2 min read

Polymer Search sales ICP case study by ProdPerfect

Prior to Polymer we were using Excel pivot tables. They were an absolute grind, and only provided limited insight. You can really only squeeze one or two variables at a time into a pivot table, and our dataset was too complex to get anything more than the most basic insights.

ProdPerfect sells a highly complex technical product to a potentially wide array of customers with not only different needs, but different technology stacks and different development strategies. It was never obvious which factors made a really great fit customer.

Of course, we spent a long time trying to sell to just about anyone who showed interest, in part just to get the data. What makes this all the more complex is that we don't simply have win/loss as outcomes: we need to understand our LTV, so we want to know which kinds of customers sign a contract, convert from our pilot, and stick around. So you can imagine a massive multivariate matrix of outcomes we're looking for.

The reason this is so important is that we invest a lot in the sale and integration of a customer. We cannot afford, long-term, to just sling mud at the wall and see what sticks. It's been mission-critical to our ability to efficiently grow the business, to be able to understand which customers are our best customers. I'd say the value of fixing this problem is "literally incalculably large," but over the next year it's probably worth a few hundred thousand dollars.

How did Polymer help you solve the above + key points of impact:

I had actually been avoiding trying to go about solving this problem using conventional methods. I'm the co-founder, and I'm busy, and everything is always on fire. I just didn't have hours and hours to sit down and take some online Excel class or find and teach someone who knows STATA or R to do the analysis for me.

Polymer made it astoundingly easy to start getting insights within a few minutes. My data was pre-processed and then presented in a way that I could just start slicing key correlations right away.

I started picking correlations and looking for outliers with a literal list of questions about our ideal customer profile (ICP). After 30 minutes, I had totally redefined it. We got a number of hugely surprising insights about what funding stage, what industry, what company size, and what testing team makeup were most successful in buying and adopting our product.

The point wasn't to have a certain statistical number; the point was to answer questions: who should we go after with our marketing dollars and sales time? We knew the answer immediately and shifted our outbound and inbound tactics that very day. It's going to make a huge difference for our aggressive 2021 growth plan.

Contrast with the previous process and solutions that ProdPerfect tried (e.g. how were you trying to solve this prior to Polymer?)

Prior to Polymer, we were using Excel pivot tables. They were an absolute grind and only provided limited insight. You can really only squeeze one or two variables at a time into a pivot table, and our dataset was too complex to get anything more than the most basic insights.

The alternative we were considering was finding someone who knew R or STATA and could run a raw regression analysis using scripts and then try to remember enough statistics from college to figure out what all the correlate factors meant. I was not looking forward to it.

How would you describe Polymer to someone else?

It's a tool where you can nearly literally ask a question about any data set and get an answer. I think the world's been waiting for something like this for a long time.

Posted on
November 19, 2020
under Blog
November 19, 2020
Written by
Alex Almeida
Founder of Connected Brands, a performance marketing agency, Author at The Conversion Lift

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