How to tell when poor UI/UX in a ML/AI application, analytics solution, or data product is impacting customer value

Today I want to tell you why your ugly, clunky, hard-to-use data/AI product or analytics solution should scare you.

But first, you, your boss, your customer, your stakeholder—somebody—has to pass that judgement on it.

They probably have, but don't expect it to necessarily come out in the words you may expect.

Just as most designers (in my experience) suck at communicating the business impact and value of design at the executive table, most product/biz and technical people are not comfortable evaluating good or bad design on their products and services (data or otherwise). In fact, less than 5% of companies in a McKinsey study reported their leaders could make objective design decisions.

So guess what?

You're probably not going to hear about "the design of our products, solutions, and internal/external experiences aren't cutting it!" as a mandate from the top, because most leaders don't understand how design can directly impact the bottom line.

However, your leadership—or your customers—will definitely react to the SYMPTOMS of bad design. 

And what are those symptoms?

I'm going to give some of these to you in the voice of YOUR customer—which some of you probably haven't heard in awhile since 40% of you actually are still not talking to your end customers (yup, see that same survey link up there ⬆︎).

Symptoms your data product, AI, or decision support solution sucks (the spoken edition - you've probably heard some of these before):

  • "I hate using this service; it frustrating, and it takes forever to get the one thing out of it."
  • "I spend hours in this tool trying to get useful information out of it. It's a huge waste of time for me every week."
  • "I'm supposed to trust this new AI thing, but I have no idea how they got to this 'insight'"
  • "This 50 pg. PDF report with all the supporting data and "proof" is not helpful!"
  • "I/my customers don't know what to do with this information"
  • "Sorry, but this is not what I meant when I asked for data about X 😕"
  • "My boss/IT made us use this new service, but we all avoid it like the plague"
  • "They've tried to make it easy to use by dumbing it down, but they took away/hid data I need to do my work."
  • "If it weren't for just this 1 feature/data point, I would NOT pay for this solution."
  • "Why am I paying for this?"
  • "How did they come up with this finding/insight?"
  • "When is our "data strategy" going to start showing some results? You guys have built a lot of infrastructure, but what did we get for it?"
  • "Using this data/tool/BI/viz/report/app is the least fun thing I do at far."

Why your design may suck (the unspoken version):

  • "This is not intuitive at all...hmmm....grr"
  • There is no feedback about what is going on behind the scenes, when it actually does matter to the end user
  • "I/we spent a lot of money on this—why is it so hard to use, and why did it take so long to get to this?"
  • "I'm supposed to understand/care about this info/insight/data, but I simply don't. It takes way too much effort."
  • "I like this, but I can't adjust the what-if scenarios to understand my risk profile." 
  • Poor visual design, ranging from use of colors, typefaces, poor visualization choices, scaling, whitespace, no directional conclusions or thought given to the customer journey, no understanding of entry/exits, giant tables of numbers, the list goes on.
  • "It's a black box—how can I trust this to make business decisions?"
  • "This does not make me want to come back for more."
  • "It takes forever to get anything useful back from the data/analytics team, and when it does, I'm like 😕🤷‍♀️"
  • It was imposed on your customer/user/stakeholder "at the end" after a gigantic technical effort to "ship something"
  • "We spent a ton of time on this, but this does not feel innovative."
  • The service/product doesn't feel inviting; it looks cobbled together, not serious, and klunky.
  • It gives weird technical errors, uses maker-jargon, and puts all the risk on the customer to know what to do next
  • It does not support the user's natural workflow
  • It makes no conclusions and forces me to explore to get any useful insight
  • It does not integrate with other services or people that the end user needs to be in sync with in order for the data to produce value

So, what does it sound like when you get design right?

  • "The value is so obvious in the new design that it sells itself" (your sales team)
  • "It's so much easier to show the value of our data science and analytics efforts."
  • Your experience helps customers save time doing work that is not fun
  • "It saves a ton of time!"
  • "It takes a lot of the risk away for me—I can make a much better decision now!"
  • It feels NEW, innovative, and different—in a good way. A significant jump from previous efforts.
  • The look, feel, utility, and usability of the data service, application, or product is solid, trustworthy, and inviting, making your brand feel innovative, unique, and valuable
  • Your data products produce meaningful decision support, and you get further requests for "more stuff like this!"
  • People can understand the meaning of the data in the context of their work, needs, or lives
  • "I can tell that somebody spent time on this and cared about making the data helpful to me and my job/work/life"
  • "I would PAY [pay more?] to use this service. It has made a huge impact on my work/life/customers."
  • "It feels very intuitive, and guided. I know what to do, when to do it, and where to go next."

You can get these outcomes too.

As the FastCompany writeup on the McKinsey survey states, and I agree, one of the best ways to start adopting design practices is to use a pilot program. You don't need to change your entire business today. You can hire some expert help, or train your staff. Whatever you do, it does matter, or the digital natives like Google, Amazon, and others wouldn't have product and UX designers helping drive the creation of their data science/ML efforts and AI products. They know that meaningful user engagement with a data-driven service is critical to ensuring that data science/analytical investment actually produces impact.

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Photo by Allan Rogers on Flickr. 

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