Marketing AI Is Overhyped, and That’s Good

Today, marketing AI is a know-it-all with a short resume. Just like Big Data and personalization, it is also a catch-all phrase that is becoming harder to define. As a result, it is no surprise that most marketers are rolling their eyes at the topic. Nevertheless, this is also the time to take the topic seriously, unless you plan to retire into seclusion in the next few years.

AI
“artificial-intelligence-2228610_1920,” Creative Common license. | Credit: Flickr by Many Wonderful Artists

Today, marketing AI is a know-it-all with a short resume. Just like Big Data and personalization, it is also a catch-all phrase that is becoming harder to define. As a result, it is no surprise that most marketers are rolling their eyes at the topic. Nevertheless, this is also the time to take the topic seriously, unless you plan to retire into seclusion in the next few years.

New research by marketing automation provider Resulticks shows that 73 percent of marketers are either skeptical, neutral or simply exhausted by the hype around marketing AI. In addition, large numbers of marketers think that vendors using industry buzzwords are full of it. This is not surprising, considering how most vendors are probably over-selling their AI solution. In the same Resulticks study, only 18 percent of marketers claim that AI vendors are delivering the goods as promised and 43 percent felt they were over-promised.

However, for those of us who have lived through (and even reveled in) industry catchphrases, from “marketing analytics” to Big Data to “MarTech,” these statistics indicate that “Marketing AI” is on a strong growth trajectory. This is because the combination of huge industry-level investments and a few success stories is generally a recipe for a new frontier of innovation. Some time ago, I wrote an article on the VC investments being made in data-driven marketing technology and many of the technology solutions were still evolving innovations, like marketing automation. Today, the phrase “Marketing AI is also heading toward becoming broad and meaningless, with heavy investments in the sector. In a few years, underneath that generic umbrella will evolve smart, pragmatic solutions which will become part of the standard tool kit. For example, under the Big Data and MarTech labels, we now have well-adopted solutions, such as CRM, programmatic buying and marketing automation. While there are still bugs and varying degrees of success, there is also a large body of fruitful use-cases which demonstrate how these tools can be very effective.

So, what is a marketer to do in this environment where marketing AI has yet to evolve to a stage where it is a stable and valued marketing tool? The most important step is to set low expectations and begin to dip your feet in the water. Experimenting now is critical, as new skills sets and operating frameworks will be required to fully take advantage of the coming AI-driven innovations, and building those individual and institutional capabilities will take time.

Playing the Amazon Game: Translating Big Data Into Big Dollars

Will 2018 be the year of Amazon (again)? The first week of the year is always filled with predictions, and there’s a good chance that most serious business predictions for 2018 will include some version of a call for businesses to respond to, react to, or create a new business model in order to compete in this age of Amazon.

Amazon boxesWill 2018 be the year of Amazon (again)? The first week of the year is always filled with predictions, and there’s a good chance that most serious business predictions for 2018 will include some version of a call for businesses to respond to, react to, or create a new business model in order to compete in this age of Amazon. Because the truth is, if you think that non-retail businesses are exempt from this challenge, you are wrong.

While Amazon may have started as an online bookseller, it is so much more than that now. It is, among many things, a cloud computing powerhouse, an award-winning original content producer and streaming content platform, a top-selling fashion house, a gamer’s paradise, the leader in AI and voice technology innovation, and the largest world marketplace for third-party sellers.

The company has innovated in pricing and subscription models, delivery systems and on-demand technologies and scared the heck out of those who previously thought their little corner of commerce was exempt from Amazon’s notice. No one is safe.

When Amazon enters a new industry or vertical — which the brand does with disquieting regularity — it changes the game for consumers and for businesses across segments and industries, challenging everyone and everything we thought we knew about consumer needs and how to sell stuff. Its impact is felt all along the business chain from suppliers and providers to adjacent businesses and directly to the consumer.

Amazon’s expansion plans and willingness to take risks, its consumer experience obsession, logistics expertise, consumer access and deep pocket investments have broad implications across categories. In 2017 alone, Amazon expanded through acquisition in non-retail directions including grocery stores (Whole Foods), cyber security (harvest.ai), gaming (GameSparks) and analytics presentation (Graphiq). And all these moves are strategically designed to strengthen its core offerings and consumer ties.

Amazon’s advantages also include a ubiquitous consumer presence in U.S. homes, (64 percent with Amazon Prime membership according to Forbes). This translates into tremendous data and insights into shopping patterns, price elasticity, promotion and offer value and critical consumer search patterns. And because it freely sells competing products, its marketplace supplies the company with nearly complete information on competitor strengths and weaknesses in not only sales data, but also consumer reviews.

This is in conjunction with the fact that it controls the marketplace and can therefore work the home-field advantage to highlight its own brands or those products that deliver the most value. In short, Amazon has a direct way to translate its big data into big dollars. This is increasingly important as Amazon aggressively expands its catalog of private label categories and products. Other key strengths include its forays into voice search, in-home electronics, alternate ordering methodologies and sheer operational excellence.

In terms of Amazon’s future endeavors, the brand has made recent investments, as well as public statements to include more acquisitions in AI and machine learning — maybe even in healthcare/genomics. And it’s probably safe to assume that we will see more proprietary devices like the Echo and Kindle that streamline consumer connections and reduce any friction in commerce while further building Amazon’s data advantage in the guise of consumer convenience and innovative experiences. Numerous patent applications in logistics, cyber security and cloud computing attest to its attention to the backbone that reliably delivers the Amazon experience.

Learning the Ways of Amazon

So how should marketers respond to such an intimidating competitor? I often think of Amazon as a wholly different planet filled with a lot of attractive consumers in active search mode for my products, but with its own set of customs, rules and laws. In order to commercially navigate on this planet, I have to familiarize myself with the environment and make some key adjustments.

