Is Loyalty Boring Customers?

Found an interesting article from September 2014 from Caroline Papadatos which discusses the gamification of loyalty programs.  It really gets the mind going, because I think the gasification side is not data driven enough and the opposite is true from the data side.

A few weeks ago, I had the privilege of judging the 2014 LoyaltyGames, an incredible week-long global challenge involving 1,500 practitioners and students from 102 countries, with 15 judges who were remarkably, never in the same room nor on the same continent.

The 2014 contest had three components: awareness building, game design and loyalty building.  The game experiences were clever and fun, and I was won over by the sheer creative genius of the contest submissions. The loyalty component was straightforward: reward and recognize customer / donor tiers without breaking the bank. With a gamification spin, it meant solving a conventional customer engagement problem with an unconventional tool set. Sounds simple enough, but as I scanned case submissions looking for earn ratios and attainability models, all I could find were badges, likes, certificates and pins.

It is fascinating how much badges and pins can get people excited.  The basis of these games has a lot of merit, but what I have a problem with is the same with social media as a channel, it is not targeted at all.  There's no meat behind the game.

My answer came from Gabe Zichermann who in a recent eight-part gamification series in COLLOQUY Magazine makes the bold statement that “loyalty isn’t fun enough anymore” and our customers are bored. Gabe clearly has a point – loyalty now competes for attention in a world where Angry Birds has been downloaded two billion times. It gets worse. At the LoyaltyGames award ceremony, a renowned gamification expert accused loyalty programs of “bribing” their customers. Now my back is up, but are we outraged or outdated? 

The truth is that loyalty programs need a shot in the arm, and while experience design always has a place in the loyalty tool set, few data practitioners are charming or entertaining. And gaming is not just for Millennials. The average social gamer is a 43-year-old-woman, which just happens to be the primary target market for grocers, drugstores and a host of other retailers. So why aren’t loyalty practitioners flocking to gaming? 

I totally agree, loyalty programs need a shot in the arm.  As I have written before, most people engaging with loyalty programs are just taking the free stuff, theres very little loyalty or behavior being driven from them.  It is fascinating to combine the rich data from the loyalty programs to the fun concepts in gamification to create a targeted loyalty gamification model.  I think this would work extremely well.

I could imagine a program where certain behaviors are awarded more points and a bounce back offer could include multiple point thresholds for buying everything in a market basket analysis.  So if the customer who usually buys a TV also buys cables, programmable remotes and a blue-ray player, the customer will get multipliers if these are purchased in the next 2 months.  This gives some fun to the loyalty program, while driving the behavior to purchase items that are typically purchased with TV's.  The best of both worlds.

There’s no doubt that loyalty programs lose their luster when they became overly programmatic, but where gaming meets transactional data analysis and customer behavior change, there are notable exceptions. BrandLoyalty’s Instant Loyalty Programs in Europe, Asia and South America have a huge fun-factor for retail shoppers – on the surface they’re a widely popular collectible game for children but there is a financial underpinning that drives incremental spend, participation and superior financial performance based on maximum turnover & transactions from family households.

Whether you’re pro-loyalty or gamification, you can certainly agree with Gabe on this: “taking something that’s crummy and putting some game frosting on it won’t magically change your customer”. But let’s face it, the mix of gaming techniques and data-driven loyalty can only be good for business. And be honest, if you were given the choice of getting on a plane for yet another industry slideshow or signing up for a multi-player gaming challenge, which would you choose?

Perfect combination, a shot in the arm.  The technology exists, lets gamify our programs.  This is what I have been harping on about for a month.  These are the types of things that create great customer experiences.    

Enrollment vs Engagement: Loyalty In Action

Getting to the checkout without hearing that phrase is a modern feat of humankind. Retailers, financial services, and expanding sectors like drugstores and restaurants know that loyalty membership programs are one of the easiest ways to get individual data on shoppers while enhancing the brand-to-consumer relationship. But when shoppers are asked (and asked, and asked again), they often can’t see the forest for the trees—that a loyalty program is an opportunity for consumers themselves to customize a relationship with brands.

The average number of loyalty programs per US household has grown to 29, based on data gathered by Colloquy. That same loyalty data shows that only 42 percent of those memberships are currently active. Just imagine what advantages consumers are missing out on when over half of their memberships are disused. Enrollment doesn’t necessarily mean engagement.

In an offline world the enrollment of a customer into the loyalty program is the main metric associates are measured to determine if they are pushing the loyalty program.  I have never liked this metric, because it doesn't measure the true purpose of a loyalty program.  

The true purpose of a loyalty program is to get your customers to engage with it.  Enrollments are a byproduct of engagement.  The metric that should be used is an engagement %.  Instead of measuring the Enrollments by associate, a better metric would be to measure the amount of sales tracked with a loyalty rewards number versus the total amount of sales for each associate.  The higher the %, the better the associate.  When this engagement % increases, I will guarantee the enrollments will increase with it.  Plus it enforces associates to be on the lookout for the best customers, not just the customers that will help them reach their goal of enrollments.

5 habits of effective data-driven organizations

Size doesn’t matter, but variety does. You would think that a data-driven organization has a lot of data, petabytes of data, exabytes of data. In some cases, this is true. But in general, size matters only to a point. For example, I encountered a large technology firm with petabytes of data but only three business analysts. What really matters is the variety of the data. Are people asking questions in different business functions? Are they measuring cost and quality of service, instrumenting marketing campaigns, or observing employee retention by team? Just getting a report at month end on profits? You’re probably not data driven.

As I have articulated previously, data-driven organizations are a culture, it is not about toolsets or data scientists.  It doesn't matter how much data you have, it matters that you have enough data to make an informed business decision.

