Nostalgic for the Future: Data That is ‘Close to You’

Last week, I had a dream — and in it, Karen Carpenter and I were friends. The following night, I had a similar dream — and this time it was Carly Simon. I literally went to bed the next night hoping for a Roberta Flack visitation.

Last week, I had a dream and in it, Karen Carpenter and I were friends. The following night, I had a similar dream and this time it was Carly Simon. I literally went to bed the next night hoping for a Roberta Flack visitation. As a result of these slumbering vocalists and songwriters, I’ve spent a good part of my leisure time over the New Year holiday listening to all their songs on my iPod. It’s yesterday, once more.

Who knows why we dream what we dream?

Sometimes, it just happens that when we’ve experienced enough in life, in play, in work some situations are bound to come around again, next week or decades later. I mean, I owned all that vinyl way back then and now I can stream it all again.

Greatest Hits: Lifecycles of Data-Inspired Marketing

So when Marc Pritchard of Procter & Gamble last week at the Consumer Electronics Show talked about “a world without ads,” I said to myself “oh, I’ve heard this song before.” And he’s right to say it.

In the world of data and direct marketing, a quest for wholly efficient advertising and a mythical 100-percent response rate actually is a 100-year science. Thank you, visionaries, such as Claude Hopkins.

• The 19th Century shopkeeper knew each customer, and conversed regularly. Ideally, each customer’s wants and desires were noted and needs anticipated to the extent that the customer was fulfilled accordingly. (Aaron Montgomery Ward and Richard Warren Sears.)
• Direct marketing originally through print, catalogs and mail, and then broadcast sought to replicate this model remotely. Measurement, attribution and response were put to science. Creativity served the science, or science served the creativity in either direction. Segmentation, analytics and differentiated communication flowed. (David Ogilvy, Stan Rapp and Alvin Eicoff, among others).
• In digital, social and mobile, direct marketing is rejuvenated this time “data-driven marketing.” Some have described this as data-inspired storytelling, or direct marketing on steroids. How responsible data collection can be used to identify prospect needs and wants, and funnel tailored communication through to sale, service and repeat purchase. (Jeff Bezos, among others.)
• And now the product itself can be designed to communicate to the customer smart appliances, smart cars, and the parts and products inside, with sensors and Internet connections and mobile app interfaces all being able to let the user know, it’s time for consideration or some other product lifecycle action.

Post-Advertising: A Reverence for Data

In all these examples, the constant is “I want to know you, so I can serve you the customer” and the facilitator is data. We exist to create and serve a customer. Period. Anything less is not sustainable. Data, in these models, is sought, analyzed and revered. It is also transparent, and its use and application has consumer buy-in. That premise is as true now in the Internet age, as it was in the direct response era before it. We all need to excel in data reverence, first, and then data analysis and application.

Advertising does have a role here, of course. Not every product sells itself and not every product meets customer satisfaction fully. The best advertising, and even the best data behind it, cannot save a bad product. There is always a need for advertising and marketing to inform the consumer, and a brand promise that serves to attract and retain beyond the product.

Every generation has its pop heroes. Tonight, I may just dream of Adele.

Factors for Marketers to Consider in Attribution Rules

At the end of each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules. Call it “Back-end Analysis” or “Campaign Analytics.” Old-timers may use terms like “Match-back.” Regardless, it is one of the most important steps in 1:1 marketing that is synonymous with what we used to call “Closed-loop Marketing.”

At the end of each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules. Call it “Back-end Analysis” or “Campaign Analytics.” Old-timers may use terms like “Match-back.” Regardless, it is one of the most important steps in 1:1 marketing that is synonymous with what we used to call “Closed-loop Marketing.” (refer to my first article on Target Marketing from 11 years ago, “Close the Loop Properly”).

In fact, this back-end analysis is so vital that if one skips this part of analytics, I can argue that the offending marketer ceases to be a 1:1 or database marketer. What good are all those databases and data collection mechanisms, if we don’t even examine campaign results? If we are not to learn from the past, how would we be able to improve results, even in the immediate future? Just wild guesses and gut feelings? I’ve said it many times, but let me say it again: Gut-feelings are overrated. Way more overrated than any cheesy buzzword that summarizes complex ideas into one or two catchy words.

Anyhow, when there were just a few dominant channels, it wasn’t so difficult to do it. For non-direct channel efforts, we may need some attribution modeling to assign credit for each channel. We may call that a “top-down” approach for attribution. For direct channels, where we would know precisely who received the offers, we would do a match-back (i.e., responders matched to the campaign list by personally identifiable information, such as name, address, email, etc.), and give credit to the effort that immediately preceded the response. We may call that a “bottom-up” method.

So far, not so bad. We may have some holes here and there, as collecting PII from all responders may not be feasible (especially in retail stores). But when there was just direct mailing as “the” direct channel, finding out what elements worked wasn’t very difficult. Lack of it was more of a commitment issue.

Sure, it may cost a little extra, and we had to allocate those “unknown” responders through some allocation rules, but backend analysis used to be a relatively straightforward process. Find matches between the mailing (or contact) list and the responders, append campaign information — through what we used to call “Source Code” — to each responder, and run reports by list source, segment, selection mechanism, creative, offer, drop date and other campaign attributes. If you were prudent to have no-mail control cells in the mix, then you could even measure live metrics against them. Then figure out what worked and what didn’t. Some mailers were very organized, and codified all important elements in those source codes “before” they dropped any campaigns.

