Machine Learning? I Don’t Think Those Words Mean What You Think They Mean

I find more and more people use the term “machine learning” when they really mean to say “modeling.” I guess that is like calling all types of data activities — with big and small data — “Big Data.” And that’s OK.

I find more and more people use the term “machine learning” when they really mean to say “modeling.” I guess that is like calling all types of data activities — with big and small data — “Big Data.” And that’s OK.

Languages are developed to communicate with other human beings more effectively. If most people use the term to include broader meanings than the myopic definition of the words in question, and if there is no trouble understanding each other that way, who cares? I’m not here to defend the purity of the meaning, but to monetize big or small data assets.

The term “Big Data” is not even a thing in most organizations with ample amounts of data anymore, but there are many exceptions, too. I visit other countries for data and analytics consulting, and those two words still work like “open sesame” to some boardrooms. Why would I blame words for having multiple meanings? The English dictionary is filled with such colloquial examples.

I recently learned that famous magic words “Hocus Pocus” came from the Latin phrase “hoc est corpus,” which means “This is the body (of Christ)” as spoken during Holy Communion in Roman Catholic Churches. So much for the olden-day priests only speaking in Latin to sound holier; ordinary people understood the process as magic — turning a piece of bread into the body of Christ — and started applying the phrase to all kinds of magic tricks.

However, if such transformations of words start causing confusion, we all need to be more specific. Especially when the words are about specific technical procedures (not magic). Going back to my opening statement, what does “machine learning” mean to you?

  • If spoken among data scientists, I guess that could mean a very specific way to describe modeling techniques that include Supervised Learning, Unsupervised Learning, Reinforced Learning, Deep Learning, or any other types of Neural Net modeling, indicating specific methods to construct models that serve predetermined purposes.
  • If used by decision-makers, I think it could mean that the speaker wants minimal involvement of data scientists or modelers in the end, and automate the model development process as much as possible. As in “Let’s set up Machine Learning to classify all the inbound calls into manageable categories of inquiries,” for instance. In that case, the key point would be “automation.”
  • If used by marketing or sales; well, now, we are talking about really broad set of meanings. It could mean that the buyers of the service will require minimal human intervention to achieve goals. That the buyer doesn’t even have to think too much (as the toolset would just work). Or, it could mean that it will run faster than existing ways of modeling (or pattern recognition). Or, they meant to say “modeling,” but they somehow thought that it sounded antiquated. Or, it could just mean that “I don’t even know why I said Machine Learning, but I said it because everyone else is saying it” (refer to “Why Buzzwords Suck”).

I recently interviewed a candidate fresh out of a PhD program for a data scientist position, whose resume is filled with “Machine Learning.” But when we dug a little deeper into actual projects he finished for school work or internship programs, I found out that most of his models were indeed good, old regression models. So I asked why he substituted words like that, and his answer was staggering; he said his graduate school guided him that way.

Why Marketers Need to Know What Words Mean

Now, I’m not even sure whom to blame in a situation like this, where even academia has fallen under the weight of buzzwords. After all, the schools are just trying to help their students getting high paying jobs before the summer is over. I guess then the blame is on the hiring managers who are trying to recruit candidates based on buzzwords, not necessarily knowing what they should look for in the candidates.

And that is a big problem. This is why even non-technical people must understand basic meanings of technical terms that they are using; especially when they are hiring employees or procuring outsourcing vendors to perform specific tasks. Otherwise, some poor souls would spend countless hours to finish things that don’t mean anything for the bottom-line. In a capitalistic economy, we play with data for only two reasons:

  1. to increase revenue, or
  2. to reduce cost.

If it’s all the same for the bottom line, why should a non-technician care about the “how the job is done” part?

Why It Sucks When Marketers Demand What They Don’t Understand

I’ve been saying that marketers or decision-makers should not be bad patients. Bad patients won’t listen to doctors; and further, they will actually command doctors prescribe certain medications without testing or validation. I guess that is one way to kill themselves, but what about the poor, unfortunate doctor?

We see that in the data and analytics business all of the time. I met a client who just wanted to have our team build neural net models for him. Why? Why not insist on a random forest method? I think he thought that “neural net” sounded cool. But when I heard his “business” problems out, he definitely needed something different as a solution. He didn’t have the data infrastructure to support any automated solutions; he wanted to know what went on in the modeling process (neural net models are black boxes, by definition), he didn’t have enough data to implement such things at the beginning stage, and projected gains (by employing models) wouldn’t cover the cost of such implementation for the first couple of years.

What he needed was a short-term proof of concept, where data structure must be changed to be more “analytics-ready.” (It was far from it.) And the models should be built by human analysts, so that everyone would learn more about the data and methodology along the way.

Imagine a junior analyst fresh out of school, whose resume is filled with buzzwords, meeting with a client like that. He wouldn’t fight back, but would take the order verbatim and build neural net models, whether they helped in achieving the business goals or not. Then the procurer of the service would still be blaming the concept of machine learning itself. Because bad patients will never blame themselves.

Even advanced data scientists sometimes lose the battle with clients who insist on implementing Machine Learning when the solution is something else. And such clients are generally the ones who want to know every little detail, including how the models are constructed. I’ve seen data scientists who’d implemented machine learning algorithms (for practical reasons, such as automation and speed gain), and reverse-engineered the models, using traditional regression techniques, only to showcase what variables were driving the results.

One can say that such is the virtue of a senior-level data scientist. But then what if the analyst is very green? Actually some decision-makers may like that, as a more junior-level person won’t fight back too hard. Only after a project goes south, those “order takers” will be blamed (as in “those analysts didn’t know what they were doing”).

