Road to Personalization

The marketing community loves buzzwords. One may say that some words just go viral. In the past, CRM was one. Server-migration (from mainframes) was another. Cloud computing – even among non-IT groups – has some magic power. Big Data has indeed been a big one the past few years, though

The marketing community loves buzzwords. One may say that some words just go viral. In the past, CRM was one. Server-migration (from mainframes) was another. Cloud computing – even among non-IT groups – has some magic power. Big Data has indeed been a big one the past few years, though it surely is losing its coolness, especially among data professionals. But, in some countries and communities, it is still gaining momentum. The latest one, I think, is “Personalization.”

Do you know how I get to find out how some words are becoming popular? The fastest way is to attend a conference and check out which session keywords are filling up the rooms. Attendance, like in the movie industry, is a sure way to measure the power of the keyword. We often see that some speeches and articles are not even remotely related to the word in question, but that doesn’t seem matter much. Everyone and their cousins start selling the word like it’s a magic potion that cures all. If you happen to come across a password to a goldmine, won’t you try it, too?

Once the word starts go viral, the power of magic starts to influence the real-life decision-making processes. Yes, I’ve been using every chance to debunk the mystery around Big Data, through this series and other opportunities. But I have to admit that those two words originally strung together for marketing purposes by software companies opened so many new doors to meetings and speaking engagements to which data geeks never dreamed of having access a mere four to five years ago. If you ask me what the best outcome of the Big Data movement is, my answer is that decision-makers, in general, became aware of the importance of analytics based on collected data. Analysts no longer have to spend a long time in meetings to justify the usage of data and analytics; we can simply dive right into the subject now.

Nevertheless, I still have a strong allergic reaction to buzzwords, like I do to syrupy pop songs of which I tire easily. The main reason, other than I just get sick of hearing them, is because buzzwords lead to kingdom-come-level promises. Overpromises lead to overinvestments, which lead to equally big disappointments (try showing decent ROI on overinvestments), which inevitably lead to finger-pointing and blame-games. That is why I, over time, tried to isolate the beneficial elements of Big Data, and attempted to put different spins and labels on it (refer to “Big Data Must Get Smaller” and “Smart Data, not Big Data”). After all, I am a believer in data and analytics for real-life (i.e., not theoretical) applications, and I want decision-makers and marketers to succeed. I want to find ways to make money with data, whatever you name that activity.

Now I see that the word “Personalization” is becoming the hot topic in conference circuits and the blogosphere. More and more, that word is uttered even during the first encounter with a potential client. Signs are everywhere that it is about to be “the” buzzword in the marketing community.

And I welcome it. Through this series, I have been repeating that the key goals of analytical activities for marketers, regardless of employed channels, should be:

  • Knowing whom to contact, and
  • Knowing what to offer through what channel, if a customer or prospect is indeed to be contacted.

An amazing amount of data that became available to marketers led to over-communication to an “everyone, all the time” level, and the response rate of any marketing endeavor cannot be sustained that way. Out of desperation, some marketers actually “increase” the contact frequency to maintain the revenue level, and some already have reached a “six times a week, per target” level. What are they going to do after reaching seven times per week? What then? Invent a new day, like Ringo Starr blurted out with “8 days a week”? Spamming more surely isn’t the way out.

Some of my colleagues ask me if we should just take a leap of faith that personalization is the key to the future of marketing, as there aren’t many – there are only few – good success stories about it yet. My answer is to look at all these marketing messages from the consumer’s point of view. Aren’t you completely sick of this barrage of irrelevant pushes, even from so-called reputable retailers? Wouldn’t you pay more attention to something that is more relevant to you, that resonates with you over countless inept and, at times, completely annoying messages? When we show a group photo to anyone, most people check themselves in the picture first. How do “I” look in it? Let’s face it, everyone cares about themselves first, and we are conditioned to pick out anything about us through all kinds of noises.

