Forget Brand Loyalty: New Ways to Capture the Repeat Customer

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Repeat shoppers are worth far more than the average customer. Second time purchasers spend three times as much as first time shoppers, and repeat customers with three or more purchases under their belt spend up to five times as much per visit as a new customer. It’s no surprise then that while repeat shoppers only represent 8% of a site’s visitors, they make up nearly 41% of total online sales. We found the same pattern emerge in our own research. We discovered the typical online store gets 43% of its revenue from repeat purchases. So, just how are best-in-class ecommerce retailers growing this valuable segment of customers?

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10 Stats to Terrify Ecommerce Holiday Marketers

Scary

1.) The costs of Facebook ads increase 55%

Not only are you competing with pictures of puppies in Santa hats, it also becomes more expensive to try to grab customers’ attention during the holidays. Tweet This!

2.) 2012 holiday emails increased 19% from 2011

The number of emails marketers are sending just keeps rising. In fact, consumers estimate that 43% of the emails in their inbox are from marketers. Tweet This!

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The Data Says: Invest in Twitter Ads

With Twitter’s IPO and Instagram announcing ads, it seems like every social network is thinking about revenue. As James Whatley says in an article in the marketing magazine, The Drum,
social networks “need their users to have data to sell and need brand advertising dollars to keep the lights on and (whisper it) actually make some money.”

Inserting ads into a previously ad-free product, though, is dicey. Will users accept the corporate intrusion into this very personal space? Will usage start to falter?

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Twitter Vine Flying Past Competition Despite Low Overall Adoption

On January 24, 2013, Twitter released Vine, a mobile service that lets you capture and share short looping videos. We set out to learn just how popular Vine has become in its first month of existence and how its performance has stacked up against competitors like Viddy and Socialcam.

We loaded data from Twitter’s API into RJMetrics and here’s what we found:

  • Overall, video creation is still an extremely underdeveloped market.  Only about 4% of highly active users shared a video through Vine or a top competitor during Vine’s first month on the market. In that same period, 98% of the same group shared at least one photo through a leading photo sharing service.
  • In its first month, Vine steadily gained market penetration to 2.8% of Twitter’s highly active users, blowing past competitors Viddy and Socialcam, which were used by 0.5% and 0.2% of the same population, respectively.
  • Twitter’s built-in tools for photo and video sharing are dominating the competition. Vine.co and pic.twitter.com are the most popular tools in their respective categories by a wide margin.

Vine Adoption

Vine showed impressively stable adoption growth over the course of its first month. We were expecting to see a spike in adoption around the time of the announcement followed by a leveling-off period, but instead the percent of new users each day has remained consistent. This is a good sign for future growth because the rate of adoption does not appear to be slowing as time goes by.

Vine vs. Viddy vs. SocialCam

In Vine’s first month, the percent of highly active users who used Vine was meaningfully higher than the percent who used competitors Viddy and SocialCam.

We were concerned that this might not be an apples-to-apples comparison since many users may have just been “trying out” Vine in this month. As a check, we looked at the average number of times each of these tools was used during the month. As it turned out, repeat usage of Vine was actually more likely than the other apps.

Video vs Photo

While Vine’s performance is impressive relative to its competitors, it’s still a tiny player in the universe of media sharing on Twitter. We looked at the percentage of users who linked to various media sharing services and found that photos represent the vast majority of the links sent out by highly active users.

As you can see in the chart above, native Twitter photo hosting (pic.twitter.com) is the dominant player, followed by Instagram and then a number of less prominent competitors. When you remove Twitter and Instagram, you can see just how small a player Vine is when it comes to sharing media.

About The Data

We decided to sample from Twitter’s most active users to find early-adopter activity. Twitter’s API was used to identify and download the twitter streams of about 2,500 randomly-selected “highly active” users, each of whom had tweeted at least 100 times so far in 2013.