  • My consumers may exist simultaneously in traditional sales channels and on planet Amazon as well as move frequently between the two. Therefore, I have to maintain a certain amount of consistency in experience and product as well as pricing unless I can distinguish an Amazon-only offering.
  • Amazon is built to provide consumers with easy access to a lot of competitive, comparative information. I better absolutely believe in the value and quality of my product before I enter this environment.
  • I must be ready to deliver at the potential scale and speed of the demand or otherwise risk a decrease in ratings and consequently, a downward sales spiral. This may require supply chain changes.
  • Planet Amazon competes directly with me and it has unfair advantages. I need to safeguard my margins to avoid giving them away.
  • The rules that helped me succeed in online marketing outside of Amazon may not help me succeed in optimizing search visibility or conversion rates within this proprietary world. I need to dedicate myself to learning the ad marketplaces, tools and options and be prepared for a dynamic environment that requires constant investment and learning.
  • I need to understand consumer expectations within this environment and work to achieve positive WOM and reviews/ratings to fuel sales.
  • I need to rethink my brand strategy within this saturated, pricing and ratings-driven marketplace.
  • I need to review my pricing strategy — including sales bundling — in light of the dense competitive field.
  • I need to carefully execute on CRM and other strategies I can control to build and develop sustainable direct connections with consumers outside of Amazon.

So by all means, plan your trip to planet Amazon, but do so carefully as it favors those that not only know its language and terrain, but also are willing to go at it with a full-fledged strategy.

Resistance Is Futile

Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into their hive-minded collective. I am talking about a rather benign-sounding subject — and my profession — analytics.

Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into their hive-minded collective. I am talking about a rather benign-sounding subject — and my profession — analytics.

When you look at job descriptions of analytics leads in various organizations, you will often find the word “evangelization.” If every stakeholder is a believer of analytics, we would not need such a word to describe the position at all. We use that word because an important part of an analyst lead’s job is to convert non-believers to believers. And that is often the hardest part of our profession.

I smile when I see memes (or T-shirts) like “Science doesn’t care about your beliefs.” I’m sure some geek who got frustrated by the people who treat scientific facts as just an opinion came up with this phrase. From their point of view, it may be shocking to realize that scientifically proven facts can be disputed by people without any scientific training. But that is just human nature; most really don’t want to change either their beliefs or their behaviors.

Now, without being too political about this whole subject, I must confess that I face resistance to change all of the time in business environments, too. Why is that? How did activities of making decisions based on numbers and figures became something to resist?

My first guess is that people do not like even remotely complicated stuff. Maybe the word “analytics” or talk of “modeling” bring back all of the childhood memories of their scary math teachers. Maybe that kind of headache is so bad that some would reject things that could actually be helpful to them.

If the users of information feel that way, analysts must aspire to make analytics easier to consume and digest. Customers are not always right, but without the consumers of information, all analytical activities become meaningless — at least in non-academic places.

An 80-page report filled with numbers and figures dumped on someone’s desk should not even be called analytics. Literally, that’s still an extension of an unfiltered data dump. Analysts should never leave the most important part of the job — deriving insights out of mounds of data — to the end-users of analytics. True, the answer may lie somewhere in that pile, but that is like a weather forecaster listing all of the input variables to the general public without providing any useful information. Hey, is it going to rain this morning, or what?

I frequently talk about this issue with fellow analytics professionals. Even in more advanced organizations in terms of data and analytics infrastructure, heads of analytics often worry about low acceptance of data-based decision-making. In many instances, the size of the data and smooth flow of them, often measured in terabytes per second as a bragging point, do not really matter.

Information should be in nugget-sizes for easy consumption (refer to “Big Data Must Get Smaller”). Mining the data to come up with fewer than five bullet points is the hardest part, and should not be left to the users. That is the primary reason why less and less people are talking about “Big Data” nowadays, as even non-data professionals are waking up to realize that “big” is not the answer at all.

However, resistance to analytics doesn’t disappear, even when data are packaged in beautifully summarized reports or model scores. That is because often, the results of analytics uncover an inconvenient truth for many stakeholders — as in, “Dang, we’ve been doing it wrong all of this time?”

If a person or a department is called out as ineffective by some analytical geeks, I can see how involved parties may want to dispute the results any which way they can. Who cares about the facts when their jobs or reputations are at stake? Even if their jobs are safe, who are these analytics guys asking us to “change”? That is not any different from cases where cigarette companies disputed that smoking was actually beneficial in the past, and oil and gas companies have an allergic reaction when the words “climate” and “change” are uttered together in present days.

I’ve seen cases where analytical departments were completely decimated because their analytics revealed other divisions’ shortcomings and caused big political hoopla. Maybe the analysts should have had better bedside manners; but in some cases I’ve heard about, that didn’t even matter — as the big boss used the results of analytics to scold people who were just doing their jobs based on an old set of rules.

You can guess the outcome of that kind of political struggle. The lesson is that newly discovered “facts” should never be used to blame the followers of existing paradigms. Such reactions from the top will further alienate analytics from the rest of the company, as people get genuinely scared of it. Adoption of data-based decision-making? Not when people are afraid of the truth. Forget about the good of the company; that will never win vs. people’s desire for their job security.

Now, at the opposite end of the spectrum, too much unfiltered information forcing decisions can also hurt the organization. Some may call that “Death by KPI.” When there are too many indicators floating around, even seemingly sound decisions made based on numbers and figures may lead to unintended consequences; very often, negatively impacting the overall performance of the company. The question is always, “Which variable should get higher weight over others?” And that type of prioritization comes from clearly defined business goals. When all KPIs are treated to be equally important? Then nothing really is. Not in this complex world.

