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After three years, top companies generate 9x more revenue than everyone else.
Number of Orders
After year one, top companies have nearly 2x the number of orders as everyone else.
After three years, top companies have 2.5x the customers than the next-best group.
Time to Loyalty
Top companies see second purchases about 12 days faster than everyone else.
Loyal Customers Spend More
Customers with 4+ purchases spent 15% more on their first order than the average.
Number of Loyal Customers
After three years, top companies have 13.4k customers making their 4th (Or higher) purchase, nearly 5k more than the other three quartiles combined.
Number of Investments
Deal volume has gone down steadily over the last 3 years. Since 2013 (a peak year with 731 deals), 2016 so far has seen only 68.
Size of Investments
The median deal size for early stage ecommerce companies has gone up from $3M in 2011 to $9.4M, so far, in 2016.
Robert J. Moore
Over the past few years, VCs and ecommerce companies have developed a somewhat tumultuous relationship. As the online retail sector continues to grow at an impressive pace for its scale, investors still struggle to separate wheat from the chaff when it comes to new entrants. This has led to a few big wins, many big losses, and an overall hesitancy for many funds to make new investments in the space at all.
For this study, we set out to uncover data that investors can use to make better decisions. The report covers three areas:
Success in ecommerce has always relied on the ability to retain existing customers during periods of rapid customer acquisition. The data in this report shows just how critical that balance is and shares the analyses that every investor should explore as part of their due diligence.
To understand ‘venture capital appetite’ in the ecommerce market, we compiled the last 5 years of funding data accreted by the sector, as tracked by Mattermark. This data was constrained to contain only United States-based companies tagged as ‘ecommerce’ by Mattermark that have raised new capital since 2011; the data set includes companies that were acquired or went public in that timeframe. We did not include biotechnology and pharmaceutical related companies.
Using that set of companies, we segmented ecommerce acquisitions, funding rounds, and IPOs into distinct charts.
The data set includes 3,043 total funding events from the past 5 years in 2,302 distinct ecommerce companies. The data includes both initial investments, and follow-on rounds taking place inside the same period. This isn’t to say that each company in our dataset raised its first round during the tracked half decade, but that if it did attract 2 or more capital infusions each was collected.
The following chart shows deal volume in ecommerce companies between 2011 and 2016, our selected time range. In terms of raw deal quantity 2012 and 2013 were peak years, with more than 720 events each. Those deals led to $3.9 billion and $4.8 billion in investment, respectively.
The median deal size for early stage ecommerce companies, ranging from Angel to Series C rounds, have steadily gone up from $3M in 2011 to $9.4M, so far, in 2016.
Similarly, late stage funding has gone up from $25M to $45M. Airbnb ($1.5B), Groupon ($950M), Wish.com ($500M), LivingSocial ($400M), and Jet.com ($350M) are some of the notable ecommerce related companies that raised rounds in the past few years.
This chart shows consistency in investment patterns in early-stage ecommerce startups, and strength among the late-stage companies operating in the genre.
In Seed, A, and B rounds there is a modest upward movement in per-round investment size over time, which mirrors broader investing trends that have seen richer valuations become the norm as the technology sector heated up in the current bull market.
Notable is Series C investments, which maintained a roughly flat pattern through the time period. Observing a slightly rising early-stage market, and a mostly flat Series C pattern, you could not be blamed for presuming that the late-stage check size would be flat, or perhaps even down.
Instead, however, as the chart plainly states, late-stage is the dramatic outlier in ecommerce investing, posting massive gains in check size in 2014, gains that it managed to first beat in 2015, and then meet thus far in 2016.
The implication is that investors, instead of falling prey to fear following the high-profile implosion of former industry-darling Fab and others, are still content to invest large sums into the more mature ecommerce startups still in the market.
It could be that the initially successful IPOs of Etsy, Shopify, and others were edifying to late-stage investors with an eye on ecommerce.
Regardless, comparing 2016 to 2015 in the above chart is to see see only modest corrections and changes in investment patterns. That indicates that, at least for now, the market is not changing its ways when it comes to how much ecommerce companies need, and can raise at their various stages.
