The Decline of Sears Is a Story About Narrow-Minded Analytics

I am a data-driven marketer, but I also talk about the dangers of using analytics for narrow-minded goals at the expense of long-term advantages. The story of Sears and its eventual bankruptcy is very illustrative of what I mean about narrow-minded analytics — used for short-term gains at the expense of longer-term goals.

I am a data-driven marketer, but I also talk about the dangers of using analytics for narrow-minded goals at the expense of long-term advantages. The story of Sears and its eventual bankruptcy is very illustrative what I mean about narrow-minded analytics — used for short-term gains at the expense of longer-term goals.

I know, because early in my career, I had spent several years at Sears. More importantly, I was there when Sears was bought out by Kmart holdings.

In 2004, Sears was already in decline. But it was still a force to be reckoned with. Despite the fact it had struggled to improve its soft lines (apparel, textiles, etc.) performance, it was still the go-to retailer for hard-line goods, such as appliances and tools. Management was also trying new formats and new product lines to rejuvenate the Sears brand.

Then the announcement came. Sears will be bought out by Kmart Holdings and ESL investments, run under the leadership of Eddie Lampert. The feeling among Sears employees was immediate demoralization. It was as if an old but proud ship was under attack by a ghost pirate ship under the flag of a cursed and dead brand.

Sensing the fear, senior management began preaching the benefits of a more efficient, data-driven management mindset that ESL investments would bring. Along with more resources, the data-driven culture would reward “smart risk-taking.” By better leveraging data, Sears would climb out of its slow descent to once again become a dominant leader in retail.

In this spirit, I became involved in an aftermarket pricing project, where we leveraged pricing and sales data to determine the optimal price of thousands of parts used in the repair and maintenance of hard-line goods. The project netted over $10 million in the first year alone, and the team was recognized with the “making money” award (Yes, that was the name of the award). As more price optimization projects came online, tens of millions of dollars in bottom-line revenue were being realized quarterly.

While the pricing initiatives were a brilliant use of analytics, senior leadership didn’t take advantage of the analytical talent to address the issue of the declining top line. Where was the data-driven strategy for top-line growth? Were we simply collecting cash for the big transformation? Was something already in the works? As we tweaked and re-tweaked algorithms to squeeze more profits, the brand atrophied. Long story short, you have what Sears is today.

However, this story is not an indictment of the transformational powers of data-driven thinking. Rather, as I have written in previous articles, such as here and here, this is an indictment of management’s ability to exercise visionary, data-driven thinking. Analytics is a powerful tool, but it doesn’t replace courage and visionary thinking.

Sears was so busy picking up loose change off the floor, it forgot to look up at the bus barreling toward it.

With analytics, this is easy to do, because it is exceptionally good at optimizing for your current environment. Changing the rules, however, requires the blend of analytics and courage.

Some argue that Eddie Lampert and ESL investments always planned to juice and kill the Sears brand. Eddie Lampert has denied this from the beginning. I believe him, because there was a time when Sears’ coterie of store brands (such as Kenmore and Craftsman) still carried immense market value. That was the time to begin stripping Sears.

This is simply a story where the potential and power of data-driven thinking was advertised as an opportunity for transformational change, but was frittered away picking up loose change.

3 Session Highlights for the 2018 FUSE Digital Marketing Summit: AI, Analytics, & How to Size Up Your MarTech Stack

The FUSE Digital Marketing Summit is quickly approaching. Subscribers to the FUSE Digital Marketing Newsletter should already have a sense of what we’ll be covering at the summit, but I just wanted to take a minute to highlight three key sessions that alone warrant marketers spending time attending the summit.

First, a little quick background on the summit:

Where & When: The FUSE Digital Marketing Summit will take place November 27 to 28 in Center City Philadelphia.

Why: With marketers constantly vetting, evaluating, and investing in new technology the two-day FUSE summit is designed to help marketers quickly identify and adopt the most relevant digital technologies. FUSE will dissect the modern martech stack and explore in-depth how the right technologies can enable marketers to achieve real business objectives.