Misguided interpretation of numbers leads to distrust in analytics. Just because someone quoted an interesting figure within or without proper context, that doesn’t mean that there is just one version of an explanation behind it. Contextual understanding of data is the key to beneficial insights, and in the age of abundant information, even casual users of analytics must understand the differences. Running away from it is not the answer. Blindly driving the business just based on certain indicators should be avoided, as well. Both extremes will turn out to be harmful.

Nevertheless, the No. 1 reason why people do not adopt to analytics is many have gotten burned by “wrong” analytics in the past, often by the posers (refer to “Don’t Hire Data Posers”). In some circles, the reputation of analytics got so bad that I even met a group of executives who boldly claimed that whole practice of statistical modeling was totally bogus and it just didn’t work. Jeez. In the age of machine learning, one doesn’t believe in modeling at all? What do you think that “learning” is based on?

No matter how much data we may have in our custody, we use modeling techniques to predict the future, derive answers out of seemingly disjointed data and fill in the gaps in data — as we will never have every piece of the puzzle nicely lined up all of the time.

In a case of such deep mistrust in basic activities like modeling, I definitely blame the analysts of the past. Maybe those posers overpromised about what models could do. (No, nothing in analytics happens overnight). Maybe they aimed for a wrong target. Maybe they didn’t clean the data enough before plugging them into some off-of-the-shelf modeling engine. Maybe they didn’t properly apply the model to real-life situations, and left the building. No matter. It is their fault if the users didn’t receive a clear benefit from analytical exercises.

I often tell analysts and data scientists that analytics is not about the data journey that they embarked on or the mathematical adventure that they dove into. In the business world, it is about the bottom line. Did the report in question or model in action lead to an increase in revenue or a reduction in cost? It is really that clear-cut.

So, dear data geeks, please spare the rest of the human collectives from technical details, and get to the point fast. Talking about the sample size or arguing about the merits of neural net models – unless the users are equally geeky as you — will only further alienate decision-makers from analytics.

And the folks who think they can still rely on their gut feelings over analytics? Resistance to analytics is indeed futile. You must embrace it — not for all of the buzzwords uttered by the posers out there, but for the survival of your business. When your competitors are embracing advanced analytics, what are you going into the battle with? More unsolicited emails without targeting or personalization? Without knowing what elements of promotions are the key drivers of responses? Without even basic behavioral profiles of your own customer base? Not in this century. Not when consumers are as informed as marketers.

One may think that this whole analytics thing is overly hyped-up. Maybe. But definitely not as much as someone’s gut feelings or so-called business instincts. If analytics didn’t work for you in the past, find ways to make it work. Avoiding it certainly isn’t the answer.

Resistance is futile.

Zooming In, Zooming Out: 2 Focal Lengths for Better Audience Understanding

Instead of a scientist measuring a particle, imagine you’re a marketer and the red dot is your audience. You really want to know who they are, so you try to get close.

I truly admire artists like Keith Haring. He created an iconic style that’s known across the world — even by people who don’t know the artist’s name. And when I think about creating an iconic, signature style like that, I imagine artists have an idea that they simply can’t shake. And they keep trying, over and over, to create the perfect expression of their vision. Only with each iteration, they are creating something that may be based on the same idea … but each expression is entirely new unto itself. And is its own version of perspective.

Now … I’m not an artist. I certainly don’t have an iconic style. But I do have an idea that I can’t get out of my head. I keep returning to it over and over again. Because I know there’s a lesson for marketers in the idea. So once again, I’m diving into The Heisenberg Uncertainty Principle — a scientific principle I’ve used in many presentations before.

In the simplest terms, the principle establishes this: There is a limit to the ability that you can understand both the exact location and the exact momentum of a particle in motion.

There’s an equation that goes along with the principle, and it includes things like The Planck Constant, central to quantum mechanics. But for the sake of this marketing article, we’ll focus on this visual expression of the idea:

John Lane artIf you’re trying to measure the precise location of the red dot, you’d want to get as close to it as possible, to plot the exact X and Y axis — down to the deepest decimal. “The fifth nine,” if you will.

If you’re trying to measure the exact momentum (to understand how fast the dot is moving from one spot to another), you’d take a view from the top, open end of the cone — so you’d have context to measure the location in relation to other objects at varied times.

To get better understanding of one aspect — location or momentum — you lose the ability to focus on the other.

Now, instead of a scientist measuring a particle, imagine you’re a marketer and the red dot is your audience. You need to know both to be successful. You need to get up close to connect. You need to see the bigger picture to create a lasting relationship. To truly understand your audience, you’re going to need to zoom in and zoom out.

Zooming In: To know your audience,  you try to get close. (At least I hope so. But for most brands, it’s more like close-ish. As close as they can get without actually talking to that audience. But that’s a different post.) You should want to do this to ensure you’re going to set up camp on a channel with a storyline that will resonate greatly.

Zooming Out. To ensure the money you’re spending on that channel and that storyline is well spent — that it isn’t wasted on a trend that passes in a hot second — you need a more broad view. You need to see all the different influences on your audience that you can only get from a broad view.

Marketers’ Most Recent Answer to Solving the Riddle of Getting Both Perspectives Is Big Data

But here’s the even newer wrinkle (and why this principle is back in my head again): The growing reliance on Big Data is actually best for the broad view. And relying on big data is steadily pulling us marketers farther and farther up the cone. It’s allowing marketers to better see where people were, and where they might be headed. But it’s taking us farther and farther away from understanding the individuals within our audience. It’s creating — and even causing us to crave — an abstracted view of our audience rather than a precise view.

