Study: 80% of Companies Will Increase Digital Marketing Budgets

Woohoo!!!  I think this is a wise move as we move into the golden age of digital marketing.  Until now I believe the many companies viewed this area as media buying and website analytics.  Digital marketing is the force that will bring the customer experience to fruition by combining online and offline behavior.  Creating consistent content and messaging from one channel to the next will be key in the coming years.

"One challenge that has been very prominent for digital marketers is the hiring of great talent, and companies are finally getting the budget to do that," said Laura McGarrity, VP-digital marketing strategy at Mondo, a technology and digital-marketing resource provider.

According to the study, the top hiring barriers are finding skilled talent (cited by 65% of respondents); the cost of quality staff (30%); attracting top talent (21%); retaining top talent (16%); and culture fit (26%).

Talent is in high demand and I think what companies have to realize is the talent they are looking for do not necessarily have many years experience in the field.  In fact, there is very little experience in the new age of digital.  Finding talent will be harder than looking at a resume and seeing if the applicant has X number of years and X degree.  These are not the metrics companies should be aspiring to hire.  The metrics should include applicants that have expressed their thoughts about digital marketing and whether their thought leadership is the direction the company is trying to go.  

"Turnover has been a really big issue," Ms. Garrity said, noting that the average tenure for digital marketing professionals is 12 months to 18 months. By comparison, average CMO tenure is 45 months, according to executive recruiting firm Spencer Stuart in a March 2014 report.

"There is such high demand and it's such a new space -- people are hopping around to find the best jobs," she added. "It is a candidate's market, particularly in digital marketing."

The top skill sets companies are hiring for this year are digital/social (54%), content creation (44%), big data/analytics (33%) and mobile strategy (30%), Mondo found.

There should also be a questioning of why there is so much turnover.  Even though it is a talent market, there should be less turnover if the work is rewarding and CMO's are really bought into the innovation.  Too many times CMO's tend to be brand focused and the digital marketer will get frustrated in that environment.  

The study also asked marketers which digital platforms will drive customer engagement in the future. It found that today, mobile is seen as a key driver of customer engagement by only 24% of respondents, but in the next three to five years, that will increase to 70%.

The 24% number is too low for mobile as a key driver.  Today is the age of mobile and if companies aren't focusing on mobile, they will be behind in three to five years.  Mobile strategy takes time to implement and companies need to start now.  

The next 12 - 18 months will be very interesting in the digital space as technology vendors are building platforms that can support the wants and needs of marketers.  Upcoming technology will push the boundaries of what is possible.  Many companies will want to leapfrog steps to get to the end goal quicker, but it is important to realize to take advantage of the next low hanging fruit before jumping too fast.  That is why it is imperative to start now on the digital strategy.

Data is the First Step to Marketing Automation

I have implemented many marketing automation solutions over the past decade and one of the perplexing findings is how organizations put the cart before the horse when they are installing their solutions.  I like to say marketing automation solutions are "dumb".  Not the kind of dumb as in "this is stupid, why are we implementing these solutions, why not do something else".  They are "dumb" in the essence of they need help from something else to be successful.  They cannot work on their own.

Marketing automation tools are a slave to the underlying data.  All marketing automation tools do is query data and create metadata that is used to create content and messaging for your customers.  Now I am minimizing the importance of the marketing automation tools in that sentence, but from a high level, it works.  

Since the underlying data is what drives the marketing automation tool, that data is the first step in implementing the tool.  Without the proper data, your implantation will fail.  Getting the data into the proper format for consumption from the automation tool is the most important step of marketing automation.  

Understand the problems to be solved

Write out all the different types of campaigns or communications to be run with the automation tool.  This step is vital to understand if there is a gap in your data collection strategy.  Also, this identifies if the data is structured properly to even run these types of campaigns.  This step comes before buying a marketing automation tool.

For example, I want to send a reminder email to all customers who bought a television that specific cables will enhance the performance of their new purchase by 50%.  For this, the data will have to be structured to understand which customer bought a television set, along with cables because you don't want to sound like you don't know your customers, within X amount of time, their email, mailing or app device ID, and the channel they prefer to be communicated with.  Now the data team can make sure they have the proper structure for just this one use case. If the data can't be structured accordingly, then the marketing automation tool will not be able to deliver this campaign.

Define success for the campaigns

This can be a simple sentence in each case.  What this determines is how the analysis of the campaigns performance will be achieved.  Analysis is also part of the marketing automation tool implantation, because I guarantee you that the executives will want to know the impact of this large investment, so the data needs to be prepared to answer these questions.

