Category Archives: Research

Twitter Vine Flying Past Competition Despite Low Overall Adoption

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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 that 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.

 

4 Lean Startup Tactics that Worked

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At RJMetrics, we pride ourselves on being a lean start-up focused on ecommerce analytics.  As a result, we’re always bringing new team members up to speed on what it means to be lean. My favorite way to educate them is through real-world examples.

Here are four of my favorite examples (from our team and others) of lean startups in action.

1. Faking A Move

To me, being lean is all about minimizing the ratio of resources consumed to insights gained. At RJMetrics, we did this to great effect when we were hiring our first employees. At that time, our company was based in Camden, NJ, and new applicants were few and far between. We suspected that our location was the reason for the talent shortage, but we had no way to prove that without making a really expensive bet and moving the business to nearby Philadelphia.

Just then, my co-founder Jake had a great idea: let’s just say we moved. We published an identical job posting but changed our address to a location in Downtown Philadelphia. The applicants started pouring in. We simultaneously started interviewing candidates and looking for new office space. We made our move literally one day before our first new employee started work.

Our Awesome Philly Office

Our Awesome Philly Office

I love this story for two reasons. First, had job applicants not increased we would have saved ourselves the trouble of moving with effectively zero downside. We then could have searched for more fundamental reasons that we weren’t getting applicants. Second, it saved us tons of time. By getting the answer to our question up-front, we were able to move and recruit in parallel rather than in sequence. I have no doubt that this accelerated our growth trajectory by months.

 

2. Actually Talking to Users

In the age of cookies, cheap storage, and abundant APIs, many companies have developed a strong bias against actually talking to their users.  Instead, they attempt to infer intent and sentiment from user actions.

While data can do a great job of telling us what is happening, it can often fall short on why it is happening.  Relying solely on data can also mask client frustrations.  Instead, from time to time companies should rely on a more time-honored tactic: asking what customers think.

Customer interviews are inexpensive, fast, and remove a lot of the interpretation risk associated with data-only strategies.  No matter how many numbers we crunch, we always learn something new when we ask our customers about their most and least favorite parts of our product.

 

3. Naming a Book

Tim Ferriss has a huge bag of tricks, but there is one that always sticks with me. When Ferriss was writing his book “The Four Hour Work Week,” he and his publishers were considering a number of potential titles. Rather than go with his gut, Ferriss devised a simple and brilliant strategy: buy Google Ads for each of the different potential titles, have them display when people were searching for content related to the book, and see what got people to click the most.

Users who clicked the ads would land on a blank page or “under construction” page.  All Tim needed to know was what moved people to click.

This inexpensive experiment allowed Ferriss to create an ad-hoc focus group consisting of thousands of ad viewers and learn which titles piqued their interest the most. Based on sales of The Four Hour Work Week, it’s quite clear that he made the right choice.   Ferriss shares this story in the video below.

 

4. Interactive Mock-Ups

Philadelphia entrepreneur Chris Cera recently turned me onto a really neat tool he uses called Axure. With Axure, you can to mock up interactive user interfaces without any backend coding. In other words, as long as testers follow pre-determined steps, they can get the impression that they’re interacting with a fully-built product.

Anyone who has done user experience work can appreciate why this is a brilliant and valuable tactic. An interactive mock UI/UX can answer huge up-front questions about how and when potential users would derive value from a product (or experience frustrations). With these questions answered, the development of the backend can be much more focused and deliberate, and there is far less risk associated with the final product’s release.

 

 

RJMetrics Winter 2013 Hackathon Results

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After the last RJMetrics hackathon, I didn’t think our team could possibly cram more innovation into a 24-hour period.  They just did.

The Projects

Team members unveiled some amazing projects, including:

Spotlight search in our new dashboard UI to provide users with fast access to charts, dashboards, and trends.

Customizations to Zendesk to make our customer support exchanges more streamlined.

A customized sales video generator to provide a personal touch to our sales prospects.