Everyone has access to some data. Almost no one has access to all of it. There are very few cultures where everyone can see nearly everything. Data breach threats and privacy requirements are top of mind for most data teams. And while these regulations certainly stunt the ability of the company to make data available, most data-driven companies reach a stage where they have developed clear business processes to address these issues.

It comes down to what data is important for each business unit.  Most business units don't need credit card information or PII information about individual customers.  Understanding what data will drive better business decisions in each unit and focusing on getting those units the needed data in a consumable format is the key.

Data is all over the place. One would think that the data is well organized and well maintained — as in a library, where every book is stored in one place. In fact, most data-driven cultures are exactly the opposite. Data is everywhere — on laptops, desktops, servers.

This can be dangerous.  Remember there is nothing worse than fighting about the validity of data.  If operating units all have their own sets of data, then it becomes a competition of who's data is right instead of what decision we should make based on the information at hand.

Companies prize insights over technology standards. Generally, the principal concern of people in data-driven businesses is the ability to get the insight quickly. This is a corollary of point #3. Generally, the need to answer a question trumps the discussion of how to best answer it. Expediency wins, and the person answering the question gets to use the tool of their choice. One top 10 bank reported using more than 100 business intelligence technologies.

I really like this, as long as you don't fall into the trap I discussed above.  To get people to adjust to a technology instead of providing insight is lost time.  Getting a huge organization on 1 platform is problematic at best, a disaster at worst.  If analysts can work in tools they have mastered, it will allow them to get insights faster.  Faster insight is a major competitive advantage.

Data flows up, down, and even side to side. In data-driven companies, data isn’t just a tool to inform decision makers. Data empowers more junior employees to make decisions, and leaders often use data to communicate the rationale behind their decisions and to motivate action. In one data-driven company, I observed a CEO present a 50-slide deck to his full team, and almost all of those slides were filled with charts and numbers. Most fundamentally, data empowers people to make decisions without having to consult managers three levels up — whether it’s showing churn rates to explain additional spend on customer services vs. marketing or showing revenues relative to competitors to explain increased spend on sales.

The old thinking was to create a business intelligence team that would provide the data for the organization.  Each operating unit should be in charge of their own data analytics.  There should be a centralized business intelligence team to provide a checks and balances, but operating units are best to answer their own questions, they know their business best.  Democratizing data throughout the organization is key to having a data-driven organization.  

Brands Don’t Know Their Customers As Well As They Think They Do

Chris Crum writes for webpronews.com:

IBM and Econsultancy have some new research out suggesting a “massive perception gap” between how well brands think they are marketing to their customers and how well customers actually think brands know them. Businesses think they’re doing a pretty good job. Consumers, not so much.

The study, which surveyed businesses and customers specifically in the United States, found that about 90% of marketers do agree that personalization of marketing campaigns is critical to their success. Even still, 80% of consumers polled don’t think the average brand understands them as individuals. This is despite consumers sharing more personal details with businesses than ever before. Some how, brands are still failing to make the most of it.

In my experience, marketers can be their own worst PR agents.  For the most part, they understand what their customers want, but they can't deliver.  However, they are constantly spinning what they are doing as to seem as though they are meeting the customers demands.  So this survey doesn't surprise me.  I'm surprised that 80% of customers don't feel like they are individuals.  It's hard to create great customer experiences with this stat.

The IBM/Econsultancy research found that 80% of marketers “strongly” believe they have a holistic view of individual customers and segments across interactions and channels. They also strongly believe in their ability to deliver “superior experiences” offline (75%), online (69%), and on mobile devices (57%). Yet just 47% of marketers say they’re able to deliver relevant communications.

Worse yet, customers don’t think they’re getting personalized experiences. Only 37% said their preferred retailer understands them as an individual. And that’s the preferred one. Only 22% said the average retailer understands them. 21% said communications from their average retailer are “usually relevant”. 35% said communications from their preferred retailers are “usually relevant”.

The biggest disconnect with marketers is in implementation.  In the survey they state they believe they can deliver "superior experiences", yet just 47% say they are "able".  So marketers believe they have the strategy to be great in the area of customer experience, the technology or knowhow to deliver these great strategies is lacking.  A lot of that comes down to the relationship with the CIO.  As I wrote in Across The Board, CMOs Struggling To Deliver An Integrated Customer Experience, until the CIO and CMO speak the same language and the CMO embraces technology, this will continue to be an issue for marketers in the future.  When only 37% of customers believe their preferred retailer knows them at all, there is an issue.

“One explanation for relevancy void may be a lack of innovation for the multi-channel lives we all lead,” IBM said. “According to the study, only 34 percent of marketers said they do a good job of linking their online and offline customer experiences. With the vast majority of dollars spent offline and the majority of product research happening on the Internet, the two are already linked for consumers but this gulf must close for marketers if they are to advance. One issue is the technology of integration, with only 37 percent of marketers saying they have the tools to deliver exceptional customer experiences.”

The technology exists today, marketers just have to embrace it.  The technology is nascent, so it is harder to implement, but this can be done today with hard work.  The results will be well worth the effort.

“The customer is in control but this is not the threat many marketers perceive it to be. It’s an opportunity to engage and serve the customer’s needs like never before,” said Deepak Advani, GM at IBM Commerce. “By increasing investments in marketing innovations, teams can examine consumers at unimaginable depths including specific behavior patterns from one channel to the next. With this level of insight brands can become of customer’s trusted partner rather than an unwanted intrusion.”  

Advani is correct in labeling this an opportunity.  For the marketers who dare to embrace the new realities of digital marketing, they will reap the benefits that come from delivering targeted content creating exceptional customer experiences.  For the marketers that don't embrace this sea-change, their companies will become less relevant in the digital age.  