Conclusion

Data and analytics businesses will continually evolve, but the math and the human factors won’t change much. What will change, however, is that we will have fewer and fewer middlemen between the decision-makers (who are not necessarily well-versed in data and analytics) and human analysts or machines (who are not necessarily well-versed in sales or marketing). And it will all be in the name of automation, or more specifically, Machine Learning or AI.

In that future, the person who orders the machine around — ready or not — will be responsible for bad results and ineffective implementations. That means, everyone needs to be more logical. Maybe not as much as a Vulcan, but somewhere between a hardcore coder and a touchy-feely marketer. And they must be more aware of capabilities and limitations of technologies and techniques; and, more importantly, they should not blindly trust machine-based solutions.

The scary part is that those who say things like “Just automate the whole thing with AI, somehow” will be the first in line to be replaced by the machines. That future is not far away.

How Marketers Can Throw Away Data, Without Regrets

Yes, data is an asset. But not if the data doesn’t generate any value. (There is no sentimental value to data, unless we are talking about building a museum of old data.) So here’s how to throw away data.

Last month, I talked about data hoarders (refer to “Don’t Be a Data Hoarder”). This time, let me share some ideas about how to throw away data.

I heard about people who specialize in cleaning other people’s closets and storage spaces. Looking at the result — turning a hoarder’s house into a presentable living quarters — I am certain that they have their own set of rules and methodologies in deciding what to throw out, what goes together, and how to organize items that are to be kept.

I recently had a relatable experience, as I sold a house and moved to a smaller place, all in the name of age-appropriate downsizing. We lived in the old home for 22 years, raising two children. We thought that our kids took much of their stuff when they moved out, but as you may have guessed already, no, we still had so much to sort through. After all, we are talking about accumulation of possessions by four individuals for 22 long years. Enough to invoke a philosophical question “Why do humans gather so much stuff during their short lifespans?” Maybe we all carry a bit of hoarder genes after all. Or we’re just too lazy to sort things through on a regular basis.

My rule was rather simple: If I haven’t touched an item for more than three years (two years for apparel), give it away or throw it out. One exception was for the things with high sentimental value; which, unfortunately, could lead into hoarding behavior all over again (as in “Oh, I can’t possibly throw out this ‘Best Daddy in the World’ mug, though it looks totally hideous.”). So, when I was in doubt, I chucked it.

But after all of this, I may have to move to an even smaller place to be able to claim a minimalist lifestyle. Or should I just hire a cleanup specialist? One thing is for sure though; the cleanup job should be done in phases.

Useless junk — i.e., things that generate no monetary or sentimental value — is a liability. Yes, data is an asset. But not if the data doesn’t generate any value. (There is no sentimental value to data, unless we are talking about building a museum of old data.)

So, how do we really clean the house? I’ve seen some harsh methods like “If the data is more than three years old, just dump it.” Unless the business model has gone through some drastic changes rendering the past data completely useless, I strongly recommend against such a crude tactic. If trend analysis or a churn prediction model is in the plan, you will definitely regret throwing away data just because they are old. Then again, as I wrote last month, no one should keep every piece of data since the beginning of time, either.

Like any other data-related activities, the cleanup job starts with goal-setting, too. How will you know what to keep, if you don’t even know what you are about to do? If you “do” know what is on the horizon, then follow your own plan. If you don’t, the No. 1 step would be a companywide Need-Analysis, as different types of data are required for different tasks.

The Process of Ridding Yourself of Data

First, ask the users and analysts:

  • What is in the marketing plan?
  • What type of predictions would be required for such marketing goals? Be as specific as possible:
    • Forecasting and Time-Series Analysis — You will need to keep some “old” data for sure for these.
    • Product Affinity Models for Cross-sell/Upsell — You must keep who bought what for how much, when, through what channel type of data.
    • Attribution Analysis and Response Models — This type of analytics requires past promotion and response history data for at least a few calendar years.
    • Product Development and Planning — You would need SKU-level transaction data, but not from the beginning of time.
    • Etc.
  • What do you have? Do the full inventory and categorize them by data types, as you may have much more than you thought. Some examples are:
    • PII (Personally Identifiable Data): Name, Address, Email, Phone Number, Various ID’s, etc. These are valuable connectors to other data sources such as Geo/Demographic Data.
    • Order/Transaction Data: Transaction Date, Amount, Payment Methods
    • Item/SKU-Level Data: Products, Price, Units
    • Promotion/Response History: Source, Channel, Offer, Creative, Drop/Wave, etc.
    • Life-to-Date/Past ‘X’ Months Summary Data: Not as good as detailed, event-level data, but summary data may be enough for trend analysis or forecasting.
    • Customer Status Flags: Active, Dormant, Delinquent, Canceled
    • Surveys/Product Registration: Attitudinal and Lifestyle Data
    • Customer Communication History Data: Call-center and web interaction data
    • Online Behavior: Open, Click-through, Page views, etc.
    • Social Media: Sentiment/Intentions
    • Etc.
  • What kind of data did you buy? Surprisingly large amounts of data are acquired from third-party data sources, and kept around indefinitely.
  • Where are they? On what platform, and how are they stored?
  • Who is assessing them? Through what channels and platform? Via what methods or software? Search for them, as you may uncover data users in unexpected places. You do not want to throw things out without asking them.
  • Who is updating them? Data that are not regularly updated are most likely to be junk.

Taking Stock

Now, I’m not suggesting actually “deleting” data on a source level in the age of cheap storage. All I am saying is that not all data points are equally important, and some data can be easily tucked away. In short, if data don’t fit your goals, don’t bring them out to the front.

Essentially, this is the first step of the data refinement process. The emergence of the Data Lake concept is rooted here. Big Data was too big, so users wanted to put more useful data in more easily accessible places. Now, the trouble with the Data Lake is that the lake water is still not drinkable, requiring further refinement. However, like I admitted that I may have to move again to clean my stuff out further, the cleaning process should be done in phases, and the Data Lake may as well be the first station.

