Don’t Blame Personalization After Messing It Up

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” But before giving up because the first few rounds didn’t pay off, shouldn’t marketers stop and think about what could have gone wrong?

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” Interesting that I started my last article quoting only about 20% of analytics works are properly applied to businesses. What is this, some 80/20 hell for marketers?

Nonetheless, the stat that I shared here begs for further questioning, especially the ROI part. Why do so many marketers think that ROI isn’t there? Simply, ROI doesn’t look good when:

  1. You invested too much money (the denominator of the ROI equation), and
  2. The investment didn’t pay off (the numerator of the same).

Many companies must have spent large sums of money on teams of specialists and service providers, data platforms featuring customer 360, personalization software (on the delivery side), analytics work for developing segments and personas, third-party data, plus the maintenance cost of it all. To justify the cost, some marginal improvements here and there wouldn’t cut it.

Then, there are attribution challenges even when there are returns. Allocating credit among all the things that marketers do isn’t very simple, especially in multichannel environments. To knock CEOs and CFOs off their chairs – basically the bottom-line people, not math or data geeks – the “credited” results should look pretty darn good. Nothing succeeds like success.

After all, isn’t that why marketers jumped onto this personalization bandwagon in the first place? For some big payoff? Wasn’t it routinely quoted that, when done right, 1:1 personalization efforts could pay off 20 times over the investment?

Alas, the key phrase here was “when done right,” while most were fixated on the dollar signs. Furthermore, personalization is a team sport, and it’s a long-term game.  You will never see that 20x return just because you bought some personalization engine and turned the default setting on.

If history taught us anything, any game that could pay off so well can’t be that simple. There are lots of in-between steps that could go wrong. Too bad that yet another buzzword is about to go down as a failure, when marketers didn’t play the game right and the word was heavily abused.

But before giving it all up just because the first few rounds didn’t pay off so well, shouldn’t marketers stop and think about what could have gone so wrong with their personalization efforts?

Most Personalization Efforts Are Reactive

If you look at so-called “personalized” messages from the customer’s point of view, most of them are just annoying. You’d say, “Are they trying to annoy me personally?”

Unfortunately, successful personalization efforts of the present day is more about pushing products to customers, as in “If you bought this, you must want that too!” When you treat your customers as mere extensions of their last purchase, it doesn’t look very personal, does it?

Ok, I know that I coveted some expensive electric guitars last time I visited a site, but must I get reminded of that visit every little turn I make on the web, even “outside” the site in question?

I am the sum of many other behaviors and interests – and you have all the clues in your database – not a hollow representation of the last click or the last purchase.  In my opinion, such one-dimensional personalization efforts ruined the term.

Personalization must be about the person, not product, brands, or channels.

Personalization Tactics Are Often Done Sporadically, Not Consistently

Reactive personalization can only be done when there is a trigger, such as someone visiting a site, browsing an item for a while, putting it in a basket without checking out, clicking some link, etc. Other than the annoyance factor I’ve already mentioned, such reactive personalization is quite limited in scale. Basically, you can’t do a damn thing if there is no trigger data coming in.

The result? You end up annoying the heck out of the poor souls who left any trail – not the vast majority for sure – and leave the rest outside the personalization universe.

Now, a 1:1 marketing effort is a number’s game. If you don’t have a large base to reach, you cannot make significant differences even with a great response rate.

So, how would you get out of that “known-data-only” trap? Venture into the worlds of “unknowns,” and convert them into “high potential opportunities” using modeling techniques. We may not know for sure if a particular target is interested in purchasing high-end home electronics, but we can certainly calculate the probability of it using all the data that we have on him.

This practice alone will increase the target base from a few percentage points to 100% coverage, as model scores can be put on every record. Now you can consistently personalize messages at a much larger scale. That will certainly help with your bottom-line, as more will see your personalized messages in the first place.

But It’s Too Creepy

Privacy concerns are for real. Many consumers are scared of know-it-all marketers, on top of being annoyed by incessant bombardments of impersonal messages; yet another undesirable side effect of heavy reliance on “known” data. Because to know for sure, you have to monitor every breath they take and every move they make.

Now, there is another added bonus of sharing data in the form of model scores. Even the most aggressive users (i.e., marketers) wouldn’t act like they actually “know” the target when all they have is a probability. When the information is given to them, like “This target is 70% likely to be interested in children’s education products,” no one would come out and say “I know you are interested in children’s education products. So, buy this!”

The key in modern day marketing is a gentle nudge, not a hard sell. Build many personas – because consumers are interested in many different things – and kindly usher them to categories that they are “highly likely” to be interested in.

Too Many Initiatives Are Set on Auto-Pilot

People can smell machines from miles away. I think humans will be able to smell the coldness of a machine even when most AIs will have passed the famous Turing Test (Definition: a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human).

In the present day, detecting a machine pushing particular products is even easier than detecting a call-center operator sitting in a foreign country (not that there is anything wrong about that).

On top of that, machines are only as versatile as we set them up to be. So, don’t fall for some sales pitch that a machine can automatically personalize every message utilizing all available data. You may end up with some rudimentary personalization efforts barely superior to basic collaborative filtering, mindlessly listing all related products to what the target just clicked, viewed, or purchased.

Such efforts, of course, would be better than nothing.  For some time.  But remember that the goal is to “wow” your target customers and your bosses. Do not settle for some default settings of campaign or analytics toolsets.

Important Factors Are Ignored

When most investments are sunk in platforms, engines, and toolsets, only a little are left for tweaking, maintenance, and expansion. As all businesses are unique (even in similar industries), the last mile effort for custom fitting often makes or breaks the project. At times, unfortunately, even big items such as analytics and content libraries for digital asset management get to be ignored.

