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.

Becoming Customer Centric is a Journey not a Destination

A very insightful article from Timothy Smith of Tahzoo.  Becoming a customer centric organization is not achieved through initiatives, it is achieved through a culture change.  

From my perspective, too many of the current efforts in financial services are internally focused, and are being solely thought about as technology projects. As I pointed out in my webinar, technology is only one part of the solution. Companies need to be thinking about transforming their business and marketing approaches in addition to their technology infrastructure. If companies don’t invest in these other transformations, they may not ever deliver on their customer centricity goals. Time and money needs to be spent on creating organizational alignment, understanding the customer journey, and deploying marketing strategies that reward, recognize and respect customers.

Companies tend to look at their technology in terms of deficiencies instead of what a technology will allow.  Technology should never be purchased until there is a specific business need the technology will solve.  If your company isn't ready to evolve beyond their current technology platform, a better tool will not help your company evolve.  To become a customer centric organization, the culture needs to evolve first and then technology can support the organization as the needs arise.  It should never be the other way around.  Customer centricity does not happen through more data about a customer, it happens when all decisions are focused on the customer.

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.  

Two Major Marketing Automation Myths

By Mat Sweezey:

Marketing Automation is Only for Marketing -- FALSE

Really I care about the second myth.  In my last position, we used our marketing automation tool for so much more than marketing.  Marketing automation tools can create business workflows and email tasks to operations folks.  We also used it in terms of creating better customer service, to alert of a customer service issue and email the best solution to the problem.  Marketing automation tools from a high level take data and triggers and provide an output.  There are numerous opportunities to enhance workflows with your automation tool.

Great Brand Apps Create Loyal Happy Customers

Mobile apps are the way we will interact with all of our loyalty programs in the digital age.  Smartphone apps can do so much more than a piece of plastic or punchcard could have ever imagined, yet so many companies have built half-baked, poorly thought out attempts at creating a customer experience.  But the good news is there are some leaders that are nailing it.

The 4 qualities a mobile app should possess are:

A mechanism to capture transactions 

At the heart of the mobile experience should be the mechanism to capture data about the customer.  This data should feed into the loyalty program of the brand.  This should come in the form of transactional, interests, surveys and geo location data.  Data is the building block for a loyalty program to succeed.  

Frictionless transactions

A mobile app has the ability to eliminate the frictions of the transaction.  For example, at an Apple Store the customer can enter the store, open the app, scan the item they would like to purchase and then leave the store, all without having to interact with a human or wait in line.  That is eliminating friction.

A mechanism to communicate with your customers

Mobile is a channel.  It is perhaps the most important channel in the new digital marketing era.  The phone is always on your customers body and that will soon include wearables.  The ability to push messages to your customers through this channel is extremely important.  The ability for your customer to open the app and see their loyalty program details makes communicating with your customer more personal than ever before.  This includes beacon support to guide the customer through the offline experience as well.  This should be the channel that receives the most focus in the coming years.

An engaging experience without a transaction

Mobile apps hold a space on the customers phone.  If you make your app engaging, even when the customer is not making a transaction with you, you may keep a good position on the phone.  Think of it as search rankings, the more prominent position, the more engagement with your brand.  Get stuck in a folder on the third page, you will only be utilized as a mechanism for transactions which is not the worst thing in the world, but doesn't drive behavior. 

Starbucks has been on the forefront in the mobile app space since it introduced its mobile app in 2011.  Starbucks took the approach of creating an app that engages customers when not in a Starbucks, along with making the transaction process frictionless.  Starbucks has long partnered with Apple by giving away free music and apps, but they also moved this functionality to the app.  By doing this, Starbucks has been able to engage their customers with their application outside of the brick and mortar stores.  I consistently look at my badges from Starbucks to see what free apps or music they are giving away this week.  Most of the time I don't get the freebies because they are not to my liking, but every once in awhile I do.  But it also has trained me to constantly go to the app.  I check my points and how far away I am for a free award and I am not even a big free award kind of a guy.

Starbucks has also made a frictionless payment process that also tracks my behavior.  I always received gift cards from Starbucks and had them strewn all over the place.  Some made it to the wallet, some were in drawers, but they were never consolidated.  Starbucks also had a loyalty program that was tied into a gift card, but it was confusing on how to interact with the program when I wasn't using that particular gift card.  Plus having to manually add money to the specific gift card was far from frictionless.  So I never really used the loyalty program and I was going to Starbucks less.  The app has removed all of this friction.  It is easy to transfer gift card money to the main loyalty account, which was a main pain point for me.  It also allowed for easy addition of funds into the card through the app.  These two items made using the program much easier.  