In contrast, the Analytics Sandbox that I often discussed in this series would be more of a data haven for analysts, where every variable is cleaned, standardized, categorized, consolidated, and summarized for advanced analytics and targeting (refer to “Chicken or the Egg? Data or Analytics?” and “It’s All about Ranking”). Basically, it’s data on silver platters for professional analysts— humans or machines.

At the end of such data refinement processes, the end-users will see data in the form of “answers to questions.” As in, scores that describe targets in a concise manner, like “Likelihood of being an early adopter,” or “Likelihood of being a bargain-seeker.” To get to that stage, useful data must flow through the pipeline constantly and smoothly. But not all data are required to do that (refer to “Data Must Flow, But Not All of Them”).

For the folks who just want to cut to the chase, allow me to share a cheat sheet.

Disclaimer: You should really plan to do some serious need analysis to select and purge data from your value chain. Nonetheless, you may be able to kick-start a majority of customer-related analytics, if you start with this basic list.

Because different business models call for a different data menu, I divided the list by major industry types. If your industry is not listed here, use your imagination along with a need-analysis.

Cheat Sheet

Merchandizing: Most retailers would fall into this category. Basically, you would provide products and services upon payment.

  • Who: Customer ID / PII
  • What: Product SKU / Category
  • When: Purchase Date
  • How Much: Total Paid, Net Price, Discount/Coupon, Tax, Shipping, Return
  • Channel/Device: Store, Web, App, etc.
  • Payment Method

Subscription: This business model is coming back with full force, as a new generation of shoppers prefer subscription over ownership. It gets a little more complicated, as shipment/delivery and payment may follow different cycles.

  • Who: Subscriber ID/PII
  • Brand/Title/Property
  • Dates: First Subscription, Renewal, Payment, Delinquent, Cancelation, Reactivation, etc.
  • Paid Amounts by Pay Period
  • Number of Payments/Turns
  • Payment Method
  • Auto Payment Status
  • Subscription Status
  • Number of Renewals
  • Subscription Terms
  • Acquisition Channel/Device
  • Acquisition Source

Hospitality: Most hotels and travel services fall under this category. This is even more complicated than the subscription model, as booking and travel date, and gaps between them, all play important parts in the prediction and personalization.

  • Who: Guest ID / PII
  • Brand/Property
  • Region
  • Booking Site/Source
  • Transaction Channel/Device
  • Booking Date/Time/Day of Week
  • Travel(Arrival) Date/Time
  • Travel Duration
  • Transaction Amount: Total Paid, Net Price, Discount, Coupon, Fees, Taxes
  • Number of Rooms/Parties
  • Room Class/Price Band
  • Payment Method
  • Corporate Discount Code
  • Special Requests

Promotion Data: On top of these basic lists of behavioral data, you would need promotion history to get into the “what worked” part of analytics, leading to real response models.

  • Promotion Channel
  • Source of Data/List
  • Offer Type
  • Creative Details
  • Segment/Model (for selection/targeting)
  • Drop/Contact Date

Summing It All Up

I am certain that you have much more data, and would need more data categories than ones on this list. For one, promotion data would be much more complicated if you gathered all types of touch data from Google tags and your own mail and email promotion history from multiple vendors. Like I said, this is a cheat sheet, and at some point, you’d have to get deeper.

Plus, you will still have to agonize over how far back in time you would have to go back for a proper data inventory. That really depends on your business, as the data cycle for big ticket items like home furniture or automobiles is far longer than consumables and budget-price items.

When in doubt, start asking your analysts. If they are not sure — i.e., insisting that they must have “everything, all the time”— then call for outside help. Knowing what to keep, based on business objectives, is the first step of building an analytics roadmap, anyway.

No matter how overwhelming this cleanup job may seem, it is something that most organizations must go through — at some point. Otherwise, your own IT department may decide to throw away “old” data, unilaterally. That is more like a foreclosure situation, and you won’t even be able to finish necessary data summary work before some critical data are gone. So, plan for streamlining the data flow like you just sold a house and must move out by a certain date. Happy cleaning, and don’t forget to whistle while you work.

Don’t Be a Data Hoarder — Why Data Governance Matters in Marketing

They say data is an asset. I say it, too. If collected data are wielded properly, they can definitely lead to financial gains, either through a revenue increase or cost reduction. But that doesn’t mean that possessing large amounts of data guarantees large dollar figures for the collector. Data governance matters.

They say data is an asset. I say it, too. If collected data are wielded properly, they can definitely lead to financial gains, either through a revenue increase or cost reduction. But that doesn’t mean that possessing large amounts of data guarantees large dollar figures for the collector. Data governance matters, because the operative words in my statement are “wielded properly,” as I have been emphasizing for years through this column.

Plus, collecting data also comes with risks. When sensitive data go into the wrong hands, it often leads to a direct financial burden for the data collector. In some countries, an assumed guardian of sensitive data may face legal charges for mishandling sensitive data. Even in the United States, which is known as the “freest” country for businesses when it comes to data usage, data breach or clear abuse of data can lead to a publicity nightmare for the organization; or worse, large legal settlements after long and costly litigations. Even in the most innocuous cases, mistreatment of sensitive data may lead to serious damage to the brand image.

The phrase is not even cool in the business community anymore, but “Big Data” worked like a magic word only a few years ago. In my opinion, that word “big” in Big Data misled many organizations and decision-makers. It basically gave a wrong notion that “big” is indeed “good” in the data business.

