a specific page, but how their purchase behaviors change over time. In these behavior patterns hide the answers for every decision your team needs to make, from campaign optimization to when to send that first retention email.

Like any metric, the inputs to how any business measures CLV will vary. For the sake of this report we’re calculating CLV as the sum of all purchases a customer makes in a given time period. In other words, we’re excluding things like customer acquisition costs, profit margins, and the time value of money. We’re also framing this metric in very strict time parameters and will be talking about:

By looking at CLV through the lense of these time periods the analysis presents actionable benchmarks for ecommerce retailers to measure themselves against.

Understanding the Behavior of the Average Customer

How much is an ecommerce customer worth?

The first thing we wanted to understand is the value of any single customer.

FOOTNOTE: Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

A new customer spends $154 over the course of their first year of doing business with a company, and they spend the majority of that within the first 30 days. This first month is the most important period of the customer relationship.

Ecommerce customers spend 69% of their first year’s spend within the first 30 days.

How much is an ecommerce customer worth by retail segment?

Of course, spending behaviors vary greatly depending on the products a customer is shopping for. We found that CLV looks very different across industry segments.

FOOTNOTE: Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

Two of these six retail segments have customers spending above the overall average of $154: Food/Drug and Housewares/Home. Due to product assortment, these two retail segments have very different buyer behaviors, and in order to highlight these differences we need to look at how these behaviors drive performance.

What drives customer lifetime value?

At its core, customer lifetime value is the result of two buyer behaviors:

Now, take a look at the industry-level CLV data again, but this time in the context of AOV and the number of orders. This view highlights some very interesting differences in the buyer behavior between industries:

FOOTNOTE: Average CLV and average number of orders (ANO) were computed as averages across customers within each industry. AOV was computed by dividing the average CLV by the corresponding average number of orders (ANO). Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

Here we see the two segments with above-average CLV -- Housewares/Home and Food/Drug -- as the largest bubbles, which represent CLV. The axes of this chart reveal the customer behaviors driving that number. Housewares/Home customers make fewer, more expensive purchases while Food/Drug customers make lower-value purchases, but come back more often. The path to customer lifetime value will be different for every company, but the end goal is always the same: make it higher.

Having high CLV is important for one simple reason: the company with above-average lifetime value has a massive competitive advantage. If your customers are more valuable, you can outbid the competition on ad spend, go above and beyond in servicing customers, and go out of your way to remarket to them. Most importantly, you can afford to invest more in the products and services you’re delivering. High CLV impacts the unit economics of every aspect of your business.

How much is an ecommerce customer worth by region?

When we analyze customer lifetime value by region we again see some notable differences in buyer behavior.

FOOTNOTE: Average CLV and average number of orders (ANO) were computed as averages across customers for each region. AOV was computed by dividing the average CLV by the corresponding average number of orders (ANO). Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

The trend among North American companies leans towards higher average order values. For European companies, the trend skews toward a higher average number of orders.

Do fast-growing companies have higher CLVs?

As we mentioned earlier, high CLV is the fuel that accelerates growth. In our Ecommerce Growth Benchmark we found striking differences between revenue growth in the top quartile of companies and everyone else. Here’s how buyer behavior breaks down based on how quickly companies are growing:

FOOTNOTE: Quartiles were defined based on the total revenue of a company in the third year of its existence. Average CLV and average number of orders (ANO) were computed across customers within each quartile. AOV was then computed by dividing average CLVs by corresponding average numbers of orders (ANO). Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

Not surprisingly, the fastest growing companies have a CLV that is 79% higher than that of their slower-growing peers. Top quartile companies show up across industry segments, so no matter what category your business is in, you can still differentiate yourself from the industry averages shown above.

The fastest growing ecommerce companies have a customer lifetime value 79% higher than their peers.

So far, we’ve looked at the average behavior of large groups of customers, but there are always customers that swing widely outside of the averages. In the next section, we'll dig into this dynamic.

Understanding the Buyer Behavior of Top Customers

How much are the best customers worth?