The Answer to This Problem Is: More Small Data!

Small Data is gathered by actually reading the comments on Instagram posts. Not just the comments they leave on your post … but the one they left on their best friend’s post yesterday. Within that comment — that comment gained through super-tight focus — are the keys to communicating in their language … to connecting with them based on a challenge, need, value or passion that that individual is expressing.

Small Data is gathered by talking to your audience. You ask for input or invite it, rather than always pushing your message. Once the input flows in — whether via suggestions or questions — you don’t stop after a two-line conversation. You cultivate the third, fourth and fifth exchanges. And then you incorporate your new discoveries based on the conversation into your storyline and lexicon. (This is qualitative input, not based solely on the Big Data algorithm breakdown of the exchange.)

Small Data Is …

Yes, Big Data is good. The bigger picture — the momentum and direction — is important. But Small Data — the highly-specific details gained by tight inspection and interaction — is just as important (if not more so) to building engagement.

So take a lesson from science! Think about the Heisenberg Uncertainty Principle. Deliberately take two views of your audience — by zooming in to Small Data, zooming out with Big Data, and continually repeating the process. Your marketing will be better for it.

Data Mining: Where to Dig First?

The main job of a modern data scientist is to answer business questions for decision-makers. To do that, they have to be translators between the business world and the technology world. This in-between position often creates a great amount of confusion for aspiring data scientists.

Data mining
“Big_Data_Prob,” Creative Commons license. | Credit: Flickr by KamiPhuc

In the age of abundant data, obtaining insights out of mounds of data often becomes overwhelming even for seasoned analysts. In the data-mining business, more than half of the struggle is about determining “where to dig first.”

The main job of a modern data scientist is to answer business questions for decision-makers. To do that, they have to be translators between the business world and the technology world. This in-between position often creates a great amount of confusion for aspiring data scientists, as the gaps between business challenges and the elements that makes up the answers are very wide, even with all of the toolsets that are supposedly “easy to use.” That’s because insights do not come out of the toolsets automatically.

Business questions are often very high-level or even obscure. Such as:

  • Let’s try this new feature with the “best” customers
  • How do we improve customer “experience”?
  • We did lots of marketing campaigns; what worked?

When someone mentions “best” customers, statistically trained analysts jump into the mode of “Yeah! Let’s build some models!” If you are holding a hammer, everything may look like nails. But we are not supposed to build models just because we can. Why should we build a model and, if we do, whom are we going after? What does that word “best” mean to you?

Breaking that word down in mathematically representable terms is indeed the first step for the analyst (along with the decision-makers). That’s because “best” can mean lots of different things.

If the users of the information are in the retail business, in a classical sense, it could mean:

  • Frequently Visiting Customers: Expressed in terms of “Number of transactions past 12 months,” “Life-to-date number of transactions,” “Average days between transactions,” “Number of Web visits,” etc.
  • Big Spenders: Expressed in terms of “Average amount per transaction,” “Average amount per customer for past four years,” “Lifetime total amount,” etc.
  • Recent Customers: Expressed in terms of “Days or weeks since last transaction.”

I am sure most young analysts would want requesters to express these terms like I did using actual variable names, but translating these terms into expressions that machines can understand is indeed their job. Also, even when these terms are agreed upon, exactly how high is high enough to be called the “best”? Top 10 percent? Top 100,000 customers? In terms of what, exactly? Cut-out based on some arbitrary dollar amount, like $10,000 per year? Just dollars, or frequency on top of it, too?

The word “best” may mean multiple things to different people at the same time. Some marketers — who may be running some loyalty program — may only care for the frequency factor, with a hint of customer value as a secondary measure.

But if we dig further, she may express the value of a customer in terms of “Number of points per customer,” instead of just dollar spending. Digging even deeper, we may even have to consider ratios between accumulated points vs. points redeemed over a certain period to define what “best” means. Now we are talking about three-dimensional matrix — spending level, points earned, and points redeemed — just to figure out what the best segment is. And we didn’t even begin to talk about the ideal size of such target segment.

Understanding long- and short-term business goals, and having “blends” of these figures is the most important step in data mining. Again, knowing where to dig is the first step.

Let’s take another example. If we introduce the “continuity” element in all of this — like in telecommunication, subscription or the travel businesses — the word “best” takes yet another different turn. Now we have to think about the longevity of the relationship, in addition to transaction and loyalty elements. For example:

  • Tenure: Expressed in terms of “Years since member signup,” “Months since first transaction,” or “Number of active months since signup”
  • Engagements: “Number of contacts for customer service, trouble-shooting, complaints, or package changes/upgrades”
  • Other Activities: Such as cancelation, delinquent payment, move or reactivation

For the airline business, “best” may mean different things for each flight. Data elements to consider could be:

  • Mileage program status
  • Lifetime mileage/YTD mileage
  • Ticket class/code
  • Ticket price paid for the flight/Discount amount
  • Frequency of the flight (Number of flights in the past 12 months, average days between flights/bookings)
  • Peripheral purchases and paid upgrades

Why do I list all of these tedious details? Because analysts must be ready for any type of business challenges and situations that decision-makers may throw at them.

Another example would be that even in the same credit card company, depending on the division — such as acquisition team and CRM team — the word “best” may mean completely different things. Yes, they all care for “good” customers, but the acquisition team may put more weight on responsiveness, while the CRM team may care for profitability above all else.

Speaking of customer care, “customer experience” can be broken down into multiple variables, again to pose different options to decision-makers. What is the customer experience made of, and what do we need to understand about the whole customer journey? In the age where we collect every click, every word and every view, defining such parameters is very important to get to the answers out fast.

