How do you create a data-driven culture in your marketing team?

Becoming a data-driven organisation doesn’t just rely on the right technology, structure and processes. The human element is essential, and without the right skills, qualities and roles, any effort to be successful at data-driven marketing is destined to struggle.

And the kinds of skills that support a data-driven philosophy are rich and varied.

This is very true.  The "art" and "science" that requires actionable data lean more to the art side in most marketing departments.  The biggest change is to respect what data can bring to the equation.  Many marketers don't respect data, they respect their gut and soft metrics like awareness.  While data doesn't solve all problems, it helps inform direction.  It helps decide what is happening with the customers you are trying to target, plus the ones that you aren't targeting and whether you should.  

“The data-driven marketing team is knowledgeable enough to converse freely with technical and statistical resources while staying laser-focused on getting the right message to the right person at the right time. But the most important quality needed in a modern marketing team is curiosity. Without that, I may as well outsource all of my data related work to a third party. Curiosity stimulates creativity and conversation, and aids decision-making.” 

I like the statement of staying laser-focused on getting the right message to the right customer at the right time.  Too many times teams lose their focus and start drifting on to answers that are either easy or different from the most relevant topics.  Understanding what data is saying is more valuable than having the person who can put the report together.  Money is made by providing the insight to the data, that is what I look for in my team.  But don't forget to have the guy that can put all the data together.

Quintero adds: “Building a data-driven culture is not an overnight process. It takes time. To me, a data-driven culture means building a safe environment where experimentation is encouraged and mistakes are tolerated. It’s less about having all the right tools in place – although that’s a critical part of the process – and definitely more about cultivating excitement around discovery and objectivity. Being data-driven is exciting and people should be encouraged to enjoy the process as much as making things happen.”

Changing a culture is a journey.  Teaching the team about why decisions are made from looking at the data and what the thought process for coming to the conclusions is critical in building the data culture.  No matter how smart a person is, if they don't understand the thought processes of decisions they will never be able to take leaps with the data.  If they understand what to look for in data, they beginning asking the right questions and delivering recommendations along with their questions.

How To Win Customers Without Data-Driven Marketing

The human element – even for online businesses – always shapes the final judgment on customer retention and acquisition.

This is the exact reason why becoming a customer-driven organization is so important, even in the age of data-driven marketing.  In most cases a human interaction will determine whether customers remain loyal or churn.  This is part of the customer experience, just as much as delivering the right content to individuals to drive the desired behavior.  While data-driven marketing can handle most customer experience, for offline businesses and even online businesses, the human interaction is critical to handling the cases where things don't quite go as well as you hoped.  

The morale of this story is: make sure that your employees are your strongest, most amazing asset because the most sophisticated technology or data-driven marketing can’t replace what your employees can deliver – caring, thoughtful, on-the-spot customer happiness.

Period. Full stop.

here is so much talk about who owns the customer experience and why CMO's are reluctant to own the entire experience.  In this article the letter describes the interaction with Terry from the call-center.  Now this interaction is as much part of the brand as an advertisement driving a customer to a website or in-store.  This experience defines the brand, yet the overall person in charge of this is not the CMO, it is more than likely an callcenter manager that reports up through an operations division.  

This is where the customer-driven organization has to be a culture, not an individual.  It doesn't matter what the position title is, there is no one person that can be the customer advocate throughout large organizations.  The customer advocate has to be all employees, period.  There could be a person leading the charge, but until an organization has changed its culture to be truly customer-centric, data-driven marketing and great advertisement will never drive the most ROI possible because the organization is not focusing on the customer experience as a whole.  

For data-driven marketing to succeed doesn't need a customer-centric organization, there is a lot of value and areas to increase revenue.  My belief is all organizations should be customer-centric, this enhances all aspects of the business, not just the data-driven marketing side.

The State and Drivers of Data Marketing

What matters most is the optimization of the customer experience, relevance and (perceived) customer value as a driver of business value. Data-driven marketing certainly is not (just) about advertising and programmatic ad buying as some believe. Nor is it just about campaigns. On the contrary: if done well, data-driven marketing is part of digital marketing transformations whereby connecting around the customer across the customer life cycle is key.

Very succinct vision of what data-driven marketing is, it's all about the customer experience.  The advent of "big data" was nothing more than gathering extra data about the customers.  Gathering data is only the first step of the process, albeit a time-consuming one.  The good news is after the hard work of gathering the data has been completed, the harder part starts.  Once you have data, making sense of the data and creating actionable outcomes to enhance the customer experience becomes the goal.  This is very hard work.  It takes plenty of analysis and insight to reach this goal.  But the companies who will do this the best will be the ones that succeed in the digital age.

Among the key takeaways of the data-driven marketing report by the GlobalDMA:

• 77% of marketers are confident in the data-driven approach and 74% expect to increase data marketing budgets this year.

• Data efforts by far focus on offers, messages and content (marketing) first (69% of respondents). Second ranks a data-driven strategy or data-driven product development. Customer experience optimization unfortunately only ranks third with 49% of respondents.

• Among the key drivers of increased data marketing: first of all a need to be more customer-centric (reported by 53% of respondents). Maximizing efficiency and return ranks second followed by gaining more knowledge of customers and prospects.

I believe the first step in the process is understanding where the puck is going to be and skate in that direction.  Marketers are understanding this data revolution is coming and they are saying the right things in surveys.  The real question will be how to get there.  It's easy to identify problems, it's hard to implement solutions.  The marketers who will show they are adept at change will thrive in this new paradigm.  

Customer analytics is something I have focused my entire career.  In the casino industry we have had the optimal opt-in mechanism for many years and have collected amazing amounts of data about our customers behavior.  We have used this to create targeted marketing campaigns to our customers, so I believe in the direction the entire industry is taking.  Always start with the customer.  It will lead to creating better experiences and more profitable results.

