Trolling for powerful backers. Wellington, New Zealand
Committment from the top is critical to the success of data science initiatives. Not surprising, since most efforts that have a broad impact in an organization need that commitment.
Commitment means that top management places enough priority and value on the initiative that its resources (time, attention, labor, capital, cash) aren’t stolen (aka reallocated). Events unfold, and new initiatives demand attention. Relative power waxes and wanes, and executives with competing priorities may succeed in sucking up the resources you need.
I think great communication is how you keep that commitment, and perhaps the most reliable way to gain it.
You need a killer KPI, one that reflects progress toward the goal, as well as the strategic purpose, the Why. Constant exposure to the KPI will cause people to kind of accept it, and kind of track it, and want it to improve.
You have to follow that up with an internal marketing and sales program (not project, not blitz, Program). Explain your data science initiative to everyone, everyone. Make sure they understand, and make sure you improve your pitch at every telling. And keep telling. Keep asking people what they hear, what they think, and especially what they don’t like. You’re not done until everyone groans or runs away when they see you coming.
The Kawarau Bridge Bungy, pictured here, is the earlist commercial bungy operation. Queenstown, New Zealand
Applying data science to business, and bungee jumping, have too much in common.
There is the committment from the top — in bungee, that’s your brain — that you’re going to make the jump. WIthout this committment, you are going nowhere, with bungee or with big data.
There is encouragement from the leaders: Others have preceded you, and report great satisfaction. Have a go!
There is the motivation: crossing a big item off your bucket list, plus bragging rights. With data science, the profits, revenues, savings, and/or strategic insights will make the effort more than worthwhile.
But then, there you are standing on the edge of precipice, cord tied to your ankle, and the truly unsafe feeling that you are going head-first, uncontrolled, into uncharted waters. (Literally, in the case of bungee jumping). With your data science inititatives, you have only a sketchy vision, an untested strategy, complete unfamiliarity with the process, and instincts that tell you to draw back.
Suddenly, committment from the top evaporates. You reach down to unhook yourself from the plan. Your resources are about to be reallocated.
In bungee jumping, this is when you get shoved off your perch.
In data science initiatives, only committment will get the organization off its perch.
Predictions are guesses about the past, present, or future. Prediction is not reporting: reporting is an organized recap of what is known.
Why predict the past? Predicting the past is the starting point for predicting the future.
Wait, don’t we know what the past is/was?
Not usually. For example, economic events are so hard to identify and measure that we constantly revise last year’s GDP and continue to argue about when the recession started or ended.
Or, closer to home, “Why did churn increase last month?” The “answer” to that question will be a hypothesis: our competitors had positive press, our company had negative press, the weather changed people’s habits, who really knows?
The causes, influences, and interdependencies are rarely clear.
A prediction engine will use whatever data you can give it to create and evaluate hypotheses to answer your question. The hypothesis that best explains the past is the starter kit for predicting the future.
And perhaps understanding the past allows you to repeat the parts you liked.
As customers, we experience digital transformation all the time—perhaps almost every day. In the past decade alone, the ways we consume music, movies, photography, news, retail, and travel have changed utterly. We are on the cusp of similar transformation of transportation and manufacturing, with the advent of onboard computers in cars, driverless cars, and 3D printing. And digital transformation won’t stop there, either.
Companies who ignore disruption of their industries will not be successful, and ultimately won’t survive.
Digital marketing is a microcosm of digital transformation that is well underway in all industries. Digital transformation of marketing means that customer interactions with a brand create rich relationships that are tracked, optimized, and enhanced throughout the customer lifecycle, and customer experience has a coherent strategy and continual measurement. The lines between marketing, sales, and fulfillment have blurred.
Customers flock to the brands that deliver an engaging and useful experience. Not surprisingly, 82 percent of marketers in a recent survey conducted by Adobe cited their greatest concern as reaching those customers. In the same survey, 68 percent of marketers said they think companies won’t succeed unless they have a digital marketing approach.
Leaders in the digital transformation were on average 26 percent more profitable and had a 12 percent greater market capitalization, according to an MIT Sloan study.
Just the Beginning
Digital transformation is entering its third decade, forever changing business models, customer experience, services, and operations for all industries. You may not be thinking in terms of digital transformation for your company, but you are likely pondering how and when to refresh your digital face or your digital heart in order to respond to customer expectations, competitive pressures, and employee innovation.
Over the past two decades, software and data innovations have disrupted whole industries: music, cinema, photography, news, magazines, retail, and travel. This is just the beginning for digital transformation. In the coming decades we will see driverless vehicles disrupt trucking, taxi, bus, and delivery markets—perhaps even leading to a precipitous drop in vehicles on the road. Three-dimensional (3-D) printing has the potential to disrupt manufacturing and its supporting industries, including shipping, supply chain services, equipment, and more.
In marketing, digital transformation has been underway in every industry for some years—with much left to be done. Just as a company cannot succeed in holding back the tide of disruption, so marketing cannot succeed by ignoring digital transformation. In an MIT Center for Digital Business study, 70 percent of executives reported pressure to transform from customers and competitors.
The digital transformation of marketing is not just marketing via digital channels, but marketing to a digital world. It is not only engaging customers throughout the lifecycle, and across channels and devices, but also generating, gathering, and using customer and interaction data to create the customer experience that will deepen customers’ relationships with your brands.
Future posts will deliver case studies of leaders in digital transformation that demonstrate how an integrated platform, skilled people, and mature optimization capabilities are the keys to success.