Let’s leave behind the concept of “the average customer” and study the behaviors of those customers that every retailer cares about most: the big spenders.

FOOTNOTE: This histogram was produced by calculating the median CLV value within each percentile, limiting to companies with total revenue in the $1M - $1B range, and further restricting to customers who made their first purchases between January 2009 and January 2014.

After one year, the top 10 percent of customers on average are worth 6 times the $154 industry average, while the top 1 percent are worth almost 18 times more. To put it in perspective, that means a single customer in the top percentile will spend more than the entire lower 50 percent combined.

The top 1 percent of customers are worth 18 times more than the average customer.

How much are the best customers worth by industry?

This basic pattern holds true regardless of industry segment:

FOOTNOTE: Industry-specific histograms were produced by computing median CLVs within each percentile for every industry, limiting to companies with total revenue in the $1M - $1B range, and further restricting to customers who made their first purchases between January 2009 and January 2014.

While the “big spenders” are disproportionate contributors across all segments, there’s some variation in just how much spending they represent. For example, compared to other segments, Housewares/Home shows CLV climbing steadily among its top 50th percentile, while Computers/Electronics increases sharply for only about the top 10 percent of customers. Regardless, it’s evident that every industry has a group of extremely high-value customers, and these customers represent an enormous opportunity for a retailer.

The ability to identify the marketing campaigns attracting these big spenders can have a major impact on a company’s ability to scale aggressively. A marketing campaign that brings in a few of these “big spenders” could be worth significantly more than a campaign that brings in hundreds of low-value customers. It’s not easy to analyze the ROI of a marketing campaign using customer lifetime value, but if you’re interested in getting more of these super-spenders, it’s what you have to do.

What does a median customer look like?

To remove the heavy influence of the “big spenders” from the previous section, it’s important to also study median customer lifetime value:

FOOTNOTE: Limited to companies with total revenue in $1M - $1B range, and customers who made their first purchases between January 2009 and January 2014.

These numbers are very different from what we looked at earlier in the report. This points to what a significant impact high-value customers have on your overall unit economics. Without them, your business would be far less successful, and attracting more of them could be game-changing.

But, how does a retailer know who is going to be in the top 10 percent without waiting for a whole year to collect that data? We’re glad you asked.

Using Your Buyer Behavior Data

How to predict the CLV of a customer?

The ability to predict the lifetime value of a customer allows you to start optimizing your strategy based on very early indications in customer behavior. For example, here’s what we found when we charted 365-day CLV across a full year.

FOOTNOTE: Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

As we saw earlier, the majority of a customer’s 365-day CLV is realized within the first 30 days as a customer. On day one, 65% of a customer’s value has already been realized, and that grows to 79% by the three-month mark.

A retailer with a strong understanding of their data doesn’t need to wait 365 days before knowing which marketing campaigns, products, or promotions are bringing in the best customers. The most intense buying behavior is happening in the first 30 days of doing business with a company, and that behavior has strong predictive power about what is to follow. Customers that are outspending their peers at 30 days have a high likelihood of continuing to do so.

We see just how valuable this can be when we segment customers by their CLV quartile, with the top spending customers in Q1.

FOOTNOTE: Customers were broken into quartiles based on their 365-day CLV. Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

On day one, the top customers are spending over three times the amount of the next closest quartile, but they also continue to steadily spend more over time. Compared to everyone else, top quartile customers spend over four times the amount of the next closest quartile after a full year.

But, the long-term value of top customers becomes even more clear when we chart a customer’s value as a fraction of their 365-day CLV, segmented by these customer quartiles.

FOOTNOTE: Customers were broken into quartiles based on their 365-day CLV. Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

We know that top customers are spending more on day one, but here we see that they also grow significantly more in value over time compared to other customers. The top customers CLV grows more than 74% during their first year, while the next best performing customers’ CLV only grows by 25%.

We saw the same story unfold when we looked at how this plays out over a three-year period. The majority of a customer’s lifetime value is realized in the first one to three months they are a customer, making the first 30 days incredibly valuable for customer interaction. And by using this type of predictive analysis, retailers can determine early on who the most valuable customers are, and use it to gain an advantage over their competitors.