Shaun Presents His Video Generator

Shaun Presents His Video Generator

BallerBoard, a TV display engine that automatically shows stats from RJMetrics, Twitter, and other services in a format that’s easy on the eyes.

BallerBoard

An End-User Query Browser to allow advanced users to query their data warehouses directly using SQL syntax.

A deployment system for MySQL stored procedures, which will greatly increase the number of analyses we can run natively in MySQL.  Last Hackathon’s Median/Percentile feature will be deployed using this system.

A trend-line overlay system that will allow users to fit regression lines to their data and forecast future data points based on these models.

TrendLine

A system for self-auditing and approving RJMetrics Trend/Metric definitions through our UI.

A new concept and 3D rendering of a new RJMetrics conference booth.

Conf

An improved RJMetrics deployment system.  This is an extension of our new AWESOM-O deployment system and its client application Butters.

Drastic improvements to our physical office environment, including a new reception area and accent walls throughout the office.

Working Capybara integration tests to monitor our UI.

 

The Results

Francis “Buck” Ryan took the crown this time around for his work on trend-line overlays.    This was a suggestion that came directly from our feature request page.  Existing users can keep an eye out for it in the beta tests of our new dashboard UI.

Buck will enjoy the grand prize: $500 cash to be spent all in one night.

I can’t wait for Spring.

 

 

Ecommerce Metrics Course

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I’m happy to announce that RJMetrics has released a free course on ecommerce metrics. This course collects some of the most valuable things we’ve learned from working with hundreds of ecommerce businessses over the past four years. This is the first time we’ve consolidated and curated all of this information in one place. Some of the topics covered are:

  • Data storage best practices
  • How to choose your metrics
  • The best process for communicating metrics
  • Improving valuation and accelerating fundraising using metrics
  • Maintaining consistent definitions
  • Case studies of successful companies
  • Benchmark report on metrics across ecommerce

This course is delivered as a series of emails over 30 days, and many of the emails have links to additional resources for further learning. You can opt out at any time. Sign up for for our course on ecommerce metrics today.

How to Get On the Front Page of Hacker News

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Obligatory Plug (let’s get this out of the way): I’m the co-founder of RJMetrics. Our hosted software helps e-commerce and SaaS businesses make smarter decisions using their data. If you’d like to see what RJMetrics can do for your business, sign up for a free 30 day trial. OK, onto the post…

I recently conducted a comprehensive Hacker News Data Analysis to settle a bet with my co-founder Jake (he won, by the way). As a byproduct of the experiment, I was sitting on a wealth of data about Hacker News submissions and their performance.

One of our team members suggested that we could use this data to learn more about how to maximize our chances of our Hacker News submissions making it to the front page. Rather than hoard the data, we decided to share it with the world in a blog post. Here’s what we found.

Continue reading →

Business Intelligence vs. Business Analytics

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When I was first hired at RJMetrics, I was a recent college grad, and the concepts of “business intelligence” and “business analytics” were still a bit new to me. I started reading up on the these fields and quickly learned that even practitioners who have been working with business data for decades can’t agree on concrete definitions.

As I waded through countless websites, forums and flame wars looking for the answers, I decided to help other people like me by condensing my findings into one convenient space. If you’re wondering about “ba vs bi” or “business analytics vs business intelligence,” hopefully this post saves you some time.

BA vs BI

When you’re talking about data and what it can do for your company, a lot of terms get thrown around. Business analytics (BA) and business intelligence (BI) are two terms heavily used, but rarely given the same definition by any two sources. Some take the stance that they’re interchangeable, and others staunchly defend their position as to the meaning of each, and what would fall under those respective umbrellas. Ultimately, I came to realize that these are two complementary concepts that have grown with, and out of, each other.

On a basic level, BI is the ability to take information resources and convert them into knowledge that is helpful in decision making. The traditional method of doing this involves cataloging and examining data from past decisions and actions, and using this as a way of setting metrics benchmarks for the future. In method, BA is an offshoot of BI. BA focuses on using data to net new insights, whereas traditional BI used a consistent, repeating set of metrics to steer future business strategies based on this historical data. If BI is the way to catalog the past, then BA could be called the way to deal with the present and predict the future.