Now we are living in a multi-channel environment, so things are much more complicated. Alas, allocating each of those coveted responses to “a” channel isn’t just technical work; it became a very sensitive political issue among channel managers. In the world where marketing organizations are divided by key marketing channels (as in, Email Division vs. Direct Mail Division), attribution became a matter of survival. Getting “more” credit for sales isn’t just a matter of scientific research, but a zero-sum game to many. But should it be?

Attribution Rules Should Give Credit Where Credit’s Due

I’ve seen some predominantly digital organizations giving credit to their own direct marketing division “after” all digital channels took all available credit first. That means the DM division must cover its expenses only with “incremental” sales (i.e., direct-mailing-only responses, which would be as rare as the Dodo bird in the age of email marketing). Granted that DM is a relatively more expensive channel than email, I wish lots of luck to those poor direct marketers to get a decent budget for next year. Or maybe they should look for new jobs when they lose that attribution battle?

Then again, I’ve seen totally opposite situations, too. In primarily direct marketing companies, catalog divisions would take all the credit for any buyer who ever received “a” catalog six months prior to the purchase, and only residual credit would go to digital channels.

Now, can we at least agree that either of these cases is far from ideal? When the game is rigged from the get-go, what is the point of all the backend analyses? Just a façade of being a “data-based” organization? That sounds more like a so-called “free” election in North Korea, where there are two ballot boxes visibly displayed in the middle of the room; one for the Communist Party of the Dear Leader, and another box for all others. Good luck making it back home if you put any ballot in the “wrong” box.

Attribution among different channels, in all fairness, is a game. And there is no “one” good way to do it, either. That means an organization can set up rules any way it wants them to be. And as a rule I, as a consultant, tend not to meddle with internal politics. Who am I to dictate what is the best attribution rule for each company anyway?

Here’s How I Set Up Attribution Rules

Now that I am a chief product guy for an automated CDP (Customer Data Platform) company, I got to think about the best practices for attribution in a different way. Basically, we had to decide what options we needed to provide to the users to make up attribution rules as they see fit. Of course, some will totally abuse such flexibility and rig the game. But we can at least “guide” the users to think about the attribution rules in more holistic ways.

Such consideration can only happen when all of the elements that marketers must consider are lined up in front of them. It becomes difficult to push through just one criterion — generally, for the benefit of “his” or “her” channel — when all factors are nicely displayed in a boardroom.

So allow me to share key factors that make up attribution rules. You may have some “A-ha” moments, but you may also have “What the … ” moments, too. But in the interest of guiding marketers to unbiased directions, here is the list:

Media Channel

This is an obvious one for “channel” attribution. Let’s list all channels employed by the organization, first.

  • Email
  • Direct Mail (or different types of DM, such as catalog, First Class mail, postcards, etc.)
  • Social Media (and specific subsets, such as Facebook, Instagram, etc.)
  • Display Ads
  • Referrals/Affiliates
  • Organic Search/Paid Search
  • Direct to Website (and/or search engines that led the buyers there)
  • General Media (or further broken down into TV, Radio, Print, Inserts, etc.)
  • Other Offline Promotions
  • Etc.

In case there are overlaps, which channel would take the credit first? Or, should “all” of the responsive channels “share” the credit somehow?

Credit Share

If the credit — in the form of conversions and dollars — is to be shared, how would we go about it?

  • Double Credit: All responsible channels (within the set duration by each channel) would get full credit
  • Equal Split: All contributing channels would get 1/N of the credit
  • Weighted Split: Credit divided by weight factors set by users (e.g., 50% DM, 30% EM, 20% General Media, etc.)

There is no absolutely fair way to do this, but someone in the leadership position should make some hard decisions. Personally, I like the first option, as each channel gets to be evaluated in pseudo-isolation mode. If there was no other channel in the mix, how would a direct marketing campaign, for example, have worked? Examine each channel and campaign this way, from the channel-centric point of view, to justify their existence in the full media mix.

Allocation Method

How will the credit be given out with all of those touch data from various tags? There are a few popular ways:

  • Last Touch: This is somewhat reasonable, but what about earlier touches that may have created the demand in the first place?
  • First Touch: We may go all of the way back to the first touch of the responder, but could that be irrelevant by the time of the purchase? Who cares about a Christmas catalog sent out in November for purchases made in May of the next year?
  • Direct Attribution: Or should we only count direct paths leading to conversions (i.e., traceable opens, clicks and conversions, on an individual level)? But that can be very limiting, as there will be many untraceable transactions, even in the digital world.
  • Stoppage: In the journey through open, click and conversion, do we only count conversions, or should the channel that led to opens and clicks get partial credit?

All of these are tricky decisions, but marketers should not just follow “what has been done so far” methods. As more channels are added to the mix, these methods should be reevaluated once in a while.