Even through a state-of-the-art AI engine, refined data works better than raw data. Your personalization efforts will fail if there aren’t enough digital assets to rotate through, even with a long list of personas and segments for everyone in the database. Basically, can you show different contents for different personas at different occasions through different media?

Data, analytics, contents, and display technologies must work harmoniously for high level personalization to work.

So What Now?

It would be a real shame if marketers hastily move away from personalization efforts when sophistication level is still elementary for the most.

Maybe we need a new word to describe the effort to pamper customers with suitable products, services and offers. Regardless of what we would call it, staying relevant to your customer is not just an option anymore. Because if you don’t, your message will categorically be dismissed as yet another annoying marketing message.

 

How Do We Leverage Data to Drive a Faster Economic Recovery?

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. However, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. And despite the global hardships that will be felt by many, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

When used well, data science will help direct scarce resources to the right opportunities and efficiently drive growth. I am convinced this will be a big differentiator versus previous recoveries of this magnitude.

Over my career, I have consistently encountered inefficient and counter-productive practices in data-driven decision management and have written about them often. They are paralleled in the crisis today. Below are three issues I would like us all to think about when we leverage data science to rebuild the national and world economy.

1. Customer Data Hoarding

Companies collect so much data that many are “drowning in data.” If you have no idea of the value of what you are collecting, then it is digital garbage.

We were led to believe that AI and data mining would help make sense of the data. It does to some extent, but more often it leads to head-scratching conclusions. We can’t leverage what we can’t understand.

As a data-driven consultant, I am amazed at how much time is spent sifting through data just trying to make sense of it all before any valuable insights can be generated. Going forward we cannot afford this luxury. If there are 10 gallons of fuel in the tank, we can’t spend five gallons trying to figure out if the engine works. However, when it comes to mining company data, we often do.

2. It’s About Qualitative, Not Just Quantitative

We can’t be slaves to the data we have. Collecting the right data is often cheap and easily done, if time is taken to plan. This means that measurement strategy can’t be a retrospective exercise. Too often, I have engaged clients in the post-mortem analysis of very important projects. In many cases, my team is often limited to the data that is available and not the data that was needed. Critical answers are sometimes left unanswered. This is a waste of time, resources and most importantly, valuable information.

3. Data Is Not the Solution, It’s the Tool

We must regularly remind ourselves that data does not solve problems or create opportunities. Rather, brave decision making solves problems and creates opportunities. Data is a valuable tool that can only inform the decisions we need to make. It can help lower the risk and provide valuable insights. Sometimes, collecting more data before acting can be wise. Other times it can also be the delay in action that leads to disaster.

What is happening today has no parallel in recent memory. While the 1918 flu pandemic had similar infection rates, the world was a different place then. Today, we have advanced tools and technology to aid our recovery.

Data science will be one of those important tools, especially if we collectively decide to use it to its true potential. As a result, I am hopeful that we can come out of this faster than we realize.

The Grand Reopening of the U.S. Economy Will Happen, Plan for It

We are in uncharted territory, much as we were in previous economic downturns and recessions. Yet, do know, another expansion will follow … eventually. There will be a grand reopening of our economy, and as marketers, we need to plan for it.

I love defaulting to optimism – even in the darkest of times. It’s been part of my survival mechanism through all sorts of crises. That being said, we are in uncharted territory in this new normal, much as we were in previous economic downturns and recessions. “The Great Recession” of 2008-2009 was largely Wall Street born and Main Street slammed. But remember, the Great Expansion followed. A possible recession stemming from COVID-19, however, would be largely reversed, with millions of livelihoods suddenly denied, and both Main Street and Wall Street being slammed in tandem. Yet, do know, another expansion will follow … eventually. There will be a grand reopening of our economy, and as marketers, we need to plan for it.

Listening to the U.S. President talk about getting parts of our country back to some semblance of normal by Easter may seem wild-eyed and some might say irresponsible. In reality, China is reportedly already back on line – after six-to-eight weeks of paralysis. Does this mean a possible “V-shaped” recession (very short), a “U-shaped” one (mild), or an “L-shaped” one (long term)? We don’t know.

It’s always dangerous to make prognostications, but we can learn from patterns elsewhere in the virology. With the United States now the most afflicted nation in sickness, we yet have a massive fight ahead to control viral spread. And doubt and fear have taken hold as two debacles have come about, one public health and one economic.

Unfortunately, there is no “on/off” switch for the viral crisis. Even when its spread is curtailed, which will happen, we’ve been shaken and edginess is going to remain. That’s only human.

Patterns of consumption will not resume as if nothing happened. Unemployment shocks will not reverse as easily as they came. So there will be a “new” normal.

However, a reopening is coming. You might say that’s my optimism, but folks – we are going to be okay in a time. It may not be of our choosing, as Dr. Fauci faithfully reports, but one that will be here nonetheless. As marketers, let’s get ready for it.

Look to Your Data to Prepare for What’s Next

Recessions are actually good times to look to the enterprise and get customer data “cleaned up.” The early 90s recession gave us CRM, and database marketing flourished. The end of the Internet 1.0 boom in 2000 brought data discipline to digital data. And the Great Recession brought data to the C-suite.

So let’s use this time to do a data checkup. Here are four opportunities:

  1. Data audits are often cumbersome tasks to do – but data governance is a “must” if we want to get to gain a full customer view, and derive intelligent strategies for further brand engagement. Quality needs to be the pursuit. Replacing cookie identification also is a priority. Understand all data sources to “upgrade” for confidence, accuracy, privacy, and permissions.
  2. March 15 might be a good date to do an A/B split with your customer data inputs – pre-virus and during-virus. What new patterns emerged in media, app usage, mobile use and website visits? Are you able to identify your customers among this traffic? If not, that’s a data and tech gap that needs to be closed.
  3. Customer-centricity or data silos? It’s always a good time to tear down that silo and integrate the data, yet sometimes healthy economic growth can mask this problem. Use the recessions to free up some time to actually get the work done.
  4. Test new data and identity solution vendors to increase match rates across your omnichannel spectrum – to better create a unified view of audiences, both prospects and customers. I’ve already seen one of my clients come up with a novel offer to analyze a subset of unidentified data to drive a substantive lift in matches.