The other app that I have been very impressed with is the Chipotle app.  This app is a little different from the Starbucks app because it is just solving one problem, waiting in line.  The app allows you to place a Chipotle order and skip the line to pick it up at a designated time.  Now I don't know if any of you have been in a Chipotle and have to wait in the line to order, but it could be a fifteen minute exercise in browsing Twitter.  The app saves your favorites and recents so it takes approximately 20 seconds to place an order.  Pay online, just walk to the cashier and they hand you your bag of goodness and you are off.  Simple, frictionless and awesome.   

Improvements can be made in both of these apps to include more of the 4 qualities.  The Starbucks app nails 3 out of the 4, but can do a better job at using the app as a personal, targeted channel.  Right now the offers they have are not very tailored to my experience.  This is a big opportunity to make the app even more engaging.  For Chipotle, they only possess 1 out of the 4.  They might be monitoring my transactions, but they don't have a loyalty program tied to the app, so I am not sure.  The app is a great start, but they could hit a home run with the addition of some functionality.  Either way, I will still use it weekly to avoid the lines.

Are You Ready for an Omnichannel World?

One of my favorite title for an article.  I think it's a funny title to be honest, because it doesn't matter if you are ready, this is the current state of the customer experience.  Customers for a few years have been living in this world and we as digital marketers are finding it hard to catch up.  What's even more scary is the rate at which technology is moving.  If digital marketers don't become more agile, they will always be playing catch up.  The issue may be is that the distance they will have to catch up will widen.

Today’s customers engage with companies in multichannel and multitouchpoint journeys, which they pause and resume over time. For example, according to the Corporate Executive Board (CEB):

• 58% of callers have visited the web before calling, and

• 34% of callers are on the web while talking to a rep

For a customer to complete a single task – buy a product, answer a question, understand a bill – they often require multiple, disconnected interactions with an organization. When a customer needs assisted service to supplement self-service, they typically must start over when they engage with the organization.  In the case of voice, it’s calling a contact center, using an IVR, and explaining their issue. In the case of chat, it’s starting a dialog with an agent without any context to their journey. These time-consuming and disconnected ‘channel shift’ experiences are one of the leading causes of missed sales opportunities and high operating expense for organizations – as well as a major source of frustration for costumers.

I remember back in 2001 having this journey with AT&T landlines.  At that time AT&T was broken into local and long distance.  Well the bills came and they looked exactly the same and this was when online banking was just getting started.  Well I was paying all the bills to long distance, because I believed they were one in the same.  The bill looked exactly the same.  I was quite surprised when they turned off my phone because I hadn't been paying.  I mean, here I was I felt like I was always paying.  When I set up my phone service I didn't call 2 numbers.  

This is something that needs to be front and center.  This is nothing more than creating great customer experiences, just using customer service as an example.  The customer doesn't want to think about telling their story over and over, they want the context of their issue to move with them as they reach each touch point.  This is expected in the digital age.  The only thing really holding organizations back is their structure.  Organizations have to structure themselves to handle the guest through this omni-channel journey.  No amount of software will help if they don't start there.   

Southwest Airlines Making an Impact in Marketing Automation

I love Southwest Airlines.  They have been the ,most profitable airlines by creating a business model which serves both their customers and their shareholders.  Southwest has managed to delight their customers and they are one of the few airlines that actually turn a profit, plus they haven't gone to the nickel and dime your customer model that has been popular in the industry.

The one area they have been weak in is database marketing/marketing automation.  The emails my wife and I get from them are very generic.  These emails have never been tailored.  This is the same in direct.  I have a Southwest Visa card and I still get an application direct mail to this day.  They also send some of these applications multiple times per week.  I tend to forgive because I am not a fan of the nickel and dime approach most other airlines employ.

Out of the blue I got an email that was actually targeted, well I hope it was targeted and not everyone received.  They sent me a tier upgrade promotion if I flew 3 roundtrips in a 2 month period.  To give a little background, I was flying much more a year and a half ago and I was an A-list, but recently I haven't needed to fly as much and I lost that status.  What I hope they are doing is looking to see that I have the propensity to become an A-List and they are betting that I will take them up on this offer. 

I happen to be taking a couple of flights in that time period, but I was going to be one roundtrip short.  Now this is where the psychology of tier benefits are interesting.  In my experience, a company doesn't necessarily get a customer to do something drastically different in their behavior to get to the next tier level.  This is true in my case.  If I hadn't been taking those 2 other trips, I would not have flown 3 roundtrips to make it to A the rest of the year.  But since I was taking those trips and I was going to be close, I decided to take 1 more trip up north and see my stepdaughters.  I would not of otherwise taken this trip.  So the promotion made them some incremental revenue and has kept my loyalty with Southwest.