Plus: FUSE Digital Marketing is a free, all-inclusive experience for qualified attendees — senior-level decision makers leading martech strategy and buying decisions. See if you qualify and learn more about the summit here.

Below are three general sessions attendees can look forward to. However, it’s worth noting the unique format of the FUSE summit – attendees will also participate in small-group boardroom case studies and have pre-scheduled 1-on-1 meetings with tech providers. And perhaps most valuable of all are the many networking opportunities with like-minded marketing executives.

3 Key Sessions at the 2018 FUSE Digital Marketing Summit

Keynote: Using AI & Deep Learning to Generate Marketing Results

In this eye-opening session, marketing AI practitioner and BrainTrust Insights co-founder Christopher Penn will explore how artificial intelligence and machine learning are changing marketing. Penn will cover what AI is – and isn’t – and what problems it’s good at solving versus the problems AI solves poorly. This session will use real-life marketing applications to illustrate how AI can elevate content marketing, lead targeting, conversion analysis, and business intelligence. And Penn will share his insight on what marketers need to do to prepare for an AI future.

Speaker: Christopher Penn, Co-Founder & Chief Innovator, BrainTrust Insights

How Do You Stack Up? Practical Advice for Constructing & Managing Your Marketing Tech Stack

In every industry, marketing technology stacks are growing in size and complexity as more products are deployed and integrated, and multiple teams throughout the organization embrace marketing technology in support of digital transformation initiatives. It’s not unusual to see companies using more than 100 different marketing tools at any one time. With a need to integrate many of those tools, building and managing the marketing technology stack has become a tremendous challenge for many organizations.

Leveraging the insights gleaned from hundreds of marketing technology stacks, this session will cover the technologies that companies are currently buying, and the hot technologies that they are looking to integrate into their stack.

Speaker: Anita Brearton, Founder & CEO, CabinetM

How the American Medical Association is Using Analytics to Grow Membership

Content marketing, digital marketing, and consumer marketing have converged to transform how organizations can interact with customers. As a digital change-agent for the past 20 years, Todd Unger, CXO of the American Medical Association, will show how he is transforming AMA’s marketing, using analytics tools to generate insights, quantify content marketing ROI and boost member acquisition and retention efforts.

Speaker: Todd Unger, Chief Experience Officer/SVP Physician Engagement, American Medical Association

Check out the full summit agenda here.

Personas, Be Gone: 1:1 Marketing Revisited

Soccer moms, coffee house professionals, gears-and-gadget guys — in the world of data marketing, the audience personas available to select from enterprising data vendors go on and on and on. Tailoring and targeting based on personas — with hundreds of variables and data elements — dominate the business rules that direct billions in media spending and gazillions of business rules built inside customer journey mapping.

Millennials are not the only ones who eschew labels.

Soccer moms, coffee house professionals, gears-and-gadget guys — in the world of data marketing, the audience personas available to select from enterprising data vendors go on and on and on. Tailoring and targeting based on personas — with hundreds of variables and data elements — dominate the business rules that direct billions in media spending and gazillions of business rules built inside customer journey mapping. Practically every retailer, every brand, has a best customer look-alike model — and segments to that model.

But ask most consumers — they say they don’t want it that way.

An international survey released last week by Selligent Marketing Cloud, reported by Marketing Charts, says that 77 percent of U.S. consumers want to be marketed to as individuals, rather than as part of a larger segment.

Credit: MarketingCharts.com

The take-away seems to be that personalization at a 1:1 level should be any brand’s consumer engagement mantra. Throw out those data segments to which you may think I, the consumer, belong. “Pay attention to what I’m doing!”

That Darn Privacy Paradox … Again

Yet there’s a paradox here. “Paying attention to what I’m doing” raises the creep factor. The same survey shows that nearly eight in 10 consumers have at least some concerns about having their digital behaviors tracked, findings that seem to echo greater societal concerns about technology and business, with real branding impact.