Hosted business intelligence solutions like RJMetrics offer a combination of BI and BA by providing a data warehousing and reporting solution alongside a flexible interface for ad-hoc analysis and data discovery that can point you toward smarter decisions.

The Evolution of Business Intelligence vs Business Analytics

In the past, BI has been used to talk about the people, processes and applications used to access and extrapolate meaning from data, for the sake of improving decisions and understanding the effectiveness of targeted decisions. But this is where BI as a baseline failed; something that runs entirely off of static, historic data severely limits a user’s ability to make predictive decisions and forecast for the future market. When an emergent situation arises on a Friday afternoon, the user doesn’t greatly benefit from looking at metrics collected prior to the introduction of that situation.

The rapid growth and demand for BA comes from this failing, and is in a way the evolved form of BI solutions. In a business world whose speed is ever-increasing, the user needs to be able to interact with information at the speed of business, not looking back over his or her shoulder at what happened in the past. BI setups alone do not support the occurrence of users asking and answering questions in the face of marketplace events as they happen. A company that is data-driven sees their data as a resource, and uses it to hedge out competition. The more current the data the user has, the better jump he or she has on the competitor, who may or may not have become a threat in a time so recent that traditional BI data reporting wouldn’t even take them into consideration.

Many companies are commonly implementing advanced analytics on top of their data warehouses, to bridge the gap between BI and current day needs. Perhaps this is the origin of the confusion between terms, as organizations pick and choose from different combinations of services and have no real understanding of what to call these mashups.

Equally relevant is the fact that more and more people are being asked to interpret data in roles that are not strictly analytical. Product managers, marketers and researchers are moving towards data as a way to formulate strategies, and traditional BI platforms make it difficult to push data into real-time situations and what-if scenarios.  With the importance of data-driven decisions increasingly becoming a realization for less tech-savvy branches of company teams, the need for more user-friendly and faster producing platforms also grows. Moreover, delivering the data that supports these decisions to a broader company team demands a more visual form of modeling tool, to improve understanding across all departments. Charts and graphs showing BA findings are quicker and more impacting than written out statistics and excel sheets full of data.

Data interpretation and the manipulation method of choice change as the market demands. While having a set of established methods is important to the effectiveness of a company’s strategy, it’s understanding the need for flexibility in the face of these changes that can be a company’s most valuable asset.

If you’re looking for a robust business intelligence tool that empowers the use of business analytics within your organization, sign up today for a free trial of RJMetrics.

Measure Twice Cut Once

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The Social Commerce Accelerator asked Jake to present to their members about how you can use data to optimize your customer experiences and become more effective at acquiring new customers. The highlights of the presentation were:

1) Use different categories of data depending on what you are trying to achieve.

Depending on what you want to measure and improve, the categories can include:

  • Revenue
  • Life-cycle analysis
  • Segmentation

2) Extend data driven optimization of the business to your social media initiatives.

Use data to understand which initiatives and channels are generating the best returns.

Social media intersects with data in the areas of:

  • Acquisition of new customers
  • Retention of existing customers
  • Referrals to friends and followers – help your customers market for you

3) More data is not always better. Focus on what is most effective.

Use actionable metrics. If seeing the data then makes you take action, then it is worthwhile.

  • Know what you are trying to accomplish, and focus on the end goal.
  • Know whether the data you need is accessible.

4) Where do companies see the biggest gains from data:

Companies see the biggest gains from data in making better customer acquisition resource decisions. RJMetrics’ customers (more than 100 e-commerce sites) see 5x difference among best and worst channels. Know your lifetime value (LTV) by channel.

5) Tactics you can use – targeting lapsed customers:

  • Remarketing: repeat purchased from existing customers are much more cost-effective than acquiring new customers.
  • Reactivation: send a targeted offer to your customers to keep them engaged.
  • Segmentation Marketing: tailor your offering so it is more interesting, and give different offers to different segments.