As we work remotely, it’s important to understand that this current state of crisis is not a permanent state. Only once the virus is conquered, on its weaknesses not ours, can we really have any timetable to resume the economy. That being the health science, it just makes great business sense now to “stage” your data for that eventual Grand Reopening.

Data Analytics Projects Only Benefit Marketers When Properly Applied

A recent report shared that only about 20% of all analytics projects work turns out to be beneficial to businesses. Such waste. Nonetheless, is that solely the fault of data scientists? After all, even effective medicine renders useless if the patient refuses to take it.

I recently read a report that only about 20% of all analytics projects work turns out to be beneficial to businesses. Such waste. Nonetheless, is that solely the fault of data scientists? After all, even effective medicine renders useless if the patient refuses to take it.

Then again, why would users reject the results of analytics work? At the risk of gross simplification, allow me to break it down into two categories: Cases where project goals do not align with the business goals, and others where good intelligence gets wasted due to lack of capability, procedure, or will to implement follow-up actions. Basically, poor planning in the beginning, and poor execution at the backend.

Results of analytics projects often get ignored if the project goal doesn’t serve the general strategy or specific needs of the business. To put it in a different way, projects stemming from the analyst’s intellectual curiosity may or may not align with business interests. Some math geek may be fascinated by the elegance of mathematical precision or complexity of solutions, but such intrigue rarely translates directly into monetization of data assets.

In business, faster and simpler answers are far more actionable and valuable. If I ask business people if they want an answer with 80% confidence level in next 2 days, or an answer with 95% certainty in 4 weeks, the great majority would choose the quicker but less-than-perfect answer. Why? Because the keyword in all this is “actionable,” not “certainty.”

Analysts who would like to maintain a distance from immediate business needs should instead pursue pure science in the world of academia (a noble cause, without a doubt). In business settings, however, we play with data only to make tangible differences, as in dollars, cents, minutes or seconds. Once such differences in philosophy are accepted and understood by all involved parties, then the real question is: What kind of answers are most needed to improve business results?

Setting Analytics Projects Up for Success

Defining the problem statement is the hardest part for many analysts. Even the ones who are well-trained often struggle with the goal setting process. Why? Because in school, the professor in charge provides the problems to solve, and students submit solutions to them.

In business, analysts must understand the intentions of decision makers (i.e., their clients), deciphering not-so-logical general statements and anecdotes. Yeah, sure, we need to attract more high-value customers, but how would we express such value via mathematical statements? What would the end result look like, and how will it be deployed to make any difference in the end?

If unchecked, many analytics projects move forward purely based on the analysts’ assumptions, or worse, procedural convenience factors. For example, if the goal of the project is to rank a customer list in the order of responsiveness to certain product offers, then to build models like that, one may employ all kinds of transactional, behavioral, response, and demographic data.

All these data types come with different strengths and weaknesses, and even different missing data ratios. In cases like this, I’ve encountered many — too many — analysts who would just omit the whole population with missing demographic data in the development universe. Sometimes such omission adds up to be over 30% of the whole. What, are we never going to reach out to those souls just because they lack some peripheral data points for them?

Good luck convincing the stakeholders who want to use the entire list for various channel promotions. “Sorry, we can provide model scores for only 70% of your valuable list,” is not going to cut it.

More than a few times, I received questions about what analysts should do when they have to reach deep into lower model groups (of response models) to meet the demand of marketers, knowing that the bottom half won’t perform well. My response would be to forget about the model — no matter how elegant it may be — and develop heuristic rules to eliminate obvious non-targets in the prospect universe. If the model gets to be used, it is almost certain that the modeler in charge will be blamed for mediocre or bad performance, anyway.

Then I firmly warn them to ask about typical campaign size “before” one starts building some fancy models. What is the point of building a response model when the emailer would blast emails as much as he wants? To prove that the analyst is well-versed in building complex response models? What difference would it ever make in the “real” world? With that energy, it would be far more prudent to build a series of personas and product affinity models to personalize messages and offers.

Supporting Analytics Results With Marketing

Now, let’s pause for a moment and think about the second major reason why the results of analytics are not utilized. Assume that the analytics team developed a series of personas and product affinity models to customize offers on a personal level. Does the marketing team have the ability to display different offers to different targets? Via email, websites, and/or print media? In other words, do they have capabilities and resources to show “a picture of two wine glasses filled with attractive looking red wine” to people who scored high scores in the “Wine Enthusiast” model?

I’ve encountered too many situations where marketers look concerned — rather than getting excited — when talking about personas for personalization. Not because they care about what analysts must go through to produce a series of models, but because they lack creative assets and technical capabilities to make it all happen.

They often complain about lack of budget to develop multiple versions of creatives, lack of proper digital asset management tools, lack of campaign management tools that allows complex versioning, lack of ability to serve dynamic contents on websites, etc. There is no shortage of reasons why something “cannot” be done.

But, even in a situation like that, it is not the job of a data scientist to suggest increasing investments in various areas, especially when “other” departments have to cough up the money. No one gets to command unlimited resources, and every department has its own priorities. What analytics professionals must do is to figure out all kinds of limitations beyond the little world of analytics, and prioritize the work in terms of actionability.

Consider what can be done with minimal changes in the marketing ecosystem, and for preservation of analytics and marketing departments, what efforts will immediately bring tangible results? Basically, what will we be able to brag about in front of CEOs and CFOs?