That is why I believe that this personalization is the future of marketing. In the age of information overload, it is the customers who are picking and choosing messages that are relevant to them, not the other way around. Everyone is exposed to at least five to six types of screens every day. And with new inventions, the noise level will certainly increase. We are no longer living in the world where marketers can just push the products and services according to their priorities. Instead, consumers are ranking products and services. Traditional “push”-type endeavors still have their place in marketing. But in the future, “every” channel will be a 1-to-1 medium, and the consumers will be in full control, choosing what they want to see and mercilessly ignoring irrelevant messages. Marketers must try their best to comply to that demand and show consumers what they may like to see, using all available data and statistical techniques. And the marketers do that right will move ahead. But only if they do it right (refer to “Personalization is about the Person”).

The road to proper personalization is a long and winding one. It starts with the data, of course, as we need to decide “who gets what message” based on them. Various technologies must be employed to display different versions of messages through multiple channels individually, still maintaining consistency. Multiple versions of copies should be written and new stack of creatives must be prepared. Collected data should be refined to be used in such personalization engines, as raw data can only do so much, even with very expensive toolsets. If required data are not explicit enough, or worse, not available at all, we will need to calculate the propensity of certain desired behaviors or consumer characteristics – as in, “not sure if the target is a health-conscious young parent for certain, but he surely looks like one.” As I stated in my previous columns, explicit data are hard to come by, even in the age of Big Data, and we all must make the most of what we get to have. No customer will wait until you have the perfect set of data.

Like in any field, may it be a musical field or martial arts, there are virtuosos (or “virtuosi”?) and grand-masters, then there are mediocre talents and complete novices. In data and analytics such levels exist, as well. Not all analysts or data scientists are on the same level, though I often argue that an unexceptional statistical model is still better than someone’s gut feeling. For end-to-end marketing executions, things get more complicated, as many different types of technologies and skills, as well as overall vision, must work harmoniously to achieve goals. Unfortunately, I often see marketers who still don’t believe in the effectiveness of advanced analytics because they “think” that they had a bad experience with it. But is it fair to dismiss time-tested methods, when many other factors could have gone wrong?

In the interest of not killing the idea of “Personalization” due to unfavorable results from rudimentary trials, allow me to share the “10 stages of personalization efforts” from a data, analytics and technology point of view (i.e., marketing creative is not considered here):

  • Not even considering personalization yet. They still think that spraying the same HTML to everyone is alright, as long as the process runs smoothly.
  • Personalization is considered, but they do not know where to start.
  • Identified basic steps toward personalization, but they do not have specific data or a technology roadmap.
  • Created the data roadmap, but they did not start thorough data inventory.
  • Identified required data sources, but datasets are not cleaned up or consolidated for 360-degree view of customers (a must-have in personalization).
  • Datasets are ready for personalization, but only with “known” (or explicit) data; statistical modeling to fill in the gaps is not considered yet.
  • Tested personalization engines through major marketing channels of choice, employing collected “known” (or explicit) data.
  • Creating “personas” (or implicit data) using statistical techniques with available data, filling in the gaps with statistical models (an ongoing effort).
  • Personalizing most messages and offers through every touchpoint, employing explicit data (known data) and implicit/inferred data (in forms of personas).
  • Collecting and utilizing results data to enhance model-based personas and personalization engines continuously, leading to automation.

So, at what stage is your organization? Are supporting datasets previously locked in channel silos merged together to form a customer-centric view? Or are you just plugging transaction or event-level data into some personalization software with a fancy name and a high price tag? Are you personalizing only sometimes through some channels to some people who happened to volunteer – explicitly or implicitly – some of their information to you, or are you doing it for most people, most times, through most channels? The differences are huge. Unfortunately, too many marketers are just personally annoying customers in the name of personalization, and most don’t even do that consistently.

