How can I help you with conversation optimization?

I just realized it’s been almost six months since I last posted on this blog. While I have plenty of ideas for posts, I figured it might be best to ask you – my readers (all three of you) how I can help you. Specifically there are two major ideas I’ve had in my head for a while and I’m debating between which one to write about next.

The first idea is a technical overview of how the web works, going into detail on web analytics and split testing. Everything someone who is not a techie needs to know in order to gain a better understanding of what the data really means from a technical perspective as well the implications on how technical decisions impact business decisions.

The second idea is making conversion rate optimization more of a science and less of an art. I’ve read just about every book out there that deals with site and page optimization. I’ve also conducted countless split tests and have analyzed more sites than I can remember. What I’ve found is that there seems to be a major gap in the process where what to do next and how to do it becomes more of an art and less of a science.

Plenty of smart marketers can see a web page and know intuitively that it won’t convert well. Often it’s even easy to identify specific elements which are “broken” and need to be fixed, but more often than not (at least for me), it’s usually not so simple to explain the internal thought process of converting an OK page into a great one. This is something I’d like to address.

So, my loyal readers, please let me know what I should write about. Even if it’s something other than the two topics I’m thinking about let me know.

Thanks,
Ophir

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About Ophir Prusak

Web Analytics and Optimization Expert

6 Responses to “How can I help you with conversation optimization?”

  1. Great idea, Ophir… one of the things I’d really like to see posted is a CRO process employed by other analysts. Particularly one which shows how a team can efficiently run tests that inform marketing decisions.

    Getting tests up and running can be very time consuming.

    • Hi Rob,

      While I can’t go into to much detail about the CRO process we follow at Adobe (don’t think my boss would be to happy about that) I can *highly* recommend the book: Cult of Analytics

      While the emphasis is on analytics and not CRO, it’s the only book I am aware of that really tackles the issue of analytics culture integration into the enterprise.

      The book provides a very detailed and thorough framework for embracing and fully integrating a healthy analytics driven decision process into a company’s standard procedures.

      Much of the framework could easily be adapted to CRO :)

      Here’s the direct link:

  2. Hi Ophir,

    Similar to what Rob said, we have good models for analyzing experiments, rejecting hypotheses or not, etc. But this is all AFTER we have picked the hypotheses to test.

    I don’t think we have good models to help with hypothesis generation. Could this be put into a process?

    Michael

    • Hi Michael,

      Just to clarify, are you looking for a process that helps decide what to test next?

      In other words, if you’re debating between testing the call to action button on the product detail page, the number of form elements on the checkout page or the messaging on the homepage headlines, which do you choose?

      Ophir

      • Exactly. I guess I am wondering if it is possible to have a model that would generate good hypotheses to test, or is it basically the art and intuition of CRO which varies from site to site?

    • Hi Michael,

      CRO is a part science and part art. After many years trying to find a good analogy, I think medicine does a good job as an analogy.

      Imagine going to a doctor saying you don’t feel well (your conversion rate is low). Finding out what’s wrong and how to fix it isn’t something that can be totally automated, but there is a science behind what you’re doing.

      Ultimately it’s a simple equation:
      X = Anticipated lift from a test (%)
      Y = Resources required to execute the test (time and $)
      and then start testing where X is high and Y is low

      Of course the hard part is figuring out X in advance which is actually more an art than a science since.

      This can (and should be) accomplished from both a UX perspective by simply looking at the page (ie your main CTA has no visual prominence on the page) and from an analytics perspective (ie a large drop-off in your funnel)

      Sounds like some good fodder for an article :)

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