Time Duration (by Channel)

Some channels have longer sustaining power than others. A catalog kept in a household may lead to a purchase a few months later. Conversely, who would dig out a promotional email from three weeks ago? This credit duration also depends on the type of products in question. Products with long purchase cycles — such as automobiles, furniture, major appliances, etc. — would have more lasting effects in comparison to commodity or consumable items.

  • Email: 3-day, 7-day, 15-day, 30-day, etc.
  • Direct Mail — Catalog: 30-day, 60-day, 90-day, etc.
  • Direct Mail — Non-catalog: 7-day, 14-day, 30-day, 60-day, etc.
  • Social: 3-day, 7-day, 15-day, etc.
  • Direct Visit: No time limit necessary for direct landing on websites or retail stores.
  • General Media: Time limit would be set based on subchannels, depending on campaign duration.

Closing Thoughts

The bottom line is to be aware of response curves by each channel, and be reasonable. That extra 30-day credit period on the tail end may only give a channel manager a couple extra conversions after all of the political struggles.

There is really no “1” good way to combine all of these factors. These are just attribution factors to consider, and the guideline must be set by each organization, depending on its business model, product composition and, most importantly, channel usages (i.e., how much money bled into each channel?).

Nevertheless, in the interest of creating a “fair” ground for attributions, someone in a leadership position must set the priority on an organizational level. Otherwise, the outcome will always favor what are considered to be traditionally popular channels. If the status quo is the goal, then I would say skip all of the headaches and go home early. You may be rigging the system — knowingly or unknowingly — anyway, and there is no need to use a word like “attribution” in a situation like that.

Toasting 2018 Silver Apple Honorees: In Their Words

You might have heard of a big event that happened last week in the USA. No, not THAT one. I’m talking about but the presentation of the Direct Marketing Club of New York’s 2018 Silver Apples honors. Here’s more about the awards, from the Silver Apple honorees themselves.

Silver Apple Honorees ballroom
Photo Credit: Edison Ballroom via DMCNY, 2018

You might have heard of a big event that happened last week in the USA. No, not THAT one. I’m talking about but the presentation of the Direct Marketing Club of New York’s 2018 Silver Apples honors. Here’s more about the awards, from the Silver Apple honorees themselves.

The Silver Apples recognize leadership, stewardship and business success mid-career in the data, direct and digital marketing field. Each honoree has (more or less) 25 years of experience, with matching achievements to point to … and all have additional contributions to our industry, community, mentoring and giving back.

With the assistance of newly named The Drum U.S. Editor Ginger Conlon, I thought it worth amplifying a few key industry insights shared by this year’s individual honorees:

Anita Absey, Chief Revenue Officer, Voxy (New York):

Favorite Data Story: “Back in the very early days when I was at Infobase, we were doing data overlays on customer databases, which was novel at the time. While working with a large insurer, doing overlays of demographic and socioeconomic data on their database, the profile and segmentation scheme that emerged from that work actually defied some of the assumptions that they had about the characteristics for their customers’ profile. The insights we provided them helped them make subtle changes in their communications and targeting to customers, which improved the overall risk profile of their customer base. It was gratifying to see how data could affirm or deny assumptions and enable our client to make decisions that helped improve the risk profile of their business.”

Measurement: “Hope is not a strategy. Your actions have to be data-based, not hopeful. Similarly, you can’t manage what you can’t measure. Unless you have data that points you to the actions and decisions that are best for the business, you’re running blind.”

Matt Blumberg, Co-Founder and Chief Executive Officer, Return Path, Inc. (New York):

On Choosing Marketing: “The thing that drew me to marketing was the Internet. I had been working as an investor at a venture capital firm that invested in software companies. Once Netscape went public and people started figuring out the short- and the long-term potential of the Internet, I got very excited about working in that field. Unbeknownst to me at the time, the Internet is all about direct marketing. For the first several years of my career, I would never have described myself as a direct marketer; but in hindsight, obviously, I was.”

On Inspiration: “It’s several sentences out of a speech by Theodore Roosevelt called ‘The Man in the Arena.’
It’s incredible. It goes:

” ‘ … The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.’

“I take it as the entrepreneur’s motto. It’s a beautiful passage that I have taped up everywhere.”

Pam Haas, Account Director, Experian Marketing Services (Providence, RI):

Overhyped: “Display and programmatic technologies are overhyped. It’s like the early days of email marketing: People just started sending millions of emails, hoping some would stick. The same thing is happening in display and programmatic. That part of the industry still needs to mature.”

Best Metric: “Right now, it’s the ROAS: Return on Ads Spent. I love that. For every dollar that the client is spending, we know that we are driving X number of dollars in sales.”

Career Advice: “Diversify. In marketing, there are so many different angles and specialties that you can focus your career on. Throughout my career, I’ve [been] able to gain experience in multiple facets of marketing: direct response, email technology, and in databases and modeling. Digital is so sexy right now, but the fundamentals still apply; so it’s important not to pigeonhole yourself into one area.

“While in a mentor program at Equifax, my mentor was a woman and she told me, ‘You have to be your own PR person. You have to make your accomplishments known, because nobody else is going to do that for you.’”