Part of the addressable media conundrum comes down to intimacy. My mailbox is outside my door. I have no issues with personalization there, and I expect it. But pop “into” my laptop and now you’re getting closer to how I spend my days and nights — moving between work, play and life. That gets even more pronounced on the most intimate media of all, my smartphone. (I suppose a VR headpiece might be the “what’s-next” level of intimacy — or an embedded chip in my forehead.)

Conflicted as a marketer? Which path does my brand follow?

Revisiting Moments of Truth

One might argue that going from mass marketing to 1:1 marketing is an easier step than going from database marketing to 1:1. I’m reminded of Procter & Gamble’s moments of truth, freshly updated. A brand doesn’t need to know everything I do all day long in order to recognize the critical moments when purchase consideration comes into play. Less in-your-face, more in-the-right moment.

“Delighted, table for one.”

Whether database or 1:1 (or some combination of both), I cannot think of a smarter marketing scenario — one that engages the consumer — that does not depend on data, analysis, insight and action. Even the beefs that consumers have with marketing — remarketing when the product is already bought, not being recognized from one screen to another, for example — are cured by more data (transaction data, graph data, respectively here), not less, and such data being applied in a meaningful way.

“I’ll order the sausage, please. It’s delicious.” (Just don’t tell me how it’s made.)

In this age of transparency, we can no longer hide behind veils of ad tech and algorithms. We must explain what we’re doing with data in plain English. Based on the Selligent Marketing Cloud survey, for most consumers, it seems the path is to tell exactly how data are collected and to serve each as individuals. And we need to be smarter when, where and how ads are deployed even ad professionals are blocking ads today.

As for vital audience data, maybe we should re-think how we explain segmentation to consumers — less about finding “lookalikes” and more about serving “you,” the individual.

Replacing Unskilled Data Marketers With AI

People react to words like “machine learning” or “artificial intelligence” very differently, depending on their interests and levels of understanding of technology. Some get scared, and among them are smart people like Elon Musk or the late Stephen Hawking. Others, including data marketers who lack strategic skills, may react based on a vague fear of becoming irrelevant, thinking that a machine will replace them in the job market soon.

People react to words like “machine learning” or “artificial intelligence” very differently, depending on their interests and levels of understanding of technology. Some get scared, and among them are smart people like Elon Musk or the late Stephen Hawking. Others, including data marketers who lack strategic skills, may react based on a vague fear of becoming irrelevant, thinking that a machine will replace them in the job market soon.

On the contrary, I find that most marketers welcome terms like machine learning. Many think that, in the near future, computers will automatically perform all the number-crunching and just tell them what to do. In marketing environments where “Do more with less” is the norm, the idea of machines making decisions for them may sound attractive to many marketers. How great it would be if some super-duper-computer would do all of the hard work for us? The trouble is that the folks who think like that will be the first ones to be replaced by the machines.

Modern marketing is closely tied into the world of data and analytics (the operative word being “modern,” as there are plenty of marketers still going with their gut feelings). There are countless types of data and analytics applications influencing operations management, R&D or even training programs for world-class athletes, but most of the funding for analytical activities is indeed related to marketing. I’d go even further and claim that most of data-related work is profit-driven; either to make more money for organizations or to cut costs in running businesses. In other words, without the bottom-line profit, why bother with any of this geeky stuff?

Yet, many marketers aren’t interested in analytics and some even have fears of lots of numbers being thrown at them. A set of numbers that would excite analytical minds would scare off many marketers. For the record, I blame such an attitude on school systems and jock cultures that have been devaluing the importance of mathematics. It is no accident that most “nerdy” analysts nowadays are from foreign places, where people who are really good at math are not ridiculed among other teenage students but praised or even worshiped.

The joke is that those geeky analysts will be replaced by machines first, as any semi-complex analytical work is delegated to them already. Or will they?

I find it ironic that marketers who have a strong aversion to words like “advanced analytics” or “modeling” would freely embrace machine learning or AI. Because that is like saying you don’t like music, unless it is played by machines. What do they think machine learning is? Some “thinking-slave” that will do all of the work without complaint or asking too many questions?