What is “good,” in a pure business sense? Simply, more money. What was the popular definition of Big Data back then? Three Vs, as in volume, velocity and variety. So, if varieties of data in large volumes move around really fast, it will automatically be good for businesses? We know the answer by now, that a large amount of unstructured, unorganized and unrefined data could just be a burden to the holder, not to mention the security concerns listed earlier.

Unfortunately, with the popularity of Big Data and emergence of cloud computing, many organizations started to hoard data with a hope that collected data would turn into gold one day. Here, I am saying “hoarding” with all of the negative connotations that come with the word.

Hoarders are the people who are not able to throw away anything, even garbage. Data hoarders are the same way. Most datasets are huge because the collector does not know what to throw out. If you ask any hoarder why he keeps so many items in the house, the most common answer would be “because you never know when you need them.” Data hoarders keep every piece of data indefinitely for the same reason.

Only Keep Useful Data

But if you are playing with data for business purposes, you should know what pieces of data are useful for decision-making. The sponsor of any data activity must have clear objectives to begin with. Analysts would then find out what kind of data are necessary to meet those goals, through various statistical analyses and cumulative knowledge.

Actually, good analysts do know that not all data are created equal, and some are more useful than others. Why do you think that the notion of a Data Lake became popular following the Big Data hype? Further, I have been emphasizing the importance of an even more concise data environment. (I call it an “Analytics Sandbox.”) Because the lake water in the Data Lake is still not drinkable. Data must get smaller through data refinement and analytics to be beneficial for decision-makers (refer to “Big Data Must Get Smaller”).

Nonetheless, organizations continue to hoard data, because no one wants to be responsible for purging data that may be useful someday. Government agencies may have some good reasons to maintain large amounts of data, because the cost of losing or misplacing data about some terrorist activities is too high. Even in that case, however, we should collectively be concerned if the most sensitive data about us — such as our biometrics data — reside in some government agency’s server somewhere, without clear and immediate purposes. In cities like London or Paris, cameras are on every street corner, linked to facial recognition algorithms. But we tolerate that because the benefit outweighs the risk (so we think). But that doesn’t mean that we don’t need to be concerned with data breach or abuse.

Hoarding Data Gives Brands the Temptation to Be Creepy

If the data are collected by businesses for their financial gains, then the subjects of such data collection (i.e., consumers) should question who gave them the right to collect data about every breath we take, every move we make and every claim we stake. It is one thing to retain data about mutual transactions, but it is quite another to collect data on our movement or whereabouts, unilaterally. In other words, it is one thing to be remembered (for better service and recommendation in the future), but it is another to be stalked (remember “Every Breath You Take” is a song about a stalker).

Have you heard a story about a stalker who successfully courted the subject as result of stalking? Why do marketers think that they will sell more of their products by stalking their customers and prospects? Since when did being totally creepy – as in “I know where you are and what you’re doing right now” – become an acceptable marketing tactic? (Refer to “Don’t Do It Just Because You Can.”)

In fact, even if you do possess such data, in the interest of “not” being creepy, you must make your message more innocuous. For example, don’t act like you are offering an item because you “know” that the target looked around similar items recently. That kind of creepy approach may work once in a while, but let’s not call that a good sales tactic.

Instead, sellers should make gentle nudges. Don’t say “I know you are looking for this particular skin care item.” The response to that would be “Who the hell are you, and how do you know that?” Instead, do say “Would you be interested in our new product for people with sensitive skin?” The desirable response would be “Hey, I was just looking for something like that!”

The difference between a creepy stalking and a gentle nudging is huge, from the receiving end.

Through many articles about personalization, I have been emphasizing the use of model-based personas, as they pack so much information in the form of answer to questions and cover the gap of missing data (as we’d never know everything about everyone). If I may add one more benefit of modeling, it coverts data into probabilities. Raw data is about “I know she is looking for a particular high-end skin care item,” where coverage of such data is seriously limited, anyway. Conversely, model scores are about “Her score for high-end beauty products is 8 out of 10 scale score,” even if we may not even have concrete data about that specific interest.

Now, users who only have access to the model score — which is “dull” information, in comparison to “sharp” data about some verified behavior — would be less temped to say “Oh, I know you did this.” Even for non-geeky types, the difference between “Is” and “Likely to be” is vast.

If converting sharp data into innocuous probability scores through modeling is too much for you to start with, then at least categorize the data, and expose data points to users that way. Yes, we are living in the world of SKU-level product suggestion (like Amazon does), but as a consumer, have you ever “liked” such blunt suggestions, anyway? Marketers do it because such personalization does better than not doing anything at all, but such a practice is hardly ideal for many reasons (Being creepy being one. Refer to “Personalization Is About the Person”).

The saddest part in all this is that most marketers don’t even know how to fully utilize what they collected. I’ve seen too many organizations that are still stuck with using a few popular data variables repeatedly, while hoarding data indiscriminately. Why risk all of those privacy and security concerns, not to mention the data maintenance cost, if that is the case?

Have a Goal for All of That Data

If analytics is part of the process, then the analysts will tell you with conviction, that you don’t need all those data points for certain types of prediction. For instance, why risk losing a bunch of credit card numbers, when the credit card type or payment method is all you need to predict responses and propensities on a customer level?

Of course, the organization must first decide what types of models and predictions are necessary to meet their goals. But that is the beginning part of the whole analytics game, anyway. Analytics is not about answering to some wishful thinking of data hoarders; it should be a goal-oriented activity, with carefully selected and refined data for clear purposes.

A goal-oriented mindset is even more important in the age of machine learning and automation. Because we should never automate bad behaviors. Imagine a powerful marketing automation engine in the hands of data hoarders. Forget about organizational inefficiency. As a consumer, don’t you get a chill down your spine just imagining how creepy the outcome would be? Well, maybe we don’t really have to imagine it, as we all get bombarded with ineffective and not-so-personal offers every day.