In the sea of data, basically we need to extract the following elements of “experience”:

  • The Subject Matter or Product in Question: Why is the customer contacting us? Start with issue classifications and related product and product category designations. If they are in free form, better get them tagged and categorized. Difficulty level of the issue resolution can be assigned, as well.
  • Number of Actions and Reactions: Expressed in terms of number of contacts/inbound calls per customer, number of outbound calls, chats or services visits per customer.
  • Resolution: In no obscure terms, what was the outcome? Resolved or not resolved? Satisfactory or unsatisfactory? If they are embedded in some call log, better employ text analytics, pronto.
  • How Long Did All of This Take? Expressed in terms of “Minutes between initial contact and resolution,” “Average minutes between actions,” “Average duration of engagements,” etc. Basically, the shorter the better for all of this.

Good customer experience, this way, can be measured more objectively. Reporting required for evaluation of different scenarios can be improved immensely when the building blocks (i.e., variables and metrics) are solid.

Now let’s move onto yet another common question of “what worked — or didn’t work — in various marketing efforts.” Consultants often encounter this type of question, and the biggest hurdle often isn’t the analytics process itself, but messy, disparate, and unstructured data. To understand what worked, well, we must define what that means. First off, what was the desired outcome?

  • Opens and Clicks: Traditional digital analytics metrics
  • Conversion: Now we need to dig into transaction data and attribute them to proper campaigns and channels
  • Renewal: If it is for B-to-B or continuity programs
  • Elevation of Brand Image: Tricky and subjective, so we would need to break down this obscure word, as well.

As for what marketers did to invoke responses from customers or prospects, let’s start breaking down that “what” of the “What worked?” question from that angle. Specifically:

  • Channel: A must-have in the omnichannel world.
  • Source: Where the contact name came from?
  • Selection Criteria: How did you choose the name to contact? By what variable? If advanced analytics were employed, with what segment, what model and what model groups?
  • Campaign Type/Name/Purpose: Such as annual product push, back-to-school sale, Christmas offer, spring clearance, etc.
  • Product: What was the main product featured in the campaign?
  • Offer: What was the hook? Dollar or percentage off? Free shipping? Buy-one-get-one-free? No-payment-until? Discount for a limited period?
  • Creative Elements: Such as content version, types of pictures, font type/size, tag lines, other graphic elements.
  • Drop Day/Time: Daypart of the campaign drop, day of the week, seasonal, etc.
  • Wave: If the campaign involved multiple waves.
  • A/B Testing Elements: A/B testing may have been done in a more controlled environment, but it may be prudent to carry any A/B testing elements on a customer level throughout.

These are, of course, just some of the suggestions. Different businesses may call for vastly different sets of parameters. I tell analysts not to insist on any particular element, but to try to obtain as much clean and dirty data as possible. Nonetheless, I am pointing out that breaking the elements down this way, upfront, is a necessary first step toward answering the “what worked” question.

I have been saying “Big data must get smaller” (refer to “Big Data Must Get Smaller”) for some time now. To do that, we must define the question first. Then we can narrow down the types of data elements that are necessary to (1) define the question in a way that a machine can understand, and (2) derive answers in more comprehensive and consistent ways.

True insights, often, are not a simple summary of findings out of fancy graphical charts. In fact, knowing where to dig next is indeed a valuable insight in itself, like in mining valuable minerals and gems. Understanding where to start the data mining process ultimately determines the quality of all subsequent analytics and insights.

So, when faced with an obscene amount of data and ambiguous questions, start breaking things down to smaller and more tangible elements. Even marketers without analytical training will understand data better that way.

People-Based Marketing: Targeting People, Not Cookies

With the promise of big data being able to provide true people-based marketing, the yearly spend for companies continues to increase. But is your data really as good as you think? Are you truly delivering the right message at the right time to the right person?

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Check out even more about personalization and artificial intelligence with FUSE Enterprise.

In 2016, eMarketer reports approximately $16.2 billion was spent on digital media. With the promise of big data being able to provide true people-based marketing, the yearly spend for companies continues to increase. But is your data really as good as you think? Are you truly delivering the right message at the right time to the right person? The overpromise of big data being able to provide marketers with this solution can create waste — both with money and time. Delivering on people-based marketing, though efficient and lucrative when done well, is turning out to be a lot harder than it sounds.

Cookies Are an Issue

Cookies have a lot of shortcomings when trying to map back to a specific individual. Cookies aren’t persistent. They can be deleted or blocked, and in most cases an individual can have multiple cookies and even have them assigned to the same device. Several advertisers have started to invest in solutions that leverage machine learning and artificial intelligence (AI) to map cookies back to an individual. But in their quest, they have forgotten the whole point of a cookie, which is supposed to be anonymous and stay anonymous. Yes, Google knows which double-click cookie maps to your Gmail account, but they’ll never share that information. That would defeat the whole purpose of why anonymous third-party cookies were created in the first place.image_1

Finding the Solution

Facebook takes credit for coining the term, and they are definitely leading the pack toward people-based marketing. The key to Facebook’s success starts with the individual versus with the cookie — they map a person’s devices and cookies back to the individual. Not the other way around. It’s a simple but important distinction, and it’s key to their success. This particular methodology works great for Facebook. After all, they already have an exhaustive list of individuals. But what about the average marketer? While Facebook will let you leverage some of what they know on their platform for a price, they definitely aren’t sharing that data. The good news is most marketers already have extensive CRM databases of their existing customers. The key is to unlock that data and target those individuals and other look-alike prospects. While it’s simple in principle, it’s challenging to put into practice.

 

image_2Learn even more about the convergence of technology and branded content at the FUSE Enterprise summit. Artificial intelligence and personalization will be featured among many other techniques and technologies.