Keira Krausz, EVP and CMO, Nutrisystem, Inc. (Fort Washington, Penn.):

On Her Current Assignment: “I’m proud of where we are at Nutrisystem and I’m particularly proud of what we’ve built as a team. Our job is wonderful, because we get to help people live healthier and happier lives. Since 2013, we’ve nearly doubled the business, which means we’ve helped a whole lot more people get healthier and happier. Along the way, we’ve revamped nearly every aspect of our business that you can think of, and we’re just getting started.”

On Mentoring: “In my first years in marketing, I was always being asked what my goals were and how I saw myself in years to come, and I always felt flummoxed, because I didn’t know what to say. I wasn’t one of those young people who had their whole life planned out when I was 25, and I often felt insecure about that. But it turns out that was OK.

“So, one thing I did that I would advise is, from early on, try to work for someone you can learn from. Somebody who you admire, who has something unique, and who can teach you something that you think you’re missing. The rest will fall together.”

Tim Suther, SVP and General Manager, Data Solutions, Change Healthcare (Lombard, Ill.):

On Career Choice: “I’ve always been technology-oriented, from learning to code when I was 17 to graduating college with a finance degree. With that background, naturally, I was suspicious of marketing. A lot of marketing felt inauthentic and superficial to me. But I had this one moment where I actually saw a dynamic gains curve for the first time and I thought, ‘Oh my god, this is one of the most interesting things I’ve ever seen.’ It was the intersection of the art of marketing and the science of data that really drew me in; and boy, did I get lucky on that one, because that’s what it’s all about today.”

On Being Data-Driven: “This might surprise you a little bit, but it annoys me when marketers say that they’re data-driven, because that’s like saying, ‘OK, it’s time to turn off my brain and just let the data drive the story.’

“I think marketers are far better off when they are data-informed, where they’re combining what the data is telling them with their own business judgment to make the right decision. Human behavior is still too complicated to purely reduce to what an algorithm tells you to do; it has to be a combination of what the data is saying, creative savvy and business judgment.”

This year, DMCNY added two special awards not tied to mid-career, but recognizing two huge drivers in our business today: advocacy and disruption. The inaugural Apples of Excellence 2018 honorees include:

Advocacy:

Stu Ingis, Chairman, Venable LLP (Washington, D.C.):

On Policy-Making: “The whole privacy concern is overhyped. What’s not getting its fair recognition, in the policy world, is all of the innovation that the marketing community brings to society. For instance, they’re bringing real-time targeted marketing to television and delivering marketing communications that consumers are interested in on a personalized basis.”

On Careers: “Take the long view. Work really hard; don’t worry about the compensation or the glory, and then persevere. Stay with it. Don’t switch jobs all the time thinking that something else is always better. If you develop your skills, the good work will come to you. You don’t have to go to it.

“I’d been representing the DMA for about two years, and I had an opportunity to leave the law firm and go out in the early Internet age at Yahoo!

“Yahoo! stock was going up. I would have made millions of dollars a day. I went to Ron Plesser and said, ‘I like working for you; I like the clients; I like the work I’m doing. But I could go get really rich working for this company.’ He said, ‘Why do you want to do that? It’ll ruin your life.’ For whatever reason, I actually believed him and agreed with him. And I stayed at my job. It was probably the best decision I ever made. I don’t regret it for a second.”

Disruptor Award, Presented by Alliant:

Bonin Bough, Founder and Chief Growth Officer, Bonin Ventures (New York):

About Bonin: “His unique approach of applying innovative technology to create breakthrough campaigns helped to reinvigorate traditional marketing brands, such as Gatorade, Honey Maid, Oreo and Pepsi.

“But his influence doesn’t stop there. Bonin believes in supporting young talent and savvy entrepreneurs. While at Mondelēz International, for example, he created internal programs to mentor young talent and launched a startup innovation program, Mobile Futures, to provide a platform for marketing-tech and agency start-ups to work with the CPG giant.

“Stephanie Agresta, global director of enterprise growth at Qnary, describes him best in her recommendation on LinkedIn: ‘Bonin is a force of nature … A true rockstar from Cleveland to Cannes, Bonin has been [at] the forefront of the digital revolution from the beginning. Smart, successful, and connected, Bonin has the pulse on what’s next. Those that know Bonin well can also attest to his generosity, commitment to mentorship and a deep belief that anything is possible.’”

Since I had the privilege of interacting with Bonin at DMA &Then18 recently, I can attest the walls fall away when you converse with him. Disrupted, indeed.

All of these honorees as well as corporate recipient Winterberry Group have many things to teach us. That’s why it’s important we continue to recognize these business leaders, as marketing today, as Matt Blumberg says, is a 100 different things. It’s the business outcomes that matter.

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.

Improved Marketing ROI Shouldn’t Be Your Metric, This Should

My team often engages in client projects designed to improve marketing outcomes. Many times, clients describe their primary objective as an increased return on marketing dollars or return on investment (ROI). However, this is often the wrong object and their real goal should be improved marketing effectiveness.

My team often engages in client projects designed to improve marketing outcomes. Many times, clients describe their primary objective as an increased return on marketing dollars or return on investment (ROI). However, this is often the wrong object and their real goal should be improved marketing effectiveness.

“That sounds like semantics,” you say? Yes, this is an argument over semantics, and in this case, semantics matter.