Conclusion

So, marketers, have clear purposes in data activities, and do not become mindless data hoarders. If you do possess data, wield them properly with analytics. And while at it, purge pieces of data that do not fit your goals. That “you never know” attitude really doesn’t help anyone. And you are supposed to know your own goals and what data and methodologies will get you there.

How Will Your Audience Receive Your New Product?

Product innovation is necessary for every company to grow and evolve in a competitive market. But if your audience “doesn’t get” your new product, success is much less of a guarantee.

Product innovation is necessary for every company to grow and evolve in a competitive market. But if your audience “doesn’t get” your new product, success is much less of a guarantee. Before you unveil your hard-won innovations, here are some ways to ensure you’re targeting the segments of your audience who will be the most receptive — both to the new product and accompanying marketing efforts.

First, Really Know Who They Are

While basic demographics like age, marital status, geographic location, hobbies and other points help you form a picture of your audience, to really know them means gaining specific, unique insights about them. You want to understand more than just who they are on paper by finding out how they think and feel and what they truly need. To do this, you have to integrate survey data with rich behavioral insights gleaned from big data.

Look at how personality profiles developed through a scan of big data reveal the personality characteristics common to the potential target audience for a new robot vacuum:

Credit: GutCheckIt

This audience ranks high for agreeableness, which points to other traits like altruistic, modest, and empathetic. So when communicating with them about the vacuum, messaging that uses a social responsibility angle will likely attract and feel relevant to them.

How your new product appeals to the individual needs and lifestyles of your audience further deepens your understanding of them. Consider in this summary of needs how the robot vacuum could hit home with the audience’s high ideals, drive toward harmony, and interest in self-expression, as well as how the vacuum could appeal to the audience majority who enjoy keeping their home tidy.

Credit: GutCheckIt
Credit: GutCheckIt

Then, Determine How Best to Reach Them

Once you’ve formed a full understanding of your audience’s personality, needs, and lifestyle, combine your learning with a study of the type of media consumed and during which times of day. For example, the vacuum audience learns about new products mainly through social media rather than television or promotional emails. They spend 7-plus hours per week on the web and using apps, mostly in the early evening hours between 5-8 pm.

Credit: GutCheckIt

To reach this audience effectively, online or mobile campaigns work best, with ads that could be shown on traditional TV in the later evening hours between 8-11 pm.

To learn what type of unique insights you could uncover about your brand’s audience before you launch a new product, visit the GutCheck website to learn more.

Do Buzzwords Get in the Way of Progress?

Have you read a column in the past week, month or year that’s void of buzzwords? Probably not. In the age of 5,000-plus choices of what partners, technologies or agencies to choose from, I find it uncanny how the marketplace is fraught with complex ways to explain simple things.

Have you read a column in the past week, month or year that’s void of buzzwords? Probably not. In the age of 5,000-plus choices of what partners, technologies or agencies to choose from, I find it uncanny how the marketplace is fraught with complex ways to explain simple things. Blame it on analysts who define industries? Blame it on a competitive marketplace and people trying to stand out with that killer phrase that describes what they do? Blame it on retailers striving to explain and justify what they do to their corporate leaders? Or startups striving to associate new ideas to mainstream challenges? Or blame it on consultants for making the simple complex and charging for it.

What it doesn’t help are retailers. In a perfect world, retailers live their brand. They look for simple ways to communicate with a broad spectrum of customers, and need creative yet practical approaches to words. You’re a merchandiser, an e-commerce company, and a lifestyle brand, and it can be a cultural challenge to balance buzzword frenzy with simple words the market needs to hear about your company. My main problem with buzzwords — and I’ve been as guilty as anyone in the use of them, just read a few of my columns — is using terms in loose context can minimize the impact of the term and make it actually more confusing. Therefore, in the spirit of no buzzwords, this column is just that: real talk for real retailers.

Lets start with a few buzzwords:

  • Disruptive technology: This begs the question of how disruptive your disruptive technology has to be for you to claim that it’s truly disruptive vs. just moderately irritating.
  • Ecosystem: This buzzword got big in mid-2014, 2015 as Luma Partners really promoted its Lumascape. Next thing you know every vendor is using it and every internal IT team began following suit to describe their “data lake strategies” and “technology road map.” I’m not sure I’ll ever get used to referring to my business interdependencies using the same terminology we use to talk about global warming and our attempt to save the planet.
  • Millennials: Are millennials really a buzzword? They might be. They’ve become more than just another generational grouping. As more millennials enter the workforce, replacing the retiring baby boomers, we will continue to spend a lot of time talking about the impact they’re having on the intersection between business, technology and our interpersonal lives. Maybe more importantly, we will continue to try to figure out why they break up with each other via text.
  • Thought leadership: This buzzword was prevalent for many years, and I still don’t really know what it means — or maybe I thought I did and really didn’t. I was awarded Thought Leader of the Year in 2016, and had trouble describing the award outside of … unfortunately, it seems to be entrenched and positioned to bother us for another year. I’ve been trying desperately to think of a new term that could supplant it, but question if I’m enough of a thought leader to make that happen.
  • Storytellling: I have to confess that I’ve coached and advised leaders to use stories to convey important things about their businesses because a good story resonates better than death by Powerpoint presentation. Now we’ve got storytelling classes, storytelling departments, and even storytelling gurus. Once gurus come into the picture, we’ve officially hit buzz status
  • Artificial intelligence/machine learning: These are likely the most overused, misunderstood and confusing buzzwords. How many times have you heard, “We have AI.” While this area of discipline and technology advances will reshape much of what we know today, any buzzword that conjures up impending doom of the human race isn’t helping in a dynamic business world.
  • Big data: I have trouble with anything that starts with “big” as a modifier of an industry trend. What’s big, and is there bigger? Much like the term disruptive, big data is an overused phrase that doesn’t serve many outside of its sellers. Google, Facebook, Amazon.com, Microsoft, Apple have big data. If you really want to understand big data in our society, there’s a great book: “The Human Face of Big Data.” Warning, this book is big, literally. In the end, the term does little to help you contextualize marketing problems or your own internal data challenges.

