This holiday season we wanted to do something as a company to contribute to those less fortunate than us. A few members of our team previously worked at companies that organize canned food drives, but I’m not a big fan. While I think that any effort to help others is worthwhile, the data indicates that canned food drives are not particularly effective. If your goal is to feed the hungry, you can help 20 times as many people by giving cash to hunger relief organizations rather than using cash to buy cans of food.
Not long ago, a prospect was kind enough to share their thought process where they evaluated us against a competitor. One thing that stood out to me was an email chain where a consultant they employ positioned our competitor as a large, established enterprise and us as “a startup with a stupid rap video.”
The not-so-subtle subtext of the consultant’s email was that their client shouldn’t feel comfortable relying on a bunch of jokers like us. It’s a fair point. We are a (5 year old) startup. We have a rap video. Guilty as charged.
When we started RJMetrics, Bob and I were able to stay on the same page without much trouble. We had similar ways of thinking, and collaboration between two people in the same room is easy.
As we have grown, it has become both impossible and undesirable for us to stay in the loop on everything the company does. We hired all of these amazing people, and we think it’s great that they conceive their own initiatives and run with them. However, it’s more important than ever to clarify what we do, why we do it, and have a guidepost for all of our actions as a company. That guidepost is our mission statement.
While our mission statement is first and foremost for our team, we’re happy to let everyone in the world know about it. We think that it will be useful for our customers, partners, vendors, and friends to have a clear sense of what drives us.
Not too long ago, Bob and I were were two guys with no customers and no product working out of an attic. The first years are tough, and companies rely on the advice and support of startup communities to make it through.
The best part of our job at RJMetrics is the chance to help new entrepreneurs succeed. That’s why we’re so excited to announce our partnership with 21212, Brazil’s premier digital startup accelerator. Through this partnership, every company in 21212 will have access to the same level of business intelligence that the industry’s leading companies use to power their decisions.
From the 21212 blog:
After completing our first two acceleration programs, and now in our third, we have come to realize the importance of data analysis in the entrepreneurial process. Starting a business is all about asking, and answering, a growing list of questions. What do my customers want from me? How much are they willing to pay? Where can I find them? Answering these questions successfully determines whether a fledgling venture will succeed or fail. And the only way to come up with answers is to dig into your data.
We couldn’t have said it better ourselves. Good luck to everyone in 21212′s third class, and we’re looking forward to helping you grow.
There are a lot of different ways to look at your data in RJMetrics, and we know that interpretation and understanding are just as important as calculation and visualization. So, I’m writing a series of blog posts where I will do a deep dive into some of our analyses and visualizations.
The first in the series is the lifetime revenue cohort analysis.
What does lifetime revenue cohort analysis mean?
This chart shows the cumulative spending per user for a period of time after they are acquired. Cohorts of users are split up by their acquisition month.
For example, the orange line above shows the average for users who were acquired in November 2011. The first data point means that in their first month, users who were acquired in November spent an average of about $200. The second data point means that by the end of their second month, these users had spent an average of about $240. Their average spending in month two was approximately $40 (240 – 200).
The different lines represent different cohorts of users. The green represents the users that were acquired in December, and the blue is users that were acquired in October.
Why is this important?
This kind of cohort analysis can be useful for several different purposes, but the most immediate benefit is often better customer acquisition decisions.
Many companies limit their marketing spend to channels that yield profitability on a customer’s first purchase. These companies will pay to acquire customers through a given channel as long as that their average first purchase yields more gross margin than it costs to acquire them. The problem with this approach is that it often results in an underinvestment in growth. If your competitors are marketing based on a deeper understanding of buying behavior, they will outgrow you.
The lifetime revenue cohort analysis helps you to understand the consequences of expanding your customer acquisition spending, and it provides an easy way to convey this to the rest of your team. If future customers behave like existing customers, then acquiring customers for a higher CPA will result in a predictable payback period. Depending on the cash position of the business, you can define what payback period you are comfortable with, find the relevant spot on the chart, and spend accordingly.
Additionally, you can use this analysis to see if you are getting better at onboarding, engaging, and generating revenue from the users you acquire. For example, this cohort analysis is a great way to see if a free shipping promotion for new users resulted in repeat buyers or one time purchasers that never come back.
How will this vary for different business models?
For most businesses, the lifetime revenue cohort analysis chart will show a large amount of spending in the initial period and then increase more slowly over time.
That initial spike is due to the fact that customers are more likely to make their first purchase soon after they are acquired than at any other time. In cases where the acquisition event itself is a purchase, 100% of customers make a purchase in their first period. In cases where registration can happen before purchases, this effect is less drastic. As an example, Groupon would likely have a much lower initial jump than Amazon, because many of the people who sign up for Groupon don’t make a purchase right away.
Unless there are a high number of refunds, this chart will slope up and to the right after the initial jump. The rate of growth tends to decrease over time because customers are usually most active when they first sign up. This causes the average to drop because the number of people in the cohort stays constant regardless of how many come back to buy more.
In subscription businesses, the slope will decay less aggressively than in businesses where people make one-off purchases. Occasionally, a subscription business will actually have a slope that increases over time. It is rare to see this, but it is a great signal for the business when it happens. This does not mean that there are zero churning customers, but rather that upgrades for customers that stay more than make up for the customers that leave.
How is this calculated?
There are two simple inputs to this calculation: how many members are in the cohort (which never changes), and how much revenue those members generated in the given period.
To determine the members in the cohort, we count the number of users who were acquired in the period in question. An acquisition can be a first purchase, account creation, newsletter sign up, or some other event.
The revenue calculation is a bit more complicated. We want to sum revenue for orders that were placed by members of this cohort and took place within a fixed time period from their acquisition date (ie the first three months).