When stating the primary objective as improved marketing ROI, the aperture is usually focused on an optimization exercise, which pits financial resources on one side of the equation and levers — such as channel spend, targeting algorithms and A/B testing — on the other side.

A couple of decades ago, marketing analytics recognized that specific activities were easier to link, with outcomes based on data that was readily available. Over time, this became the marketing ROI playbook and was popularized by consultants, academics and practitioners. This led to improved targeting, ad buys and ad content. These improvements are very important, and I would argue that they are still a must-do for most marketing departments today. However, resources are optimally allocated across channels, winning ads identified and targeting algorithms improved, marketing is still not as effective as it can be. Now is when the hard part of building a more effective marketing function actually begins.

For a moment, let’s imagine a typical marketing ROI project from the customer’s perspective. Imagine you are actively shopping for a refrigerator. A retailer uses data to appropriately target you at the right time, across multiple channels, with the right banner ad and a purchase naturally follows, right? Of course not.

  • What about helping you understand the variety of features, prices and brands available?
  • What about helping you understand the value of selecting them over other retailers?
  • What about the brand affinity and trust this process is developing in the consumer’s mind?

Because this purchase journey can play out over weeks or months, these marketing activities are more difficult (but not impossible) to measure and are often left out of the standard ROI project. However, these activities are as impactful as the finely tuned targeting algorithm that brought you to the retailer’s website in the first place.

Back to why semantics over ROI and marketing effectiveness matter. Today, the term “marketing ROI” is calcified within a relatively narrow set of analytical exercises. I have found that using marketing effectiveness as the alternative objective gives license to a broader conversation about how to improve marketing and customer interaction. It also lessens the imperative to link all activities directly to sales. Campaigns designed to inform, develop relationships or assist in eventual purchase decisions are then able to be measured against more appropriate intermediate metrics, such as online activity, repeat visits, downloads, sign-ups, etc.

What makes this work more challenging is that it requires marketers to develop a purposeful and measurable purchase journey. In addition, it requires a clear analytics plan, which drives and captures specific customer behavior, identifies an immediate need and provides a solution so the customer can move further down the purchase journey.

Finally, it requires developing an understanding of how these intermediate interactions and metrics eventually build up to a holistic view of marketing effectiveness. Until marketers can develop an analytical framework which provides a comprehensive perspective of all marketing activity, marketing ROI is merely a game of finding more customers, at the right time and place who will overlook a poorly measured (and, by extension, poorly managed) purchase journey.

Marketing Success Metrics: Response or Dollars?

It’s tempting to ask about whether marketing success metrics should be response rates or money. But you don’t need to ask marketers what they want. Basically, they want everything.

It’s tempting to ask about whether marketing success metrics should be response rates or money. But you don’t need to ask marketers what they want. Basically, they want everything.

They want big spenders who also visit frequently, purchasing flagship products repeatedly. For a long time (some say “lifetime”). Without any complaint. Paying full price, without redeeming too many discount offers. And while at it, minimal product returns, too.

Unfortunately, such customers are as rare as a knight in white armor. Because, just to start off, responsiveness to promotions is often inversely related to purchase value. In other words, for many retailers, big spenders do not shop often, and frequent shoppers are often small item buyers, or worse, bargain-seekers. They may just stop coming if you cut off fat discount deals. Such dichotomy is quite common for many types of retailers.

That is why a seasoned consultants and analysts ask what brand leaders “really” want the most in marketing success metrics. If you have a choice, what is more important to you? Expanding the customer base or increasing the customer value? Of course, both are very important goals — and marketing success metrics. But what is the first priority for “you,” for now?

Asking that question upfront is a good defensive tactic for the consultant, because marketers tend to complain about the response rate when the value target is met, and complain about the revenue size when goals for click and response rates are achieved. Like I said earlier, they want “everything, all the time.”

So, what does a conscientious analyst do in a situation like this? Simple. Set up multiple targets and follow multiple marketing success metrics. Never hedge your bet on just one thing. In fact, marketers must follow this tactic as well, because even CMOs must answer to CEOs eventually. If we “know” that such key marketing success metrics are often inversely correlated, why not cover all bases?

Case in point: I’ve seen many not-so-great campaign results where marketers and analysts just targeted the “best of the best” segment — i.e., the white rhinoceros that I described in the beginning — in modeled or rule-based targeting. If you do that, the value may be realized, but the response rate will go down, leading to disappointing overall revenue volume. So what if the average customer value went up by 20%, when only a small group of people responded to the promotion?

A while back, I was involved in a case where “a” targeting model for a luxury car accessory retailer tanked badly. Actually, I shouldn’t even say that the model didn’t work, because it performed exactly the way the user intended. Basically, the reason why the campaign based on that model didn’t work was the account manager at the time followed the client’s instructions too literally.

The luxury car accessory retailer carried various lines of products — from a luxury car cover costing over $1,000 to small accessories priced under $200. The client ordered the account manager to go after the high-value target, saying things like “who cares about those small-timers?” The resultant model worked exactly that way, achieving great dollar-per-transaction value, but failing at generating meaningful responses. During the back-end analysis, we’ve found that the marketer indeed had very different segments within the customer base, and going only after the big spenders should not have been the strategy at all. The brand needed a few more targets and models to generate meaningful results on all fronts.