This could be a less targeted approach and I just happen to think it is because of my propensity.  They send me an email last week reminding me of the promotion ending, however they did not reference I was 1 roundtrip away, so they aren't exactly where they need to be yet.  But, if Southwest can put together a strong direct program with their superior business model, then other airlines will have even more to worry about.  Here is to hoping they are moving in that direction.

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.  

Using Smartphones and Apps to Enhance Loyalty Programs - NYTimes.com

I am such a big fan of using rewards on a smartphone.  There is no better way to communicate with a customer than with the device they are carrying around in their pocket.  The next evolution for rewards programs is moving from a card in the hand or a punch card mentality to devices that allow even smaller businesses to compete against bigger competitors.  

Smartphones and loyalty apps have begun offering small businesses enhanced program features and automated administration capabilities once affordable only to large companies like airlines and hotel chains. These capabilities also offer the equivalent of a real-world psychology lab for easily evaluating the effects of offerings and incentives on customer loyalty.

The key to any reward program is to capture data about a customers behavior.  If your program isn't allowing you to capture transactional level data in conjunction with the program, there may be a need to consider this approach.  If only to capture the amount spend and the date, this will allow a lot more opportunity for the business.  As I wrote in The True Purpose of a Loyalty Rewards Program, it is imperative to have a program that incentivizes a customer to share their data with you, but not over-incentivize.  The key is to drive behavior by targeting the customer, rather than giving everyone the same rewards.

“Clearly, this is the best of times for loyalty programs,” said Mr. Bolden of the Boston Consulting Group, who recommended that small businesses “focus on the non-earn-and-burn aspects of the program.” He suggested that spas consider a separate waiting room for their app-identified best customers.

“Or when the treatment is over, you hand the customer a glass of Champagne and strawberries,” he added. “If you’re an apparel retailer and you get in a new line from a new designer, invite the top 5 percent of your customers in first so they can see it before anyone else.” The point is that many effective rewards need not cost much to bestow.

Driving behavior is not all about a discount.  Understanding what your customers want and delivering them an experience is more important than a discount.  Because a customer that is coming just for a discount is more than likely not your most loyal customer.

“With apps you now can target specific customers and influence specific behaviors and keep track of all the results and understand the results,” Mr. Smylie said. “Because the check-level detail is now tied to a customer’s profile, we can understand what their purchasing behavior is, what their interests are and cross-reference that against their social media profiles and market to them more effectively and involve them at a deeper level with our brand.”

 

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.

Next Generation Customer Experience - business2community

Terry Green writes for business2community:

...we all talk about customer journey mapping but how many of us have actually done it? No I don’t mean sitting listening to a boring presentation about the subject whilst fiddling with my mobile phone or making a half arsed attempt at it with no commitment – I mean really done it like we meant it?

I’ve always said that the key to getting any customer oriented change programme though an organisation is to get the business leaders to walk a mile in the customer’s shoes. Cliched? Yes but no longer enough. The challenge now is to get everyone inside your organisation to see themselves from the customer’s perspective and to understand how it makes them feel to interact with you.

What better way than customer journey mapping?

It is so important for organizations to do this mapping, but I agree with Green, it never seems to resonate with a larger team.  I feel because people going through this exercise tend to treat this as a transactional exercise instead of an emotional one.  What I mean by emotional is customers have an emotional attachment to their journey, they don't feel like what they are doing is a transaction.  

I believe one person should own the customer journey and bring people into the process for specific parts of the journey.  Journey mapping is a very overwhelming experience, but when broken up into pieces it could generate great conversations from the entire organizations.  When taken into pieces, the organization can concentrate on the emotion of a specific piece of the journey without having to get overwhelmed by the entire journey itself.  

It’s only when you have got your people to stop thinking like vendors and truly moved them into the customer’s headspace that you can start their journey towards customer centricity.

Keeping Up With Today’s Loyalty Demands

Originally posted on IBM’s Smarter Commerce blog:

Loyalty marketing is more and more prominent in today’s retail landscape. It is becoming common knowledge that customer acquisition costs are increasingly rising, and data-driven customer retention is a key area filled with untapped growth potential. But loyalty marketing is evolving and is more intricate than just offering discounts to existing customers. As many marketers realize, there are three common problems that they run into when trying to implement an effective loyalty program:

1. They often feel stuck offering dollars-off discounts and are losing their margins without sustainably changing their customer behavior.

2. Personalization is not going further than using much more than a first and last name, and is not connecting to the customer and building customer relationships.

3. Their loyalty members are not actively participating and being engaged, and consequently not influencing long term results.

It is a buyers market as they would say in the real-estate business.  Customers have the ability to buy from a multitude of companies with fairly frictionless transactions.  Years ago, a customer would be limited to their location to buy many of the items they can now purchase online, which makes loyalty marketing a much harder task today.  If the customer does not like an experience they have with your company, the friction to switch providers is much easier than in the past.