The result was 2.3 million tweets that were sent between January 24th and February 24th. 320,000 of these tweets contained links, which we followed through any link shorteners to find their final destinations.

The data was then loaded into RJMetrics, where we generated this analysis with just a few clicks.

Conclusions

Twitter’s efforts to add native photo and video sharing into its service are proving fruitful.  These tools have quickly become the most popular options for end users, causing a major impact on the market for 3rd party apps.

Vine appears to be establishing itself as the de facto tool for short video creation and sharing. However, the significance of this move will only be felt as its market matures.  Today, Vine is a service only used by a small minority of even the most highly active users.

Surprising Hacker News Data Analysis

Obligatory Plug (let’s get this out of the way): I’m the co-founder of a company called RJMetrics. We develop hosted software that helps online businesses make smarter decisions using their data. I used RJMetrics to do all of this analysis and only scraped the surface of what our tool can do. If you’d like to see what RJMetrics can do for your business, sign up for a free 30 day trial. OK, onto the good stuff…

A few days ago, I was lamenting to my co-founder Jake about a frustrating problem: my blog content had stopped making it to the front page of Hacker News. While my posts are admittedly formulaic (I usually get my hands on some never-before-seen data and analyze it in RJMetrics), they always seemed to work their way to the top.

But lately I’ve been coming up dry. My TechCrunch guest post on how start-ups approach patents? Nah. My piece on never-before-seen Pinterest data? Fail. How about new data on the behind-the-scenes world of VC deal sharing? Another bomb.

I had some self-serving theories: Hacker News had devolved, succumbed to voter rings, or maybe just become too mainstream. Jake, as he often does, offered up alternative theory: my content sucks.

Jake proposed that the content landscape has become more competitive as HN has grown and that my content hasn’t improved fast enough to keep up.

As with most of our arguments, we decided to let the data decide. I used ThriftDB’s HNSearch API to pull down a complete history of Hacker News submissions, comments, and scores. I then plugged the data into an RJMetrics Dashboard and went to work answering some questions about the evolution of community, content, and competition on Hacker News.

Read on to see the data behind findings like these:

  • On Hacker News, the rate of new user registrations grew explosively in 2010, was flat in 2011, and is down in 2012.
  • The total number of active users continues to grow because a high percentage of historical users continue to participate on HN even years after their initial registrations.
  • Despite growth in the user population, the number of submissions made to Hacker News each week has held steady since 2011.
  • If you want upvotes, use profanity and talk about hot startups. Steer away from big companies and sensationalist headlines.

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The Clubby World of Venture Capital

As an entrepreneur who previously worked in VC, I’ve become fascinated by how much investors rely on each other to get comfortable with deals. I’ve always observed venture investing to be a “clubby” game where politics, rivalries, and friendships play a large role in how companies get funded.

I decided to test my theory by looking at the data. I used RJMetrics dashboards to analyze data from Crunchbase, a public database of VC deals. This data included investment history for over 12,000 venture-backed technology companies and their 6,000 investors. (Note that Crunchbase is a community-edited resource that is not fully comprehensive and includes sparse data before the early 2000’s. Despite this, we believe it to be a representative population of modern venture investments.)

Here’s what I found:

  • 60% of venture-backed companies have more than one investor.
  • Among firms who have made at least 50 investments, the average firm is the sole investor in just 10% of their portfolio companies.
  • Prolific co-investors like SV Angel and DAG Ventures are the sole investor in under 2% of their investments.
  • Benchmark Capital may be the most cliquey investor when it comes to co-investing with other leading firms. They share 36 investments with DAG Ventures (more than any other pair of highly active investors) but only one with Draper Fisher Jurvetson (fewer than any other pair).
  • Well-known firms like Kleiner Perkins, First Round Capital, and Accel Partners appear to keep it in the family, investing in the highest number of companies with the smallest number of distinct co-investors.