I understand that not all marketing organizations have to achieve ninth-degree black belts in personalization, as from company to company, business models, channel usage, success metrics, budget limitations and available data are undeniably different. Nevertheless, I dare to say that personalization will be more important for the survival of most businesses, as companies that are better at it are visibly leaping ahead. Look at the ways that some big name retailers are doing it from a consumer’s perspective; they are clearly not operating under the old paradigm of “marketers push, consumers respond.” Even when committed to the concept, before any organization gets into the thick of things, decision-makers must set the data and technology roadmap first. The order of operation is important here, and it would be easier to prove the worthiness of the endeavor in baby steps, too. Dismissing the whole idea after trying a few rudimentary steps out of order would be a real shame.

Like any guru would say, awareness is the first step toward improvement. Understanding how far one must go is at the core of any learning process. Isn’t that what Master Yoda tried to teach a young Jedi named Luke Skywalker on that swampy planet of Dagobah?

Perspectives Matter in Analytics

When we observe a certain phenomenon, we should never do so from just one angle. We’ve all heard the fable about blind men and an elephant, where each touched just one part of the animal and exclaimed, “Hey, this creature must be like a snake!” and “No, it feels like a thick column!” or “I’m sure it is like a big wall!” We certainly don’t want to fall into that trap.

When we observe a certain phenomenon, we should never do so from just one angle. We’ve all heard the fable about blind men and an elephant, where each touched just one part of the animal and exclaimed, “Hey, this creature must be like a snake!” and “No, it feels like a thick column!” or “I’m sure it is like a big wall!” We certainly don’t want to fall into that trap.

In the world of marketing, however, so many jump to conclusions with limited information from one perspective. Further, some even fool themselves into thinking that they made scientific conclusions because they employed data mining techniques. Unfortunately, just quoting numbers does not automatically make anyone more analytical, as numbers live within contexts. With all these easy-to-use visualization tools, it’s equally easy to misrepresent the figures, as well.

When we try to predict the future – even the near future – things get even more complicated. It is hard enough to master the mathematical part of predictive analytics, but it gets harder when the data sources are seriously limited; or worse, skewed. When the data sources are contaminated with external factors other than consumer behavior, we may end up predicting the outcome based on the marketer’s action, not on consumer behaviors.

That is why procuring and employing multiple sources of data are so important in predictive analytics. Even when the mission is to just observe what is happening in the world, having multiple perspectives is essential. Simply, who would mind the bird’s-eye view when reporting a high-speed car chase on TV news? It certainly enhances the picture. On the other hand, you would not feel the urgency on the ground without the camera installed on a police car.

I frequently drive from New Jersey to New York City during rush hour. (I have my reasons.) I have been tracking the number of minutes in driving time between every major turn. Not that it helps much in reducing overall commuting time, as there isn’t much I can do when sitting helplessly on a bridge. But I can predict the arrival time with reasonable accuracy. Now armed with smartphone apps that collect such data from everyone with the same applications (crowd sourcing at its best), we can predict ETA to any destination with a margin of error narrower than a minute. That is great when I’m sitting in the car already. But do such analytics help me make decisions about whether I should have been in the car in the first place that morning? While it is great to have a navigator that tells me every turn that I should make, do all that data tell me if going to the city on the first day of school in September is the right decision? Hardly. I need a different perspective for that type of decision.

Every type of data and analytics has its place, and none are almighty. Marketers literally track every breath you take and every move you make when it comes to online activities. So-called “analytical solution providers” are making fortunes collecting data and analyzing them. Clickstream data are the major reasons why data got so big; and, thanks to them, we started using the term “Big Data.” It is very difficult to navigate through this complex world, so marketers spend a great amount of time and resources to figure out where they stand. Weekly reports that come out of such data are easily hundreds of pages (figuratively), and before marketers get to understand all those figures, a new set of reports lands on their laps (again, metaphorically). It is like having to look at the dashboard of a car without a break when driving it at full speed. Such a cycle continues, and the analysts get into a perpetual motion of pumping out reports.