Machine learning is one of many ways of modeling, whether it is for prediction or pattern recognition. It just became more attractive to the business community as computing power increased over time to accommodate heavy iterations of calculations, and because words like neural net models were replaced by easier sounding “machine learning.”

To wield such machines, nonetheless, one must possess “some” idea about how they work and what they require. Otherwise, it would be like a musically illiterate person trying to produce a piece of music all automatically. Yes, I’ve heard that now there are algorithms that can compose music or write novels on their own, but I would argue that such formulaic music will be a filler in a hotel elevator, at best. If emotionally moving another human being is the goal, one can’t eliminate all human factors out of the equation.

Machines are to automate things that humans already know how to do. And it takes ample amounts of “man-hours” to train the machine, even for the relatively simple task of telling the difference between dogs and cats in pictures. And some other human would have decided that such a task would be meaningful for other humans. Of course, once the machines are set up to learn on their own, a huge momentum will kick in and millions of pictures will be sorted out automatically.

And as such evolution goes on, a whole lot of people may lose their jobs. But not the ones who know how to set the machines up and give them purposes for such work.

Let’s Take a Breath Here

Dialing back to something much simpler: Operations. In automating reports and creating custom messages for target audiences, the goals must be set by stakeholders and machines must be tweaked for such purposes at the beginning. Someday soon, AI will reach the level where it can operate with very general guidelines; but at least for now, requesters must provide logical instructions.

Let’s say a set of reports come out of the computer for the use of marketing analysis. “What reports to show”-type decisions are still being made by humans, but producing useful intelligence in an automated fashion isn’t a difficult task these days. Then what? The users still have to make sense out of all of those reports. Then they must decide what to do about the findings.

There are folks who hope that machine will tell them exactly what to do out of such intel. The first part may come close to their expectation sometime soon, if not already for some. Producing tidbits like “Hi, human: It looks like over 80% of your customers who shopped last year never came back,” or “The top 10% of your customers, in terms of lifetime spending level, account for over 70% your yearly revenue, but about half of them show days between transactions far longer than a year.” By the way, mimicking human speech isn’t easy, but if all these numbers are sitting somewhere in the computer, yes, it is possible to expect something like this out of machines.

The hard part for the machines would be picking five to six of the most important tidbits out of hundreds, if not thousands of other “facts,” as that requires understanding of business goals. But we can fake even that type of decision-making by assuming most businesses are about “increasing revenue by acquiring new valuable customers, and retaining them for as long as possible.”

Then the really hard part would be deciding what to do about it. What should you do to make your valuable customers come back? Answering that type of question requires not only an analytical mindset, but a deep understanding in human psychology and business acumen. Analytics consultants are generally multi-dimensional thinkers, and the one-trick ponies who just spit out formulaic answers do not last too long. The same rule would apply to machines, and we may call those one-dimensional machines “posers” too (refer to “Don’t Hire Data Posers”).

But let’s say that by entering thousands business cases with final solutions and results as a training set into machines, we finally get to have such machine intelligence. Would we be free from having to “think” even a bit?

The short answer is that, like I said in the beginning, such folks who don’t want to analyze anything will become irrelevant even sooner. Why would we need illogical people when the machines are much cheaper and smarter? Besides, even future computers shown in science fiction movies will require “logical” inquiries to function properly. “Asking the right question” will remain a human function, even in a faraway future. And the logical mindset is a result of mathematical training with some aptitude for it, much like musical abilities.

The word “illiterate” used to mean folks who didn’t know how to read and write. In the age of machines, “logic” is the new language. So, dear humans, do not give up on math, if self-preservation is an instinct that you possess. I am not asking everyone to get a degree in mathematics, but I am insisting that we all must learn about ways of scientific approaches to problem-solving and logical methods of defining inquiries. In the future, people who can wield machines will be in secure places — whether they are coders or not — while new breeds of logically illiterate people will be replaced by the machines, one-by-one.