When you go after any type “look-alikes,” do not just go after the ideal targets in your head. Always look at the customer profile reports to see if you have dual, or multiple universes in your base. A dead giveaway? Look at the disparity among the customer values. If your flagship product is much more expensive than an “average” transaction or customer value in your own database, well, that means most of your customers are NOT going for the most expensive option.

If you just target the biggest spenders, you will be ignoring the majority of small buyers whose profile may be vastly different from the whales. Worse yet, if you target the “average” of those two dichotomous targets, then you will be shooting at phantom targets. Unfortunately, in the world of data and analytics, there is no such thing as an “average customer,” and going after phantom targets is not much different from shooting blanks.

On the reporting front — when chasing after often elusive targets — one must be careful not to get locked into a few popular measurements in the organization. Again, I recommend looking at the results in every possible way to construct the story of “what really happened.”

For instance:

  • Response Rate/Conversion Rate: Total conversions over total contacted. Much like open and click-through rate, but I’d keep the original denominator — not just those who opened and clicked — to provide a reality check for everyone. Often, the “real” response rate (or conversion rate) would be far below 1% when divided by the total mail volume (or contact volume). Nonetheless, very basic and important metrics. Always try to go there, and do not stop at opens and clicks.
  • Average Transaction Value: If someone converted, what is the value of the transaction? If you collect these figures over time on an individual level, you will also obtain Average Value per Customer, which in turn is the backbone of the Lifetime Value calculation. You will also be able to see the effect of subsequent purchases down the line, in this competitive world where most responders are one-time buyers (refer to “Wrestling the One-Time Buyer Syndrome”).
  • Revenue Per 1,000 Contacts: Revenue divided by total contacts multiplied by 1,000. This is my favorite, as this figure captures both responsiveness and the transaction value at the same time. From here, one can calculate net margin of campaign on an individual level, if the acquisition or promotion cost is available at that level (though in real life, I would settle for campaig- level ROI any time).

These are just three basic figures covering responsiveness and value, and marketers may gain important intelligence if they look at these figures by, but not limited to, the following elements:

  • Channel/Media
  • Campaign
  • Source of the contact list
  • Segment/Selection Rule/Model Score Group (i.e., How is the target selected)
  • Offer and Creative (hopefully someone categorized an endless series of these)
  • Wave (if there are multiple waves or drops within a campaign)
  • Other campaign details such as seasonality, day of the week, daypart, etc.

In the ultimate quest to find “what really works,” it is prudent to look at these metrics on multiple levels. For instance, you may find that these key metrics behave differently in different channels, and combinations of offers and other factors may trigger responsiveness and value in previously unforeseen manners.

No one would know all of the answers before tests, but after a few iterations, marketers will learn what the key segments within the target are, and how they should deal with them discriminately going forward. That is what we commonly refer to as a scientific approach, and the first step is to recognize that:

  • There may be multiple pockets of distinct buyers,
  • Not one type of metrics will tell us the whole story, and
  • We are not supposed to batch and blast to a one-dimensional target with a uniform message.

I am not at all saying that all of the popular metrics for digital marketing are irrelevant; but remember that open and clicks are just directional indicators toward conversion. And the value of the customers must be examined in multiple ways, even after the conversion. Because there are so many ways to define success — and failure — and each should be a lesson for future improvements on targeting and messaging.

It may be out of fashion to say this old term in this century, but that is what “closed-loop” marketing is all about, regardless of the popular promotion channels of the day.

The names of metrics may have changed over time, but the measurement of success has always been about engagement level and the money that it brings.

Marketing Decision-Making: Similarity to the 94% Who Don’t Trust Mainstream Media

Marketing decision-making is a science for some, a gut reaction for others. And the latter group is concerning, because people are easily misled when presented with things they want to believe.

Marketing decision-making is a science for some, a gut reaction for others. And the latter group is concerning, because people are easily misled when presented with things they want to believe.

Writing in his book, “Thinking Fast and Slow,” Daniel Kahneman says:

“The psychologist Paul Slovic has proposed an affect heuristic in which people let their likes and dislikes determine their beliefs about the world … His work offers a picture of Mr. and Ms. Citizen that is far from flattering: guided by emotion rather than by reason, easily swayed by trivial details, and inadequately sensitive to differences between low and negligibly low probabilities.”

Sounds like some marketing decision-making.

Essentially, people believe what they want to believe. I was amused when someone shared this tidbit about the mainstream media in my Facebook News Feed. Having done a good amount of market research, I’ve found that it’s unusual for more than 90% of people to agree on anything. Taking the bait, I replied that you might get more than 90% of people to agree that they love their mother (unless you pulled your research sample from people who were abused and neglected as children). To get this result about the mainstream media, the research sample must have been pulled from Sean Hannity’s Twitter followers, if there was a study at all.

Marketing Decision-Making’s Similarity to the 94% Who Don’t Trust Mainstream Media
Credit: Chuck McLeester

Marketers and researchers can easily succumb to the affect heuristic. We have to be cautious about letting personal beliefs, opinions, and biases guide decisions and conclusions. We may desperately want to believe certain things about our target audience because that’s what we feel. Of course, our feelings might not be supported by the numbers.

