Most Conversion Optimizers focus on the technical aspects of improving your site. They get adequate results.
The Conversion Co. gather deep feedback on your customers to find out what they REALLY want. When we give it to them, the results can be spectacular
Data × Psychology = CRO to the power of two.
Step 1: Gather Data
Charts aren't enough
The biggest wins in conversion come from your customers.
The things they want, and the things they're trying to get rid of.
That's why we'll look at your dashboards and analytics, but we're far more interested in talking to people who are in your market, right now.
CRO2 is based on what people do x what they feel while they are doing it. It's an equation that multiplies results.
Step 2: Diagnose Problems
Are you giving your customers what they want?
Tiny points of friction add up to sales-stoppers. We identify these points and look for ways to encourage the sale instead. Every issue is graded on it's likely impact vs how hard it is to implement, so we can find the quickest wins early.
Step 3: Design Alternatives
Don't panic. We're not selling you a re-design
Because they never end well. Instead we find incremental changes that add up to more than a rip-and-replace project ever would.
Step 4: Tune the Toolset
Because good decisions need good data
Before we test anything we'll make sure your analytics setup is as near perfect as it can be. A strong analytics setup, coupled with testing tools that measure their effect on long term sales is what makes the difference between companies that grow and companies that guess.
Step 5: Run the Tests
What gets measured gets done
We don't take pot-luck on what will work and what won't. We're dealing with humans, not spreadsheets here. Despite thousands of tests, or maybe because of it, one thing we know is that there are no universal rules. We also protect your existing business by keeping the majority of your traffic going through known paths.
Step 6: Evaluate Winners
We care about why
Every test, win or lose, brings us more data. We use that to create new test ideas, find segments in the data who act differently, and find principles we might be able to apply elsewhere. And then we start again, always looking for improvement.