For example, I want to see the redemption rate and revenue generated, along with the expenses for delivering and cost of goods for the customers who returned to the store and purchased upgraded cables for their televisions.  For this the data will have to meld together the ID for the offer, in this case the cable, along with the purchase item along with the expense data from the marketing automation tool and the sales system.  These tasks aren't easy, but they will pay dividends if this legwork is done upfront.  There is nothing worse than flying blind with your marketing automation..  

 The expectations for campaign execution times

This is one that almost always gets missed.  I have heard of campaigns that run almost all day because the data is not organized in a fashion that is not optimized for the marketers.  That kind of performance may be acceptable if the campaigns are run once a month, but for most businesses that is not the speed of digital marketing.  

For example, I want to be able to run the campaign for the television purchasers every day.  This includes time to run the automation, send out proofs for the collateral and have the deliveries out to the customer by 10AM.  This allows the data team to be able to optimize the data structures to make sure the data can be pulled fast and efficiently for all your automation campaigns.  

This by no means is an exhaustive list, but it is a start to having a successful marketing automation implementation.  No matter how many bells and whistles the marketing automation tools have, if the data does not support the wants and needs of the marketer, it doesn't matter because the tool is "dumb".  It needs the data to perform magic.     

Big Data: How Netflix Uses It to Drive Business Success

Bernard Marr writes how Netflix uses data to fuel their business:

Netflix is said to account for one third of peak-time internet traffic in the US. Last year it announced that it had signed up 50 million subscribers around the world. Data from all of them is collected and monitored in an attempt to understand our viewing habits. But its data isn’t just “big” in the literal sense. It is the combination of this data with cutting edge analytical techniques that makes Netflix a true Big Data company.

Netflix is a fascinating company.  They were able to build a business model that put a giant industry, retail movie rentals, out of business and then pivot to streaming before being out innovated by other companies.  They are constantly ahead of the curve when it comes to recognizing the next new technology and digital strategy.  They recognized early that original content was also a key to success, so they are pivoting into becoming greater than HBO at their own game.

More recently, Netflix has moved towards positioning itself as a content creator, not just a distribution method for movie studios and other networks. Its strategy here has also been firmly driven by its data – which showed that its subscribers had a voracious appetite for content directed by David Fincher and starring Kevin Spacey. After outbidding networks including HBO and ABC for the rights to House of Cards, it was so confident that it fitted its predictive model for the “perfect TV show” that is bucked convention of producing a pilot, and immediately commissioned two seasons comprising of 26 episodes.

This is how data-driven organizations behave.  They look at their customers and use data to determine the optimal next move.  All their strategy and tactics are based on using what they know about their customers and what they will do.  So many times organizations are obsessed with what other companies are doing, regardless of what their data is telling them.  They will copy their competitors for fear they are missing out on opportunities.  

The question I always ask is, "how do you know what the other guys are doing is working?"  What you see as a threat, may be a disaster because they haven't set up the correct means to measure the performance or are looking at the wrong KPI's.  Worse yet, they may be attracting an entirely different customer than what you are trying to target.  

A data-driven organization looks at their data and reacts.  Netflix, I am assuming, saw that many of their users were binge watching TV series as soon as they came out.  I'm sure this started with Breaking Bad, Mad Men, great content.  They saw an opportunity to create this content on there own as the majority of the time spent on Netflix is binge watching TV.  They looked at their own data and saw the opportunity to increase time on Netflix and add subscriptions by creating content.  But not just any ole content.  They had the data which showed what their customers loved watching and what resonated with them.  They were able to see what shows were being dropped off of the binge halfway through.  They saw what types of shows were most addictive.  

The content creators gave their biggest competitor the keys to the kingdom, data.  Now Netflix is poised to put a lot of the content creators out of business because they know way more about their customers behaviors than the content creators know.  Because Netflix controls the entire experience, from creation, to delivery, to analyzing the behavior, they can create superior content.  It is a model that is brilliant.  Netflix will continue to dominate, especially in the age where people are looking to become "cord-cutters".  I believe we will see even better content coming out of Netflix in the near future as they learn even more about what we like to watch.

Building credibility for your analytics team—and why it matters

If you work with data regularly, chances are you trust it. You know how it's collected and stored. You know the caveats and the roadblocks you face when analyzing it. But, when you bring your findings to those further removed, you're asking them to take a leap of faith and trust in data they may know very little about.

Multiple times in my career I had to come into organizations and take teams that were not trusted in the organization and help build them into the trusted source of data accuracy and insights.  This journey is never easy.  It takes patience and requires a lot of persistence to change an organizations perception of the department.  But these points are good advice on a roadmap to do this.