We’re in a world of endless information. Buzzwords in my opinion distort real talk and make complex concepts harder for the masses to address in situational marketing. Have fun with it by infusing a NO Buzzword culture or, better yet, force the offender to fully explain the term in the context of your business. And remember the goal of words is not to show how smart you are versus; they are a way to level set on complex ideas.

Make the complex simple!

Why Behavioral Science Is Critical to Marketing and Research

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What Is Behavioral Science?

Behavioral science isn’t a new industry, but within the past few years is something of an emerging topic in marketing and research. At its core, behavioral science and the research that results from it, seeks to understand the many aspects related to someone’s habits or decision-making. Most importantly, as we noted, it helps to understand why people make certain decisions.

If you think of that in the context of our marketing and product strategies, it’s clear why behavioral science plays a role in market research. There are a variety of methods that can get close to truly understanding consumer behavior, but much of them can fail to capture empirical evidence — sensory information captured through observations and the documentation of behaviors through experimentation.

As a result, the importance and rise of behavioral science in marketing and research is no small subject. Just in the past year, there have already been numerous events discussing behavioral science specific to gathering and analyzing data to understand why consumers make decisions — but marketers and researchers, by and large, are still figuring out how to leverage it.

Leveraging Behavioral Data

Big data can be used as a possible solution for at least two reasons. First, it gives us access to more data than ever before, including data based on actual behavior from purchasing, web analytics, subscriptions, and more. As a result, big data can reduce the struggles we sometimes have with differences between stated versus observed behavior.

Second, there are big data sources that allow us to understand motivations of our consumers by examining the big 5 personality traits for millions and millions of people. By understanding different personalities, we can begin to realize if being “extroverted” or “conscientious” drives consumers’ purchasing. Some suggest that behavioral science and the resulting data on motivations behind decision making will be the new normal for market research. We agree that understanding what people don’t tell us in surveys is as important as what they do. Together, these two types of data give us a more well-rounded picture of consumer behavior, and with the right methodology, you can gain this knowledge quickly.

In a specific use case, a brand was looking to understand their target audience for a new product innovation. They had hypothesis’ about what this audience would look like, and likely could have gained that knowledge through standard quantitative research. However, by incorporating an approach that combines survey data and big data, they were able to understand who their audience was, but also what would motivate them to purchase this particular new product. The moral of the story? Consumers are more than just the people that buy your product.

Marketing Success Sans ‘Every Breath They Take, Every Move They Make’

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

But before we get into boring analytics talk, citing words like “predictive analytics” and “segmentation,” let’s talk about what kind of data are required to make predictions better and more accurate. After all, no data, no analytics.

I often get questions like what the “best” kind of data are. And my answer is, to the inquirer’s disappointment, “it depends.” It really depends on what you are trying to predict, or ultimately, do. If you would like to have an accurate forecast of futures sales, such an effort calls for a past sales history (but not necessarily on an individual or transactional level); past and current marcom spending by channel; web and other channel traffic data; and environmental data, such as economic indicators, just to start off.

Conversely, if you’d like to predict an individual’s product affinity, preferred offer types or likelihood to respond to certain promotion types, such predictive modeling requires data about the past behavior of the target. And that word “behavior” may evoke different responses, even among seasoned marketers. Yes, we are all reflections of our past behavior, but what does that mean? Every breath you take, every move you make?

Thanks to the Big Data hype a few years back, many now believe that we should just collect anything and everything about everybody. Surely, cost for data collection, storage and maintenance has decreased quite a bit over the years, but that doesn’t mean that we should just hoard data mindlessly. Because you may be deferring inevitable data hygiene, standardization, categorization and consolidation to future users — or machines — who must sort out unorganized and unrefined data and provide applicable insights.

So, going back to that question of what makes up data about human behavior, let’s define what that means in a categorical fashion. With proliferation of digital data collection and analytics, the definition of behavioral data has expanded considerably.

In short, what people casually refer to as “behavioral data” may include this to measure success:

  • Online Behavior: Web data regarding click, view and other shopping behavior.
  • Purchase: Transactional data, made of who, what, when, how much and through what channel.
  • Response: Response history, in relation to specific promotions, covering open, click-through, opt-out, view, shopping basket, conversion/transaction. Offline response may be as simple as product purchase.
  • Channel: Channel usage data, not necessarily limited to shopping behavior.
  • Payment: Payment and related delinquent history — essential for credit purchases and continuity and subscription businesses.
  • Communication: Call, chat or other communication log data, positive or negative in nature.
  • Movement: Physical proximity or movement data, in store or store area, for example.
  • Survey: Responses to various surveys.
  • Opt-in/Opt-out: Sign-up specific 2-way communications and channel specific opt-out requests.
  • Social Media: Product review, social media posting and product/service-related sentiment data.

I am sure some will think of more categories. But before we create an exhaustive list of data types, let’s pause and think about what we are trying to do here.

First off, all of these data traceable to a person are being collected for one major reason (at least for marketers): To sell more things to them. If the goal is to predict the who, what, when and why of buying behavior, do we really need all of this?

The ‘Who’ of Buying Behavior

In the prediction business, predicting “who” (as in “who will buy this product?”) is the simplest kind of action. We’d need some PII (personally identifiable information) that can link to buying behaviors of the target. After all, the whole modeling technique was invented to rank target individuals and set up contact priority — in that order. Like sending expensive catalogs only to high-score individuals, in terms of “likely to respond,” or sales teams contacting high “likely to convert” targets as priorities in B2B businesses.