Lone Rangers and Socialites

According to Crunchbase, 60% of funded companies have taken money from more than one investor. This may seem high, but it makes sense if you consider that seed-stage investments are increasingly being made by syndicates of angel funds and later-stage investments are almost always follow-ons to investments made by other investors.

So, who are the lone rangers who are investing in the other 40% of companies? We looked at the percentage of “solo investor deals” made by firms with at least 50 investments to find the answer.

The average firm was the lone investor in only 10% of their portfolio, but Edison Venture Fund topped the list by investing alone on 53% of their companies. Next was German VC High-Tech Gruenderfonds with 42%, followed by a steep drop-off with Austin Ventures at 28%. (Note that there may be some sampling bias here. I suspect that High-Tech Gruenderfonds may have such a high number because the investments of other European VCs are under-represented in Crunchbase.)

On the other end of the spectrum, Jafco Ventures and Sutter Hill Ventures each have 59 investments but were not the sole investor in a single one of them. DAG Ventures was the sole investor in just 1 of their 101 portfolio companies, and SV Angel was the sole investor in just 2 of their 147 investments.

Best Friends and Worst Enemies

Some investors appear to get along very, very well. Benchmark Capital and DAG Ventures share a whopping 36 investments, more than any other pair of investors in Crunchbase. Lerer Ventures and SV Angel, who openly share deal flow, come in second with 30. (Disclosure: SV Angel and Lerer Ventures are investors in RJMetrics)

So what about the investors who were least likely to work together? We used RJMetrics to isolate every possible pairing of the 10 most active investors and found the answer. Among these 45 pairs, the average pair has made 7 investments together and every pair shares at least one portfolio company.

However, Benchmark Capital and Draper Fisher Jurveston only share a single investment (Prosper). Next in line is Benchmark Capital and Kleiner Perkins, who only share two (Friendster and Good Technology).

Benchmark is an interesting case here because it is part of both the most and least frequent investor pairs, despite being only the 10th most active investor overall. Playing in these extremes suggests that Benchmark may be the most cliquey investor in Crunchbase.

Welcome to the Club

So, who are the clubbiest VCs? To quantify this, I looked at the number companies a firm has co-invested in vs. the number of distinct firms it has co-invested with. In other words, this “clubbiness ratio” grows higher as a firm invests in more companies alongside the same tight network of co-investors.

To reduce the noisiness of the data, I only included firms who had co-invested in at least 50 companies (there were 77 of them).

While the average ratio was six investments per co-investor, SV Angel topped the list with a whopping thirteen. Next up was a four-way tie at ten companies per co-investor between Kleiner Perkins, DAG Ventures, First Round Capital, and Accel Partners. Sequoia Capital and NEA were next with nine companies per co-investor.

Sierra Ventures had the lowest ratio at 3.3 companies per co-investor. The only other funds with ratio below four were Rho Capital Ventures (3.7), BDC Venture Capital (3.7), and VantagePoint Venture Partners (3.8).

It’s noteworthy that the high-ratio group includes some of the best known and most successful firms out there, whereas the middle of the pack is full of lesser-known players who still do a high volume of investments. In other words, it appears that many of the most prolific investors are also the clubbiest.

Conclusion

In case there was any lingering doubt, industry politics and personal networks are a major factor in how venture capital deals get done. However, the data from Crunchbase suggests that the networks of investors may be just as influential as the networks of the investees.

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RJMetrics is an online tool that helps online businesses make smarter decisions using their data. For a free 30-day trial of RJMetrics, visit our website.

For more exclusive reports, be sure to follow us on Twitter @RJMetrics.

The Importance of Customer Lifetime Value in the Daily Deals Market

Customer lifetime value is an important metric for every business, but it is especially critical for e-commerce, daily deal and flash sale sites. For companies like these, a key to success is profitably acquiring new customers. Without a firm grasp on customer lifetime value, companies run the risk of acquiring unprofitable customers or getting outspent and outgrown by a competitor who better understands the metrics of their model.