Start Small

When trying to get people to believe in your team, it can be tempting to chase the biggest problems first. These problems often take a long time to answer, and can take several tries to get right. It's often better to first establish trust by picking early projects that you know you can win, and win quickly.Try starting with basic arithmetic to answer crucial business and product questions. For startups, some example questions might be:

• What are the most engaging features of your product?

• What is the company's core demographic? What do they like about the product?

Often, people don’t judge the answers to these questions on technical rigor, they judge them on business impact. Starting small can open doors to the big questions that you may have wanted to start with; if you've earned credibility along the way, you'll have more time, flexibility—and maybe resources—to tackle them.

I always find it helpful to start answering questions that are not currently being answered.  As Derek Steer points out in the article, don't start by trying to solve the worlds problem.  If your team tries to tackle tough problems, there will be a much more critical eye on the work and the data produce.  Allow your team to get some wins under its belt.  Remember, this is a journey, not a sprint.  Trust comes with wins, not home runs.

Know your audience

Keep your audience in mind as you begin to craft the story from your data. Add in the appropriate amount of detail your audience needs to focus on decisions rather than methods. What context might they need? Spending a little time thinking about what your audience cares about most also helps you anticipate possible questions and prepare answers in advance. Few things can help establish credibility faster than fielding a question during a presentation and immediately flipping to a slide that answers it.

This point is critical to garner trust.  When presenting data, make sure there is a story that is being told along with the data.  Guide the audience to the answers that you have found, don't let them have to figure it our themselves.  Be sure to explain to the audience what they are seeing and why it matters.  Make it simple, quick and insightful.

Don’t be a House

House, a brilliant albeit fictional doctor, routinely diagnosed rare diseases but had abysmal bedside manner. The thing was, House didn’t have to win his patients over—they were so desperate to survive that they would listen to his every word.

It's subtle, but consider your findings a conversation starter. Understand that the non-analysts have valid points too: they have experiences you don't have and they likely know something you don't. These discussions aren't about winning an argument, but making the right decision for the business.

Never use data for evil.  This is fairly common in Finance departments, but it is important not to attack decisions, but rather try to initiate conversations to come to the best business decision.  Once there is a tone of implication in the analysis, your team will lose the trust of the department that you are creating the analysis for.  Those departments made decisions that didn't have data to drive their decisions, so treat them as a partner, not someone that needs help.

Be Transparent

Analytics can feel like a black box to many people—making that leap of faith appear even larger. By showing even just the basics of your process, you can help others believe in it. To increase transparency try:

• Making your work simple and understandable. Monica Rogatti would urge you to try division before doing anything harder. As your audience becomes more comfortable, up the game to simple regression models—it's not usually difficult for folks to understand the direction and magnitude of coefficients.

• Finding simple ways to convey advanced concepts. For example,confidence intervals and p-values can be confusing for many people, but charts with error bars make these concepts easy to understand.

• Using stories. If you're presenting information about feature usage, or events with technical backend names, paint a picture of how a user would see these features, or put events in plain-English names.

Numbers are difficult to interpret at times, taking the complex and being able to tell a story with it is an art.  Most analysts are great at finding data and creating insights if they have domain knowledge, however they can be terrible at communicating their findings.  Always have available the methodology for coming up with the answers, even if you believe it is a waste of time.  The haters in the organization will demand this, but it also humanizes the process for the non-analytical audience members that you want on your side.  

The most important part of the journey is to persevere.  The beginning of the journey is the hardest part.  I remember in my last position, the department I took over was the laughing stock of the organization.  It took them weeks to come up with an answer and no one believed in what they were saying because they were just being report monkeys, instead of providing any insights.  By the time we got going, we were the defacto data source for the organizations.  We created analysis for parts of the organization we didn't have anything to do with, but when the organization needed something done right, it came through our team.  That was because we had many wins along a journey.  

Turn Your Data Into Smart Data

Great insights from Scott Houchin regarding data.

To harness and convert data into stronger business strategies and overall profitability, approach data practices with a holistic integration of people, process and technology, following three key steps: collection, strategy and alignment.

A data strategy is the first step in becoming a data-driven organization.  Setting up the structure and expertise of the organization has to start before jumping into data strategies.  This can happen outside of the confines of IT.  The business leaders should own the data, as long as they have the expertise and knowledge to do so.  Try to set up procedures to be agile with your processes.  The longer it takes to implement changes in data, the less of a competitive advantage your organization has.  It will also be near impossible to become data-driven if there is a constant wait for data to be delivered to the end users.