If Algorithms Know All, How Much Should Humans Help? - NYTimes.com

Steve Lohr writes for NYTimes.com:

Armies of the finest minds in computer science have dedicated themselves to improving the odds of making a sale. The Internet-era abundance of data and clever software has opened the door to tailored marketing, targeted advertising and personalized product recommendations.

Shake your head if you like, but that’s no small thing. Just look at the technology-driven shake-up in the advertising, media and retail industries.

This automated decision-making is designed to take the human out of the equation, but it is an all-too-human impulse to want someone looking over the result spewed out of the computer. Many data quants see marketing as a low-risk — and, yes, lucrative — petri dish in which to hone the tools of an emerging science. “What happens if my algorithm is wrong? Someone sees the wrong ad,” said Claudia Perlich, a data scientist who works for an ad-targeting start-up. “What’s the harm? It’s not a false positive for breast cancer.”

I have written here many times of analytics being a combination of "art" and "science".  Having data and insight leads to the most action, yet some data scientists want to remove the "art" part of the equation.  The belief is that computers and algorithms can see more about the data and the behavior than a human ever could.  Also, once there is so much data about an individuals behavior, there is no "art" left, all the data points are accounted for so the "science" is indisputable.  

However, I have a hard time believing that "art", or the human insight, will ever be replaceable.  There are so many variables still left unknown and a computer can't know all of them.  The "science" portion will always get better at explaining the "what" happened, but they don't understand the business operations and strategy that goes behind the decisions that were made. I am a true believer in the "big data" coming of age.  I believe it is fundamentally changing the way companies have to do business, but never forget about the human side, the "art" of understanding "why" the data is telling you "what" is happening.  

These questions are spurring a branch of academic study known as algorithmic accountability. Public interest and civil rights organizations are scrutinizing the implications of data science, both the pitfalls and the potential. In the foreword to a report last September, “Civil Rights, Big Data and Our Algorithmic Future,” Wade Henderson, president of The Leadership Conference on Civil and Human Rights, wrote, “Big data can and should bring greater safety, economic opportunity and convenience to all people.”

Take consumer lending, a market with several big data start-ups. Its methods amount to a digital-age twist on the most basic tenet of banking: Know your customer. By harvesting data sources like social network connections, or even by looking at how an applicant fills out online forms, the new data lenders say they can know borrowers as never before, and more accurately predict whether they will repay than they could have by simply looking at a person’s credit history.

The promise is more efficient loan underwriting and pricing, saving millions of people billions of dollars. But big data lending depends on software algorithms poring through mountains of data, learning as they go. It is a highly complex, automated system — and even enthusiasts have qualms.

“A decision is made about you, and you have no idea why it was done,” said Rajeev Date, an investor in data-science lenders and a former deputy director of Consumer Financial Protection Bureau. “That is disquieting.”

Blackbox algorithms have always been troubling for the majority of individuals, even for the smartest of executives when trying to understand their business.  Humans need to see why.  There is a reason why Decision Trees are the most popular of the data models, even though they inherently have less predictive prowess than their counterparts like Neural Networks.

Decision Trees output a result that a human can interpret.  It is a road map to the reason why the prediction was made.  This makes us humans feel comfortable.  We can tell story around the data that explains what is happening.  With a blackbox algorithm, we have to trust that what is going on inside is correct.  We do have the results to measure against, but as these algorithms become more commonplace, it will be imperative that humans can trust the algorithms.  In the above bank loan example, when making decisions regarding bank loans, a human needs to understand why they are being denied and what actions they can take to secure the loan in the future.  

This ties into creating superior customer experiences.  Companies that will be able to harness "big data" and blackbox algorithms and create simple narratives for customers to understand will have a significant competitive advantage.  Creating algorithms to maximize profits is a very businesslike approach, but what gets left out is the customer experience.  What will happen over time is the customer will dislike the lack of knowledge and communication and they will not become future customers.  A bank may say, this is good, they would have defaulted anyway.  But what happens in the future when too many people have bad customer experiences?  I don't believe that is a good longterm strategy.  

In a sense, a math model is the equivalent of a metaphor, a descriptive simplification. It usefully distills, but it also somewhat distorts. So at times, a human helper can provide that dose of nuanced data that escapes the algorithmic automaton. “Often, the two can be way better than the algorithm alone,” Mr. King said.  

Businesses need to also focus on the human side.  When we forget there is also an "art" to enhance all of these great algorithms, businesses will be too focused on transaction efficiency instead of customer experiences which in turn will lead to lower sales.  

Across The Board, CMOs Struggling To Deliver An Integrated Customer Experience

Daniel Newman writes for Forbes:

Back in January of this year in an article entitled Are CMOs Poised To Take Over Technology Purchasing? I wrote that “Whether they (CMOs) are ready or not, technology is fast becoming an inextricable part of the CMO’s functions, and they need to participate in making tech decisions in order to determine the ROI for purchases.”

Based upon the results of a recently released study from The CMO Club and Oracle Marketing Cloud a great number of CMOs are indeed not ready to utilize the technology that is available to them as a means to deliver upon long sought after integrated customer experience.

The days of a CMO not being technology savvy are over.  CMO's need to understand technology as well as they do brand.  The tools being developed in the marketing cloud space are very compelling, but they are nascent, so the demands to implement are greater than they will be 5 years from now.  Implementing technology toolsets are not for the faint of heart and the better the CMO understands the toolsets, the faster to market.  

CMO's should be data savvy.  They should understand where the data lives, how it flows and what the data is telling them about the customer.  It all starts with the data.  

