It’s All About Ranking
The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?
Yes, "choice" is the keyword in all of these questions. And if you picked Paris over other places as an answer to the last question, you just made a choice based on some ranking order in your mind. The world is big, and there could have been many factors that contributed to that decision, such as culture, art, cuisine, attractions, weather, hotels, airlines, prices, deals, distance, convenience, language, etc., and I am pretty sure that not all factors carried the same weight for you. For example, if you put more weight on "cuisine," I can see why London would lose a few points to Paris in that ranking order.
As a citizen, for whom should I vote? That's the choice based on your ranking among candidates, too. Call me overly analytical (and I am), but I see the difference in political stances as differences in "weights" for many political (and sometimes not-so-political) factors, such as economy, foreign policy, defense, education, tax policy, entitlement programs, environmental issues, social issues, religious views, local policies, etc. Every voter puts different weights on these factors, and the sum of them becomes the score for each candidate in their minds. No one thinks that education is not important, but among all these factors, how much weight should it receive? Well, that is different for everybody; hence, the political differences.
I didn't bring this up to start a political debate, but rather to point out that the decision-making process is based on ranking, and the ranking scores are made of many factors with different weights. And that is how the statistical models are designed in a nutshell (so, that means the models are "nuts"?). Analysts call those factors "independent variables," which describe the target.
In my past columns, I talked about the importance of statistical models in the age of Big Data (refer to "Why Model?"), and why marketing databases must be "model-ready" (refer to "Chicken or the Egg? Data or Analytics?"). Now let's dig a little deeper into the design of the "model-ready" marketing databases. And surprise! That is also all about "ranking."
Let's step back into the marketing world, where folks are not easily offended by the subject matter. If I give a spreadsheet that contains thousands of leads for your business, you wouldn't be able to tell easily which ones are the "Glengarry Glen Ross" leads that came from Downtown, along with those infamous steak knives. What choice would you have then? Call everyone on the list? I guess you can start picking names out of a hat. If you think a little more about it, you may filter the list by the first name, as they may reflect the decade in which they were born. Or start calling folks who live in towns that sound affluent. Heck, you can start calling them in alphabetical order, but the point is that you would "sort" the list somehow.
Now, if the list came with some other valuable information, such as income, age, gender, education level, socio-economic status, housing type, number of children, etc., you may be able to pick and choose by which variables you would use to sort the list. You may start calling the high income folks first. Not all product sales are positively related to income, but it is an easy way to start the process. Then, you would throw in other variables to break the ties in rich areas. I don't know what you're selling, but maybe, you would want folks who live in a single-family house with kids. And sometimes, your "gut" feeling may lead you to the right place. But only sometimes. And only when the size of the list is not in millions.
If the list was not for prospecting calls, but for a CRM application where you also need to analyze past transaction and interaction history, the list of the factors (or variables) that you need to consider would be literally nauseating. Imagine the list contains all kinds of dollars, dates, products, channels and other related numbers and figures in a seemingly endless series of columns. You'd have to scroll to the right for quite some time just to see what's included in the chart.
In situations like that, how nice would it be if some analyst threw in just two model scores for responsiveness to your product and the potential value of each customer, for example? The analysts may have considered hundreds (or thousands) of variables to derive such scores for you, and all you need to know is that the higher the score, the more likely the lead will be responsive or have higher potential values. For your convenience, the analyst may have converted all those numbers with many decimal places into easy to understand 1-10 or 1-20 scales. That would be nice, wouldn't it be? Now you can just start calling the folks in the model group No. 1.
But let me throw in a curveball here. Let's go back to the list with all those transaction data attached, but without the model scores. You may say, "Hey, that's OK, because I've been doing alright without any help from a statistician so far, and I'll just use the past dollar amount as their primary value and sort the list by it." And that is a fine plan, in many cases. Then, when you look deeper into the list, you find out there are multiple entries for the same name all over the place. How can you sort the list of leads if the list is not even on an individual level? Welcome to the world of relational databases, where every transaction deserves an entry in a table.
Relational databases are optimized to store every transaction and retrieve them efficiently. In a relational database, tables are connected by match keys, and many times, tables are connected in what we call "1-to-many" relationships. Imagine a shopping basket. There is a buyer, and we need to record the buyer's ID number, name, address, account number, status, etc. Each buyer may have multiple transactions, and for each transaction, we now have to record the date, dollar amount, payment method, etc. Further, if the buyer put multiple items in a shopping basket, that transaction, in turn, is in yet another 1-to-many relationship to the item table. You see, in order to record everything that just happened, this relational structure is very useful. If you are the person who has to create the shipping package, yes, you need to know all the item details, transaction value and the buyer's information, including the shipping and billing address. Database designers love this completeness so much, they even call this structure the "normal" state.