Collection

Start with a clear understanding of project goals and requirements to guide the collection process. Establishing this helps ensure data collected is “smart” or meaningful. Collection shouldn’t narrowly focus on new data. Many organizations already have a goldmine of owned data that should be tapped. To make the most of historical data, scan legacy systems, such as social pages or purchase history, map findings back to strict uniform terminology, and fill in the gaps where data is missing across the organization.

Having a process for collecting new data and examining historical data up front ensures quick and accurate collection, minimizing time spent on governance practices and carving down unnecessary data sets.

There is a treasure trove of data already being collected in most organizations.  Ensure that this data is being properly collected and stored.  The goal is to ensure as many people can get to the data as possible, data democratization.  If data is stored and is hard to get to, takes complicated joins and there are no tools available to the organization to easily access the data, then more has to be done to reach these goals.

Strategy

Once data is collected, work with data-marketing specialists to analyze and align functional uses and marketing’s business goals. This requires a team of analysts and strategists who have both high levels of industry and domain expertise to identify sources, manage collection and road-map operations processes.

Teams of analysts can help organizations identify, collect and integrate data from sources and channels, like web traffic, Facebook, Salesforce, etc., into a proprietary database. Once established on a datamart, it can be integrated into current campaign tools through human labor. Having this data integrated into marketing tools gives brand-side marketers the insights to improve customer experiences, measure performance of digital assets, predict customer decision stages, etc.

Data should not be financial focused, it should be customer focused for the greatest impact on ROI.  Marketers have to own their data.  Hiring analysts and data domain expertise is imperative for success.  If ownership lies outside of the marketing resources, there is a much higher likelihood of failure.  Remember, CMO's and CIO's don't speak the same language.  

Alignment

Another example can be demonstrated with IT and marketing. Marketers spend more on technology than some IT departments now, but need alignment to ensure data is stored, platforms are integrated and in-house technical support is available. Alignment between these two departments appeases both marketer’s need for autonomy and IT’s domain over platforms, allowing for the integration of datamarts into other units’ datasets from the onset.

IT is still very critical for success with this strategy.  Just because IT does not own the data, doesn't mean they aren't extremely important.  IT needs to ensure the network is working, data is flowing and collection tools are working.  They also need to be support for when things break and they should control the access to the systems.  Make sure IT understands the goals and agree on the toolsets being chosen, so they can support them.  

To Benefit From Big Data, Resist The Three False Promises

From Forbes.com:

Gartner recently predicted that “through 2017, 60% of big data projects will fail to go beyond piloting and experimentation and will be abandoned.” This reflects the difficulty of generating value from existing customer, operational and service data, let alone the reams of unstructured internal and external data generated from social media, mobile devices and online activity.

Yet some leading users of big data have managed to create data-driven business models that win in the marketplace. Auto insurer Progressive PGR -1.22%, for instance, uses plug-in devices to track driver behavior. Progressive mines the data to micro-target its customer base and determine pricing in real time. Capital One, the financial services company, relies heavily on advanced analytics to shape its customer risk scoring and loyalty and offer optimization initiatives. It exploits multiple types of customer data, including advanced text and voice analytics.

I believe what most people miss when they hear these success stories is the amount of human capital that gets thrown at these problems.  Hundreds of data scientists create thousands of models, of which very few are actually incorporated into final production.  The reason the Gartner stats ring true is most companies don't have the kind of resources to throw at the problem and most companies won't realize an ROI even if they could throw these types of resources at a problem.

Promise 1: The technology will identify business opportunities all by itself.

This is the direction the technology is moving towards, but it is not there yet.  The technology enables a group of data scientists to identify the opportunities, it's not magic.

Promise 2: Harvesting more data will automatically generate more value. 

The temptation to acquire and mine new data sets has intensified, yet many large organizations are already drowning in data, much of it held in silos where it cannot easily be accessed, organized, linked or interrogated.

More data does not mean better ROI on your initiatives.  In fact, most companies don't take advantage of the data they already have to generate the maximum ROI.  I always use a rule of thumb when purchasing new technology.  If as an organization you don't believe you are already using the technology you currently posses to its fullest, then its not time to move on to something better.  Your current technology should be preventing you from innovating, if its not then you either have the wrong technology or the wrong people.

Promise 3: Good data scientists will find value for you. 

To profit consistently from big data, you need an operating model that deploys advanced analytics in a repeatable manner. And that involves many more people than data scientists.

Remember, data + insight = action.  Actionable data is a combination or art and science.  data scientists provide the science, however you need the team with the business acumen to provide the insight, this is the art.  Data scientists will create a lot of questions that you never thought to ask of your data, but they cannot provide a solution in and of themselves.  