In this post, we’ll use the customer acquisition strategies of Groupon and LivingSocial to frame a discussion about the importance of optimizing customer lifetime value, customer acquisition cost and other analyses for daily deal sites.

Groupon versus LivingSocial Customer Acquisition Debate

Groupon and LivingSocial have different views on “loss leader” customer acquisition deals, which may be due to different views about repeat purchase rates and lifetime value. As a publicly traded company, Groupon releases statistics on its customer acquisition costs to the public. LivingSocial, a private company, does not disclose such data. However, we can find proxies for acquisition cost by examining some of their deals (more on that later).

Acquisition costs in the daily deal space have increased dramatically from when these two companies pioneered the market. In fact, Groupon’s customer acquisition costs grew 485% between the first quarter of 2010 and the first quarter of 2011 to more than $30 per email address. However, once customers are acquired, they are believed to be create a profitable annuity of repeat purchases, although how profitable and the duration of that annuity is still unknown.

Groupon CEO Andrew Mason explained the company’s acquisition cost philosophy in an email memo to employees: “Once we have a customer’s email, we can continually market to them at no additional cost…. There is no cost of reacquisition — that’s unusual. If Johnson wanted to follow the Groupon strategy, he would have to start a free daily newspaper about bandages and then run Band Aid ads in it every day”

Do Deals with High Profile National Merchants have a Lower Lifetime Value?

If customers are as valuable as Mason says, and the incremental cost per sale once Groupon acquires a customer is trivial, why not acquire customer in large volumes at a loss like competitor LivingSocial has done with their Amazon and Whole Foods deals?

LivingSocial presumably offers deals such as promotions where customers spend $10 to get $20 in merchandise from Amazon or WholeFoods in order to acquire new subscribers. Users acquired through these deals may represent up to a 66% discount off of Groupon’s current acquisition cost.

Groupon’s CEO Andrew Mason also addressed this topic in his email to employees by stating, “…Our marketing team has tested this tactic enough to know that it’s generally a bad idea, and not a profitable form of customer acquisition.”

Depending on the proportion of the coupons that were bought by new users and the percentage of coupons that were redeemed, LivingSocial might be acquiring users for less than Groupon’s cost per acquisition. The other critical element is the value of the customers acquired through this channel. Groupon’s cohort analysis on these users may have shown that customers from those deals are unlikely to be repeat customers.

Whether or not that is the case, identifying repeat high value customer segments by acquisition source versus those likely to churn is invaluable information in the daily deal market. LivingSocial and Groupon surely have different rates of repeat purchase; the question is how much and is there a distinct difference in rates by deal type.

Learn which Deals and Sources generate Your Most Profitable Customers

In spite of the fact that customer lifetime value is so critical to success, young e-commerce, flash sale, and daily deal companies face several challenges that make it difficult to pull these numbers. First, with a limited operating history, it can be difficult to draw high-confidence conclusions about the length of your customer lifecycle or how the average customer will ultimately behave. For example, Groupon attributed a lower than expected profit to refunds associated with a specific cohort that had higher than average customer dissatisfaction rates associated with them.

Another challenge is that e-commerce sites are often started by excellent merchandisers who don’t have a core competency around technology and quantitative marketing. This can make it difficult to find the internal resources to run complex calculations and ensure that the data is clean and consistent for long term analysis.

One tactic that we recommend to all of our e-commerce clients is splitting the customer lifetime value calculation into several separate metrics that address different stages of the customer lifecycle. This makes individual parts of the product or acquisition strategy easier to optimize, and it ensures the calculations can be understood and communicated with the entire team.

A few examples of these customer lifecycle metrics are:

    • Percentage of members converted into buyers
    • Time from account creation until first purchase, first purchase to second, second to third, etc.
    • Revenue and gross margin generated in first 30, 60, 90, 365 days
    • Invitations and social referrals in first 30, 60, 90, 365 days

Groupon is not required to disclose this level of detail on their unit economics, but you can be sure that they and LivingSocial are monitoring these statistics carefully on their different customer and deal types to decide which are most profitable.

