There are so many voices out there talking about customer experience and different ways to improve them that it’s easy to get lost in the every changing sea of opinion. But fear not, because perhaps the only way to understand customer experience is by establishing an never-changing foundation from which you measure it. If the approach is a variable, your equation has no constants.
Let’s take the buyer’s journey. It seems as though the visual marketers still have strong foothold in this space. The sort of imagery, below, is considered to be a deliverable-quality output that a client can act upon. Yet, there is little meaningful data behind them, and far too many assumptions (take, for example, personas).
Often times, this imagery is merely a fresh overlay for tired, and useless “frameworks” such as LBGUPS. If you’ve never heard of that, count yourself lucky — and don’t bother googling it as you’ll be wasting your time.
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Learn, Buy, Get, Use, Pay, Support is an imaginary customer journey as only a company could see it. Unfortunately, we are all customers, so we know there is a lot more going on in and around these steps. There is a reason we are interacting with a company, and the companies rarely understand when I’m not able to get my job done with their product — and they should have known in advance that there might be better options
Regardless of how pretty the output is, if it’s not drenched in data and can’t drive a clear market strategy that ends up working for more than a month…it’s worthless. By drenched in data, what I’m saying is that “ideating” your customer personas and then creating imaginary journey maps is a fools errand — and a fool with a tool is still a fool, so they say. Unfortunately, ideas have proven over time to be worthless (unless you’re happy with a ~5% success rate).
So what kinds of questions should we be expecting answers to that we’re not hearing from our consultants and marketing practitioners today?
- How can large portions of the population of customers / consumers hide within our traditional segmentation analyses? A: we’re not looking at the problem properly
- How can we isolate unmet needs within different parts of a product or service (e.g. different types of products for a retailer, or different parts of a service for a service provider) A: We need to break the problem down
- How should we identify the new questions we need to ask? A: Don’t keep asking if you can make the horse go faster
We need to talk about Jobs-to-be-Done
No, we will not be going from one set of pretty pictures to another. Pretty pictures and lack of structure around job identification falls too low on the innovation capability maturity model for my taste. You should feel the same way…hope you do.
There are some questions that we need answers to, and unfortunately we aren’t going to get them with the designer’s approach to Jobs-to-be-Done. (actually, it’s a marketer’s approach). Looking back at why customers switched from one product to another product is…product-centric and backward-looking. What we need is the industrial approach! We want certainty and precision as we move forward in our evaluation of our customers’ buying journey. I call that progress!
The progress and struggling moment stuff is kind of an inside joke. Sorry
In the Jobs-to-be-Done world we can just call this something as simple as Buying a Product. If we were in the process world, we would call the process Buy a Product and would test it by flipping from verb-noun to noun-is-verbed, or Product is bought. We really need to use the same sorts of tests using the Jobs-to-be-Done framework powered by outcome driven innovation to ensure a mature and stable lens. See Giving Customers a Fair Hearing for details.
One thing you will hear from practitioners who use what I call the Level 2 model (scale of 1–5) of JTBD is “Job Stories.” These take the form of contexts, or job statements with high level outcomes attached. These are really quite useless. I wrote the Data-driven Job Story to explain; but Tony Ulwick also launches into it in his new book; which is a good read if you want to understand the 25 year history of the Jobs-to-be-Done framework — which he pioneered.
The Consumption Chain Job of Buying a Product
The core Job-to-be-Done of a customer looks like decorate a new home. However, there are many steps (and projects) that hide inside that simple statement. There is an entire hierarchy of jobs-to-be-done related to this (or this might be a small part of buying a new home; which could also include getting a mortgage!).
But for each solution that is hired to help with one or more steps, or jobs, there is the challenge of buying, learning to use, storing, maintaining, etc. the various solutions. These are known as consumption chain jobs, and they are also jobs that must be mapped out using the Level 5 approach to Jobs-to-be-Done if you want to investigate them in detail. It’s unclear why the older version of Jobs-to-be-Done is better than the newer one — drifting off into deep thought...