The KellyAnne Conway School of Customer Service

It’s just a few weeks into a new year and unless you’ve been living in a cave, you’ve been exposed to interviews with White House Counselor KellyAnne Conway. She has masterfully demonstrated how to dodge questions, provide “alternate facts” and generally frustrate the media in their efforts to get to the truth. In a recent interaction with Samsung, I’m convinced that the customer service agent received training from KellyAnne, as I’ve never experienced such a roundabout set of back-and-forth email communications from any major brand — ever!

KellyAnne Conway[Editor’s note: Update — Today, White House officials told CNNMoney that Kellyanne Conway has been sidelined from TV appearances because her comments last week about former National Security Adviser Michael Flynn contradicted those of the White House. On Fox News, she denied being sidelined.]

It’s just a few weeks into a new year and unless you’ve been living in a cave, you’ve been exposed to interviews with White House Counselor KellyAnne Conway. She has masterfully demonstrated how to dodge questions, provide “alternate facts” and generally frustrate the media in their efforts to get to the truth.

In a recent interaction with Samsung, I’m convinced that the customer service agent received training from KellyAnne, as I’ve never experienced such a roundabout set of back-and-forth email communications from any major brand — ever!

Let me start with a little background: I don’t know about you, but I am not happy when it comes time to replace my mobile phone. Just as I get all my settings to work the way I want, and can flick screens, open apps and manipulate my device with minimal effort, the device inevitably starts to fail. First, it started shutting itself down when my power level fell below 50 percent, then it would freeze at the most inopportune moments, and finally, when it refused to hold any charge at all, I cried “Uncle!”

Okay, all you iPhone owners can start snickering now … because I own a Samsung Galaxy (and no, not the kind that self-ignites), and have done so since my Blackberry became a dangerously obsolete option (I still miss that qwerty keyboard!)

I braced myself for that ugly visit to the AT&T store. The one where no one seems to know how to import my contacts, or set up my email; true in keeping with my past experience, I was in the store for a full two hours and left with my old phone, a new phone and a promise to return in 24-hours after I had figured out how to set up my Exchange Server email myself. But that’s a story for another day.

The fun really started after I was upsold a Samsung tablet for $0.99 in the AT&T store. That probably should have been my first clue …

About 24-hours after my purchase, I received an email from Samsung congratulating me on my Tablet purchase and offering me 30 percent off on a tablet cover. Since I planned to carry my Tablet in my bag as a notepad, I figured a cover was a wise purchase decision. I copied the promotional code, and clicked the link.

The landing page presented me with a number of colorful Tablet cover options. I carefully looked at each one, compared the colors, the way they opened/closed, made my purchase selection, pasted the promotional code and checked out.

But when the Tablet cover arrived 10-days later, it was too big for my Tablet!

I immediately went to the Samsung customer service link and advised them of my plight. The customer service agent, Brian, started the conversation just like KellyAnne had taught him. Repeat the key word used in the question, but take your answer in another direction.

Even though I had clearly laid out the details of my transaction, Brian advised me that if my tablet type and the tablet cover purchased “matched” I would be offered a full refund. Since this was my first clue that there was a “tablet type” we all know where this is going … clearly they were not going to match because the cover didn’t fit!

After a very convoluted set of email exchanges, it turns out there are multiple tablet types, and even though Samsung knew what type of tablet I had purchased (it’s all about BIG data!), it never occurred to Samsung marketing people to send me to a landing page that presented tablet covers that would actually fit the device I had purchased. Instead, knowing I might own multiple tablets and want to purchase one for every tablet I owned, they presented me with all their tablet cover options. Never once did they point out “make sure you select a tablet cover that fits YOUR particular tablet type” or “Hey you idiot, there are multiple tablet types. Check your receipt to learn which tablet type you purchased and match it to the tablet cover.”

Call me dumb, but I honestly thought marketing would have linked their email to a landing page with covers that fit my device, and then offered a link to additional covers in case I owned additional devices. Now that would have made for a smooth customer journey.

Brian was not very helpful either. He ignored any facts relating to the email conversation I presented, he was dismissive of any data exchanges between AT&T and Samsung, and his reality was that I made a purchase error … and it was all my problem. Golly gee, KellyAnne trained you very well!

Now I can’t decide if I should pay to return the cover and get a new one, or simply sell the cover on e-Bay or sell the cover and the Tablet and call it a day. If you’re interested in any of these options, email me and I’m sure we can cut a deal that doesn’t involve Russia.

Why Buzzwords Suck

Let’s talk about why buzzwords are bad for the data and analytics business. I don’t entirely deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase.

bees-44507_640In my previous column, “Don’t Hire Data Posers“, I wrote that one of the first signs of a poser is excessive use of buzzwords. This month, let’s talk about why such buzzwords are bad for the data and analytics business — besides the obvious annoyance of overuse.

I don’t deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase. Big data, CRM (in the past), customer 360, personalizationcustomer experiencereal-time modeling or in-database scoring are some examples.

For instance, the term big data acts as an umbrella for many different ideas that not-so-technical people may not be familiar with. But by saying that magic term, we can cut to the chase much faster. Marketers and decision-makers often interpret the term as “all data and analytics activities that enable data-based decision-making processes,” regardless of the actual data sets and processes in question. So data players like me no longer have to take 15 minutes to explain what we do for a living, and data geeks have more succint voices in executive meetings nowadays.

Similarly, creation of a single customer view or a 360degree customer view may include many intricate steps, but who has time to list them all in a planning meeting? Just drop the term customer 360, and people will get the general idea.