So, before you freely invite advanced thinking machines into your marketing operations, think carefully if you are either the one who gives purpose to such machines (by understanding what’s at stake, and what those numbers all mean), or one who can train machines to solve those pre-defined (by humans) problems.

I am not talking about some doomsday scenario of machines killing people to take over the world; but like any historical events that are described as “revolutions,” this machine revolution will have real impact on our lives. And like anything, it will be good for some, and bad for others. I am saying that data illiterates who would say things like, “I don’t understand what all those numbers mean,” may be ignored by machines — just like they are by smartass analysts. (But maybe without the annoying attitudes.)

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.

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.

By Association: Brands, Data and Marketing Finally Have Come Together

Call it marketing data’s destiny. On July 1, if membership approves, the Data and Marketing Association (DMA) will be owned and operated by the Association of National Advertisers (ANA). Perhaps a merger more than 100 years in the making.

Call it “marketing data’s destiny.”

On July 1, if membership approves, the Data and Marketing Association (DMA) will be owned and operated by the Association of National Advertisers (ANA).

The former first began in 1917 — the latter in 1910. Perhaps this moment is destiny 100-plus years in the making.

In 1915, William Wrigley sent chewing gum to every household listed in every phone book in America — more than 1 million at the time. That was “direct marketing.”

What David Ogilvy Knew, We All Must Know Now

One of the greatest advertising practitioners of all time – David Ogilvy – knew that “direct response” advertisers — no matter what the medium — knew which ads worked, and which didn’t, because of their discipline to measure. Direct marketing was Ogilvy’s “secret weapon.”

Google did not invent analytics — direct marketers were always data-driven, and have been testing and analyzing and measuring every piece of advertising real estate under the sun. Google helped to introduce analytics to digital-first marketers.

Early on, direct marketers recognized Amazon as what it truly is — front end to back end: “direct marketing on steroids.”

DMA knows data. Its conferences, content, professional development — and advocacy and representation — have always advanced the discipline of data-driven marketing, in quality and quantity. Accountability, efficiency, return on investment, testing and audience measurement — these attributes, for perhaps decades too long — were relegated “second-class” citizenship by Madison Avenue, general advertising and the worship of creativity.

Oh, how times have changed.

Data Streams — What Direct Response Started, Digital Exploded

Even before the Internet was invented, smart brands — leading brands — started to recognize the power of data in their advertising and marketing. While some had dabbled in direct mail, most pursued sales promotion techniques that mimic but do not fully commit to direct marketing measurement. It was the advent of database marketing — fueled by loyalty programs, 800 numbers and credit cards — that gave many “big” advertisers their first taste of audience engagement.

Brand champions were curious, and many were hooked. Nothing helps a brand more than customer interaction. Data sets the stage for such interaction through relevance — and interactions enable behavioral and contextual insights for future messaging and content.

Digital marketing — and mobile since — have exploded the availability of data.  So all-told, brands must be data-centric today, because that’s how customers are found, sustained, served and replicated. In fact, data-centricity and customer-centricity are nearly indistinguishable.

ANA and DMA coming together — it’s as if brands understand (or know they need to understand) that data champions the consumer and serves the brand promise. Data serves to prove the effectiveness of all the advertising, marketing and engagement brought forth.

ANA has been acquiring organizations — Word of Mouth Marketing Association, Brand Activation Association, Business Marketing Association and now the Data and Marketing Association. There certainly may be more to this most recent transaction than my humble point of view here today.

But I’d rather believe that data-driven marketing, finally, has received an accolade from brands 100 years due. Congratulations are in order.

 

Great Marketing Analytics Can’t Drive Managerial Courage

Great marketing analytics can’t drive managerial courage, but the reverse is true. Recently, I decided to have coffee with an old acquaintance of mine. He has been in almost every company imaginable and has such a specialized role that he is in constant demand.

Great marketing analytics can’t drive managerial courage, but the reverse is true.

Recently, I decided to have coffee with an old acquaintance of mine. He has been in almost every company imaginable and has such a specialized role that he is in constant demand. Every few years, there is an explosion on innovative management books designed to put him out of business — yet he remains in high demand.

