Getting the green light for a machine learning project


How do you convince decision makers in your enterprise to give a machine learning (ML) project the green light?

You might be super excited about machine learning – as many of us are – and might think that this stuff should basically sell itself! The value proposition can seem totally obvious when you are already invested in it. The improvement to current operations is a "no-brainer." And the core ML technology is nifty as heck.

But to get traction for a new initiative, to sell it to decision makers, you need to take a step back from the excitement that you feel and tell a simple, non-technical business story that is sober rather than fervent.

Start with an elevator pitch

99.5% of our direct mail is ineffective. Only half a percent respond.

If we can lower that nonresponse rate to 98.5% — and increase the response rate to 1.5% — that would mean a projected $500,000 increase in annual profit, tripling the ROI of the marketing campaigns. I can show you the arithmetic in detail.

We can use machine learning to hone down the size of our mailings by targeting the customers more likely to respond. This should cut costs about three times the amount that it will decrease revenue, giving us the gains and ROI I just mentioned.

A short pitch like this is the best place to start before asking for questions. Get straight to the point – the business value and the bottom line – and then see where your colleagues are coming from. Remember, they're not necessarily excited about ML, so in this early stage, it is really, really easy to bore them. That’s why you must lead with the value and then get into the ML technology only to the degree necessary to establish credibility.

Keep your pitch focused on accomplishing these three things

  1. Your pitch must lead with the value proposition, expressed in business terms without any real details about ML, models, or data. Nothing about how ML works, only the actionable value that it delivers. Focus on the functional purpose, the operational improvement gained by model deployment – and yet, in this opening, don't use the words "model" or "deployment."
  2. Your pitch must estimate a performance improvement in terms of one or two key performance indicators (KPIs) such as response rate, profit, ROI, costs, or labor/staff requirements. Express this potential result in simple terms. For example, the profit curve of a model is “TMI” (Too Much Information) – it introduces unnecessary complexity during this introductory pitch. Instead, just show a bar chart with only two bars to illustrate the potential improvement. Stick with the metrics that matter, the ones people care about — that is, the ones that actually drive business decisions at your company. Make the case that the performance improvement more than justifies the expense of the ML project. Don't get into predictive model performance measures such as lift.
  3. Stop and listen -- keep your pitch short and then open the conversation. Realize that your pitch isn't the conclusion but rather a catalyst to begin a dialogue. By laying out the fundamental proposition and asking them to go next, you get to find out which aspects are of concern and which are of interest, and you get a read on their comfort level with ML or with analytics in general.

So, does the wondrous technology of machine learning itself even matter in this pitch? Can you really sell ML without getting into ML? Well, yes, it does matter, and usually you will get into it, eventually. But you need to interactively determine when to do so, to what depth, and at what pace.

With machine learning, leading with the scientific virtues and quantitative capabilities of the technology that you are selling – predictive modeling algorithms, the idea of learning from data, probabilities, and so on – is like pitching the factory rather than the sausage. Instead, lead with the business value proposition.

It's more common than you may realize for the business professional to whom you're speaking to feel nervous about their own ability to understand analytical technology. The elevator-pitch format serves as an antidote to this type of "tech aversion." Lead with a simple story about how value is delivered or how processes will improve.

These tactics for green lighting compose just one part of machine learning leadership. For machine learning projects to succeed, a very particular leadership practice must be followed. To fully dive in, enroll in my SAS Business Knowledge Series course, Machine Learning Leadership and Practice – End-to-End Mastery. (This article is based on one of the course’s 142 videos.) I developed this curriculum to empower you to generate value with machine learning, whether you work as a techie, a business leader, or some combination of the two. This course delivers the end-to-end expertise that you need, covering both the core technology and the business-side practice. Why cover both sides? Because both sides need to learn both sides! Click here for more details, the full syllabus, and to enroll.


About Author

Eric Siegel

Founder at Predictive Analytics World

Eric Siegel, PhD, is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the Predictive Analytics World and Deep Learning World conference series, which have served more than 17,000 attendees since 2009; the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery”; a popular speaker who’s been commissioned for more than 110 keynote addresses; and executive editor of The Machine Learning Times. He authored the best-selling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sang educational songs to his students. Eric also publishes op-eds on analytics and social justice. Follow him @predictanalytic.

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