As he watched the presentation we were giving him on the machine learning project we were working on, I couldn’t but help notice his furrowed brows.
I knew him to be a natural sceptic, one who loved asking tough questions that dug deep into the heart of the matter. Though these questions occasionally bordered on what I felt was an annoying stubbornness, especially when I was on the receiving end of them, they were oftentimes great at uncovering issues one may not have thought of; or, at the very least, making sure that important issues were discussed out in the open and transparent to all who mattered.
Our machine learning project had to do with the estimation of how likely a customer was to convert. I won’t delve into too much detail given the need for confidentiality, but on a high level what the model we built provided us was a very good estimate of how likely a customer was likely to pay a deposit, the next stage of the Sales pipeline.
In other words, we had a great predictive model – one that helped us to predict what would happen.
“But,” he said, “how does that help us know what to do?”
We, the project group, looked at each other. I’m not sure if the others knew how important this question was, but I did. It was the very question I had been asking early on, but one that I decided we could only answer later.
Given the quality and quantity of our activity data (i.e. the logging of activities by our salespeople, and/or the collection of activities carried out by our customers and partners etc.), and the Sales processes we had historically in place, there simply wasn’t enough standardisation and control for a sufficient time to use in our models, something I was working on as the head of Sales Operations to fix (ah, the beauty of holding both Sales Ops and Analytics hats!)
“In effect,” he continued, “what you’re doing is forecasting what’s going to happen, but not what we should do to get better outcomes. In a way predicting the past, and not influencing the future.”
Spot on, dear sir.
The model we had was a prediction model, not quite yet a prescription one. A prescription model was what we were working toward: what can we tell the Sales team to do in order to improve their conversion efficacy? Do we contact our customers, or do we not? (e.g. though possibly counter-intuitive, it might actually be better to leave customers alone in order to improve conversion rates!) Do we make 1 call or 3 or 5?
We needed more data, but we were not quite there yet. The model we had would be great for forecasting, sure, but in terms of prescribing an activity or activities not quite, yet.
So what’s all this got to do with tackling the missing middle of adoption? Well, you see, when we had started with machine learning I knew it was going to be a tough sell. Machine learning isn’t standard in the industry I am in (i.e. Higher Education), unlike technology or finance. There’s huge untapped potential, but it’s potential we can’t get at if we don’t start.
Together with several forward-thinking senior leaders in the organisation (including most importantly my boss!) we made the decision to go ahead with machine learning on a small scale, to “get our feet wet”, and iterate ourselves to success as we learned through doing.
You don’t go from zero to a hundred without first encountering 20, 50, and 70. This exploration phase (“exploration” because we knew it wasn’t going to be perfect and was not quite the “end goal”) was a necessity. Sometimes, it might even seem a little like giving up on the promise of progress – to continue the analogy, slowing down.
And as per the image of this post, you’ll have noticed that in order to get to our destination, sometimes the best move is “backwards”, getting to “the middle” before we get on a highway from where we accelerate to our desired destination.
To have avoided this “middle” would have made achieving the “end” very much harder – notice the curved, narrow roads in the image? – reminds me of how it’s sometimes much easier to go around a mountain than to tunnel through it!
In the missing middle of adoption, we always tend to forget that in order to achieve our innovation goals, we sometimes need to take up an option that’s not quite perfect, and may at first glance seem like a detour. We just need to make sure we don’t fall into that other trap: complacently thinking that our detour is the final destination! (But more on that for another day.)
I love to read and write. Professionally, data science, technology, and sales ops are my thing. In my non-professional life, I aspire quite simply to be a good person, and encourage others to do the same. For those who care, I test as INFJ in the MBTI.