I am not discounting the value of such reporting at all. When a rocket ship is being launched, literally hundreds of people look at their screens all simultaneously just to see how the process is going. However, if the rocket ship is in trouble, there isn’t much one can do by looking at the numbers other than, “Uh-oh, based on these figures, we have a serious engine problem right now.” And such reporting certainly does not tell anyone whether one should have launched the vehicle at that particular moment in time with that pre-set destination. Such analytics are completely different from analyzing every turn when moving at a full speed.

Marketers get lost because they look at the given sets of numbers looking for answers, while the metrics and reports are designed for some other purpose. At times, we need to change the perspective completely. For instance, looking at every click will not provide accurate sales projections on a personal or product level. Once in a while it may be correct, but such predictions can easily be thrown off with a slight jolt in the system. It gets worse when there is no direct correlation between clicks and conversions; as such things are heavily dependent upon business models and the site design (i.e., actions of marketers, not buyers).

As I emphasized numerous times in this series, analytical questions must be formed based on business questions, not the other way around. But too often, marketers seek to find answers to their questions within the limited data and reports they get to see. It is not impossible to gauge the speed of your vehicle based on the shape of the fur of your dog who is sticking his head out the window, but I wouldn’t recommend using that method when the goal is to estimate time of arrival with a margin of error of less than a minute.

Not all analytics are the same, and different types of analytical objectives call for different types of data, big and small. To understand your surroundings, yes, you need some serious business intelligence with carefully designed dashboards, real-time or otherwise. To predict the future outcome, or to fill in the blanks (as there are lots of unknown factors, even in the age of Big Data), we must change the perspective and harness different sets of data. To determine the overall destination, we need yet another types of analytics at a macro-level.

In the world of predictive analytics, predicting price elasticity, market trends or specific consumer behaviors all call for different types of data, techniques and specialists. Just within the realm of predicting consumer behavior, there lie different levels of difficulties. At the risk of sounding too simplistic, I would say predicting “who” is relatively easier than predicting “what product.” Predicting “when” is harder than those two things combined, as you may be able to predict “who” would be in the market for a “luxury vacation” with some confidence, but predicting “when” that person would actually purchase cruise ship tickets requires a different type of data, which is really hard to obtain with any consistency. The hardest one is predicting “why” people behave one way or the other. Let’s just say marketers need to listen to anyone who claims that they can do that with a grain of salt. We may need to get into a deep discussion regarding “causality” and “correlation” at that point.

Even that relatively simple “who” part of prediction calls for some debate, with all kinds of data being pumped out every second. Some marketers employ data and toolsets based on availability and price alone, but let us step back for a second and look at it from a different perspective.

Hypothetically speaking, let’s assume we as marketers get to choose one superpower to predict who is more likely to buy your product at a mall, so that you can address your prospects properly (i.e., by delivering personalized messages properly). Your choices are:

  • You get to install a camera on everyone’s shoulder at the entrance of the mall
  • You get to have everyone’s past transaction history on an SKU level (who, when, for how much and for what product)

The choice behind Door No. 1 offers what we generally call clickstream data, which falls into the realm of Big Data. It will record literally every move that everyone makes with a time stamp. The second choice is good old transaction data on a product level, and you may call it small data; though in this day and age, there is nothing so small about it. It is just relatively smaller in size in comparison to No. 1. Now, if your goal is to design the mall to optimize traffic patterns for sales, you surely need to pick No. 1. If your goal were to predict who is more likely to buy your product, I would definitely go with No. 2. Yes, some lady may be looking at shoes very frequently, but will she really make a purchase in that category? What does her personal transaction history say?

In reality, we may have to work just with No. 1, but if I had a choice in this hypothetical situation, I would opt for transaction data any time. In my co-op data business days, I looked through about 50 model documents per day for more than six years, and I have seen the predictive power of transaction data firsthand. If you can achieve accurate answers with smaller sets of data, why would you pick any reroute?

Of course in real life, I would like to have both. Because more varieties of data – not just these choices, but also demographic, geo-demographic, sentiment and attitudinal data, as well – will help you zoom into the target with greater accuracy, consistency and efficiency. In this example, if the potential customer is new to the mall, or has been dormant for a long time, you may have to work with just cameras-on-shoulders data. But such a judgment should be made during the course of analytics, and should not be predetermined by marketers or IT folks before the analysis begins.