Nobody was already at the café when I arrived. He was sitting in the middle of the café wearing a shiny grey suit, black shirt and sunglasses perched on his slicked-back salt and pepper hair, purposefully baiting my awe and contempt. He flashed a big toothy grin as I approached.

“Hi, ‘Nobody.’ I hope I did not keep you waiting,” I said, trying to hide my disdain.

“Nah, it’s all good,” he replied. “I was just people watching.”

“So what have you been up to?” I asked.

“Same old, same old … consulting business is as good as ever.” To punctuate his point, he grinned and leaned back with hands behind his head, as if he were ready to fall back into a hammock.

“Yeah, tell me what you do, again?” I asked.

“My consultancy focuses on accountability. It is really a simple model. When something breaks down in the workplace, or there is a failure to perform, I am called in to take accountability. Usually, when I show up, people will be stressed out. The guilty parties think someone else is responsible or are looking to share the blame, leadership does not want to create a toxic environment, and everyone wants to just move on. As a result, I come in. Everyone points to me, and they agree that it is Nobody’s fault.”

“Wow! And what do you charge for this service?”

“Depends, but it is usually a large percentage of gross revenue or net profit, depending on the size and type of failure I assume responsibility for. Business is great!”

My memory of our last conversation is suddenly jarred.

“That’s right; last we talked, we discussed how the wave of data-driven management was going to put you out of business. Wasn’t there some concern that measurement and analytics were the new wave of human capital management and that through measurement, greater accountability would come about?”

“Nobody” brightened up and leaned forward. His eyes opened up and his jaw slackened in awe of his luck.

“Yeah, that was what I was afraid of,” he said, “but it turns out, this big data threat has turned out to be a big hoax. You see, I was not called in because accountability was difficult; I was called in because accountability was icky. No amount of data and measurement will help my clients generate a healthy approach toward accountability if they don’t have the vision of what good accountability looks like. “

I had always disliked Nobody. While he feared the disinfecting power of data, I spent a good part of my career preaching the gospel of insightful data. I had always seen him as a Luddite; someone unaware and clinging to old ways. However, after this insightful confession, I found a sudden rush of respect for him. He knew things about business that I was now just learning for myself.

“Nobody, you are right,” I said. “I don’t deal with accountability directly, but I am often asked to help clients with data-driven customer strategy and marketing effectiveness. I have found that the analytics part is easy. However, it is often lack of clarity, purpose, and vision that prevents data and analytics from being effective.”

He smugly flashes that familiar, self-satisfied, toothy grin and instinctively my resentment reappears. However this time, it’s a different resentment. This time, my disdain is seeded with a healthy and well-deserved sense of respect and fear.

“You know your business model is still destined for obsolescence,” I insist. “It is just a matter of time. Wait till artificial intelligence shows up.” I am embarrassed as soon as the words part my lips. I feel small and helpless, like a kid fighting off a bully by threatening to call in an older sibling.

“Nobody” senses the change in our dynamic. He leans in closer than at any time in our conversation. Like a Bond villain, secure in his advantage, unafraid to share a horrifying truth.

“YOU-DON’T-GET-IT.” He pauses after each word, maximizing the dramatic effect, entirely playing out the Bond villain cliché.

“Data, AI, analytics — none of this matters, unless you have the courage and vision to use it in transformative ways. In fact, in this data-driven age, managers are so enamored by what they CAN do, it is hard to think about what they SHOULD do. As a result, my friend, managerial courage and vision are harder than ever. ”

Damn, he’s good.

The Cost of Marketing to the Wrong Consumer, and How to Get It Right

We all know that Internet marketing is easy and cheap. But regardless, marketing to the wrong retail customer can come at a high price. Here are some suggestions for how to keep your marketing judicious and well-targeted, so you’re reaching the right audience.

Internet marketing is easy and cheap. That’s all the more reason to use it judiciously, because the cost of marketing to the wrong retail customer can cost big. Here are some suggestions to make sure you’re targeting the right audiences.