The news, of course, is not all good. If you contrast the deal pace data from before with the check size chart above, it’s easy to see that aggregate investment over time has declined; 2014 saw a large rise in the median late stage check, but with total deals in steep decline it’s a mixed bag. Also, it’s encouraging that late stage managed a fresh high in per-investment dollars, but that same year posted yet another decline in total ecommerce deals.
Investors seem more than willing to write the same checks this year as the last, and the year before, just to fewer companies. The rest of the research in this report will focus on the metrics and patterns investors should look for when evaluating ecommerce companies.
Throughout this report we will refer to “top ecommerce companies” and “top quartile companies,” by which we mean the top 25% of companies. As we found in previous research, this segment exhibits growth patterns that are dramatically different from their counterparts.
Let’s start by looking at revenue. Starting in the very first months of business, top quartile companies already show differentiated performance.
By month six, they’re generating $490k in monthly revenue. The next closest quartile is bringing in $135k in monthly revenue – a 3x difference. By month 15 in business, the top quartile are clearing $1M in monthly revenue, while companies in the second quartile haven’t yet crossed the $500k monthly revenue threshold.
Let’s step away from dollar values for just a bit and look at order volume. Here we see top companies processing far more orders per month.
For an ecommerce company, any given purchase will come from one of two customer types: new or repeat buyers. We see that by month two in business, top companies already break away from the pack when it comes to new customers, acquiring nearly 2x more new customers per month.
This gap only widens over time. By the end of year two, top performers acquire 5x more customers every month than their counterparts.
Rapid acquisition is a defining feature of top-performing companies. Not all ecommerce companies who go on to be mega-hits will see these kinds of numbers on day one, but this is the average growth trajectory among those who find breakout success.
Getting customers in the door is exciting, but you can’t build a business on acquisition alone. Our research shows that top ecommerce companies excel at both acquisition and retention.
Already in month one, top ecommerce companies generate 20% of their revenue from return customers. And by the close of year three, they’ve experienced periods when they generate nearly 60% of their revenue from return customers. Now, compare this revenue breakdown with that of the bottom three quartiles.
In month one, bottom quartile companies only see 13% of their revenue come from return customers. By the end of year three, they have yet to see that number go north of 55%. As we’ve seen in previous research, once companies hit this point in their lifecycle, they fluctuate between the 45-55% mark. It’s safe to say that this window represents “predictable retention” – a term we’ll use going forward in the report.
Of course, there is no “right” amount of revenue a company should be getting from return customers. While a dollar from repeat purchases is almost always more profitable than a dollar of new revenue, having 60% of your revenue come from repeat customers isn’t necessarily a good thing; it could simply indicate that you shut off an acquisition channel and have a higher percentage of revenue coming from return customers.
These findings provide us with two takeaways:
The best way to understand the interplay between acquisition and retention is with one key metric: customer lifetime value (CLV). While it’s possible to inflate acquisition numbers with unsustainable spending or heavy discounts, CLV cuts through that noise by showing the value of a customer over the long-term.
The data is clear: top ecommerce companies acquire more valuable customers. While CLV is more of an output than an input, the lesson here is still quite important: you cannot win on transaction volume alone -- revenue quality matters. In the first year with a business, customers at top companies spend nearly $100, while CLV for the bottom three quartiles lands somewhere between $45 and $50. When considered alongside the phenomenal acquisition numbers of top performers, a more clear picture of how powerful this acquisition/retention engine is begins to emerge.
So far we’ve established that top companies outperform on five ecommerce KPIs: Revenue, Orders, New Customers, Repeat Customers, and Customer Lifetime Value. In this section, we’re going to dig into the customer retention patterns behind their phenomenal growth.
One customer retention pattern that is markedly different amongst top ecommerce companies is the speed at which they turn first-time buyers into loyal customers.
On average, top companies see customers making their second purchase on average twelve days faster than their counterparts. This trend holds true across the entire customer lifecycle.
We saw in our Buyer Behavior Benchmark that repeat purchase probability increases with each order. In the average company, for example, there’s only a 32% chance that a customer who has made one purchase will make a second purchase, but there’s a 53% chance that a customer that has made two purchases will make a third.