Remember to walk before you run when it comes to data initiatives.  It's always good to have a goal of using "big data" to improve your business and create ROI from where it didn't previously exist, however the journey to "big data" is more important.  These examples of success with "big data" did not happen over night.  They happened because advanced companies were butting up against the limits of their current technology and they were ready to take the next step.  

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.    

The Current State of Email Marketing in 9 Fascinating Stats [#SlideShare]

Some very interesting stats on the slideshow, just click the title and you can view them.  The interesting stat to me was 20% of marketers link their primary revenue back to email.  I think this number is too low.  Email is still the best channel for businesses.  

Now I believe mobile will overtake email soon because it is a more direct channel.  Mobile apps have the potential to be the greatest marketing channel in our lifetime.  The phone is always with us.  It knows where we are, where we've been.  It knows if we are moving or standing still.  It knows where in our store our customers have been.  It is also always on, always available.

Email is not dead, but it already knows who will kill it.   

The Messy Business of Reinventing Happiness - Fast Company

Austin Carr wrote a fascinating piece in Fast Company about the behind the scenes struggles to implement Disney's MagicBand at Disney World.  It details the infighting and politics at one of the most revered brands in the world.  The MagicBand is a new innovation to Disney Theme Parks (only at the Orlando Disney World Theme Park at this time) which brings NFC technology to life in a 40 year old product.  The ideas behind the MagicBand were well thought out and they were trying to solve real problems at Disney World, but they couldn't deliver on the entire dream and it proves that Customer Experience is a cultural change more than an initiative which I have been writing about for a few weeks now.  

The article is very in depth and I think points out a few mistakes in launching an initiative this grandiose.  The main point it proves is how hard it is to change a culture when it comes to customer experience, because so many people in the organization want to eep their points of power rather than thinking of the customer.  It is human nature to be scared of technology that may serve the customer better, it puts people in a defensive mode.  Even when a company as big as Disney commits $1 Billion to the initiative, without the cultural change it makes it near impossible to create magic.

Dream Big, Implement in Stages

The dream was large for the Next Generation Experience (NGE) team at Disney.  They wanted to solve the real world problems that were influencing customer satisfaction at the park.  Long lines, juggling multiple pieces of paper and keys were bringing down an experience that is supposed to be one of enjoyment.  One of the main issues is they were trying to bite off more than they could chew with their implementation.  At one point they were trying to change the airport arrival and had meetings with TSA on airport security procedures.  I understand controlling the entire experience, something Steve Jobs has taught all of us, but at some point these types of distractions take away from the big picture, which is implementing and iterating.  

The biggest problems Disney was trying to solve was long lines and handling of multiple items (tickets, Fast Pass tickets, money, hotel room keys, etc.).  The team was 2 years late delivering on their initiative because they forgot what the main goal of the project was.  The team was distracted with all the technology could do, instead of solving the immediate problems and then iterating on the technology to enhance other experiences.  Always handle the low hanging fruit first, then iterate to enhance the next set of opportunities.  

Keep the Team Small for as Long as Possible

Once the team grows to include more people to implement, projects start spinning out of control.  Change is very hard for people and they will fight it especially when it comes to areas they control within an organization.  Because the plan was growing larger than solving the immediate problems, more people from the organization had to be brought in which slowed the project down to a crawl.  The leaders of the areas being affected wanted control and they wanted a say in the development of the technology.  Embrace the leaders of the areas that will be affected and make sure they are represented on the early small team.  If they embrace the change and feel they had a part in the development, they will get the troops aligned once implantation begins.

Clearly Articulate Goals

The goals of the initiative in my eyes were to enhance the customer experience at the theme park.  What happens in these large initiatives is the organization gets hung up on the technology instead of what the technology is trying to help solve.  Technology in and of itself is worthless, unless used for a purpose to solve a real world problem that cannot be solved another way more efficiently.  NFC technology in a bracelet does not solve any problems by itself, it is the implantation of this technology that is the magic.  Always keep the goals, which is enhancing the customer experience in this case, front and center.  Never start from the technology and work backwards, start from the problem and work forward to how the technology can help solve the problem.

I am fascinated how organizations behave.  Each culture is very different, but they all tend to have the same issues.  The bigger the organization becomes, the harder it is to accomplish innovative change.  Politics and human ego can be the death of innovation.  The Disney project succeeded through sheer will to get it done, but proves that even throwing money at something doesn't guarantee success.  The culture of the company has to be customer-centric before it can solve the problems of the customer.   

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.   

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.

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.

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.