But the trouble with the relational structure is that each line is describing transactions or items, not the buyers. Sure, one can "filter" people out by interrogating every line in the transaction table, say "Select buyers who had any transaction over $100 in past 12 months." That is what I call rudimentary filtering, but once we start asking complex questions such as, "What is the buyer's average transaction amount for past 12 months in the outdoor sports category, and what is the overall future value of the customers through online channels?" then you will need what we call "Buyer-centric" portraits, not transaction or item-centric records. Better yet, if I ask you to rank every customer in the order of such future value, well, good luck doing that when all the tables are describing transactions, not people. That would be exactly like the case where you have multiple lines for one individual when you need to sort the leads from high value to low.
So, how do we remedy this? We need to summarize the database on an individual level, if you would like to sort the leads on an individual level. If the goal is to rank households, email addresses, companies, business sites or products, then the summarization should be done on those levels, too. Now, database designers call it the "de-normalization" process, and the tables tend to get "wide" along that process, but that is the necessary step in order to rank the entities properly.
Now, the starting point in all the summarizations is proper identification numbers for those levels. It won't be possible to summarize any table on a household level without a reliable household ID. One may think that such things are given, but I would have to disagree. I've seen so many so-called "state of the art" (another cliché that makes me nauseous) databases that do not have consistent IDs of any kind. If your database managers say they are using "plain name" or "email address" fields for matching or summarization, be afraid. Be very afraid. As a starter, you know how many email addresses one person may have. To add to that, consider how many people move around each year.
Things get worse in regard to ranking by model scores when it comes to "unstructured" databases. We see more and more of those, as the data sources are getting into uncharted territories, and the size of the databases is growing exponentially. There, all these bits and pieces of data are sitting on mysterious "clouds" as entries on their own. Here again, it is one thing to select or filter based on collected data, but ranking based on some statistical modeling is simply not possible in such a structure (or lack thereof). Just ask the database managers how many 24-month active customers they really have, considering a great many people move in that time period and change their addresses, creating multiple entries. If you get an answer like "2 million-ish," well, that's another scary moment. (Refer to "Cheat Sheet: Is Your Database Marketing Ready?")
In order to develop models using variables that are descriptors of customers, not transactions, we must convert those relational or unstructured data into the structure that match the level by which you would like to rank the records. Even temporarily. As the size of databases are getting bigger and bigger and the storage is getting cheaper and cheaper, I'd say that the temporary time period could be, well, indefinite. And because the word "data-mart" is overused and confusing to many, let me just call that place the "Analytical Sandbox." Sandboxes are fun, and yes, all kinds of fun stuff for marketers and analysts happen there.
The Analytical Sandbox is where samples are created for model development, actual models are built, models are scored for every record—no matter how many there are—without hiccups; targets are easily sorted and selected by model scores; reports are created in meaningful and consistent ways (consistency is even more important than sheer accuracy in what we do), and analytical language such as SAS, SPSS or R are spoken without being frowned up by other computing folks. Here, analysts will spend their time pondering upon target definitions and methodologies, not about database structures and incomplete data fields. Have you heard about a fancy term called "in-database scoring"? This is where that happens, too.
And what comes out of the Analytical Sandbox and back into the world of relational database or unstructured databases—IT folks often ask this question—is going to be very simple. Instead of having to move mountains of data back and forth, all the variables will be in forms of model scores, providing answers to marketing questions, without any missing values (by definition, every record can be scored by models). While the scores are packing tons of information in them, the sizes could be as small as a couple bytes or even less. Even if you carry over a few hundred affinity scores for 100 million people (or any other types of entities), I wouldn't call the resultant file large, as it would be as small as a few video files, really.
In my future columns, I will explain how to create model-ready (and human-ready) variables using all kinds of numeric, character or free-form data. In Exhibit A, you will see what we call traditional analytical activities colored in dark blue on the right-hand side. In order to make those processes really hum, we must follow all the steps that are on the left-hand side of that big cylinder in the middle. Preventing garbage-in-garbage-out situations from happening, this is where all the data get collected in uniform fashion, properly converted, edited and standardized by uniform rules, categorized based on preset meta-tables, consolidated with consistent IDs, summarized to desired levels, and meaningful variables are created for more advanced analytics.
Even more than statistical methodologies, consistent and creative variables in form of "descriptors" of the target audience make or break the marketing plan. Many people think that purchasing expensive analytical software will provide all the answers. But lest we forget, fancy software only answers the right-hand side of Exhibit A, not all of it. Creating a consistent template for all useful information in a uniform fashion is the key to maximizing the power of analytics. If you look into any modeling bakeoff in the industry, you will see that the differences in methodologies are measured in fractions. Conversely, inconsistent and incomplete data create disasters in real world. And in many cases, companies can't even attempt advanced analytics while sitting on mountains of data, due to structural inadequacies.
I firmly believe the Big Data movement should be about
- getting rid of the noise, and
- providing simple answers to decision-makers.
Bragging about the size and the speed element alone will not bring us to the next level, which is to "humanize" the data. At the end of the day (another cliché that I hate), it is all about supporting the decision-making processes, and the decision-making process is all about ranking different options. So, in the interest of keeping it simple, let's start by creating an analytical haven where all those rankings become easy, in case you think that the sandbox is too juvenile.
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.