Be the customer champion every step of the way: CMOs need a clear understanding of how customers and prospects interact with their brands at every stage, from consideration, to engagement, to purchase and advocacy. They are the voice of the customer, translating insights to actions across every organizational function.

This was a big focus of Adobe Marketing Cloud Summit 2015.  Their tagline "Marketing beyond Marketing", which didn't resonate as much as they hoped, is what the customer experience is all about.  Marketing has to be involved with all touchpoint throughout the organizations.  This involves operations units which have not been a priority for marketing in the past.  

Become BFFs with your CIO: Of those surveyed, only one of 110 respondents referenced a positive relationship with their CIO. A critical action item for a CMO is to reach out to their CIO to collaborate, plan, and integrate activities.

This may be easier said than done.  Most CIO's and CMO's do not speak the same language.  If a CMO is technologically savvy, it will be easier to communicate with the CIO to create the technology roadmap for the customer experience.  The scary part of this is only 1 out 110 CMO's surveyed have a positive relationship with their CIO.  Either the CMO has to move toward technology or the CIO has to move towards marketing.  I prefer the former.  

Co-design the optimal customer-driven technology roadmap: CMOs need to develop an understanding of the technology that is required to deliver the optimal customer experience and co-design the technology roadmap with the CIO, allowing flexibility in design to incorporate new technology and third party applications.

Again, this becomes impossible if the CMO and CIO are not in sync.  Both sides have to respect each other for the relationship to become collaborative and if the CMO is not also a technologist, the chances of this item happening are slim.  

Rethink your marketing organization and processes: There are many formal and informal opportunities to create collaboration across marketing departments and technology. As critical as it is to building the right culture and cross-functional environment, it’s also critical to hire the right talent.

As I wrote in Agile is the Key to Digital Marketing Success, the structure of the marketing organization needs to be changed.  Marketing organizations need to include technology resources in order to be agile in the digital marketing age.  Developing a technology culture within the marketing organization is a main component for delivering great customer experiences.

Establish a system for continuous improvement: The customer is outpacing companies in terms of their expectations for personalized service compared to a company’s ability to act on the information – both technologically and analytically. The CMO of today must – in addition to being agile – be open to taking chances and remain risk receptive.

If you're not failing you're not trying.  Marketing is a living breathing entity, especially in the digital age.  There will never be a time when a marketing organization can implement a plan and then check it off the list.  CMO's need to have their fingers on the pulse of society and the technology that customers are moving towards.  Just when a company has implanted their mobile strategy, here comes the watch and the Internet of Things that may change the way marketers have to think.  Having a technologist as the CMO will increase the chances that the organization will stay in touch with the customers, no matter where they move to next.

Business Intelligence for the Other 80 Percent

Ted Cuzzillo writes for Information-Technology:

We give business people everything. They’ve got data, and often it’s clean. They’ve got tools, and many are easy to use. They’ve got visualizations, and many of them speed things up. They’ve got domain knowledge, at least most do. Tell me: Why hasn’t business intelligence penetrated more than about 20 percent of business users?

This is a great question.  So many organizations have executive leadership that says they want information, dashboards and realtime information, yet when provided to them, it goes unread.  How does this happen?  The answer is what most executives want is a story.  They want someone to interpret the analytics and let them know what they should be looking at.  The dashboards act as content for speaking points.  Executives want the most important numbers at their fingertips so they can spit them out at a moments notice.  

What executives want is the rest of the data to be fed to them in a story with a narrative.  Here is the data, here is what we believe it says and here is what we are going to do about it.  It coincides with my article Data + Insight = Action.  

What executives need is all of these parts (data, insight and action) in one analysis.  They need to see the data, using visualizations to make the data easier to read.  They need the insight of the business experts in the form of a commentary, succinct and to the point.  Then they need what action is the business going to take with this newfound knowledge.  With all of this information to arm the executive, they can understand and make a decision on what to do.  

To reach "The Other 80 Percent," let’s turn away from the “data scientist” and to the acting coach. “A lot has to do with intangible skills,” said Farmer. A lot also has to do with traditional story structure, which appeals to “a deep grammar that’s very persuasive and memorable.”

Storytelling isn’t a feature, it’s a practice. One practicing storyteller, with the title “transmedia storyteller,” is Bree Baich, on the team of Summit regular Jill Dyché, SAS vice president, best practices.  While others talk about stories, she said, most people seem to start and end with data and leave out the storytelling art. They fail to connect data with any underlying passion. “What we need are translators, people who understand data but can tell the human story from which it arose.”

There is always an assumption that is made from an analyst that a visualization or a table of data is plain and understandable.  A good rule of thumb is to assume the audience of an analysis doesn't see what the analyst is seeing.  If analysts start with this assumption, they can then tell a story of why this data is fascinating.  An analysis without text that explains why the data is interesting is going to fall on deaf ears.  Once the analysis gets to a higher level, the executives will not have time to create the "insight" portion of the data and they will either send the analysis back, or ignore it completely.  Always remember to include the data, with the insight as a story and what action is going to be taken.  With this formula analysts will become more than report generators.  

Social Media: Stop It With Pointless Metrics

From Martin McDonald:

We’ve all been there, sat in a meeting with your boss, or client, and they’ve said something like:  “Our competitors have got 40,000 Facebook likes and 20,000 followers on twitter more than we do, we need to double down on our Social Media!”.

Let’s be perfectly clear, tracking social media based on likes, or follower numbers, is a pointless metric. For a start, both can be easily gamed, but increasingly platform are moving towards more sophisticated content targeting which for many companies means their chances of getting an ROI out of social media is significantly reduced.