This has led to a race to the bottom with most companies.  Instead of competing on differentiation, companies rely on sales and discounting to compete in this new world.  Relying on discounts is not differentiated at all.  Any competitor can match a price or beat the price if they are willing to decrease their margins for the business.  As I wrote in Busy is Not a Strategy, many of your competitors will look at metrics like volume as their key metric which will force them to decrease margins and hurt your business.  

Increasing Self-Identification

Loyalty incentivizes customers to provide more information about themselves and engage across channels, which leads to a richer understanding of your customers and how they interact with your brand. You may be surprised how many of them are open to providing information about themselves in order to receive more relevant communications and offers.

Spending most of my time in the casino industry has shown me that consumers willingly give away information in return for a richer experience.  In the case of the casinos this comes in the form of comps, but in other industries this does not have to be a giveaway.  This could be access to sales, in the case of grocery stores.  Find out what your version of the comp is to increase customer self-identification.  It may start off as a giveaway, but don't let it drive the future customer experiences with that customer.  

Taking Personalization to the Next Level

In addition to increasing customer self-identification, you should track and analyze metrics such as order frequency, average order value, and from which channels customers are purchasing. Modern loyalty programs gather this customer data and provide a centralized hub which is used to personalize meaningful incentives and rewards for higher customer redemption and satisfaction, and also to send personalized messages. These messages can be targeted towards specific actions and customer segments, and are used to maintain relevance and build upon customer-brand relationships by making customers feel like you are paying attention to what they want.

If you aren't tracking the purchases of your customers then you aren't going to be successful in loyalty marketing.  Creating meaningful customer experiences relies on gaining insight to the behavior of the customer.  By getting the customer to opt-in, it allows the business to create the true value from the loyalty program as I wrote here.  Targeted content will create meaningful customer experiences and this rich data is at the core.  

Cohesive Omni-Channel Capabilities

With today’s consumer having the ability to interact with your brand across all channels, it is essential to have cohesive communication, connectivity of data, and customer access to your program and rewards at all touch points. Different consumers like to interact with brands through different channels – whether in-store, social media, or email – and your program should be available in their preferred channel.

Providing the same experience for the customer, no matter what channel they are using, is the key to creating meaningful customer experiences.  This is the hard part of the new customer experience paradigm.  Keeping the content and messaging across channels in an online and offline world can be complex, but is very rewarding.  Customers don't care that different divisions in the company have different responsibilities and the online team doesn't communicate effectively with the operations team.  Customers expect their experience to be seamless across channels and it is imperative that businesses adjust to create this seamless customer experience. 

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.  

When it Comes to Data, Small is the New Big

A great example of taking the data you already have by Anthony Smith:

Customer data is a commonly unrecognized superpower. Companies that provide software as a service (SaaS) are especially likely to have massive amounts of uncategorized, unmanaged customer data, creating a well of potential that largely goes unused. This data, when analyzed properly, can provide companies with unlimited opportunities to improve product functionality, increase customer satisfaction and stimulate business growth.

As I wrote in Data + Action = Insight and Back Again, big data is not necessarily the next key driver for a business.  The next key driver may be harnessing the data you currently have and using that to glean more insight.  Once you have this insight, you can develop new strategies and tactics to deliver targeted content and create great customer experiences, which in turn lead to increased revenue.

Anthony talks about how he did this with his company and I believe so may companies can do this too.  Make sure you have exhausted all current data possibilities before leaping into "big data".  The data you have may be the next key driver for your business.

Meerkat is dying – and it’s taking U.S. tech journalism with it

About three days after it received a lavish new funding round, Meerkat died an ugly and embarrassing death. It is hard to decide whether the Great Meerkat Debacle that has unfolded over the past week is a tragedy or a comedy — probably a bit of both.

The mobile streaming app that had whipped U.S. tech journalists into a frenzy announced $14 million in new funding on Thursday. Money poured in from Jared Leto, Greylock Partners and other illustrious sources. On the same day, Twitter launched its rival streaming app called Periscope. Apparently, investors didn’t stop to ponder why Meerkat people rushed to cash in so aggressively only a month after the app had debuted.

When there is talk about another tech bubble, this will be where they point to.  I won't say it's easy to get the attention of many with an app, but we have seen very little staying power with apps.  The demise of Zynga points to their premature purchasing of very basic games that didn't have long staying power.  Meerkat was popular for like a week.  I have been using Periscope for the last few days and it may take a lot more to keep the staying power.  Theres a lot of terrible content on the service.  Twitter will have to solve finding good content.  Otherwise this will be a fad and that will be too bad because I do think it has the opportunity to be amazing.