Finally, we divide the revenue by the number of members in the cohort for each time period in the chart and add this value cumulatively over time.
What are the variations of this chart?
There are many different kinds of useful cohort analyses. The most common variation is filtering by user acquisition source. For example, you might want to look at this chart for customers who came from organic search, paid search, or an affiliate program.
This will help you understand if the customers from one acquisition source are more loyal or valuable than another. Thrillist’s subsidiary JackThreads used this analysis to understand that one of their most expensive acquisition sources was actually its most profitable. After learning this, Thrillist shifted their marketing budget to the more expensive acquisition source and accelerated their growth.
Another way to look at the data is with an incremental, rather than cumulative, data perspective. This shows the incremental amount that an average user spends in each month after they are acquired. This is useful for forecasting the amount of repeat purchases you will get from existing users.
We can look at this with other things besides revenue as well. Some examples include margin as well as non financial metrics like invites, votes, or messages.
Lifetime spending cohorts are a powerful way of looking at at your customers’ buying behavior. Stay tuned for more information on how to use and interpret your metrics.
We’re hiring someone to help us capture the tremendous opportunity in front of us. This person will play a pivotal role in defining and executing on our growth strategy. We are moving very quickly with this position, so please apply before the end of the day on Friday December 14th if you are interested.
A significant part of this job will be analyzing different marketing channels and strategies to determine best fit and ROI. Day to day responsibilities will include content marketing, quantitative data analysis, paid advertising, funnel optimization, and partner management.
We want this person to be comfortable contributing on all aspects of marketing-related projects and able to work autonomously.
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.
I’m proud to announce that RJMetrics is now a recommended vendor on Start2Cloud, a research firm that evaluates cloud solutions for B2B software.
Jan Kocfelda, Start2Cloud’s head of business intelligence, had this to say about RJMetrics:
We at Start2Cloud love RJMetrics. It is one of the best BI applications out there, and they know that great analytics is not only about excellent software but skills and experience in the field are crucial too. Pursuing this notion, RJMetrics provides free implementation and training on top of every package offered. RJMetrics also manages your data warehouse continuously and helps with any additional adjustments. There is no reason we wouldn’t recommend RJMetrics for any business looking for a great BI solution.
We’re in a book! Grab a copy of The Entrepreneurial Instinct.
Monica Mehta, reporter for BusinessWeek and frequent contributor to Fox News, MSNBC, and ABC news, just published a new book titled The Entrepreneurial Instinct. The book features interviews with successful entrepreneurs and discusses the circumstances and the thought processes that drove them to start their companies.
Beyond the stories, Mehta also dives into the psychology and neurochemistry of entrepreneurship and risk taking. She analyzes the personality traits that correlate with and enable entrepreneurship. Mehta examines the crucial role of the brain chemical dopamine, and she explains how its interacts with different parts of the brain during the risk taking decision process.
Finally, she goes into detail on practical exercises and personal finance tactics that can help people get comfortable with the risk, uncertainty, and anxiety that come along with the plunge into a new venture.
Bob and I were interviewed for this book, and we’re thrilled to be a part of it. You can read about us in chapter 10, “Finding Inspiration,” on page 124. Get your copy of The Entrepreneurial Instinct today, I highly recommend it.
Magento is a great platform. It’s incredibly flexible, and it is used to power everything from early stage e-commerce sites to some of the biggest sites on the internet. However, like any platform, some areas of its functionality are stronger than others. In this post, we explore an area that often leaves users unimpressed: Magento reports.
Existing Magento Reports
The default Magento reports are very basic. The main things you can do in the Magento reporting interface are:
- See your top 5 items, search terms or customers
- See your most recent 5 customers, orders, or search terms
- Plot a line chart of revenue or orders for a few different time ranges
- Pull simple lists of orders, products, customers, and search terms
These are useful for business intelligence, but they won’t allow you to build a data driven business. To take things to the next level, you need to supplement Magento’s reporting capabilities with additional analysis.
What’s missing from Magento’s Reports?
The list of things you can do with your data is only limited by the number of questions you have. While it can be tempting to run as many analyses as you have time for, it’s important to focus on actionable metrics.
Some of the analyses and metrics that savvy ecommerce companies study are:
- Lifetime value
- Cohort analysis
- Lists of lapsed customers for targeted follow up
- Advanced segmentation by customer and product attributes
The tools and features that help companies study and manage such metrics include:
- Custom dashboards
- Flexibile visualizations
- Different views and permission levels for various stakeholders
- Tools to incorporate data stored in other databases, Google Analytics, or spreadsheets
Augmenting Magento Reports With SQL Queries
The first thing that most e-commerce companies try when they want to answer questions with data is to ask a member of the development team run SQL queries.
This works if there’s only one data question, if you don’t need the answer immediately, and if the business user can work with the results in Excel to get what they need. However, if you want your business to use data to drive decisions on a day to day basis, this is not a sustainable solution.
I’ve spoken with hundreds of e-commerce businesses here at RJMetrics and in my previous job in venture capital. I don’t remember ever meeting someone whose IT team had plenty of time for pulling data for business users.
Manual queries can be particularly tricky because of Magento’s entity-attribute-value data model. This allows for a lot of flexibility when building out your store, but it also makes building and maintaining analytical queries much trickier.
Enhancing Magento Reports with Hosted Business Intelligence
Here at RJMetrics, we work with many companies that are on the Magento platform, and we’ve learned a lot about the platform’s strengths and weaknesses. We’ve spent even more time working with e-commerce businesses on all different kinds of platforms to understand their business challenges and how we can help use data to solve them.
We’d love to make your data understandable and actionable. Sign up for the RJMetrics free trial today.