When to Put Analytics Projects First

Prioritization of analytics projects should never be done solely based on data availability, ease of data crunching or modeling, or “geek” factors. It should be done in terms of potential value of the result, immediate actionability, and most importantly, alignment with overall business objectives.

The fact that only about 20% of analytics work yields business value means that 80% of the work was never even necessary. Sure, data geeks deserve to have some fun once in a while, but the fun factor doesn’t pay for the systems, toolsets, data maintenance, and salaries.

Without proper problem statements on the front-end and follow-up actions on the back-end, no amount of analytical activities would produce any value for businesses. That is why data and analytics professionals must act as translators between the business world and the technical world. Without that critical consulting layer, it becomes the-luck-of-the-draw when prioritizing projects.

To stay on target, always start with a proper analytics roadmap covering from ideation to applications stages. To be valued and appreciated, data scientists must act as business consultants, as well.

 

What I Hope to Learn in Orlando’s Magic ‘Data’ Kingdom

The Association of National Advertisers (ANA) inaugural 2020 Masters of Data and Technology Conference kicks off today. It will be interesting to learn how brands see themselves transformed by all the digital (and offline) data surrounding prospects and customers at this Magic Data Kingdom in Orlando.

As I get ready to embark to the Association of National Advertisers (ANA) inaugural 2020 Masters of Data and Technology Conference (beginning today), I’m very curious to listen in and learn how brands see themselves transformed by all the digital (and offline) data surrounding prospects and customers.  With CMOs telling ANA that this topic area is a strategic priority, I don’t think I’ll be disappointed this week in Orlando’s Magic Data Kingdom.

Are “they” — the brands — finding answers to these questions?

  • Do they have command of data in all the channels of customer engagement?
  • Are they deriving new sources of customer intelligence that had previously gone untapped?
  • Can they accurately map customer journeys — and their motivations along the way?
  • Are they truly able to identify customers across platforms accurately with confidence?
  • How do data science and creativity come together to make more effective advertising — and meet business real-world objectives?
  • What disruptions are shaking the foundations of B2C and B2B engagement today?
  • Are investments in data and technology paying dividends to brands and businesses in increased customer value? Do customers, too, value the data exchange?
  • Is there a talent pool in adequate to deliver data-derived, positive business outcomes? What more resources or tools might they need?
  • What impacts do barriers on open data flows — walled gardens, browser defaults, privacy legislation, “techlash” — have on relevance, competition, diversity in content and other business, economic and social concerns? How can these be managed?
  • Are “brand” people and “data” people truly becoming one in the same in marketing, and in business?

Admittedly, that’s a lot of questions — and perhaps the answers to some of these may be elusive. However, it’s the dialogue among industry peers here that will matter.

The mere emergence of this conference — “new” in the ANA lexicon — is perhaps a manifestation of where the Data & Marketing Association (acquired by ANA in 2018) hoped to achieve in its previous annual conferences and run-up to acquisition. The full promise of data-driven marketing — and “growth” in an Information Economy — can only happen when brands themselves (and, yes, their agencies and ad tech partners, too) have command of data and tech disciplines, and consumers continue to be willing partners in the exchange.

Imagination lives beyond the domain of the Magic Kingdom (where we all can take inspiration from Disney, nearby). Likewise, aspirations can be achieved. Let’s listen in and learn as ANA takes rein of this brands- and data-welcomed knowledge share. Growth is a beautiful thing.

 

Data Love Story in the USA With a Few Spats, Too

You might call this time of year, Jan. 15 to March 15, marketing data’s “high season,” based on all of the goings-on. There’s a lot of data love out there — and, like all relationships that are precious, they demand a huge amount of attention, respect, and honor — and celebration.

I’ve been enjoying Alliant’s “Data and the Marketer: A Timeless Love Story” postings this month, leading up to Valentine’s Day.

You might call this time of year, Jan. 15 to March 15, marketing data’s “high season,” based on all of the goings-on:

The Alliant infographic download got me thinking of some other “key” dates that might also be recognized on the Data Love calendar, reflecting other aspects of the love story. Not all love affairs are perfect — are there any? Sometimes there’s a quarrel and spats happen, without any abandonment of a full-on love affair.

  • 1960 — The Direct Marketing Association (then, DMAA) develops its first self-regulatory ethics code for data and lists, in an early industry initiative to separate the good from bad players. It becomes the basis for practically every data protection (and consumer rights) framework since.
  • 1971 — The Mail Preference Service is launched (today DMAChoice) the first marketing industry opt-out control program for consumers — the essential framework for every consumer choice tool in marketing (in-house and industry-wide) since.
  • 1973 — The U.S. Department of Health, Education, and Welfare introduces and adopts eight Fair Information Principles. In 1980, the Organization of Economic Co-operation and Development adopts these principles for trans-border data flows. In 1995, The European Union, among other governments, enact variation and interpretation of these formally into law, eventually adopting the EU General Data Protection Regulation in 2018.
  • 1991 — Jennifer Barret is named Acxiom’s privacy leader — among the first enterprises to name what essentially would become a “chief privacy officer.” In 2000, Trevor Hughes launches the International Association of Privacy Professionals. A nascent cottage industry evolves into a huge professional education and development organization that today includes tens of thousands of members.
  • 1992 — A nonprofit and privacy advocacy organization, the Privacy Rights Clearinghouse, is formed, and soon thereafter begins tracking data security breaches, both public and private sector. Its breach list since 2005 is posted here. Data privacy and data security, as evidenced in Fair Information Practice Principles, go hand-in-hand.
  • 1994 — The first online display ad appears on the Internet, by AT&T. (And the first commercial email perhaps the same year.) So marked the humble beginnings of Internet marketing — “direct marketing on steroids.” I thought Jeff Bezos used this term in Amazon (formed 1994) early days during a DMA conference – but alas, I’m having a hard time sourcing that one. Perhaps this quote was related to Google (formed 1998) and the real-time relevance of search!
  • 1995-96 — Subscriber Ram Avrahami asserts a property right to his name in a lawsuit against S. News and World Report. Because he thwarted the spelling of his name on the magazine’s list – in a bid to discover who else the magazine rents its subscriber list to – the court ultimately rejects his challenge. The case, however, introduces a novel concept and set of questions:Is the value of any list or database tied to the presence of any one individual name on that list, a penny a name in this case?  Or, is its value because of the sweat of the brow of the list/database creator (a business, nonprofit group, or other entity) that built a common attribute to which a list may derive commercial value?The “walled gardens” of today’s Digital Giants largely were built on such data collection. These two questions recognize that a “data-for-value” exchange must be perceived as mutually beneficial, or else consumer trust is eroded. “Who owns the data?” (a 20th Century assertion) might be better substituted today as “Who has a shared interest in the value and protection of data?” (a 21st Century proposition).
  • 2006 — Facebook is formed, among the first companies that created a “social network.” (I’m sure the adult content sector preceded it, as it often points us the way.) In one industry after another, digital disruption reorders supply chains, consumer-brand relationships, shopping practices, and name-your-own-business here. The Great Recession, and venture capital, serves to speed the quest for data-defined efficiency and transformation.
  • 2017 — Equifax, one of the United States three leading credit and information bureaus on Americans, experiences a breach of epic proportions. While the nation was fascinated with subsequent public hearings about Facebook, its data deals, and its (ahem, beneficial) targeted advertising practices, a potentially much more egregious purveyor of harm – sponsored government hacking of the highest order – largely gets a ho-hum from the general public, at least until this past week.
  • 2020 — California fragments online privacy protection in the United States – only underscoring the need for the federal government to act sooner than later. Support Privacy for America.

