Source: Gartner Blog Network On:
Last week was interesting and illuminated an issue that has been bouncing around in my brain for a while. In decision making, it is nearly impossible to gain perfect information to inform that decision. And we are fine with that. We do the best we can (or follow Colin Powell’s advice which is to gather an estimated 40% to 70% of the info you need, then go with your gut).
Then consider all of the data that Gartner presents. I’ve never seen a statistic or “data fact” that was “100% of respondents did x.” That type of perfect information is either very, very rare or very, very limited in value.
And we’re fine with that.
Until, a new idea comes along.
Source: Photo by Ikowh Babayev from Pexels. Cropped by Me.
Then, we expect the new idea to be perfect or to deliver perfect information. I experience this with our Enterprise Technology Adoption (ETA) Profiles. A lot. The big objections to them:
There is no list you can buy of companies by ETA Group.
If multiple respondents in a company complete the assessment (https://surveys.gartner.com/s/ETAProfile) and their responses differ then the information must be wrong.
If sales teams complete the survey, what if they are wrong.
But, then I counter with the positives:
We have evidence across multiple surveys across multiple years that show that ETAs do a better job of distinguishing buying approach than any other data cut imaginable.
The simple act of taking the assessment improves understanding of an organization (whether done by someone working at the company or a sales team working with them).
Different answers are an opportunity for discussion–Discussion leads to even greater understanding.
Fundamentally, ETAs should be thought of as well constructed hypotheses, with an evidence base to form them. Like any hypotheses, you then conduct experiments (or in sales interactions and continuous qualification) and seek to prove or disprove the hypotheses.
I’ll contend that this is better than the alternative. When we segment by traditional firmographics, we might actually have that 100% perfect data. We know the orgs we target are from North America; or in the Insurance industry, or are mid-size. That’s fine, but it doesn’t get you far enough to really make progress. Once you get past that, its all about understanding imperfect information and signals.
Are you happy with your marketing results and conversion? Or are they a bit (or a lot) too imperfect?
To break free, we have to kill the expectation of perfection for new ideas. It’s self defeating and keeps you stuck in a cycle of marginal effectiveness from low value information.
With ETAs, there is a simple way to get started. Make the ETA assessment part of your work in developing an ideal customer profile. Then (and we have lots of tools in this area coming soon for clients), tune your messaging to appeal to your ideal ETA groups. Choose content formats and information types that they prefer. Leverage channels and independent information sources that flock too. For those that engage, you can start with a hypothesis that they are in your target ETA group. Then you qualifications and interactions to discover if that is the case. Learn and refine, learn and refine.
I have a follower on social media who is not even a Gartner client. But using the free assessment and the information I’ve shared in blogs, he and his clients have used ETAs to try to understand customers and target them. They developed an ideal customer profile. The applied it (using mostly their own ingenuity). Analyzing deals that they closed, 85% were with accounts that fell into that ideal customer profile.
That’s not perfect, but it is pretty darn good.
Stop applying unrealistic expectations to new ideas. The status quo is not perfect.
Why get paralyzed by an expectation of perfection for something new when perfection doesn’t exist for much of anything we already do? The paradox of perfection paralysis.
Break free….Try…Learn….Grow…and Win (like the example above). And if it doesn’t work for you, try something else. There are always new things to try to be better.