The ‘What’ of Buying Behavior

The next difficulty level lies with the prediction of “what” (as in “what is that target individual going to buy next?”). This type of prediction is generally a hit-or-miss, so even mighty Amazon displays multiple product offers at the end of a successful transaction, by saying “Customers who purchased this item are also interested in these products.” Such a gentle push, based on collaborative filtering, requires massive purchase history by many buyers to be effective. But, provided with ample amounts of data, it is not terribly difficult, and the risk of being wrong is relatively low. Pinpointing the very next product for 1:1 messaging can be challenging, but product basket analysis can easily lead to popular combinations of products, at the minimum.

Big Data: What It Is and How to Analyze It

What really is big data? Big data encompasses extremely large datasets that can be analyzed to reveal more in-depth insights, patterns, trends and even help predict future outcomes. But what actually makes up these “extremely large datasets” can be much more exhaustive, and understanding them can significantly improve our overall knowledge of big data and how to use it.

What really is big data? Big data encompasses extremely large datasets that can be analyzed to reveal more in-depth insights, patterns, trends and even help predict future outcomes. But what actually makes up these “extremely large datasets” can be much more exhaustive, and understanding them can significantly improve our overall knowledge of big data and how to use it.

Big data is just data: The following types of big data can be used to define any data in today’s world. But the goal of understanding the different types of data is to help determine how they might be used together to provide the answers to the questions marketers are asking.

3 Types of Big Data

First and foremost, big data can be defined based on its structure. The structure of data depends on how organizable it is. In other words, whether it can be formatted into tables of rows and columns. There are three types of big data when defining it by the structure:

  1. Structured: Data that is structured is often already stored in a database or other data management platform, and it can be easily accessed and processed to provide an ordered output.
  2. Unstructured: Usually larger datasets — the majority of big data is unstructured, meaning it can’t easily be organized or classified.
  3. Semi-Structured: As the name implies, semi-structured data isn’t inherently organized at the start, but as it is analyzed or digested it can begin to take on a more structured form.

Both structured and unstructured data can be either human-generated or machine-generated. Human-generated, structured data can be contact information or website form details directly collected from an individual. Human-generated unstructured data can be any form of website activity and social data such as video, audio, or social posts shared by a person.

On the other hand, examples of machine-generated, structured data include GPS tracking, inventory tracking, or transaction data. Unstructured forms of machine-generated data include information gathered through satellite such as images or weather sensory information.

Each of these types of data can be analyzed in many different ways. However, there are certain types of analysis that will serve their own purpose depending on the objectives at hand.

4 Types of Analysis

There are many reasons to look to big data for insights. Whether it’s combining big data and survey data for detailed audience intelligence or combing through it to predict purchase data, they all fit into four types of analysis:

  1. Prescriptive Analysis: Data analysis that provides answers to what actions should be taken.
  2. Predictive Analysis: An analysis of data that can be used to predict what situation or number of situations may results.
  3. Diagnostic Analysis: Data analysis that provides insight into what happened in the past and why.
  4. Descriptive Analysis: Data analysis that can be real-time or leveraged to see what is currently happening.

Mapping your analytics and marketing strategy to the type of big data needed and the type of analysis can help understand what tools and solutions may be best to bring it all together. Specifically, the type of data and analysis will lead you to the type of big data analytics required.

4 Benefits of Applying Marketing Analytics

Marketing analytics is no small subject in today’s world of business. In fact, according to Transparency Market Research, the marketing analytics industry is set to grow by roughly 14% by 2022. Why such growth? Marketing analytics has a tremendous impact on a marketing organization’s activities, but also on a brand’s overall understanding of their entire company’s success.

Marketing analytics is no small subject in today’s world of business. In fact, according to Transparency Market Research, the marketing analytics industry is set to grow by roughly 14% by 2022. Why such growth? Marketing analytics has a tremendous impact on a marketing organization’s activities, but also on a brand’s overall understanding of their entire company’s success.

There are four unique benefits marketing analytics provides, and combined together, these benefits give a holistic view of an organization’s past, present and future.

But First: What Is Marketing Analytics and Why Is It Important?

Marketing analytics is a result of the technology and influx of data we use as marketers. Early on, marketing analytics was a relatively simple concept. It encompassed the process of evaluating marketing efforts from multiple data sources, processes or technology to understand the effectiveness of marketing activities from a big-picture view — often through the use of metrics. Fundamentally, it’s all about quantifying the results of marketing efforts that take place both online and offline.

Today, marketing analytics has become an entire industry that’s changing the way we work and the type of work we do as marketers. 

It’s important to measure the financial impact of not just marketing but of a variety of efforts from product and sales — which marketing analytics also can provide. As a result, knowing and understanding the different types of analysis and the benefits they provide within marketing analytics, can help to identify what metrics to focus on for what objectives — because objectives can be an endless list of how to understand or increase ROI, monitor trends over time, determine campaign effectiveness, forecast future results, and so on.

The 4 Benefits of Applying Marketing Analytics

1. Learn What Happened

Marketing analytics can first lend insight into what happened in the past and why. This is instrumental to marketing teams in order to avoid making the same mistakes. Through descriptive analysis and the use of customer relationship management and marketing automation platforms, analytics bring to light not only what happened in the past but also provide answers to questions on specific topics. For example, you can ask more about why a specific metric performed the way it did, or what impacted the sales of a specific product.