So, yes, there’s a lot of Data Love out there — and, like all relationships that are precious, they demand a huge amount of attention, respect, and honor — and celebration. See you soon in Orlando!

 

 

‘Too Much’ Is a Relative Term for Promotional Marketing

If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month?

If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month? Just because it’s a huge brand? I bet it’s because “some” of its promotions are indeed relevant to your needs.

Marketers are often obsessed with KPIs, such as email delivery, open, and clickthrough rates. Some companies reward their employees based on the sheer number of successful email campaign deployments and deliveries. Inevitably, such a practice leads to “over-promotions.” But does every recipient see it that way?

If a customer responds (opens, clicks, or converts, where the conversion is king) multiple times to those 20 emails, maybe that particular customer is NOT over-promoted. Maybe it is okay for you to send more promotional stuff to that customer, granted that the offers are relevant and beneficial to her. But not if she doesn’t open a single email for some time, that’s the very definition of “over-promotion,” leading to an opt-out.

As you can see, the sheer number of emails (or any other channel promotion) to a person should not be the sole barometer. Every customer is different, and recognition of such differences is the first step toward proper personalization. In other words, before worrying about customizing offers and products for a target individual, figure out her personal threshold for over-promotion. How much is too much for everyone?

Figuring out the magic number for each customer is a daunting task, so start with three basic tiers:

  1. Over-promoted,
  2. Adequately promoted, and
  3. Under-promoted.

To get to that, you must merge promotional history data (not just for emails, but for every channel) and response history data (which includes open, clickthrough, browse, and conversion data) on an individual level.

Sounds simple? But marketing organizations rarely get into such practices. Most attributions are done on a channel level, and many do not even have all required data in the same pool. Worse, many don’t have any proper match keys and rules that govern necessary matching steps (i.e., individual-level attribution).

The issue is further compounded by inconsistent rules and data availability among channels (e.g., totally different practices for online and offline channels). So much for the coveted “360-Degree Customer View.” Most organizations fail at “hello” when it comes to marrying promotion and response history data, even for the most recent month.

But is it really that difficult of an operation? After all, any respectful direct marketers are accustomed to good old “match-back” routines, complete with resolutions for fractional allocations. For instance, if the target received multiple promotions in the given study period, which one should be attributed to the conversion? The last one? The first one? Or some credit distribution, based on allocation rules? This is where the rule book comes in.

Now, all online marketers are familiar with reporting tools provided by reputable players, like Google or Adobe. Yes, it is relatively simple to navigate through them. But if the goal is to determine who is over-promoted or adequately promoted, how would you go about it? The best way, of course, is to do the match-back on an individual level, like the old days of direct marketing. But thanks to the sheer volume of online activity data and complexity of match-back, due to the frequent nature of online promotions, you’d be lucky if you could just get past basic “last-click” attribution on an individual level for merely the last quarter.

I sympathize with all of the dilemmas associated with individual-level attributions, so allow me to introduce a simpler way (i.e., a cheat) to get to the individual-level statistics of over- and under-promotion.

Step 1: Count the Basic Elements

Set up the study period of one or two years, and make sure to include full calendar years (such as rolling 12 months, 24 months, etc.). You don’t want to skew the figures by introducing the seasonality factor. Then add up all of the conversions (or transactions) for each individual. While at it, count the opens and clicks, if you have extracted data from toolsets. On the promotional side, count the number of emails and direct mails to each individual. You only have to worry about the outbound channels, as the goal is to curb promotional frequency in the end.

Step 2: Once You Have These Basic Figures, Divide ‘Number of Conversions’ by ‘Number of Promotions’

Perform separate calculations for each channel. For now, don’t worry about the overlaps among channels (i.e., double credit of conversions among channels). We are only looking for directional guidelines for each individual, not comprehensive channel attribution, at this point. For example, email responsiveness would be expressed as “Number of Conversions” divided by “Number of Email Promotions” for each individual in the given study period.

Step 3: Now That You Have Basic ‘Response Rates’

These response rates are for each channel and you must group them into good, bad, and ugly categories.

Examine the distribution curve of response rates, and break them into three segments of one.