Not all datasets are created equal, and we need all kinds of data. Each set of data comes with tons of holes in it, and we need to fill such gaps with data from other sources, from different angles. Too often, marketers get too deep into the rabbit hole simply because they have been digging it for a long time. But once in a while, we all need to stick our heads out of the hole and have a different perspective.

Digging a hole to a wrong direction will not make anyone richer, and you will never see the end of it while you’re in it.

Tracking Mobile Ad Effectiveness With Real-Time Data

The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

When analyzed effectively, this data can provide business insight on user sentiment, behavior and even physical movement patterns. Due to the sheer number of mobile devices in use, Big Data practitioners can tap mobile Big Data analytics to better understand trends across vast populations and sub-segments of users. This understanding helps business to improve engagement tactics and to optimize the delivery of services.

Mobile device data becomes particularly useful for analytics purposes when combined with extended data from outside sources. For example, weather and economic allow practitioners to correlate macro-level trends to a targeted set of users. These consumer segments have traditionally grouped users together based upon similarities. However, industry is increasingly focusing upon targeting towards individuals based upon their interests or upon their past behaviors.

Below you will find a number of ways you can apply real-time data analytics to your mobile marketing and advertising campaigns.

  • More Personalized and Targeted Ads
    Big data allows brands to better target users with more personalized interactions that drive engagement. We increasingly see ads featuring products and services we might actually want to use to make our lives better. These more personalized, more targeted ads are all based on massive amounts of personal data we constantly provide. Everywhere we go nowadays, we leave data crumbs. Following these trails reveals information about what we we’re doing, saying, liking, or sharing. Thanks to our mobile devices, this data trail now also hints at where we’re going.
  • Hyper-Localized Advertising
    The proliferation of mobile devices, primarily smartphones, has created a major opportunity for marketers to deliver contextual advertisements. These mobile-specific ads target the right people at the right time. For instance, through the combination of social data and location data, stores that shoppers are near and might be interested in can send out ads offering percentage discounts or other incentives. Delivered by shops to their shoppers in real time, these ads get consumers to walk through their doors. Hyper-localized advertising has been shown to increase customer engagement and conversion rates.
  • Leveraging Attribution to Achieve Mobile Engagement
    Leveraging Big Data about user behavior helps brands more accurately and completely quantify the effectiveness of their mobile marketing initiatives. Big data helps marketers determine whether their campaigns are creating the desired results. The ways users respond to branding assets can be used to literally create “rules of engagement” for each user or for each type of user. Marketers optimize their results through understanding varying levels of consumer engagement and through understanding the contributions of different campaigns across the path-to-purchase.
  • Real-Time Data Analytics Across the Complete Mobile Lifecycle
    In the past, conventional database solutions could be relied upon to effectively manage and analyze massively large data sets. But they did so at a snail-like pace, taking days and even weeks to perform tasks that often yielded “stale” results. By contrast, the big data analytics platforms of today can perform sophisticated processes at lightening-fast speeds, allowing for real-time analysis and insights. Shorter time to insight allows marketers to make real-time decisions and take immediate action based on fresh, reliable and relevant information.
  • Flip Traditional Consumer Profiling Upside-Down
    In the context of ubiquitous, real-time consumer data brands can now ask the data who their customers are. Contrast this to the erudite consumer profiling where consumers are targeted towards based upon their goodness of fit into an expected consumer picture. Rather than relying upon arcane consumer characteristics, instead we can now quantitatively choose targeting characteristics based upon the congruence of these characteristics with the desired call-to-action.

Brands are in desperate need for solutions that will help them understand the impact of their mobile advertising spend in the grand scheme of their broader marketing plan. This requires brands to go well beyond the click-through to be able to attribute their spend to in-store visits and better yet, sales.