Patients Aren’t Ready for Treatment?
In my job of being “a guy who finds money-making opportunities using data,” I get to meet all kinds of businesspeople in various industries. Thanks to the business trend around analytics (and to that infamous “Big Data” fad), I don’t have to spend a long time explaining what I do any more; I just say I am in the field of analytics, or to sound a bit fancier, I say data science. Then most marketers seem to understand where the conversation will go from there. Things are never that simple in real life, though, as there are many types of analytics — business intelligence, descriptive analytics, predictive analytics, optimization, forecasting, etc., even at a high level — but figuring what type of solutions should be prescribed is THE job for a consultant, anyway (refer to “Prescriptive Analytics at All Stages”).
The key is to an effective prescription is to listen to the client first. Why do they lose sleep at night? What are their key success metrics? What are the immediate pain points? What are their long-term goals? And how would we reach there within the limits of provided resources and put out the fire at the same time? Building a sound data and analytics roadmap is critical, as no one wants to have an “Oh dang, we should have done that a year ago!” moment after a complex data project is well on its way. Reconstruction in any line of business is costly, and unfortunately, it happens all of the time, as many marketers and decision-makers often jump into the data pool out of desperation under organizational pressure (or under false promises by toolset providers, as in “all your dreams will come true with this piece of technology”). It is a sad sight when users realize that they don’t know how to swim in it “after” they jumped into it.
Why does that happen all of the time? At the risk of sounding like a pompous doctor, I must say that it is quite often the patient’s fault, too; there are lots of bad patients. When it comes to the data and analytics business, not all marketers are experts in it, though some are. Most do have a mid-level understanding, and they actually know when to call in for help. And there are complete novices, too. Now, regardless of their understanding level, bad patients are the ones who show up with self-prescribed solutions, and wouldn’t hear about any other options or precautions. Once, I’ve even met a client who demanded a neural-net model right after we exchanged pleasantries. My response? “Whoa, hold your horses for a minute here, why do you think that you need one?” (Though I didn’t quite say it like that.) Maybe you just came back from some expensive analytics conference, but can we talk about your business case first? After that conversation, I could understand why doctors wouldn’t appreciate patients who would trust WebMD over living, breathing doctors who are in front of them.
Then there are opposite types of cases, too. Some marketers are so insecure about the state of their data assets (or their level of understanding) that they wouldn’t even want to hear about any solutions that sound even remotely complex or difficult, although they may be in desperate need of them. A typical response is something like “Our datasets are so messy that we can’t possibly entertain anything statistical.” You know what that sounds like? It sounds like a patient refusing any surgical treatment in an ER because “he” is not ready for it. No, doctors should be ready to perform the surgery, not the patient.
Messy datasets are surely no excuse for not taking the right path. If we had to wait for a perfect set of data all of the time, there wouldn't be any need for statisticians or data scientists. In fact, we need such specialists precisely because most data sets are messy and incomplete, and they need to be enhanced by statistical techniques.
Analytics is about making the best of what we have. Cleaning dirty and messy data is part of the job, and should never be an excuse for not doing the right thing. If anyone assumes that simple reports don’t require data cleansing steps because the results look simple, nothing could be further from the truth. Most reporting errors stem from dirty data, and most datasets — big or small, new or old — are not ready to be just plugged into analytical engines.
Besides, different types of analytics are needed because there are so many variations of business challenges, and no analytics is supposed to happen in some preset order. In other words, we get into predictive modeling because the business calls for it, not because a marketer finished some basic Reporting 101 class and now wants to move onto an Analytics 202 course. I often argue that deriving insights out of a series of simple reports could be a lot more difficult than building models or complex data management. Conversely, regardless of the sophistication level, marketers are not supposed to get into advanced analytics just for intellectual curiosity. Every data and analytics activity must be justified with business purposes, carefully following the strategic data roadmap, not difficulty level of the task.
The main reason why it is hard to capture the whole essence of "the right thing to do" in the data business is because there multiple viewpoints that must be considered at the same time. Allow me to share a few essential dimensions here.
- Industry: Often consulting companies and service providers build their practices around industry verticals. I don’t fully agree with such an approach, as there are many more critical dimensions that I am about to list here. Nonetheless, the industry that a company belongs to — finance, banking, hospitality, entertainment, online and offline retail, publishing, telecommunication, utility, non-profit, etc. — is indeed a very important factor, not just because the business models could be vastly different, but also because the shape of collected data and their success metrics inevitably calls for different types of analytics. On top of the industry breakdown, we must also examine B-to-C and B-to-B cases separately.
- Marketing and Sales Cycle: Even within similar industries, we often observe immensely dissimilar marketing practices. Sales-oriented marketing organizations, for example, require different solutions in comparison to more mass marketing-oriented companies.
- Marketing Channels Employed: I have been emphasizing the importance of the buyer-centric view in marketing, but the reality is that most companies even break down their marketing departments and all related activities solely based on channels. For that reason, channel usage certainly is one of the most important factors, as each channel produces a different type of data and calls for different messaging strategies.
- Target Buyers Lifecycle: Buyers go through different cycles of becoming a customer of a company, and each stage yields a different type of data. For example, available data don’t even look similar for prospecting and CRM stages from the marketer’s point of view. For win-back programs, marketers would have to deal with aged data and third-party data.
- Data Availability and Shape: As I mentioned earlier, some data are messier than others. Marketers and analysts should never give up on any data source so easily, and unorganized datasets certainly call for separate treatments. Plus, consolidating disparate data sources around customers to create a 360-degree view is one of the most important, yet often neglected steps.
- Existing Teams, Divisions and Vendors: Political barriers among divisions are often the main reasons why data initiatives get derailed. Navigating through political mines, unfortunately, is part of the data player’s job, as guardians of real of fictitious domains often become bottlenecks.
- Level of Sophistication of Users: Expert, intermediate and novice users require different types of solutions and toolsets; not because they must go through set courses of analytics in a certain order, but because marketers ultimately get to make decisions with resultant analytics within the confinement of their skillsets. Some may want to put their hands on the data with sophisticated tools, while others may not even want to look at more than a few pages of reports.
- Immediate Pain Points: All companies have pain points, even advanced ones. The key is to fix immediate problems without losing sight of the long-term goals. Too many analytical solutions are nearsighted, and too many projects are designed just for quick results, leading to “Oh, no” moments later. Even for quick fixes, data projects must begin with a clear roadmap.
Yes, analytics consultants certainly have many factors to consider when prescribing solutions. However, from the user’s point of view, data must be something they can access easily, regardless of hardships and headaches that professional data players must have gone through. Much like daily weather reports that we take for granted, we can all imagine weather forecasting is anything but simple and easy.
Data scientists must be always mindful that, from the users’ point of view, data must be:
- Easy to understand and intuitive to all, not just experts
- Small, bite-size answers, not mounds of unrefined information
- Consistently reliable, accurate and effective
- Available most times, not just sometimes
- Easily accessible via users' favorite devices and channels
This is what I mean by “Smart Data” (refer to “Smart Data, Not Big Data”). There are all kinds of users, business models, challenges, and clean or messy data out there. And navigating through them is what data players signed up to do. Good data scientists should never complain about the users, though some need to be told rather bluntly that they are on a wrong path. As, in the end, even bad patients deserve the best possible treatments, too.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.