I couldn't agree more.  I remember when we were first launching our social media sites for our brands at a casino/hotel company I was working.  We were so obsessed with gaining followers, yet no one was really engaging with the content we were providing.  Gaining followers was important, but if we weren't producing relevant content, then the followers would not lead to any brand equity.  

The analytics that Facebook and Twitter are putting out are a good start:

Social media should never be considered a “broadcast medium” ,  its no longer suitable as a one to many distribution – it should be considered a discussion medium, where you can engage your audiences with your message, your brand and your personality.

Moving away from messaging and towards discussion and interaction reveals the true metrics you should be concerned with: Engagement rates!

Measuring Social Media Effectively

Thankfully, both Twitter and Facebook provide lots of metrics, and have robust, free, analytics platforms.

Twitter recently revamped their entire analytics platform and its accessible to everyone with an account just by going to http://analytics.twitter.com and it provides in depth statistics on a per tweet basis. 

Being able to manage engagement has always been something I have been very interested in.  Content is king and just broadcasting what you're selling or information that doesn't appeal to the many of your followers will result in ignoring your messages.  This is very similar to email marketing.  

7 Limitations Of Big Data In Marketing Analytics

Anum Basir writes:

As everyone knows, “big data” is all the rage in digital marketing nowadays. Marketing organizations across the globe are trying to find ways to collect and analyze user-level or touchpoint-level data in order to uncover insights about how marketing activity affects consumer purchase decisions and drives loyalty.

In fact, the buzz around big data in marketing has risen to the point where one could easily get the illusion that utilizing user-level data is synonymous with modern marketing.

This is far from the truth. Case in point, Gartner’s hype cycle as of last August placed “big data” for digital marketing near the apex of inflated expectations, about to descend into the trough of disillusionment.

It is important for marketers and marketing analysts to understand that user-level data is not the end-all be-all of marketing: as with any type of data, it is suitable for some applications and analyses but unsuitable for others.

There are a lot of companies looking towards "big data" as their savior, but just aren't ready to implement.  This leads to disenfranchisement towards lower level data.  It reminds me of the early days of Campaign Management (now Marketing Automation) where there were so many failed implementations.  The vendors were too inexperienced to determine how to successfully implement their products, the technology was too nascent and the customers were just not ready culturally to handle the products.  This is "big data" in a nutshell.  

1. User Data Is Fundamentally Biased

The user-level data that marketers have access to is only of individuals who have visited your owned digital properties or viewed your online ads, which is typically not representative of the total target consumer base.

Even within the pool of trackable cookies, the accuracy of the customer journey is dubious: many consumers now operate across devices, and it is impossible to tell for any given touchpoint sequence how fragmented the path actually is. Furthermore, those that operate across multiple devices is likely to be from a different demographic compared to those who only use a single device, and so on.

User-level data is far from being accurate or complete, which means that there is inherent danger in assuming that insights from user-level data applies to your consumer base at large.

I don't necessarily agree with this.  While there are true statements, having some data is better than none.  Would I change my entire digital strategy on incomplete data?  Maybe if the data was very compelling, but this data will lead to testable hypothesis that will lead to better customer experiences.  Never be afraid of not having all the data and never search for all the data, that pearl is not worth the dive.

2. User-Level Execution Only Exists In Select Channels

Certain marketing channels are well suited for applying user-level data: website personalization, email automation, dynamic creatives, and RTB spring to mind.

Very true.  Be careful to apply to the correct channels and don't make assumptions about everyone.  When there is enough data to make a decision, use that data.  If not, use the data you have been working with for all these years, it has worked up till now.

3. User-Level Results Cannot Be Presented Directly

More accurately, it can be presented via a few visualizations such as a flow diagram, but these tend to be incomprehensible to all but domain experts. This means that user-level data needs to be aggregated up to a daily segment-level or property-level at the very least in order for the results to be consumable at large.

Many new segments can come from this rich data and become aggregated.  It is fine to aggregate data for reporting purposes to executives, in fact this is what they want to see.  Every once in awhile throw in a decision tree or a naive bayes output to show there is more analysis being done at a more granular level. 

4. User-Level Algorithms Have Difficulty Answering “Why”

Largely speaking, there are only two ways to analyze user-level data: one is to aggregate it into a “smaller” data set in some way and then apply statistical or heuristic analysis; the other is to analyze the data set directly using algorithmic methods.

Both can result in predictions and recommendations (e.g. move spend from campaign A to B), but algorithmic analyses tend to have difficulty answering “why” questions (e.g. why should we move spend) in a manner comprehensible to the average marketer. Certain types of algorithms such as neural networks are black boxes even to the data scientists who designed it. Which leads to the next limitation:

This is where the "art" comes into play when applying analytics on any dataset.  There are too many unknown variables that go into a purchase decision of a human being to be able to predict with absolute certainty an outcome, so there should never be a decision to move all spending in some direction or change an entire strategy based on any data model.  What should be done is test the new data models against the old way of doing business and see if they perform better.  If they do, great, you have a winner.  If they don't, use that new data to create models that will maybe create better results than the current model.  Marketing tactics and campaigns are living and breathing entities, they need to be cared for and changed constantly.

5. User Data Is Not Suited For Producing Learnings

This will probably strike you as counter-intuitive. Big data = big insights = big learnings, right?

Actionable learnings that require user-level data – for instance, applying a look-alike model to discover previously untapped customer segments – are relatively few and far in between, and require tons of effort to uncover. Boring, ol’ small data remains far more efficient at producing practical real-world learnings that you can apply to execution today.