These leaders in digital transformation foster a culture of experimentation, customer experience management supported by mature optimization programs, and measurements relevant to their goals. Customers and expectations change rapidly, and the leaders who are successful have an integrated platform that enables them to personalize customer interactions and measure success by managing, delivering, and tracking content across channels and devices.
There is no holding back this tide. You can jump in now or linger on the deck, but ultimately, you’ll have to get your feet wet. Just as it’s a rare company that is successful without a web site today, it will be a rare company that is successful without digital marketing tomorrow.
Filled with despair because your data is a mess? Welcome to the club, and it is a big one: everyone’s data is a mess. It is always incomplete, inaccuarate, out-dated, redundant, plagued with typos, and unavailable in the desired timeframe. It can’t truly answer the questions you need to ask right now, and the questions it can readily answer may not be worth asking.
You need a plan and a roadmap. What questions do (and will) we need answered? Ok, what data is required? How are we going to create or acquire it? When? At what cost?
Your plan ensures that as weeks (months, years) roll by, your data and your questions become richer and more satisfying.
If you are working through your Big Data roadmap and hoping for guidance from the Google, well, hope may be all have.
I recently studied the top offerings for my search “big data buyers guide.” These docs clearly state that its complicated, and there are a lot of choices. Good to know.
One piece I can definitely recommend: Buyer’s Guide to Big Data Integration, by CITO Research, sponsored by Pentaho. It does a fine job of explaining where we are, how we got here, and where you want to be going. Insightful, accurate, and does not pander to vendors. You might think that the focus is the usually ugly problem of integration, but that’s not the case.
The report was written in September 2013 — 18 months ago — but remains completely relevant. Just shows how far we’ve come.
Michel Manago is CEO of Kiolis, a Paris-based startup that develops software for personalization and recommendation. I interviewed Michel about the MyCoachNutrition project, which applies case-based reasoning and collaborative filtering technologies to guide subscribers to eat and exercise well. It strikes me that the project combines 3 of Michel’s keenest interests: algorithms, starting companies, and food. Given his achievements in these 3 disciplines, Michel is always at the top of my list for dinner companions.
- How would you describe the current status of MyCoachNutrition?
MyCoachNutrition is in alpha. The broad commercial launch, through a distributor that will promote us to 2.5 million French families, is scheduled for August 2015. Beta is scheduled in May with a limited commercial launch in June.
- What challenge surprised you in getting to this point?
The biggest challenge was to create a value proposition that is sufficiently attractive for our users. Some of our users are used to getting free content, such as cooking recipes, on most web site. However, they do not necessarily trust this information. Content has to be free or fantastic as one of my clients used to say. We are not in the business of providing free content, therefore our content must be fantastic.
Our content has previously been published by a professional publishers, but our dietician and our pharmacologist have reviewed every single bit of it in order to check the accuracy of our nutritional facts. We originally assumed that our content would be fantastic because it had been published by a professional publisher but, unfortunately, we quickly realized that you do not publish in the same way on the web as you do in books. The content was not unified because each book lives its own life and has its own constraints. When we combined the material from the different books (we have access to over 10,000 published cooking recipes for example), we quickly noticed that the metadata to classify this content was different in each book. Even basic parameters, such as “how easy it is to cook this recipe”, “what is the cost of the recipe”, “how long it takes” were not consistently provided for every recipes in every book. Not to mention other more advanced criteria such as “the country”, “the theme” etc. So we had to unify this content and enhance it so that :
A). our users can search based on these criteria.
B). our recommendation engine can use these criteria to create personalized recommendations based on what we know about our users (cooking skills, budget, available time, type of food they like etc.)
Because we knew our users wanted to increase their skills, we also develop our own video content to teach cooking techniques. We reviewed many web sites with video and designed our own guidelines. For example, a video should not be more than 3 minutes at most. It should be divided in steps that match the cooking steps etc. We ended up developing our own player so the user can view the steps in text form and pause in between steps. A video’s function is not merely to be nice, but it has to be almost like an individual e-learning project. What do we want to teach through this video? How effective are we? Can someone, who does not have the skills of our chef, easily reproduce what we show on the video?
- How did you achieve your value proposition?
We tested the concept early on some focus groups. We ran tests with 4 focus groups of 5 people for 2 hours each. Originally, we assumed that our main audience would be people who want to lose weight or people who eat specific types of food (ex: vegetarians, or people with allergies). We then discovered, during these early tests, that these were not the most promising audience for myCoachNutrition. We now focus on families and on people who want to feel in good health. We also found out that while our users appreciated being told how to exercise and what to eat, they also wanted to learn and to understand why we made the recommendations. We switch from a system that would propose ready-made meal plans to a system that is totally interactive. The user receives assistance but only if and when he/she requests it. It’s almost like an interactive game where one learns how to balance his/her own meals and learn what to order do when he/she is not at home to prepare his/her own meals.
- What is the biggest challenge you expected, and how did you address it?
The biggest challenge was to develop a system that is at the same time entertaining and fun to use, but that has the credibility of a proven scientific approach. During a three year joint research project (the fiora e-health project www.fiora.pro), we gathered the know-how of the best French specialists in nutrition and health and combined it with the big data expertise of IT experts from the public and private sectors. The next challenge was to put the technology to use and make this advanced know-how accessible to a wide audience as well as gain visibility so that we will generate revenues through subscriptions. We achieved this by making a deal with a French distributor who gives us visibility with their 2.5 million subscribers. Finally, health-data privacy is a major challenge and we make sure that our users understand that their data is secure and remain private. We do not sell their data to third parties, nor do we generate revenues through advertising. The guarantee that the data from our users will remain safe with us has a cost for our users (the monthly subscription fee), but this builds trust and trust is key to our business.