I have to beg your indulgence on this article. It’s an article about predictive and proscriptive analytics. Part of my reason is having just returned from Dreamforce 2015, as you would guess, a lot of the sessions talked about analytics. Part of the reason for this post was driven by a thoughtful question from Anthony on my “Who Should We Be Talking To,” Post. It also ties to my “The Intersection Of Seth Godin And Challenger/Insight Selling” Post.
I know you are looking at the title of this post, thinking, “Dave, here you go again on one of those esoteric rants—What’s analytics have to do with butterfly’s landing on leaves?”
Somewhere, years ago, I heard the story—there are thousands of variations– A butterfly lands on a leaf in a Brazilian rainforest, ultimately causing the tree to fall, which blocked the flow of water, which impacted the volume of water flowing from the Amazon (the river 😉 ) into the ocean, which 2 years later caused a Tsunami in Japan.
It’s a story with lots of lessons, but basically looks at cause/effect and interrelated systems. It helps us learn how something that is seemingly unrelated–because of interrelated systems–impacts thins in completely different areas. It could be melting icecaps and heat waves in Europe, the mysterious death’s of honeybees and the impact on world food supplies.
It even applies to us in sales and marketing. If we understand the relationships between disparate, seemingly unconnected events, we might choose to take actions with certain customers based on events that impact them.
Today, we do this in the most simple ways, largely looking at their interactions with us–whether through the web, white papers they download, phone calls they make. We score them, trying to predict their buying readiness, ultimately creating a MQL.
Some emerging tools in predictive analytics are incorporating events and actions our customers take, outside of interactions with us, that influence actions we might take with those people. Using Dreamforce as and example, Analytic software competitors to Salesforce.com might be interested to know what analytics sessions I attended at DF15, targeting me with specific content.
These tools help refine our understanding of customers and predict their information needs, and ultimately their likelihood of buying. They enable us to more effectively reach the right customer at the right time with the right message and right offer.
Yeah, yeah, you’re still stuck on the damn butterfly…….
As a curious observer of butterfly’s landing on leaves in the Rainforest, I might start reaching out for information on what it means. Googling this, I might learn that butterfly’s cause trees to fall down, as a result, I might be interested in chain saws, axes, maybe how to sell lumber. Clever suppliers might watch my query patterns, might even track my visit to the Manaus Home Depot to look at chain saws. Based on the patterns of my behavior, predictive analytics, might say, “Get to Dave with this information and this offer, he looks like he is buying ready. Maybe toss in a free butterfly net as an incentive to buy the super deluxe chain saw.” (OK, I tossed that last part in myself.)
Predictive analytics (particularly Omni channel analytics) can watch our information gathering patterns and behaviors, helping us to understand, based on their actions, what actions and information buyers might next take and when the person might be buying ready.
Proscriptive analytics is very different, in this case, it might tell us to sell flood insurance to people on the coast in Japan, not wasting our time with anyone/anything else. Proscriptive analytics is the foundation of appropriate, disruptive insight. Proscriptive analytics enables us to recognize patterns that may impact certain categories of customers–before they are even aware they might have a need.
It enables us to reach out preemptively, perhaps to challenge them. We might go to people on the coasts of Japan saying, “Did you realize earlier this year, a butterfly landed on a tree in a Brazilian Rainforest?” The astute people, might be stunned (They have heard the same story). Aghast, they might reply the Japanese equivalent of “OMG! I need to move or buy flood insurance!” Actually, it’s there’s probably a little more education involved to get to this point, but you get the idea.
Today, we waste a lot of our customer’s and our time trying to do all things for all customers. If this month we are supposed to provide Insight on some topic, we provide endless amounts of content and sales people make lots of Insight prospecting calls—all in the hopes of finding enough customers to raise their hands. We try to use educated guesses, maybe some sort of scoring approach, to help refine our approaches, but largely, we tend to cast very wide nets, hoping enough people are interested in having a conversation.
Analytics, predictive and proscriptive, tell us who to disrupt with what and when they might be most receptive to that disruption. Now we have the right conversations with the right people at the right time–rather than wasting time and customer equity by covering too many in an unfocused manner. We have the capability of targeting people who really want to talk and talking to them about the things that are most relevant.
Lest you think this is all pure hyperbole, a number of years ago, working with a client and their customers, leveraging these analytic tools we were able to move conversions from less than 15% to over 70%. All by simply reaching someone at the moment they were most interested in what the vendor had to say.
Predictive analytics are very powerful, marketing and sales needs to learn how to leverage these to great impact. Predictive and proscriptive analytics, used together enable us to change the business and professional lives of our customers (individually and organizationally). If you aren’t learning about these, if you aren’t experimenting–you should be. Many organizations are creating great results with rich predictive models. Proscriptive analytics is a little newer, but offers, at least in my opinions, much richer capability.
(For the data science folks reading this post, I recognize I have hugely oversimplified things and ask your patience. After all, I’m speaking to sales and marketing people .-) )
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