In some cases yes, but don't discount the learnings that can come from this data.  Running this data through multiple modeling techniques may not lead to production ready models that will impact revenue streams overnight.  These rarely happen and takes many hundreds of data scientists with an accuracy rating of maybe 3% of the models making it into production.  However, running data through data mining techniques can give you unique insights into your data that regular analytics could never produce.  These are true learnings that create testable hypothesis that can be used to enhance the customer experience.

6. User-Level Data Is Subject To More Noise

If you have analyzed regular daily time series data, you know that a single outlier can completely throw off analysis results. The situation is similar with user-level data, but worse.

 This is very true.  There is so much noise in the data, that is why most time spent data modeling involves cleaning of the data.  This noise is why it is so hard to predict anything using this data.  The pearl may not be worth the dive for predictive analytics, but for data mining it is certainly worth the effort.

7. User Data Is Not Easily Accessible Or Transferable

Oh so true.  Take manageable chucks when starting to dive into these user-level data waters. 

This level of data is much harder to work with than traditional data.  In fact, executives usually don't appreciate the time and effort it takes to glean insights from large datasets.  Clear expectations should be set to ensure there are no overinflated expectations at the start of the user-level data journey.  Under promise and over deliver for a successful implementation.  

What to Do When People Draw Different Conclusions From the Same Data

Walter Frick writes for HBR:

That famous line from statistician William Edwards Deming has become a mantra for data-driven companies, because it points to the promise of finding objective answers. But in practice, as every analyst knows, interpreting data is a messy, subjective business. Ask two data scientists to look into the same question, and you’re liable to get two completely different answers, even if they’re both working with the same dataset.

So much for objectivity.

But several academics argue there is a better way. What if data analysis were crowdsourced, with multiple analysts working on the same problem and with the same data? Sure, the result might be a range of answers, rather than just one. But it would also mean more confidence that the results weren’t being influenced by any single analyst’s biases. Raphael Silberzahn of IESE Business School, Eric Luis Uhlmann of INSEAD, Dan Martin of the University of Virginia, and Brian Nosek of the University of Virginia and Center for Open Science are pursuing several research projects that explore this idea. And a paper released earlier this year gives an indication of how it might work.

I believe it is best practice to have multiple analysts look at a problem to at least devise what their methodology would be for a certain problem.  In fact, I always like to take a crack myself when the problem is particularly difficult, just so I have an idea of what the data looks like and how certain variables are influencing the results.  

I think too many executives are unwilling to dig into the data and work with a problem.  I believe it is very important to have a deep understanding of the data issues so, as an executive, you can make better decisions on how to guide the team.  Many times the answer is not a deployable model, but a data mining exercise that will glean some testable hypothesis.  

Though most companies don’t have 60 analysts to throw at every problem, the same general approach to analysis could be used in smaller teams. For instance, rather than working together from the beginning of a project, two analysts could each propose a method or multiple methods, then compare notes. Then each one could go off and do her own analysis, and compare her results with her partner’s. In some cases, this could lead to the decision to trust one method over the other; in others, it could lead to the decision to average the results together when reporting back to the rest of the company.

“What this may help [to do] is to identify blind spots from management,” said Raphael Silberzahn, one of the initiators of the research. “By engaging in crowdsourcing inside the company we may balance the influence of different groups.”

I do believe in internal "crowdsourcing".  The minute tough problems start to be outsourced, the company loses the great insight their analysts and business owners have that can bring insight tot he data that many analysts outside of the company could never understand.  I truly believe analytics is art and science, but too many times the art is under appreciated.  

Top Players by OPS in 2013 & 2014

Since baseball season is around the corner, I wanted to look at the best players by OPS the past 2 years and whether they were increasing or decreasing.  Here's a little viz that shows that along with being able to take a look at their career and what makes up their OPS.  

Busy is not a Strategy

One of my favorite people once taught me the mantra of "Busy is not a Strategy".  So many businesses use the wrong metrics or KPI’s when measuring success of the business.  For brick and mortar companies, their eyeballs tend to deceive them and they use that as their main metric (we were so busy).  For other industries it is market share.  How many widgets can we sell.  The problem can be using the wrong KPI’s along with having the wrong culture can lead to an unprofitable business.

I have implemented the “Busy is not a Strategy” with resounding success before.  We had a casino/hotel in a declining market that had 1,800 rooms.  They were moderately successful considering their location, but they were using the wrong metrics.  Their KPI’s were hotel occupancy and casino revenues.  Now anyone who knows the casino/hotel business is going to ask, what is wrong with those metrics?  They had good casino revenues for the market and an occupancy of 87%.  Most anyone would love these numbers.  Plus, they were really busy.

When we took over the business strategy of the property we saw to get these impressive numbers, there were a lot of giveaways and very low hotel room rates.  To drive the wrong metrics, they were servicing a large number of unprofitable guests.  The belief was if the hotel is full, more profits would eventually flow to the bottomline.  There was just one problem, the other centers of business were not large profit centers and the customers coming in at very low hotel rates did not gamble, because they didn’t have a lot of money.  

To increase profits, we decided we were not going to busy, we would focus our attention on the best customers and try to drive more frequency from these guests while sacrificing the low-end of the business.  This resulted in decreased occupancy and decreased casino revenues.  Uh oh.  Hotel occupancy went down to 44% and casino revenues were down 10%.  The operators were crying “the business is being ruined”.  Even competitors were coming over and asking the operators “are you going to be able to remain open until the end of the year”.  There was pure panic.  That was until the financials came out.  EBITDA was up 100% for the quarter.