But there are definite downsides to these over-simplifications. So, let’s list the harmful effects of abusing buzzwords:

  • Over-simplification in itself is bad already, as it undervalues the efforts. Just because it takes less than a second to say it, doesn’t mean the actual steps are just as quick and easy. Executors still have to sift through painstaking details to get anything done. I’ve seen marketers who actually thought that properly executing personalization would be simple and easy, when the reality of it is that even the very definition of the word deserves a lengthy consideration. Is it about content, delivery, data or analytics? The answer is all of the above, and one must plan for every aspect separately. Calling personalization simple is like saying, “Why don’t we make more movies like ‘Star Wars’ and make tons of money?” Well, can you make that lightsaber look real in someone’s hand?

Machine Learning and AI — What’s ‘Real,’ What’s Required

Big data has gone full-cycle. Quite a while ago, big data had its beginning within the realm of academic research. Recognizing its usefulness, niche businesses then began implementing big data. Massive companies, such as Google, began commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big. This all makes for a lot of noise in the marketplace.

Data graphicBig data has gone full-cycle.

Quite a while ago, big data had its beginning within the realm of academic research. Niche businesses then began implementing big data after recognizing its usefulness. Next, massive companies (like Google) started commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big.

This all makes for a lot of noise in the marketplace.

Today, we hear folks without applied mathematics or computer science backgrounds talking big data, algorithms and artificial intelligence (AI) at cocktail parties. The fluency has grown rather quickly: A CMO I’ve known for years used to wince when we talked analytics, but now she enthusiastically discusses her firm’s AI initiatives. She’s not running marketing at Google or IBM Watson, either — she sells clothing online.

While we’re likely in one of the most amazing periods in history to be in business, it does not come without its challenges. These days, you have to sift through all of the clutter when it comes to innovations in the marketing space.

Let’s see if we can simplify what the data pundits are tweeting and discern where the value really is.

Machine Learning

Machine learning (ML) occurs through networks of algorithms.

First, the good news: ML really works.

As we’ve discussed in “Marketing Machines — Possible or Pipedream?” ML is used to ingest large amounts of data and identify patterns in that data. The machine “learns” by ingesting, transforming and then conditioning a learning algorithm with your dataset.

ML will find the statistical relationships (models) between your various data points to articulate how efficiently your business is running. By calculating the best potential models, it can also show you what improvements you can make. ML can deduce your most profitable business targets. It can tell you who is likely to buy shoes priced over $800, or which production line is most likely to break down in the wintertime.

But ML Isn’t Foolproof

Machine Learning can surely help us find structure and patterns in data through statistics and the power of cloud computing. Amazon’s ML cloud computing capability, for example, isn’t specific to any domain and arguably works with any inputs. It will consistently output a result or target. Yet that very flexibility is where ML can prove risky:

“If you can dump anything into an ML process, and have it come up with an answer, you’d be wise to be wary of that answer.”

ML techniques all require you provide it with a “universe”. This universe consists of all the likely permutations representative of your purpose. If your conditioning data is skewed heavily to sneakers under $75, it will prove very hard to predict what customers are likely to buy $800 shoes.

This may sound like an unfair example, but consider the marketers who are out to break into the higher-end sales but only have data from their pre-existing customers. If skewed interpretations were applied to new-customer marketing (and they can be), your returns could be even worse than without any ML interference. The fact is, there are far more experiments where ML doesn’t produce a valuable outcome than those that do. But as technology and big data are refined over time, better results will be achieved across the board.

Analytics and model-building are highly iterative processes. If an ML process is focused on only a particular niche, the likelihood of getting better results sooner is higher — but still iterative. Despite its current limits, AI offers a deeper and more layered method of applying iterative math to break down large data questions than raw manpower.

Google’s AlphaGo AI beat champion Lee Sedol in a tournament of Go by exponentiating component questions, covering as many bases as it could. While AlphaGo works similarly in many ways to the human mind in this way, it did also have the advantage of iteratively playing against itself thousands of times.

Humans can’t do that.

The Bottom Line: Good Data In, Good Comes Out

Whether Google’s AlphaGo, Amazon’s ML tools or your home-grown mashup, the quality of the data that goes into ML is the largest factor you can control in creating value with systems-driven optimization.

In an age where many organizations have siloed data or cumbersome messes, along with marketing organizations that don’t even have a reliable marketing operations database, this is no small challenge. Getting your data centralized, organized and accessible is a requisite first step. Get that right, and there may be opportunities ahead to drive value up.

What Does Personalization Mean to You?

“Personalization” is the next big thing after “Big Data.” … And that is really too bad for the users of data, technology and analytics. Why? Because many users end up thinking that they are doing a good job at it, while in reality, they are only touching the surface.

We are still living through the aftermath of the Tower of Babel, though the main language of choice in the marketing, data and analytics industry remains English. Outside of the U.S., I speak at conferences and events in Korea, Brazil and the U.K. Even when I presented in Korean — with a PowerPoint presentation consisting entirely of English — I called data “Data,” though the pronunciation is more like “dei-tah” there. Korean business people love to say “Big Data” in English, though the meaning is quite different from what I am accustomed to. They use it with a much broader meaning than we do in America; they literally imply anything and everything related to data activities, small or big, raw or analyzed. Conversely, I have encountered groups of people in America who have a very narrow definition of it, whether it be about literal size, complexity or even specific platforms, such as Hadoop. I am sure each of you has a different notion of the word.

Recently, I participated in a retail conference in London regarding “Personalization.” I was a panelist, and I noticed they spelled the word “personalisation.” I didn’t want to argue about how funny that spelling looked among folks from a country where the English language was literally spawned, but what is the point of having the letter “z” in the alphabet if they are not using it for a clear “z” sound? In any case, they too seemed to be searching for the meaning of the word in marketing, as the very first question to the panel was “What does personalisation mean to you?” Not surprisingly, each panelist provided different answers.

