Omnichannel: Everyone talks about it but where's the beef?

Generating meaningful data regarding customer channel selection and experience is typically full of bias and hypotheticals

I was fortunate enough to be able to run a customer journey survey on a (conflated) JTBD in the retail industry, but I included some channel experience questions as dimensional data. Something I’ve noticed in what is considered to be leading edge marketing research is that they deal with hypotheticals like:

• How likely are you to purchase [product] in the next 12 months?

• If you were to purchase a [product]…would you prefer to do so on a website, or in a store?

I’ve already written a post on this subject but I’m still laughing about it 👇

Asking the Right Questions of the Right Audience in Jobs-to-be-Done Research

The topic of channels is an interesting one in an age where digital options abound, but there are still a lot of brands that have heavy investments in physical stores and exuberant customer-facing teams. So, there is a ton of inertia, bias and “executive opinion” that prevents us from seeing the true picture because researchers can be intimidated by the thought of getting the wrong conclusion from their work…even if it’s 100% right. Know what I mean?

The other thing we see is very simplistic surveys that address obvious elements of a channel and ignore other things that might be going on in an end users mind. So, when you look at these models, try to convince yourself that there is a larger way to look at things. Yes, there are a few elements that I would consider eliminating. There are obvious steps that could become success statements in another step. This is research, it’s not a final product, so use it to accelerate your work. The questions you’ve been asked to answer will determine the scope of your quantitative research.

Note: I’m getting much more concise success statements in the Alternative Metrics section. I’m working to make that better in the phases but I highly recommend that you review the alternative statements because they are closer to what I would want in a survey. They’re not grouped by phase, however.

AI has been really good at covering all the bases (and I’ve put limits in place) but if you’re a purist in the origins of the methodology you might think it’s too verbose. I would argue that it’s considering things that are missed in interviews and at least it gives us a portfolio of options to reduce instead of wondering what the consultant missed. Something to think about. I’ve already begun working on reduction prompts but right now I think you should consider more data rather than less as a starting point.

Two sides of the coin

We need to recognize the balance between the needs of customers and the needs of the business. If one side wins, the other side can’t (or won’t) participate for very long. Also, this isn’t just about building channel capabilities, a company may need to decide at each interaction which channel it’s willing to offer a customer - for a variety of reasons. Every decision comes with a cost, and not all customers are worth the same as another to the brand.

Here’s something that doesn’t jump out at me as I look through these catalogs; it didn’t strongly consider the value of a customer when deciding which channels to offer. In B2C that may not be a super big issue, but in B2B there are customer interactions that can be far too expensive for some customers based on their lifetime value. So, nothing’s perfect. I’ll eventually be training my own model :)

Two Jobs-to-be-Done research catalogs for the price of one….$0

Click the image

Have a great day!

Book 30 minutes with me if you have some unique qualitative research you’d like to get done in a day or two. I promise, it won’t cost you hundreds of thousands of dollars and we’ll do it exactly the way you want it. We can also talk about anything else you have on your mind about JTBD