The Mad World News post about mainstream media invited readers to share if they were one of those who mistrusted the mainstream media. The post got 41,405 shares — from only 2% of the Mad World News 1.8 million followers. Granted, not everyone who agrees with the post is going to share it. But to validate the claim, 47 times as many Mad World News followers would have to share it. When challenged, one sharer doubled down on his belief.

Beware the affect heuristic.

How to Make Marketing a Revenue Center and Not a Cost

Many marketers struggle to have their departments viewed as the valuable, revenue-generating entities that they are. They are viewed as sales support teams, at best. That shouldn’t be the case, and if it is, it may be your own fault. To get your marketing viewed as a revenue center and not a cost requires the right metrics, solid sales and marketing integration, and excellent analytics.

In a word: metrics.

Many marketers struggle to have their departments viewed as the valuable, revenue-generating entities that they are. They are viewed as sales support teams, at best.

That shouldn’t be the case, and if it is, it may be your own fault. If you can’t create a line that ties your activities to actual revenue, you can’t prove that your marketing activity is generating revenue.

You only make the problem worse if you insist on talking to C-level folks about fans, followers, likes, and subscribers. They just don’t pay the rent. And acquiring them has costs.

But drawing the line between marketing and revenue isn’t always easy, particularly for B2B marketers without transactional websites. Which means that my claim for metrics being the answer is a bit simplistic. You really need:

  • The right metrics
  • Integration between sales and marketing systems
  • A measurement process

The Right Metrics

As I mentioned above, not just any metrics will do. Process metrics like fans, followers, clicks, etc. are important to us as marketers but not important to those with profit-and-loss responsibility and not important to most businesses as a whole. (If you’re in the publishing business, that’s another story.)

The metrics you need to seek out are business metrics. These are metrics related to profit, revenue, sales, lead volume, lead quality, and so on. The problem, of course, is that not only are these metrics harder to measure, they are harder to tie to specific marketing actions.

Despite the increased degree of difficulty, this is the first step in turning marketing from a cost to a revenue generator. In fact, you may have to make inroads here before you can secure the resources you’ll need to take the next step.

Sales and Marketing Systems Integration

That next step is tying your various sales and marketing systems together in such a way that you can track not just how many times a piece of content, for example, has been consumed, but what content a particular prospect has consumed. This requires coordination between your CRM system and the CMS that underpins your website.

It’s of even more value if you can track which pieces of content are most frequently consumed by prospects who convert to customers.

As an added benefit, coordination like this can also increase your marketing’s effectiveness by allowing you to tailor the content you present to individual visitors. For example, once you know what content a site visitor has already consumed, you can replace a static “You Might Also Like” links in your sidebar with links to content that might be the next logical step for someone who has already consumed introductory materials.

Progressive profiling, as it’s called, will also help you hone your content offerings and create content ladders that lead from introductory materials through education and establishing trust to, we hope, conversion.

Measurement Process

Finally, we need to measure what’s working and what is not. We need to know what content resonates with our audience and which audience segments we’re connecting with. Much of the data you’ll need for this will be available in your CRM, though you may need to tie in other analytics data gathering tools

The only downside to this is that implementation of these ideas tends to be quite customized. There’s no off-the-shelf solution that is likely to fit your needs – your audience, your CRM and CMS, your goals. Making yourself an educated consumer is critical, even if you aren’t going to implement with internal resources. Different vendors will present different solutions and doing an apples-to-apples comparison requires at least a basic understanding of the various moving parts.

Turn the Funnel Upside Down for Better ROI Planning

Many conventional marketers depict the progression from prospect to buyer as a funnel starting with impressions at the top and working down through the sales cycle to responses, leads, qualified leads and finally buyers. This approach tells a top-to-bottom chronological story of the promotion process.

Many conventional marketers depict the progression from prospect to buyer as a funnel starting with impressions at the top and working down through the sales cycle to responses, leads, qualified leads and finally buyers. This approach tells a top-to-bottom chronological story of the promotion process.

Better ROI chart
Credit: Chuck McLeester

But turning the funnel upside down provides a much more useful approach to planning ROI. It has its roots in the fundamental principles of direct response: customer lifetime value (LTV) and allowable acquisition cost (AAC).

Better ROI chart 2
Credit: Chuck McLeester

You can start out at the top of the upside-down funnel using your customer lifetime value, or if you’re interested in getting a specific return on a short-term promotion, you can use the value of a one-time transaction. Either way, you’re starting with the value of a customer — be it short-term or long-term.

Once you determine a revenue point to work with, set a target ROI and calculate your AAC (allowable acquisition cost). For this illustration, let’s assume that the transaction is worth $200 and our target ROI at 2:1. This results in an AAC of $100; that is, the amount we can spend to get the transaction.

AAC chart by Chuck McLeesterAs you move to the lower portions of the upside-down funnel, you apply assumptions about the conversion rates at each stage. You may have some historical data on which to base these assumptions, but if you don’t apply industry standards or make educated guesstimates. Ultimately, you’ll learn what the actual rates are in a well-constructed test scenario. For example, if you assume that 30 percent of all qualified leads will convert to buyers, then the allowable cost per qualified lead is $30.