Not surprisingly, we saw some variation when we broke down our analysis by industry segment.

FOOTNOTE: Customers were broken into quartiles based on their 365-day CLV. Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

As we saw in earlier charts, we’re again seeing differences in buyer behavior based on industry. For example, Housewares/Home and Computer/Electronics is relying significantly more on new customers spending a large amount of money upfront compared to Food/Drug or Mass Merchant, who are relying more on repeat purchases growing over time.

Using this analysis, an ecommerce site can start to make extremely accurate predictions about which customers will turn into big spenders, and they can segment this analysis further by dimensions like:

By understanding the impact of these factors, a retailer can identify their key CLV drivers and better predict the future spending of any given customer.

Improving Customer Lifetime Value

How to improve customer lifetime value?

Growing the lifetime value of your customer base is dependent on two things:

  1. Acquiring more high-value customers
  2. Increasing the value of your existing customers

We’ve already discussed the merits of optimizing customer acquisition strategy to bring in high-CLV customers. In this final section, we’ll focus on growing the customer lifetime value of your existing base, and this comes down to improving AOV and repeat purchases.

The chart below shows the percent of customers that make a certain number of purchases in their first year.

FOOTNOTE: Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

Only 32% of customers actually order a second time over the course of the first year. That’s a massive drop-off after the first purchase. But what’s the solution?

Only 32% of customers place a second order during their first year.

Look at this same data presented in a slightly different light. In the chart below, you’re looking at the likelihood of a customer to continue making purchases. For example, after their original purchase, the likelihood of a customer to make a second purchase is close to 30%, and after they make their second purchase, the likelihood of a third purchase jumps past 50%.

FOOTNOTE: Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

This means that once a retailer gets a customer to make a second purchase, there’s a much higher likelihood of getting that customer back through the door a third, fourth, even fifth time. Combine this with the knowledge that most spending happens in the first 30 days of doing business with a company, and retailers have a playbook on how to turn first-time buyers into loyal customers.

A customer’s repeat purchase rate steadily increases with each incremental purchase.

Average order value follows a similar trend as repeat purchases.

FOOTNOTE: Limited to companies with total revenue in the $1M - $1B range, excluding customers with a customer lifetime value above the median of the 100th percentile and below the median of the 1st percentile, and further restricting to customers who made their first purchases between January 2009 and January 2014.

The majority of customers have an average order value less than $50. And the next largest percentage of customers spend only between $50-100 per order.

The majority of ecommerce customers have an average order value less than $50.

Finding incremental ways to boost AOV represents another opportunity for retailers to boost CLV, without the additional costs of customer acquisition. There are multiple tactics that retailers can apply with the aim of increasing average order value. This ranges from site design tweaks to improving user experience, to mastering the right free shipping threshold and price- setting strategy.

The Rise of Customer Centricity

As Kevin Hillstrom stated, “the days of easy customer acquisition are ending.” Online retailers can no longer rely on a growth strategy that solely focuses on capturing as many customers as possible. As we discovered in our Ecommerce Growth Benchmark, the fastest growing companies are achieving breakout success because they excel at two things:

  1. Acquisition: they acquire customers three and a half times faster
  2. Retention: after three years in business a majority of their revenue is coming from repeat purchases

This report revealed the biggest challenges retailers face in mastering this growth strategy. Only 32% of customers purchase a second time in a twelve month time period, and the window for effectively engaging a first time purchaser is within the first thirty days. Using customer lifetime value to understand buyer behavior is the solution for discovering how to overcome these challenges.

And companies that figure out how to put this data to work will have an advantage. While 75% of senior executives say that customer lifetime value is an extremely important indicator for success, 42% of them admit to not regularly calculating CLV. The competitive advantage is clear. An ecommerce business using CLV to understand buyer behavior is ahead of the curve, empowered with the data it needs to turn first time buyers into loyal customers.

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