How Fab.com Raised $40 Million with Data


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Last week, we talked with Robert J. Moore, CEO, RJMetrics about how to start a company by replacing your own job. This week, we're taking a look at a couple of examples of companies that are using RJMetrics to get ahead.

In a behind-the-scenes post about how he raised $40 million usind data, Jason Goldberg, founder & Chief Executive Officer at Fab.com opens the kimono on his site's metrics. Fab.com started as a daily deal site and is evolving into an online destination to rival Amazon.com.

 

RJMetrics works as follows

You spend a couple of hours connecting your production database to RJMetrics and then it spits out the most amazing graphs and charts on all facets of your business. It updates every couple of hours based on your actual production data.

As a dashboard it’s fantastic, but the real power of RJ is in its cohort analysis — RJ enables you to effectively monitor each cohort of users by join date and then evaluate their revenue contribution, payback periods, engagement levels, and lifetime value.

Roberts says Fab.com uses its own site's data not only on the customer analysis side but also the strategic side. When speaking with investors, the founder was able to show the lifetime value of a typical customer.

Thus the conversation shifted beyond the value of one customer to how much should you be willing to spend to acquire a customer, which might not be as evident as you think.

Say for example you're spending $40 to acquire that customer. If you know that customer is willing to make multiple purchases in their first few weeks, you should be willing to invest more than that.

 

Gaining statistical confidence for decision making

 

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The utility of RJMetrics deep dive is to help you separate customers into cohorts that are similar. For example, when they joined, the referral source they came from, the first product they purchased, it's about finding like patterns for customers that behave in a certain way.

Talking about eCommerce, some of the advantages are

  1. you have this data, or your Website wouldn't work, your back-end database includes a lot of this key information
  2. there's a lot of that data, in the order of magnitude of ten, twenty-thousand customers, in some client cases, it's ten or twenty million, with a volume of data like that, you gain statistical confidence that a person who fits a certain profile that makes them specialized, is likely to do a certain incremental thing within a certain amount of time or make an incremental purchase in a certain way

When you have that level of knowledge you get to what the potential lifetime value of a customer is and that gives you a lot of power about customer acquisition decisions, merchandizing, and around strategic things like raising capital.

Because you can really demonstrate with statistical confidence, on paper, that your business is worth a certain amount and your customers bring you a certain value. This analysis wasn't practical to run especially for a startup ten, even five years ago. 

 

Customer segmentation, and reading intent in social

Problem solving for questions like:

  • how do I segment my customer base based upon their purchase patterns?
  • how can you tell intent from the data?
  • how can you make better decisions on customer acquisition costs?

Watch the video for the answers on intent — part art and science. My next question on intent is about learning to extract more value from the qualitative information we have with the integration of social media.

It's about being proactive and reactive in using social signals in networks to create some form of intent — kind of like inception.

Using the analytics RJMetrics provides, for example, you can identify those customers who are actually more likely to respond to a particular kind of marketing and message, then use social media as a channel to deliver that message.

Instead of doing an email blast to thousands of customers, by this method, you sell single customers a very particular product. You can make your likelihood of being more targeted much higher and you get the side benefit that you resist brand fatigue, which happens when people are mis-targeted too frequently and get frustrated with the product or brand.

This is a preview of the questions and examples we'll use at the Conversation Agent workshop on April 19, 2012.