Get Industry Statistics on Daily Deal Customer Lifetime Value and Repeat Purchases

Update: We published the report, and you can access it here.

We will be publishing our first daily deal, flash sale and general e-commerce industry benchmarks of metrics like customer lifetime value, time between purchases and more in June, so please check back and get your copy.

We have a great view into the evolution of best practice metrics for e-commerce, and we would love for you to try RJMetrics and leverage our experience for your business.

New Google Plus Data Shows Weak User Engagement

Google CEO Larry Page recently announced that Google Plus crossed over the 100 million user mark and continues to see strong user growth.

Despite these strong numbers, however, the service continues to be pummeled in the press. Many outlets have claimed that engagement is poor and that growth is only fueled by Google forcing membership upon users of its other products.

Rather than rely on third-party reports, we decided to pull publicly available data on a random population into an RJMetrics online dashboard and see for ourselves.

Here are some of our most interesting findings:

  • The average post has less than one +1, less than one reply, and less than one re-share.
  • 30% of users who make a public post never make a second one. Even after making five public posts, there is a 15% chance that a user will not post publicly again.
  • Among users who make publicly-viewable posts, there is an average of 12 days between each post
  • A cohort analysis reveals that, after a member makes a public post, the average number of public posts they make in each subsequent month declines steadily. This trend is not improving in newer cohorts.

How We Did It

We began by selecting a population of 40,000 random Google Plus users. For each user, we downloaded their entire public timelines (which consist of all publicly-visible activities for that user). Only one third of the users in our population had any public activity, so this sub-set of the population is the main focus of many of our statistics.

Once we had the data, it was a snap to upload it to RJMetrics and pull the insights seen here with just a few clicks.

Since we are looking at public data exclusively, we want to point out that this data is not necessarily reflective of the entire population of users. These are simply insights into the public-facing actions of Google Plus users based on a population that is known to post publicly.

Repeat Posters

Once a user has made one public post, the chances that they will make a second post are quite strong: around 70%. After that, however, Google Plus does not perform as well as other social services that have analyzed. In charts like these, we typically expect to see the probability of repeat posts shoot up to well north of 90% by the time the user has made several posts. This is basically the “once you’re using it you’re hooked” principle.

With Google Plus, however, this number never crosses the 90% mark. Even after having made five such posts, the chance of making a sixth is only 85%. The means that 15% of people who have made five posts never came back to make a sixth.

Cohort Analysis

The cohort analysis below shows the rate at which new publicly-viewable posts are created by users who made their first post in different months throughout time.

This is a cumulative chart, so we’re basically showing the “average number of total posts made” as it grows over time for users in each cohort.

The decay rate here is very concerning. Users are less and less likely to make additional posts even a few months after initially joining. While it may not be an apples-to-apples comparison, it’s interesting to contrast this with the same chart from our Pinterest Data Analysis, which shows no decay whatsoever.

Time Between Posts

We were surprised at the by the length of time between public posts among users. On average, a user waits 15 days between making their first public post and making their second. This number declines with each subsequent post, but not drastically. There is an average of 10 days between a user’s fifth and sixth public posts.

The overall average time between any two public posts by the same user is 12 days.

Remember that, since we are only looking at public posts, it is very possible that users are making non-public posts in between the ones that we were able to see. Despite this, however, we were still quite surprised by the large amount of time between public posts.

+1s, Replies, and Sharing

Of all the categories, we feel that this is the least likely to be biased by the fact that we only studied public posts. These public posts will still be visible to each member’s private networks, and actually could attract +1s, shares, and replies from external users as well. If anything, we would expect our numbers here to be higher than in the general population.