Effectively used, marketing has the power to connect the right consumers with brands and turn them into loyal, repeat customers. But what happens when it’s not, and what’s the cost incurred? Bigger than you think — bad campaigns are deadly on a number of fronts. It’s not just lost sales. They result in lost loyalty and a confused target market. They can quickly alienate some of a retailer’s most valuable potential and current customers. That leads to further difficulty attracting and maintaining relationships with the very people who could have been your best customers, brand ambassadors or social media amplifiers.

Because it’s easier to reach out in today’s digital environment, retailers can more easily connect with their client base now than ever before, for better or worse. Just because they can, doesn’t mean they should. It’s very easy to try a new type of campaign or use digital tools like social media, but it’s just as simple for poor planning and execution to lead to a negative result.

With the rise of digital marketplaces and the vast increase in shopper options, the way shoppers buy products has drastically changed. This means that retailers must regularly adjust, refine and improve their approaches to marketing. It’s critical to understand that just using the internet as a marketing tool isn’t enough —it’s easy to market in a tone-deaf manner. As with any other campaign, success depends on careful planning during every stage of development and the judicious use of accurate, current data and relevant analytics tools. When marketers don’t do this, they risk the consequences of directing their marketing initiatives at the wrong consumer. And there are far too many marketing strategies that don’t lead to the generation of value or a customer transaction.

What Sets Great Modern Marketing Campaigns Apart?

It starts with careful and thoughtful direction of resources involves gathering data, collecting and securely storing it, and effectively using analytics tools to derive useful, actionable insights that form and bolster relationships. Drilling down, certain qualities of effective marketing campaigns set them apart from other, less-successful efforts. Here are a few of the most important concepts for reliable, powerful and positive results:

  • Focus on a well-defined customer type: Great campaigns don’t cast too wide a net. Instead, they have a clear idea of whom they’re targeting.
  • Don’t worry about long-tail keywords: Unless your company can compete with the giants of your market segment — and giants of every segment, like Amazon — it’s best not to put too much stock in these keywords.
  • Emphasize qualified leads: A qualified, well-understood customer persona is much more than an email address. With a thoroughly developed customer profile, including data about budgeting and identity, companies have better results. This is one of many areas where powerful, effective analytics comes into play.
  • Align large and small details to the defined personas: A strong campaign should feel relevant, attractive, focused and engaging to its recipients.
  • Segment your database, continually: Building the difference between prospective and existing customers into targeted variations of the same campaign, for example, helps retailers realize the best results. Continually segmenting databases through the use of effective big data and analytics tools is one difference that sets retail leaders apart from the rest of the pack.
  • Properly value existing customers: You already have a stronger relationship with existing and past customers than with potential ones. An incentive like a coupon or discount — with the exact terms defined in part through analytics and big data — is often enough to secure a new purchase.
  • Gather feedback: Valuable intelligence about your products, customer service and brand experience comes from social media and many other online communities. Retailers need to be where their customers congregate online, then gather feedback for review by staff and use in automated analysis.
  • Build emotional connections: Lasting, meaningful connections with core customers are more important than customer service in many instances. Building these relationships means encouraging purchasing over the long term. Consider these examples:
    • Target determined it was too narrowly labeling bedding and toys for children based on gender. Taking changing attitudes about gender fluidity into account, the retailer stopped marketing based on gender. It now markets bedding and toys with a more inclusive strategy.
    • Dick’s Sporting Goods announced it would stop selling assault rifles and raise its minimum age for purchasing firearms to 21. CEO Edward Stack decided this would provide an overall benefit and strengthen bonds with customers throughout all of its product lines.

A large part of the fine-tuning involves drawing on the power of data and analytics to ensure they can move at the speed of the modern consumer and connect to them effectively. Many aggressive, short-term campaigns use crowdsourcing, social media and apps to build strong, short-term connections. Carried out properly, these efforts increase positive sentiment among the customers you know are interested in shopping with your company. This turns the digital world into an invaluable public space in which businesses can interact with customers, using existing and custom-built tools to quickly and efficiently reach them. The costs of marketing to the wrong consumer are both clear and substantial. So focus on your current and prospective customers and leverage big data and analytics tools to market to the right ones.