  1. Under-promoted (the top part, in terms of response rate),
  2. Adequately Promoted (middle part of the curve),
  3. Over-promote (the bottom part, in terms of response rate).

Consult with a statistician, but when in hurry, start with one standard deviation (or one Z-score) from the top and the bottom. If the distribution is in a classic bell-curve shape (in many cases, it may not be), that will give roughly 17% each for over- and under-promoted segments, and conservatively leave about 2/3 of the target population in the middle. But of course, you can be more aggressive with cutoff lines, and one size will not fit all cases.

In any case, if you keep updating these figures at least once a month, they will automatically be adjusted, based on new data. In other words, if a customer stops responding to your promotions, she will consequently move toward the lower segments (in terms of responsiveness) without any manual intervention.

Putting It All Together

Now you have at least three basic segments grouped by their responsiveness to channel promotions. So, how would you use it?

Start with the “Over-promoted” group, and please decrease the promotional volume for them immediately. You are basically training them to ignore your messages by pushing them too far.

For the “Adequately Promoted” segment, start doing some personalization, in terms of products and offers, to increase response and value. Status quo doesn’t mean that you just repeat what you have been doing all along.

For “Under-promoted” customers, show some care. That does NOT mean you just increase the mail volume to them. They look under-promoted because they are repeat customers. Treat them with special offers and exclusive invitations. Do not ever take them for granted just because they tolerated bombardments of promotions from you. Figure out what “they” are about, and constantly pamper them.

Find Your Strategy

Why do I bother to share this much detail? Because as a consumer, I am so sick of mindless over-promotions. I wouldn’t even ask for sophisticated personalization from every marketer. Let’s start with doing away with carpet bombing to all. That begins with figuring out who is being over-promoted.

And by the way, if you are sending two emails a day to everyone, don’t bother with any of this data work. “Everyone” in your database is pretty much over-promoted. So please curb your enthusiasm, and give them a break.

Sometimes less is more.

More Rules and Regulations for Content Marketers

So, content marketers, let’s talk about the regulatory environment more broadly, because one thing is for certain: the web, as wild and woolly as online discourse may be, is no longer the Wild West. Online marketing is now being held to a much higher standard.

Privacy protection, accessibility, and copyright —  oh, my!

Last time around, we talked about data privacy regulations as they apply to non-transactional sites. As confusing a landscape as those regulations currently present, they’re not the only regulations with which you need to be aware and compliant.

So, let’s talk about the regulatory environment more broadly, because one thing is for certain: the web, as wild and woolly as online discourse may be, is no longer the Wild West. Online marketing is now being held to a much higher standard than it has been, so you’ll want to be sure you have a plan in place to build your site by the book and to remain compliant. Otherwise, you risk spending more time talking to lawyers than to prospects.

Accessibility

If you built your website without accessibility in mind, chances are you’re not going to be happy when your website developers tell you what it’s going to cost to make it compliant. In many cases, it can make more sense to start from scratch, given the investment involved.

On the plus side, the cost to design and build a new website with compliance in mind is only incrementally greater than building that same site without WCAG Level AA compliance as your goal.

There is some extra work to be done, but for the most part, compliance requires a change in mindset for designers and some slightly different coding tactics for the dev team. Once that’s in place, it’s really only a matter of making sure new content additions are made in a compliant manner. (Image alt tags must be included, for example.)

You’ll want to include an accessibility statement on your site that includes a way for visitors who are having trouble consuming your content to contact you and seek remediation.

Privacy and Data Protection

As we’ve discussed, you need a privacy policy and you need to abide by it. If you haven’t told people that you’re planning on selling their email addresses to the highest bidder, you probably can’t. (Regulations differ by jurisdiction and industry; check with a lawyer.)

Once you have a collection of data, you need to take steps to keep that data safe, both in storage and in any transmittal or other use. Again, your industry may have specific compliance standards that you have to meet, and you may need to document the protections you’ve put in place.

Copyright

If you don’t own it, don’t publish it. This should be obvious, but often marketers make mistakes that can be costly.

Images are the most common area where errors occur. Doing a web search and then publishing any old image you find is a recipe for disaster. Going through a respected stock image library and paying for the images you use is the safest approach.

If you’d prefer not to go that route, you can use the Google Advanced Image Search tool. It is an excellent way to search for images to use in your digital marketing if you filter to include only those that are “free to use, share, or modify, even commercially.”

Don’t even think about trying to use an image from a stock image library without licensing it. They can and will find you. They can and will demand payment, usually well beyond what the initial license would have cost. (Also worth noting is that technically, for most stock image libraries, any image you use should be licensed under your firm’s name rather than by your design agency. That approach is also just smart business, because you may not always be working with that design team.)

When copy is purloined, it’s even easier to track down. Even if you get away with it, the search engines may very well penalize you for publishing duplicate content. There are other ways to get on the search engines’ bad sides, so be careful if you’re republishing content from other sources, even if it’s content that you have the right to republish.

Finally, think twice before stealing code. It’s an open source world, but that doesn’t mean you’re free to take and use anything you find in your travels. At the very least, attribution may be required. Most code libraries, snippets, etc., may require license fees — regardless of how they’re used. Some require payment only if you want updates or support. This can be harder for marketers to police, so be sure to have a regularly scheduled review with your dev team.

Spend Time on This

These regulations — and whatever may be coming down the pike in the future — make investing in digital expertise ever more important. Your team needs the time and mandate to stay on top of what regulations apply to your business and best practices for remaining compliant.

1 Year Later: Gen Z College Students Weigh in Again on Personal Data Collection

Last February, I reported on some of the things my Gen Z students wrote in response to an assignment about who gains the most from the value exchange of convenience-for-personal-data. A year later, I gave the same assignment with the same supplemental readings to students, and the results were notably different.