By focusing on the best and most profitable customers, this property saw increases where it mattered most, the bottomline.  How did this happen?  The expenses to drive the KPI’s that were important to this property were astronomical.  They were essentially competing for market share instead of profit.  What happened through time, is the best customers started to come more often as that was the new focus of the property.  Casino revenues started to increase through time to levels much higher than before the strategy change, however occupancy remained at 44%.  They did this by focusing on:

• Increase frequency of their top tier from the players club
• Increase hotel room rate
• Target giveaways to the more profitable sector of the database
• Increase customer satisfaction of the best customers

This is very similar to what I see is happening in the phone industry.  There are many manufacturers and most of them are focusing on “Busy” as a strategy.  Now the metrics for busy in this industry are phones sold and market share.  Android accounts for approximately 80% of the worldwide market share for phones sold.  Yet when it comes to profit, that metric is almost reversed.  In fact it is a lot less than 20% in the last quarter.  So how can this be?

The phone manufacturers are selling basically the same thing.  They run Android software that they manipulate in small ways, but all the apps are compatible with their competitors.  This creates an experience that cannot be differentiated in any way but price.  This is the same thing that happened in the PC industry.  All manufacturers ran the same operating system, Windows, and they had to compete on price which forced them to make deals of adding bloatware onto their machines that destroyed the customer experience.  This is where the phone industry is heading.  When price is the main differentiator, businesses eventually will go out of business unless they can outlast the competition.  

So these OEM’s sell many millions of phones to increase market share which leads to…  To what?  I don’t know.  From what I have read these manufacturers have a decent amount of customers that are buying new phones, but they are buying them for the price.  So the manufacturer sold an unprofitable phone so they can gain a customer who will buy another unprofitable phone.  That doesn't sound like a sustainable strategy.  There is nothing that differentiates the experience of the customer enough to make that return customer more profitable.  It is a vicious cycle.  

The only company that is running a different strategy is Apple.  Apple is making almost all of the profit in the phone industry by having a differentiated product that is customer focused.  Apple is doing the same thing in the PC industry, their Macs account for about 10% of the market, but more than 50% of the profits.  Apple has been able to run the “Busy is not a Strategy” strategy to ultimate success.  Sure Apple sells a lot of phones and they would like to sell more, but these sales are the outcome of their strategy, not the focus.  Apple has a culture that is design focused which leads to a product that has a better customer experience.  

Apple is dominating the phone industry because they do not bow down to the marketshare gods.  They focus on the customer first through their design culture.  They make profitable, differentiated products which bring in the majority of the profits in the industry, which then allows them to spend more money on R&D to create more products and services to keep their customers in the ecosystem.  These customers buy new phones at a nice profit which creates a beautiful cycle.  All because Apple is NOT implementing “Busy as a Strategy” 

The Content Marketing Paradox - TrackMaven

TrackMaven has put out a white paper documenting 13.8 million pieces of content from 8,800 brands.  With the massive amount of options to post content, are organizations providing relevant content to their users.  The key to digital marketing is providing relevant (targeted) content to customers to provide great customer experiences.  With that in mind, have marketers been using relevant content or is there still a shotgun approach.  

TrackMaven documents Pottery Barn on an initiative they had to increase their "engagement" on Pinterest.  The mistake they made, which a lot of marketers make when it comes to social media channels, is to increase the amount of content.  Increased content = increased impressions = increased engagement, right?  Not so fast.

Pottery Barn Engagement and Posts

What is happening in this example is the number of pins are increasing, however the average interactions per pin is decreasing at about the same rate.  So in effect, they are working much harder to engage the same amount of customers.  I love this chart, because it shows that more is not necessarily better.  In the database marketing world, my push is to always increase frequency, whether that be purchases or trips to a casino, etc.  But the key metric to look when frequency starts to increase, is does the worth of the customer per transaction decrease and at what rate.  

In this case, frequency of pins is increasing, but the engagement is not, so the content is not relevant, there's just more of it.  Pottery Barn isn't the only guilty party when it comes to this tactic.  As TrackMaven saw, a lot of brands were implementing the same tactics.

The good news is Potter Barn (using TrackMaven software of course) was able to identify this and change tactics.  Instead of focusing on content designed to sell certain items, more like an advertisement, Pottery Barn started to pin content that helped their customers make their home better.  Of course Pottery Barn goods were front and center in this approach, but they posted less and focused on relevancy to their customer to change the engagement equation.  So the new equation is:

More relevant content = increased engagement

Pottery Barn results with new relevant content strategy

Much better content has resulted in the end goal, more engagement through Pinterest.    

Managing Your Mission-Critical Knowledge - HBR

Martin Ihrig and Ian MacMillan for HBR:

Tantalizing as the promise of big data is, an undue focus on it may cause companies to neglect something even more important—the proper management of all their strategic knowledge assets: core competencies, areas of expertise, intellectual property, and deep pools of talent. We contend that in the absence of a clear understanding of the knowledge drivers of an organization’s success, the real value of big data will never materialize.

This continues to be a theme I am seeing with "big data" and I wrote about here.  This applies to "regular ole" data also, the key is to apply the knowledge and insight of the operation with the data to create actionable strategies and tactics.  Data analytics, big and small, is a combination of Art and Science.

This is a fascinating paper and one that I would recommend reading.  It attempts to identify company knowledge to find strengths and opportunities within that base.  It also attempts to spread that knowledge and expertise throughout the company to different divisions, which would assist on where to use that knowledge for the most profitable or strategic initiatives.  Very high level and difficult work, but very valuable.  

Big data: are we making a big mistake? by Anum Basir

Anum Basir writes for Analytics Weekly:

“Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever.

This is an article every executive should read about "big data".  I believe it fits right in to my narrative about event with data, companies need art along with the science to have true insight as I wrote here.  The article is a long read, but it details the promises of "big data" along with the pitfalls that come in only trusting the results without having the proper insights and testing.