Examples of how to segment your customer base:

  • Those who haven’t bought in the past 90 days
  • People referred by a friend
  • People who have referred friends
  • Customers with the highest order values

Keep it simple to start. Split customers by multiple segments and keep testing.

The webinar is posted here on inSparq and is part of the inSparq Social Commerce Accelerator (SCA). SCA is a 10 week program for established retailers, brands & e-commerce sites aimed to revive their social commerce strategy to drive sales life.

Best Practices in Optimizing Online Marketing Spend

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Know your lifetime customer value (LTV) by source, by individual affiliate, ad variation, keyword or whatever level of granularity will enable you to best optimize ROI on marketing spend.

Have LTV data by source handy when you are making marketing spend decisions or optimizing campaigns.

Have an LTV marketing optimization strategy that includes:

  • A plan for how much you can scale each channel without diminishing returns. For example some Internet retailers find a few keywords to be phenomenally profitable, but they can only scale those terms to the available ad inventory. Often, while a portion of a search or display media campaign under-performs the rest of the campaign in terms of LTV, the company still needs the volume from the other keywords in order to hit revenue targets.
  • The frequency that LTV is calculated and is available for optimization. Some companies get daily LTV updates and can track variations in small time periods. Others only batch LTV calculations quarterly and new merchandising mixes or first time media buys will not yield data until the close of the quarter. The most sophisticated online marketers know the frequency with which their customers make repeat purchases, site visits, or other metrics that they track and optimize.
  • Your most important metrics and how they relate to each other. For example, many online marketers have their own methodologies for optimizing campaigns. Some reallocate spend every week, month or quarter based on their best/worst performers. Others know that on average their customers make their second purchase 30 days after the first and use this information to assess the success of acquisition channels before scaling spend further.
  • Shorter cycle-time CLV optimization. If you know that re-purchasing patterns or churn rates are fairly consistent after 90 days, then use 90 day CLV as your optimization metric and track correlations between initial purchase CLV, first 30 day purchase CLV and 90 day CLV. If your CLV data updates weekly, you can then optimize in 5 weeks and again in 13 weeks after you know how these customers compare to your average customers in terms of repeat purchases. Online marketers are limited in their performance by access to data. The more a marketer can use data to optimize their decisions, the more they will produce results that hit the goals of the company as a whole.

Online Marketing Best Practices for Internet Retail

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In any online marketing role, you have performance targets in terms of both volume and CPA. However, for Internet retail, subscription and other businesses where revenue grows after the initial acquisition and can vary dramatically over time, many online marketers struggle with how to measure customer lifetime value by acquisition source, and how to use that information when making spend and customer acquisition decisions.

At RJMetrics, we work with some really advanced online marketers, and I had the opportunity to ask them how they addressed this issue and what their optimal solution would be.

Accurate Customer Profitability Data by Source

An all-too-common frustration of online marketers is not having visibility into data they need to allocate their acquisition budget. In order to optimize for the highest ROI in terms of customer lifetime value, online marketers need to access that data in both the right format and level of detail. In high performance companies, online marketers are the “client” in instances where another team maintains customer profitability data warehouses.

Validate Hypotheses on Acquisition Channel Performance

Many online marketers have hunches on the lifetime value of customers produced through coupon sites, affiliates, paid search, unpaid search and social media. For example, some ecommerce companies suspect that certain acquisition channels produce much lower customer lifetime values than others, but they do not have data to validate their assumptions. So, they keep funding what they suspect are less profitable channels.

Actionable Customer Lifetime Value Data

Customer lifetime value data is only as effective as the marketer’s ability to use the data in their day to day marketing activities. Some bid management tools and email service provider interfaces let online marketers override CPA targets with customer lifetime value metrics to optimize for profitability. Having this data handy when optimizing search, affiliate, digital display, re-marketing and other spend is the best way to ensure the highest CLV and not just the highest first purchase ROI. The two figures may differ dramatically and CLV is more important to the profitable growth and sustainability of a company.

The Importance of Customer Lifetime Value in the Daily Deals Market

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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.