Last February, I reported on some of the things my Gen Z students wrote in response to an assignment about who gains the most from the value exchange of convenience-for-personal-data between consumers and marketers.

A year later, I gave the same assignment with the same supplemental readings to a similar group of 40 students from Rutgers School of Business Camden, and the results were notably different.

Last year, I wrote, in “Gen Z College Students Weigh-in on Personal Data Collection — Privacy Advocates Should Worry”:

“Some Gen Zers don’t mind giving up their personal data in exchange for the convenience of targeted ads and discounts; others are uneasy, but all are resigned to the inevitability of it. However, the language they use to describe their acquiescence to data collection should be troubling to privacy advocates.”

This year’s students are far more concerned about the collection and sale of their personal data, but they are just as resigned to the inevitability of it. At the same time, some bask in the advantages it brings them and they’re sympathetic to the needs of marketers to provide a personalized data-driven experience to consumers.

The privacy concerns of the current group are more pronounced than the previous group.

“I used to believe that the consumer benefitted from the perks of technology. But more and more, I believe that marketers benefit more. Social media, search engines, TVs, refrigerators, Alexa or Google Home, Kinsa Thermostat are all ways that marketers can reach the consumer with things we use in our everyday lives. Some people don’t even realize they’re feeding right into it just by providing some information about yourself.”

Another wrote:

“Privacy has almost become a thing of the past. Places like our kitchens, bathrooms, and bedrooms have transformed from places behind closed doors to areas that are willingly shared with thousands of others on the receiving end of the data being collected for business purposes.”

Yet, like last year’s group, they are resigned to giving up personal data for access to information and services.

“Consumers are beginning to realize how often what they do, speak, and read are all being recorded. Personally, I’ve been more aware than ever of what is being tracked. I’m more aware of every ad I look at and every website I clicked on. This lifestyle is something that can’t be avoided.”

A common complaint involves the lengthy user agreements that consumers must accept to use web-based services and Internet-connected devices:

“This type of ultimatum often means that consumers regularly grant permission on their personal devices, rather than lose their access to a particular product.”

The proliferation of the Internet of Things may be behind much of the change in attitude since last year. (Caveat: I confess that I’ve warned about small sample sizes in the past [“Beware the Small Sample”]. I’m not drawing quantitative conclusions here, but rather reporting on a trend from qualitative research done with 40 students each year).

“Some people who purchase these tech-savvy devices often don’t understand the policies of the product. Understanding the policy and happily opting-in for your information to be used is one thing, but complying because you’re unsure is another. Did you know that brands can start tracking your information at the age of 13? How can a child understand the policy and process of how this works if a grown adult cannot?”

Another stated:

“The terms of agreement can exceed 10,000 words and not be accessible unless the consumer searches the web for it. Consumers don’t get the full story of how much the companies invade their personal lives. Even aspects like your political preference are being monitored and can aid in influencing your votes.”

One student is mounting a fierce resistance:

“I am one of those people that have a Post-it over the camera on my laptop. I shut off the location on my phone, even though I feel like it is being monitored without my consent a lot of the time. My smart TV is not connected to the Internet, and I rarely use streaming devices, such as Netflix or Hulu — if I do, it is usually on my computer. Devices like Google Home and Alexa completely freak me out and I do not believe I would ever purchase one for my home. Even some of the newer home security systems — like Xfinity Home or the video doorbell, Ring — introduce new ways for people to hack in and monitor your personal activity.”

Data leaks and potential misuse are another concern. One student worried about home assistant devices mishearing innocuous phrases as legitimate commands to record and send private conversations:

“Families could be going through a family matter and these devices are listening and recording what is being said. Next thing you know, it is being sent to your boss or colleagues who did not need to hear or know what is going in in the comfort of your home. Also, the refrigerators that know exactly what is inside can share this information with marketers who then share it with insurers who can possibly charge consumers more for unhealthy diets.”

But it’s not all gloom and worry. One student who recently booked a trip to Disney World was delighted by the collection and use of her personal data:

“Being able to get discounted magic bands and Disney exclusive accessories catered for my needs has been a huge bonus. This also benefits Disney, as they are getting my credentials and can alter their research based on my specific data. A part of the reason they are so successful is because of how personal they make the process feel. Even from the first search, they are there to help guide you and aid in your conversion to purchase. (They) get you to come back, because they have that initial information and the personal details of your preference.”

(BTW, how great is Disney? Offering discounts on those magic bands that they use to track your movement and purchases throughout the park. They not only get you to agree to it, they get you to pay for it and be grateful for the discount).

So the time may be right for privacy advocates to gain a foothold among the generation whose members have gone so willingly into the world of sharing personal data.

How to Make Actionable Sense of Customer Sentiment Analysis

Creating a better customer experience is a top priority for most businesses, with 72% of companies saying improving CX is their No. 1 goal, according to data from Forrester. However, figuring out what drives a better user experience is a total guessing game, unless you take a deep dive into customer sentiment analysis.

Creating a better customer experience is a top priority for most businesses, with 72% of companies saying improving CX is their No. 1 goal, according to data from Forrester. However, figuring out what drives a better user experience is a total guessing game, unless you take a deep dive into customer sentiment analysis.

Understanding the responses and reactions that customers give out after using your products can help your brand immensely. Of course, conducting market research and surveys, and gathering feedback from customers are all small but essential steps toward improving your product or service, as well as its user experience. However, these reports are mostly a whole lot of confusing numbers and statistics; they offer no action plan or recommendations, or even insights on what to do next.

Making actionable sense of the numbers can be tricky, especially if there are no clear problems or opportunities that were identified through your research.

So, what should you do? Let’s go step-by-step.

Pinpoint Common Threads in Customer Reviews

While it’s typically a company’s first reaction to try to remove negative reviews that could deter future customers, these actually may be your best resource for fixing hidden issues.