Strategyn (the firm that pioneered JTBD) recently partnered with Harte Hanks, a global marketing agency, to create a customer buying journey diagnostic tool. Think about it, a marketing agency with people swimming in a sea of experience with journey mapping and persona making; and yet they shift gears. You should read the ebook to understand why.
While this collaborative effort leverages a standard outcome driven approach to segmentation, they were specifically investigating how brick-and-mortar retail stores could complete in an age of growing online competition. Traditional approaches to segmentation were resulting in absolutely no differentiation between groups. Trying something a bit different, they created a new perspective using the simple tool serious Jobs-to-be-Done practitioners know — the job map — for buying a solution
Step 1 — Qualitative Foundation of Jobs & Outcomes
This is a fairly granular look at a functional job using qualitative data (the job). They also captured 134 customer metrics aligned to various steps; which are also considered qualitative. It became even more granular. Together, the jobs and outcomes created the stable foundation of Jobs-to-be-Done research — bringing a constant to the equation of innovation.
Step 2 — Quantitative Research for Customer Data
I said the bad word…data. If this were Twitter there would be a tweet storm of words like progress and causality. But as Tony Ulwick clearly pointed out…
Clearly, data is the enemy of pretty pictures, so no wonder designers fear data so much. That, and I’m sure the design schools probably promote the idea that material design (in the UX space) is more important than designing functional solutions that GET JOBS DONE. Oh well, if there were no differentiation in the world, Harte Hanks wouldn’t have been searching for it.
Designers / Marketers, don’t hate me. I’m trying to help
The data captured was in the form of satisfaction and importance for 134 customer metrics, plus additional attributes necessary to help describe the profile of the segment once the data reveals it. This is not different than process bench-marking data, other than the fact that internal process data is backward looking and what we’re talking about is forward-looking. They both generate lots of good data — to be used for different purposes.
Step 3 — Use Statistics to Locate Differentiated Segments
I doubt they teach statistics at design school. It’s hard. I know, I was an Economics major. Ugh. Thankfully, you can outsource the labor to a geek if you need to.
What’s harder is creating a product that succeeds in the market.
What’s important is finding those hidden segments that exist within a population of customer data that is otherwise invisible.
This is done around unmet needs, not ideas, and not demographics. In the case with Harte Hanks, they were unable to find any differentiated segments when investigating opportunities for brick-and-mortar retail business…until they applied outcome-based segmentation.
Apparently, nearly 40% of the population of buyers had unmet needs. That sounds like opportunity to me! And while conventional theory around customer value would have you think that selling across more channels increases customer lifetime value (as does selling across more product categories), there were clear opportunities for brick-and-mortar retailers to compete by finding ways to meet these needs just in the brick and mortar store (no online eCommerce needed).
To top it off, there was even more differentiation when looking at various product categories. Hmm…data. Causality proven by data and not over cocktails!
Step 4 — Formulate a Strategy
Most founders, designers, etc. start by coming up with ideas first — which almost always fails as an approach. We can only guess right so many times. At Step 4, we’re only now just getting to where we can evaluate if our old ideas and new ideas will provide any value in the market. But, unlike other innovators, we have data that tells us which needs are unmet, information on how to profile these customers accurately, and precise targeting to select ideas that create value, and reject ideas that don’t.
There is simply no better way, and no better data. And sometimes you simply have to move on.
We’re talking about customers here, their journey, and how they perceive value. Your ideas and thoughts will only mean something if you help customers tell you what they ‘re trying to accomplish, the steps they take to accomplish it, the metrics they implicitly use to measure success across competing solutions, and how they organize themselves into groups of customers with similar unmet needs.
You can’t design your way out of the extremely high failure rate that the traditional forms of product development and innovation have given us. You can’t market your way out of it either. If you want to be a differentiated product or service provider, then it’s time to get out of the skinny jeans and get a nice pair of Carhartt coveralls from Tractor Supply. We’ve only touched the tip of the Jobs-to-be-Done iceberg. Maybe it’s time to switch your approach so you can make some progress.
Another hastily put together blog. Hope you liked it. Feel free to disagree in the comments or on Twitter @mikeboysen