2. Gauge What’s Happening Now

Marketing analytics can also help you understand what’s currently taking place in regards to your marketing efforts. This helps determine if you need to pivot or quickly make changes in order to avoid mistakes or make improvements. Using dashboards to display current engagements in an email track or the status of new leads are examples of marketing analytics that look to assess the real-time status of marketing efforts. Usually, these dashboards are created by employing business intelligence practices in addition to a marketing automation platform.

3. Predict What Might Happen

Some could say the predictive aspect of marketing analytics is the most important part of it. Through predictive modelings such as regression analysis, clustering, propensity models and collaborative filtering, we can start to anticipate consumer behavior. Web analytics tracking that incorporates probabilities, for example, can be used to foresee when a person may leave a site and when. Marketers can then utilize this information to execute specific marketing tactics at those moments to retain customers.

Or perhaps it’s marketing analytics that assesses lead management processes to prioritize leads based on those similar to current customers. This helps identifies who already has a higher propensity to buy. Either way, the goal of marketing analytics for the future will be to move away from a rear-view strategy to focus on the future. Luckily, the influx of data, machine learning, and improved statistical algorithms mean our ability to accurately predict the likelihood of future outcomes will rise exponentially.

4. Optimize Efforts

This last benefit only comes when you combine your analytics with your market research objectives — but if you do so you could see the greatest impact. In fact, if you’re not ensuring your marketing analytics and market research work together, then you could be missing out on a lot of opportunities. Essentially, it’s about translating marketing analytics findings into market research objectives. A common mistake marketers make in conducting marketing analytics is forgetting to gather real customer feedback. This activity is important to bridge the gap between analytics insights, a marketing strategy and activation.

In addition to the first three benefits or approaches, brands should use marketing research as a tool to push their marketing analytics from just learning about lead generation and sales metrics to actually understand customers in the context of their marketing opportunities.

Understanding and Leveraging Big Data for Audience Insights

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data?

Data mining, big data
Creative Commons license. | Credit: Flickr by KamiPhuc

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data? Some data management platforms (DMPs) have over 900 million consumer profiles globally with 10,0000 different data points associated with it. To get even close to that amount of data from consumer surveys, you’d have to run about half a billion surveys.

So while big data has been most impactful to programmatic advertising and media, it also provides a lot of opportunity for other marketing efforts and market research. Due to the insight it provides into consumer behavior, as budgets continue to shrink and the speed of decision making continues to increase, big data is necessary. 

The 4 V’s of Big Data

Today, big data is bigger than ever with more people engaging and utilizing tools that offer big data integration. However, incorporating big data is a marathon, not a sprint and companies have to take the right steps before making the leap. If you’re not already using big data, before getting started, you’ll want to familiarize yourself with the 4 V’s of big data:

  • Volume: the amount of data available
  • Variety: the different types of data
  • Velocity: how frequent, real-time, or up to date the data is
  • Veracity: how accurate and applicable the data is

The biggest challenge when it comes to data is the “veracity” of it. Because there is so much and such a variety of data, it can be difficult to assess its accuracy and application to your business. Discerning the signal from the noise is where most innovation teams will spend their time interpreting the data. In other words, veracity helps to filter through what is important and what is not, and in the end, it generates a deeper understanding of data and how to contextualize it in order to take action.

Data Veracity: The Most Important “V”

Data veracity, in general, is how accurate or truthful a data set may be. In the context of big data, however, it takes on a bit more meaning. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is. Removing things like bias, abnormalities or inconsistencies, duplication, and volatility are just a few aspects that factor into improving the accuracy of big data.

Unfortunately, sometimes volatility isn’t within our control. The volatility, sometimes referred to as another “V” of big data, is the rate of change and lifetime of the data. An example of highly volatile data includes social media data, where sentiments and trending topics change quickly and often. Less volatile data would look something more like weather trends that change less frequently and are easier to predict and track.

The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs and the output is pertinent to objectives. Interpreting big data in the right way ensures results are relevant and actionable. Further, access to big data means you could spend months sorting through information without focus and without a method of identifying what data points are relevant. As a result, the velocity of data and agile methods come into play here — big data should be analyzed in a timely manner, as is difficult, otherwise the insights would fail to be useful.

Big data is highly complex, and as a result, the means for understanding and interpreting it are still being fully conceptualized. While many think machine learning will have a large use for big data analysis, statistical methods are still needed in order to ensure data quality and practical application of big data for better marketing activation. For example, you wouldn’t download an industry report off the internet and use it to take action. Instead you’d likely validate it or use it to inform additional research before formulating your own findings. Big data is no different; you cannot take big data as it is without validating or explaining it. But unlike most market research practices, big data does not have a strong foundation with statistics—luckily, integrating it with survey data can help.

Integrating Big Data With Survey Data for Market Insights

While big data can answer when, where, and what, it can’t answer why. Integrating primary research, particularly with an agile methodology that can keep up with the velocity of big data, can help to analyze and connect the dots — easier said than done.

The obvious benefits of using big data in marketing includes gaining a better understanding of people, content, and media. By combining big data with survey data, you can identify a market opportunity, understand your target audience, and incorporate findings into your messaging and creative execution. Infusing survey research with big data also means the volume of questions that need to be asked are reduced as big data provides more answers. So our understanding of consumer behavior is going to grow exponentially over time as we bring these two worlds together. The ability to incorporate big data to use fewer questions can also deliver more speed and value.

The result is unique audience intelligence. The benefits of this approach mean removing the guesswork during activation, letting the audience identify the opportunity for you, creating more effective messaging, and ultimately increasing value on ad purchases. There are certainly challenges to infusing survey research with big data before organizations can reap the benefits. Since DMP’s were originally meant for advertising, they haven’t been made research-grade, so oftentimes there are errors or inconsistencies in the data related to an audience. However, there are solutions out there — and more to come for sure — that will be able to overcome the challenges and provide an accurate depiction of audience insights through big data.