About 25% of consumers have left a review for a local business because of a bad experience, but this doesn’t mean that 100% of these reviews are helpful to either companies or other customers. It’s best to turn to a reliable system here that can sift through emotionally exaggerated (and practically useless) or downright fake reviews and uncover valuable information that could point you toward better solutions.

A review platform, such as Bazaarvoice, allows brands to collect genuine ratings and reviews from customers, respond to their questions and concerns about their products, display moderated content created by customers on social media, and even implement a product sampling program based on the reviews you’ve collected.

Similarly, an interaction management tool, like Podium, gets you in the game earlier, helping you connect and interact with prospects on multiple channels. It enables team collaboration on lead generation and nurturing, as well as solving customer problems, leading to a consistent customer experience.

Customer Sentiment Analysis image
Credit: Podium.com

More customers tend to leave reviews with brands that use customer review management tools. This results in more data for your sentiment research, eventually ensuring better targeting and success of your product marketing campaigns.

Watch out for repeated keywords throughout these reviews, such as issues with customer service, packaging, delivery, or pricing. Looking for patterns in your customer reviews lies at the core of identifying the problems and coming up with solutions.

Use Smart Segmentation

Customers never fit into the one-size-fits-all category. Even if you cater to a small niche or if your product has a very specific use, there will be subsets, segments, and cohorts, all influenced by varying demographics and regulations, who could affect opinions of your business. This is why smart segmentation is important when reviewing customer sentiment analysis.

Again, these segments may need different targeting strategies, depending on whether your company is a B2C or B2B entity.

B2C

B2C marketers need to look at the:

  • age:
  • location:
  • income: and
  • in-the-moment needs of their customers.

B2B

B2B marketers, on the other hand, need to address non-personal variances, such as:

  • company size:
  • budget; or
  • objectives.

By pairing demographic and quantitative data, customer sentiment may make more sense and provide even deeper insight than before. For instance, customers who are motivated by finding the best deal may say that your shipping costs are too high; whereas, customers with FOMO may be ready to pay extra for next-day delivery. When you have multiple datasets of behavioral data that you can compare against one another, your team can understand how to cater to various customer segments by understanding their motivations.

Note that customer “segments” vary from “profiles” or “personas.” They are not as specific, and typically only focus on one or two variables rather than a list of unique qualities. There are countless ways to segment your audience, so be sure to find the segmentation model that best fits your business.

Customer Sentiment Analysis photo
Credit: MeaningCloud.com

Identify Engagement Intent

Understanding the “why” behind your customer’s actions will shed some light on their sentiment reactions. Your expectations always influence your experience, so a customer’s engagement intent could play a part in their response.

The rise of search as a marketing channel has made it clear that there are essentially four engagement intent categories that consumers fall into today:

  • informational;
  • navigational;
  • commercial; and
  • transactional.

Each of these steps correlates well with the traditional AIDA sales funnel model.

Informational

The first is searching for information on a particular subject that may or may not be a problem for them. These are typically prospects who are just entering the marketing funnel. They simply want to know more, so if your website does not offer the information they are looking for, their interest in your brand or product will not develop at all.

Navigational

People in the navigational category are looking for a specific product, service, or piece of content. This group knows what they want, and they will be easily frustrated if they can’t find it.

Commercial

The commercial investigation intent group is interested in buying, but they just aren’t quite ready yet or aren’t convinced that your product offers the best solution for them. They fall just above the action segment of the sales funnel and are often looking for the last bits of information before they make a purchase.

Transactional

And finally, the transactional group has the intent to buy. They have already made their decision to buy a specific product; however, any hiccups in the buying or checkout process could deter them.

Identifying Engagement Intent

Of course, identifying their engagement intent is a little tricky, especially after the interaction has been completed. But with some digging and martech tools, there are ways to figure out the motivations behind every brand-customer engagement.

One of the clearest ways to identify engagement intent is through carrying out intent research, attribution modeling, and analyzing their behavior on your digital property. If they just read a post on your blog, chances are they were looking for more information on a topic related to your industry. If they clicked an ad and filled up a form on your landing page, they are probably interested in availing themselves of your service.

Once their intent has been identified and understood, it will be much easier to understand their sentiment post brand engagement or product usage.

Experiment With Changes

Finally, the only way to make customer analysis actionable is to, well, take action. However, just switching things up without constantly analyzing the results will only put you back at Square One.

Many marketers rely on A/B/n or multivariate testing strategies to compare different changes, whether it be in the design or layout to an entire product or service experience. However, A/B testing can be a long and arduous process that yields murky results. It may even mislead you, if you over-rely on seasonal or contextual variables. Unsurprisingly, AI technology has been a huge help in the A/B testing realm by improving the accuracy and reliability of the process, resulting in few conversion opportunities lost.

AI-based algorithms are able to gather and analyze massive amounts of data at a time. They can compare results of multiple tests against each other simultaneously at various interaction points along the buyer journey.

Tools like Evolv use machine learning (ML) to find which experiences and customer journey paths work best (make profits) for you and nudge customers down those paths accordingly. You can set up experiments on your landing pages with goals and KPIs, and let the algorithm tweak the UX for each customer by presenting various combinations. The data from these experiments help you understand how satisfied the customer is with the interaction, and also develop new hypotheses to keep testing further or make decisions related to product development or service delivery.

The Way Ahead

By understanding the root causes behind your customer’s reactions and feelings, you can go as far as to influence sentiment, improve brand loyalty ,and encourage advocacy. Always be looking for overlaps and commonalities among complaints. This will help you avert PR disasters, deliver exceptional customer service, and stay ahead of the competition.

Use sentiment analysis to understand where your customers are coming from by segmenting them and uncovering their intents at every interaction. Finally, track the effects of all your initiatives and take action responsibly to ensure they stay delighted at all times.