Despite that, our population of nearly 70,000 posts yielded the following properties:

  • An average of 0.77 “+1s” per post
  • An average of 0.54 replies per post
  • An average of 0.17 re-shares per post

Conclusion

From what we can see from the outside looking in, Google Plus has a long way to go before it becomes a real threat to the social networking landscape. While user growth is strong, it is unclear how much of that is driven by tie-ins with other Google products.

At the end of the day, Google Plus simply does not show the same level of ravenous user adoption and engagement that we’ve seen in other social networks (see our reports on Pinterest Data and Twitter Data for examples).

Airbnb Data Analysis: 6 Million Users by Year-End, Only 20% Active

Airbnb is one of the hottest sites on the internet. The Y Combinator graduate has raised $120 Million of funding to change the way people find places to stay around the globe.

As fans of Airbnb with a passion for startup data, we decided to try and learn more about the site’s user base by looking at the publicly-available profiles of its members. We sampled just over 60,000 users and were able to draw some interesting insights using an RJMetrics business intelligence dashboard.

Some highlights include:

  • Airbnb has over 2.1 million registered users and is growing about 250% year-over-year. At this rate, they’ll have 3 million users by the end of June and 4 million by the end of August.
  • Almost 85% of Airbnb’s userbase has never received a review as a host or a guest. Our sample suggests that there may be as few as 350,000 reviewed users among the userbase of over 2 million.
  • Usage is addictive — with each additional stay booked through Airbnb, users become increasingly likely to book again.

User Growth

Since Airbnb uses auto-incrementing IDs for its users and does not appear to have skipped any range of ID values, it is quite easy to track user growth over time.

Airbnb has very seasonal growth patterns, with most new users signing up in the summer (peaking in August) and significantly fewer signing up in the winter months (reaching a low point in December). These user analytics were easily extracted using RJMetrics.

The current user count is approximately 2.1 million.

For the past several months, the year-over-year growth rate has been steady at around 250%. Extrapolating this out for the rest of the year puts the site’s user count at over 3 million by the end of June and over 4 million by the end of August. At its current growth rate, the site will approach 6 million registered users by the end of 2012.

Usage

Since we didn’t have direct access to data on actual stays, we used reviews as a proxy for activity. Reviews are the lifeblood of the Airbnb community, so we think it’s fair to assume that the number of reviews is a good proxy for the number of stays.

Most sites we study show signs of the “80/20 rule,” which suggests that 80% of the activity comes from 20% of the users. In Airbnb’s case, it’s more like the “100/20″ rule — only 16% of the user base has been reviewed as a host or a guest.

Here are some other usage statistics:

Only about 14% of users (or about 300,000 users) have been reviewed as guests.

Only about 2.3% of users (or about 50,000 users) have been reviewed as hosts.

A mere 0.5% of the userbase has been reviewed as both guest and host.

5% of users (or about 100,000 users) have active listings, but only 2% (or about 40,000 users) have received reviews from guests. This suggests that more than half of the people listing properties have yet to host a guest.

Repeat Activity

By relying on the same techniques we use to track repeat purchase probability in RJMetrics, we were able to profile the average user’s likelihood of using Airbnb with each additional stay.

As you can see, while only about 14% of the userbase ever books a stay (as indicated by a first review from a host), 22% of those users who book once go on book a second stay via Airbnb. By the time a user has booked five stays, the likelihood that they will book another stay on Airbnb is over 50%.

Note that these percentages are based on the behavior of the existing user population, the majority of which has been registered for less than a year. Since so many users have a limited history on the site, it’s quite likely that these numbers will increase over time.

Conclusion

Airbnb continues to explode in popularity and experience tremendous user growth as a result. As with most consumer sites, however, the population of active users is much smaller than the total registered user count.

Airbnb’s key to continued success will be to both grow its user base and convert more of its registered users into paying customers. As we’ve seen, with each additional booking users become more likely to book again.

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