Don’t Let Old Habits Dictate Your Marketing Thoughts

When marketers play with data, we often get confined within the limitations of the datasets that are available to us, or worse, tool sets through which we get to access data. Some bad habits live through an organization for multiple generations, as we all get trained in marketing thoughts, in the beginning of our careers, by others who have been doing similar jobs.

marketing thoughts
Credit: Pixabay by Mohamed Hassan

When marketers play with data, we often get confined within the limitations of the datasets that are available to us, or worse, tool sets through which we get to access data. Some bad habits live through an organization for multiple generations, as we all get trained in marketing thoughts, in the beginning of our careers, by others who have been doing similar jobs.

When a few iterations of such training go on through a series of onboarding processes, the original intents of data, reporting and analytics get diluted. And the organization ends up just using those marketing thoughts to go through motions of producing lots of reports that no one cares about or benefits from. I’ve heard some radical claims that the majority of decision-makers today won’t miss over half of automatically generated reports.

We shouldn’t really look at a single report or initiate data-related projects without setting a clear goal first. Often, the most important role of a consultant is to remind clients “why” they should do anything in the first place.

For example, why should we all watch clickthrough rates every day, often locked in a set frame of time parameters? As in, compared to the same time last year, the clickthrough rate went down by 0.8%! The horror! Why do marketers make a big fuss about it, when the clickthrough rate is just one of many indicators, not even the most effective one at that, of actual purchases? Because someone in the past set the KPI reports up that way?

In other words, sometimes marketers and analysts who help them needed to be reminded that the goal is to sell more things and retain customers, not live and die with open and clickthrough rates. I am not flatly dismissing those important metrics at all; I’m just pointing out that we need to have a goal-oriented mindset when dealing with data and analytics. Otherwise, we end up in a maze of metrics and activities that do not really help us achieve organizational goals.

What are those ultimate goals? Not that I want to be a smart ass who would say “From Earth” to an innocuous question “Where are you from?”, but let’s really go to that high level for a moment; we play with data (1) to increase the revenue, or (2) to decrease the cost. Since Profit=Revenue-Cost, well, we can even reduce this whole thing to just one goal: Increase the Profit.

Why am I pointing out the obvious? Because I’ve seen too many data players who just go through motions without questioning the original intent of the activity or key metrics, and blindly believe that all that hard work will somehow lead to success. Unfortunately, that is far from the truth.

If you run on an airplane midflight, would you get to the destination any sooner? Definitely not. In fact, the captain may even go back to the originating airport to drop such crazy person off, further elongating the length of the journey.

You may think this analogy is silly, but in the world of data and analytics, such detours happen all of the time. All because no one questioned how and why any activity set in motion in the distant past would continue to help achieve long and short-term organizational goals – especially when goals need to be constantly adjusted thanks to ever-changing business environments. Nothing in scientific activities, no marketing thoughts, should be carved in stone.

That is why the first question by a seasoned consultant should be what the organization’s long and short-term goals are. Okay, we can all easily agree that we are all in this data and analytics game to increase profit, but what are the specific goals, and what are the immediate pain points? Of course, like any good doctor, a consultant must remedy immediate pain points first. But what do we call those doctors who make the patient’s condition worse just to relieve immediate pain? We call them quacks.

Bringing back this discussion to the world of marketing, having the clear long and short-term goals for every data and analytical activity is a must. If you do that, you may never need an expensive consultant just to remind you that you are wasting resources digging wrong places. Clear business goals beget proper problem statements (not just list of all symptoms and wish lists), which beget appropriate measurement metrics, which in turn lead us to proper digging points in terms of data and methodology, which would minimize waste of time and energy to achieve predetermined goals. In short, we can avoid lots of mishaps and detours just by remembering the original intents of data and analytics endeavors.