Category: Health/ Beauty | Growth Rate: 242.1% | Source: Growth Secrets of the Top 500
Dollar Shave Club analyzed how different products impact customer loyalty. They found that customers who purchase the Shave Butter product are more likely to buy other products as well. Using this insight, they updated their sample program, targeting only customers who purchased the Shave Butter. Today they see a 100% ROI on their sample program. Leveraging this “golden motion” allows Dollar Shave Club to focus their loyalty program on their best prospects, effectively moving more customers toward loyalty, faster.
Being able to predict customer lifetime value means companies can increase spend on high ROI campaigns without waiting for several months of data. And it’s possible to spot these predictors from a customer’s very first purchase.
Customers that are in that top 10% of first order value on average place .6 more orders than the bottom 10%. The difference isn’t huge but there is a clear trend upward, indicating that AOV is an early predictor of future lifetime value.
If we take a different approach, the effect becomes more pronounced. When you bucket customers by the number of orders they place, then look at how their first order values compare to the average, we see definitively that loyal customers have higher first purchases.
Repeat purchasers spend more on their first purchase than the average, and one-time purchasers are the only group that spends less than the average.
Companies can use analysis like this on their own customer base to predict the campaigns that will bring in the highest marketing ROI. Predicting customer behavior as early in the customer lifecycle as possible can enable tight feedback loops and decisive actions. This results in the growth rates experienced by top companies.
Category: Apparel | CLV Growth Rate: 120% | Source: Predictive Customer Lifetime Value is a perfect fit for a brand that’s re-inventing men’s retail
When the data became too much for Bonobos to handle using spreadsheets, they turned to a real-time CLV calculator and channel analysis tool. Once they began their analysis, they were able to recognize that their “Guideshops,” were bringing in customers with the highest lifetime value. They focused their attention on that channel, expanding their marketing efforts for Guideshop, and as a result, their average CLV went up. Many decisions like this later, and they’ve increased their CLV by 20%, increasing the effectiveness of the business across the board.
Loyal customers are worth more to a business because of their higher CLV, but also because of their ability to bring new customers in the door via word-of-mouth referrals.
Here’s the crux of the ecommerce challenge: customer retention is incredibly difficult. When we look at the revenue breakdown between top and bottom performers in year three, there’s no meaningful difference.
The only notable difference is that top companies acquire new customers at a faster rate (see Monthly New Customers by Quartile). So, while the ratios are exactly the same, the actual numbers show that top quartile companies have more customers at every order frequency; more specifically, top companies have a higher volume of loyal customers.
In year three of business, top ecommerce companies have 13.4k customers making their 4th (Or higher) purchase, nearly 5k more than the other three quartiles combined. This larger base of loyal customers means top companies have more people talking about the brand on social media, telling their friends, and sharing referral codes. And this fuels acquisition.
This loyal customer base refers other customers, creating a flywheel of efficient acquisition.
Category: Food | Repeat Order Rate: 200% | Source: RJMetrics Customers Page
Harris Farm Markets measures Net Promoter Score (NPS) to make sure customer interactions with their brand are as positive as possible. When a customer submits a score below 7, an email is automatically sent from Zendesk to their team, letting them know that they need to resolve the issue. Because of this improvement in response time, Harris Farm Markets was able to keep customers coming back, increasing the number of repeat customers by 2x. These efforts also increased new customer acquisition by 51%.
Not all companies exhibit these growth patterns in their earliest days. Remember, it took Nasty Gal five years to hit $24 million in annual revenue, a threshold that top quartile companies in our research crossed during year two in business. But today, its annual revenue is estimated to be around $250 million.
The purpose of this research isn’t to attempt to predict the future of every ecommerce startup, but rather to provide a rough guide to understanding the growth patterns of healthy companies. While few companies will perfectly match the data points in this report, there are five patterns to keep an eye out for:
RJMetrics is the analytics platform of choice for over 400 online businesses, many of whom are ecommerce retailers. Our global customer base ranges from new ecommerce companies with less than $1 million in annual revenue to some of the fastest growing companies in the IR 500.
The conclusions in this report are based on analysis of the anonymized data of these companies.
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