Qualified lead formula for better ROI
Credit: Chuck McLeester

Similarly, you can calculate the allowable cost per lead, cost per response and cost per impression all the way to the base of the upside-down funnel. So if you estimate that two-thirds of your leads will be qualified, your allowable cost per lead is $20, and so on.

Allowable cost/lead formula for better ROI
Credit: Chuck McLeester

 As you reach the bottom of the upside-down funnel, you can determine the required response rates from each medium under consideration. You can either make an assumption about the percentage of clicks, calls or responses that will turn into leads, or you can go straight to calculating the number of leads you need from each medium based on the media cost as shown here.

Final graphic for better ROI
Credit: Chuck McLeester
  1. Divide the cost of the media by the allowable lead cost to determine the number of leads required from each medium
  2. Divide the number of leads required by the circulation or number of impressions associated with that medium

For example,

Final formula for better ROI
Credit: Chuck McLeester

 (These calculations can also be done on a CPM basis).

Then, do a gut-check. Is that response rate realistic? Don’t know? Test it. A carefully controlled small test will quantify your assumptions at each point of the upside-down funnel.

CMO Accountability: What’s the Time Horizon?

What are CMOs held accountable for at the end of the year? Let’s say we invest 5 percent of revenues in a given year in marketing, what do the CEO and the board of directors expect in return?

What are CMOs held accountable for at the end of the year? Let’s say we invest 5 percent of revenues in a given year in marketing, what do the CEO and the board of directors expect in return?

  1. Incremental sales to the tune of 20x their investment?
  2. Incremental market share or new market penetration?
  3. Incremental profits?
  4. Incremental customer loyalty, customer satisfaction and customer value?
  5. Increasing shareholder value?
  6. All of the above?

Whereas sales representatives may have a one quarter horizon, can the CMO afford to invest in marketing functions with such a short time horizon in mind? In our post last month, we discussed these six major functions for which marketing is responsible, presumably so that they can deliver on the list above:

  1. Gather customer requirements, defining markets and the product/service sets
  2. Help create and retain customers with demand generation programs, content marketing, events, social, etc.
  3. Increase brand equity
  4. Channel marketing and technology partner management
  5. Empower the sales channels with market data, prospect data, competitive data and sales tools & collateral
  6. Participate in the support and delivery of the “whole product” to customers

Reconciling the investments in each of the latter six functions with the results described in the former list of six outcomes is a herculean task. So, let’s focus on just one aspect: Which marketing functions require the CMO to have a longer time horizon than one year?

  1. Defining the markets, and defining the products/services required to successfully penetrate those markets are tasks usually associated with product management (PMM). But the final decision-making requires participation from representatives in nearly every function in the company. The ROI period for these efforts could be three to five years or more. What share of the marketing budget should go to PMM knowing that it is a long-term investment? Most firms tie this to market share changes and revenue/profits that the PMM forecasts over a multi-year period.
  2. If the sales cycle is six months or less, it is conceivable that the ROI for demand generation could be viewed as a near-term investment. As a result, many marketing organizations focus their ROI reporting solely on their promotion and content budgets and ignore ROI calculations on many of the other marketing investments.
  3. Increases in brand equity can be measured, but it is definitely a long-term investment. The benefits are obvious to most: increased brand awareness, brand loyalty, perceived quality, and clearly defined brand attributes improve lead acquisition, increased loyalty, and lower cost of acquiring new business.
  4. In many cases the management and nurturing of channel partners, resellers and VARs lean on marketing to support these players with educational materials, training, and product information. Ie the Partner Managers are in marketing. Additionally, if products or services from an OEM are an integral part of the product or solution sold, those relationships are also managed in marketing by product marketing managers. The ROI for investments in these relationships is near term and can be measured.
  5. Sales enablement with tools, content, templates, training, competitive data target, customer and prospect data and market data is a requirement, and in most cases the ROI is both near and long term. The return is an increase in productivity in the sales teams and sales channels. It is easy to measure, but difficult to allocate how much credit falls to marketing initiatives. Also, much of the tools and content (but not all) will be accounted for in No. 2 above.
  6. How do you do an ROI on marketing’s role as part of the product or service delivery? If marketing is doing follow-up communications with new customers to ensure adoption and satisfaction how are the benefits measured? If marketing owns the e-commerce platform an ROI is easy. How about marketing communications around support contract renewals?

The long-term investments for marketing, at a minimum, are product marketing/management, brand equity and of course any infrastructure investments (technology, data and process). Brand equity investments are usually rolled up under promotion and demand generation efforts as if they were near-term investments. Investing in infrastructure is usually accounted for by tying it to increasing marketing productivity, enabling marketing to be more competitive, or improving customer experience (leading to greater acquisition and loyalty presumably).

The conclusion here is that the CMO is accountable for a portfolio of investments, related to different functions and with both near and long-term return horizons. The methods for measuring these returns vary, and the outcomes for the business from these investments also vary. The CMO has to rebalance the portfolio quarterly and they must adopt an “agile” approach without taking their eyes off the goals. Although for sales, this may be the most important quarter in the company’s history, the CMO has too many long-term investments to have a short time horizon.