Since then, I have been attending marketing and technology conferences quite diligently this season. While a great many panel discussions, industry tracks and keynote speeches were about personalization, I found that literally everyone meant different things by saying it. Unfortunately, some presenters were as confused as their audiences, and some were downright clueless (more on the subject of useless conference tracks in future articles). Yes, all of that popularity means “Personalization” is the next big thing after “Big Data,” and it truly reached the buzzword status. And that is really too bad for the users of data, technology and analytics.

Why? Because many users end up thinking that they are doing a good job at it, while in reality, they are only touching the surface. Such an attitude leads to investment in the wrong places, while other vital steps could be missed completely. It is not much different from patients in a placebo group thinking that they are taking the real trial drug. It is even worse than that in marketing, as users may have paid a good sum of money to check off that little box called “personalization.” The first blame should be on the service providers who overpromised the effectiveness of the toolset (as in “All your problems will be solved if you buy this!”), but the users must be more educated about it, too.

So, what does personalization mean to you? Allow me to list a few possible answers:

  • Addressing your customers by their first names?
  • Suggesting more of the same products that they just purchased through collaborative filtering?
  • Collecting explicitly expressed preferences and reacting to them?
  • Keeping in touch with your customers all of the time?
  • Customizing emails and landing pages based on customer preference?
  • Knowing when to contact them and through what channel?

I think we can safely agree that calling someone “Dear Jane” in an email isn’t the end of personalization. Suggesting more of the same products? Such practices, joined with “keeping in touch with customers all of the time,” often leads to “personally annoying your customers,” not necessarily personalization (refer to “Personalization Is About the Person”).

I happened to have caught a rather technical presentation (with a title that includes “personalization”) by a reputable provider of a personalization engine, and I was quite impressed with all of the complex and ingenious algorithms they applied to the effort. I am not a mathematician, and I do not mean to criticize those brilliant scientists about their efforts. But I must say that three out of four their steps were about products, not people, though they left a step for behavior-based segments. Presented segmentation methods and variable sets were not by any means at the level of as-good-as-it-gets, but adding behavioral segmentation is a very hopeful move, indeed.

Regardless of the complexity, stringing up related products together, using collaborative filtering, popularity hierarchy and/or clever methods to harness unstructured meta-data are still more about the product, not the consumers. People have an uncanny ability to smell machines, even through remote channels. Personalization definitely requires some human touches (or at least illusions of it), and that come from understanding the target’s current and past behaviors (refer to “Data Atrophy”).

So, what do marketers to do, if even the most advanced kind of personalization engines are still more about products, not people? We need to fill in the gaps with data and analytics. To get there, let’s first break down what personalization is made of:

  • Content
  • Delivery
  • Data
  • Analytics

I am a firm believer that every personalization (or any type of 1:1 messaging) must start with data. But for the purpose of being pragmatic, I reversed the order here.

Simply, if a marketer doesn’t have enough content that matches different types of customer demand and their personas, the effort will be pointless, even with an ample amount of data. Contents — literal and graphic — must be created with potential targets in mind, and they should be properly managed through DAM (Digital Asset Management) systems. We are talking about something far more organized than some memory sticks sitting in a desk drawer in a creative agency. For many marketers, this is “the” personalization effort, as content creation is an age-old marketing function, and effectively managing it is at the heart of digital marketing.

Then, the marketer needs to acquire the ability to show different contents to various types of customers. This is where all of those commercial solutions come into play. If it is about the website, is it modularized, so that various parts of the pages can be customized? If it is about email campaigns, can each email be tailored with different offers and feature products? If it is about offline campaigns, how flexible can versioning be? There are already supermarket chains that customize almost every coupon book with different binding sequences and contents. The ability to deliver customized messages to customers and prospects is a must-have, not an option, for any type of personalization initiative.

Next, are all of these efforts data-driven? What types of data are being used? Just product meta-data and product-level sales data? Or individual behavioral and demographic data? If so, are they just based on snapshot data of the present, or the person’s historical data, as well? Are product-, event- and transaction-level data summarized to an individual level for proper personalization?

That leads to analytics (and this “analytics” has many meanings, too). Are data converted to forms of segments or personas, or are the raw data still being plugged into the engine? The difference in effectiveness is huge, as even machines prefer clean and simple data. Further, even with ample amounts of transaction- or event-level data, we often find lots of huge holes in data when aligned around the person, as there is no way to know everything about everyone all the time. Such gaps should be filled with statistical models, while we often label those with different names, such as segments and personas. (This leaves yet more room for serious misunderstandings.)

Stephen H. Yu: 3-step approach to complete personalization

Illustrated is a three-step approach to personalization, starting with installation of a commercial personalization engine. Then test-run the engine with simple segments, based on available data. After all, reacting to immediate customer needs and displaying different versions of content based on known explicit data is not simple or easy. That would still be more like “personalize contents only sporadically for some people through some channels.” To reach the stage of “personalizing content constantly for everyone through all channels,” event- and product-level data must be realigned around target individuals, and personas must be built to fill in the gaps (refer to “No One Is One-Dimensional”).

Personalization is definitely the most popular buzzword these days, though it means different things to a lot of people. What does not change is that this movement is here to stay in the age of information overload, as marketers must stand out, for their survival, with relevant messages to ever-distracted consumers.

What we simply refer to as “personalization” is made of multiple components, and that is why many of us are confused by it. Therefore, we must aspire to reach a true personal level with our customers through a step-wise approach, not a single giant leap. Let us not make the mistake of calling the mere first few steps the whole thing, when more important data and analytics steps are not even in play yet.