As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell.

I believe in more data, not a term of "big data"  When people are trying to sell "big data" to corporations, are they really helping?  

But the “big data” that interests many companies is what we might call “found data”, the digital exhaust of web searches, credit card payments and mobiles pinging the nearest phone mast.

In some circumstances they might be, but what Basir discusses in this article is the idea of "found data".  This is data that already exists inside the company, or data that is just not being tracked and analyzed.  As I wrote here, companies are sitting on a treasure trove of data that they aren't using optimally already.  Adding "big data" may send the corporation down a path they are not ready for.  Always search for the next best data that will solve the answers to the questions that need answering.  

Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.

Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”

Basic goes on in the article to punch holes in these four claims.  "Big Data" is very promising, but it is a destination for most companies.  When something is a destination, there is a path that needs to be taken to get there.  The path may change and there are detours on the way, but most companies can't just jump all in on "big data" or "found data".  Companies must build an analytics culture, live in their data and use that data to make decisions with the business acumen they have built up for many years.  

As Basir points out in the article, the problems with data do not go away with more of it, they just get bigger.  

Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.

The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about ­correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”.

But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down.

Correlation without causation arguments do not go away with "big data".  Having insights to enhance the results is key to successful analytics.  We are all familiar with the story of ice cream sales and shark bites are strongly correlated, so selling more ice cream causes shark bites?  Well thats just silly and obvious, we all know because sales of ice cream and swimming in the ocean increase in the summertime.  

But the example brings up a crucial point, do not trust the output of the data without using your vast knowledge on the subject as a barometer.  Anyone can see the shark bite, ice cream example has nothing to do with each other, but findings of big data can be a lot more tricky to detect.  What may look to be a relatively reasonable explanation of data from a model a data scientist created may actually ruin a business because the data scientist had no knowledge of the subject matter.  When just solely relying on data, all the great human knowledge about the business are thrown away.  This is art and science.  Treat it as such.  Get the artists into the room with the scientists and find the best answer, not the cheapest and easiest one.  Actionable analytics is hard, don't underestimate the complexity of the problem.     

Big data as a driver of organizational change

Analytics can “open up many doors for healthcare organizations” of all types, including life sciences companies aiming to get new medications to patients faster or to “provide regulatory bodies with evidence of drug safety.”  Or, for payers, analytics can “answer questions about future growth, profitability and sustainability,” or help them to detect and prevent fraud.”

It still amazes me that big organizations in so many industries are still talking about what analytics can do for them.  Of course, this headline is a little deceiving as there really is no "big data" to be found.  You get a lot of clicks when you have "big data" in the headline though.

500 Homerun Club

Wanted to see what the impact of the steroid era had on the 500 homers club.  Seems that the list would not be the same.  The steroid era had such an effect on baseball, I would argue it might have also saved the game.  The Mark McGwire and Sammy Sosa home run race was the best baseball television of those years.

Are CMOs wasting money on faulty marketing analytics?

Manji Matharu writes:

CMOs are now at a crossroads between data quality and data results. It’s no longer enough to dabble in analytics and come out with the richness required for informed decision-making. The business needs integrated systems across IT infrastructure, and marketers — not IT pros — must champion the call for improved data controls and governance as their cause.

Data quality is the first step in all marketing processes.  Ensuring this is boring and hard, but it is a necessity.  This is the first step when I come into an organization, determine the quality of the data and work to fix that.  Once there is a trusted version of the truth, marketing analytics come to life.  

Data + Insight = Action and Back Again

Adobe Summit brought with it a great nirvana of a near-future where marketers are able to deliver relevant content to customers creating great experiences.  The words that permeated throughout the conference were those, content, experiences and data.  The big stars of the show were content and customer experiences, which I believe are extremely important as I have written before.  

However, there is no right content delivered at the perfect moment to create wonderful customer experiences without data.  Data is the key to making this all work, and not just any data.  No, I'm not talking about "big data", I'm talking about actionable data.  

Most companies are sitting on a treasure trove of data already.  Without purchasing third-party data, understanding every click, customers have data that can transform their business.  The issue is in interpreting the data, making it actionable.  Actionable data isn't a product, it's a culture.  

Actionable data is the combination of art and science.  The path to actionable data isn't necessarily going out and hiring a bunch of talented data scientists, though it doesn't hurt to have these people on your team.  The path to actionable data is marrying the data with the business acumen.  It's not enough to have data telling you something happened, there has to be an understanding of the business as to why it happened.

Once there is an understanding of what happened (science) and why it happened (art), you have actionable data.  Now you can create optimal tactics to deliver relevant content to create targeted experiences in the digital age.  The great thing about this process is it's circular.  Once a company creates great targeted experiences for their customers, customer behaviors will change and the entire process starts all over again.  There are always puzzles to solve and amazing content and experiences to create.  

How to Make Big Data Work for You

This article is the problem with Big Data.  Everyone wants to jump so many steps on their way to true 1-to-1 marketing using data as the cornerstone.  Great marketing is always an evolution.  One step forward using data brings results and different behavior is gleaned from that data.  Then that data is taken and different questions are asked of the data based on the results using the previous data set.  This is how marketing problems are solved using data.

Marketers can't take a dataset that is fairly large, one they are already struggling to make the most of anyway, and then be given a much larger dataset and told to "go make magic".  Marketing with data is a disciplined venture.  As a marketer, make sure you are making the most out of the data you already have before worrying about what keystrokes the customer is making or the "Internet of Things".  

Always make sure the next step in the data is one that will bring you value today.  Have a long term understanding of where the data can take you, but be disciplined in getting there or you just might miss a lot of insight on the way.