 

Sears Could Disrupt Throwaway Tech Culture

It's funny the timing of this article.  I was just talking with my wife about Sears and how it seems they have no future, it's only a matter of time before the Sears retail store as we know it will no longer exist.  Then to read a headline about Sears disrupting?  Heck ya I'm interested.

The company has launched a Seattle office, and recruited retail tech execs to help it get a handle on the data it has amassed from the 40,000+ daily service queries its Home Services group collects on washing machines, refrigerators, and other appliances. It turns out that the industry average is that about 1 out of every 4 customers don’t get their appliance woes fixed on the first visit. 

“Each truck carries about 400 parts, yet those annual service calls require something like 168,000 different parts,” explained Arun Arora, the group’s president. “We’d have to have our 7,000 certified technicians driving semis around to anticipate them.”

"Big data" has so many applications and to see Sears trying to disrupt in a way that doesn't make headlines is impressive.  This kind of disruption, even though on the surface looks like a cost-savings initiative, can revolutionize the service of appliances.  Why does that matter?  Because loyalty is the name of the game.  If they make the experience of owning a machine better, even when it is getting old and needs some new life breathed into it, they can increase their base of loyal active customers.  

The more customers that are active with a company the more they will make.  If Sears can increase the number of loyal customers by offering a superior customer experience of ownership, they can drive more sales in other areas.  It is the process of rebuilding trust with a brand.  If I knew buying an oven will have a longer shelf-life and the company where I was buying it can make that happen, then it makes where I buy more interesting.  

So many times in the retail space it comes down to price.  Everyone sells ovens and mostly from the same manufacturers, so there is very little to differentiate.  The easiest rode to differentiation is price.  The problem is when competing on price, the business can never win.  They are not cultivating loyal customers, in fact they are probably selling to the exact wrong customer.  If a customer is only going to choose on price, they are by definition not loyal customers.  If Sears can differentiate beyond price and experience in the stores, they can grow their loyal database.  That's a big win.   

The Dangers of Data-Driven Marketing

Marketing has gone digital, and we can now measure our efforts like never before. As a result, marketers have fallen in love with data. Head over heels in love—to the point where we want data to drive our marketing, instead of people, like you and me. I think this has gone too far.

I'm a big proponent of data-driven marketing, in this article Ezra Fishman uses semantics to say this is bad, but what he is trying to get is there is a need to go beyond just the data.  As I wrote in Data + Insight = Action, data all by itself cannot create actionable outcomes.  

Data-informed marketing

Instead of focusing on data alone, data-informed marketing considers data as just one factor in making decisions. We then combine relevant data, past experiences, intuition, and qualitative input to make the best decisions we can.

Instead of poring over data hoping to find answers, we develop a theory and a hypothesis first, then test it out. We force ourselves to make more gut calls, but we validate those choices with data wherever possible so that our gut gets smarter with time.

This is what I was trying to articulate in my article.  To be an excellent data-driven marketing organizations takes a little bit of "science" and a little bit of "art" to determine the best course of action.  When a data scientist is driving your organization, there are years of experience being unused to help him understand even further what the data is saying.  

Most times when a data scientist is off on their own, it takes an inordinate amount of time to come up with a conclusion, mostly because they lack the context of how the business is generating the data.  How the strategy manipulates the data.  How a customer being underserved may be an intentional outcome.

The ease of measurement trap

When we let data drive our marketing, we all too often optimize for things that are easy to measure, not necessarily what matters most.

Some results are very easy to measure. Others are significantly harder. Click-through rate on an email? Easy. Brand feelings evoked by a well-designed landing page? Hard. Conversion rate of visitors who touch your pricing page? Easy. Word-of-mouth generated from a delightful video campaign? Hard.

Right on!  Of course the organizations that take the easy way out are ones that I would not consider to be data-driven.  KPI's are a great item, but they can be deadly.  There are usually so many moving parts that make up the business and the data being generated.  This can cause business KPI's to look fine, yet drilling down into the performance from a customer perspective may show some very scary trends that would cause alarm.  However, a non data-driven company will continue with their strategy because of the KPI's (hello RIM/Blackberry).  

The local optimization trap

The local optimization trap typically rears its head when we try to optimize a specific part of the marketing funnel. We face this challenge routinely at Wistia when we try increase the conversion rate of new visitors. In isolation, improving the signup rate is a relatively straightforward optimization problem that can be "solved" with basic testing.

The problem is, we don't just want visitors to sign up for our Free Plan. We want them to sign up for our Free Plan, then use their account, then tell others how great Wistia is, then eventually purchase one of our paid plans (and along the way generate more and more positive feelings toward our brand).

This can be combined with the previous bullet.  When analytics is only seen from a high level, simple statements like "we need to increase the number of signups, which will flow down at the same rate as we currently have, will increase conversion."  Nothing could be further from the truth.  To increase anything there needs to be an additional action.  This action may include advertising to a different group of individuals or giving an incentive that will increase signups.  The issue with this thinking is these aren't the same individuals that are converting in your current funnel.  The proper strategy is to figure out the converters and try and target customers like them, which may actually decrease the size of the funnel if done right.

The data quality trap

We are rarely as critical of our data as we ought to be. Consider, for example, A/B tests, which have become the gold standard for marketing experimentation. In theory, these tests should produce repeatable and accurate results, since website visitors are assigned randomly to each page variant.

In practice, however, there are lots of ways even the simplest A/B tests can produce misleading results. If your website traffic is anything like ours, visitors come from a variety of sources: organic, direct, referral, paid search, and beyond. If one of those sources converts at a much higher rate than others, it's easy to get skewed results by treating your traffic as a single, uniform audience.

One should rarely just take the conversion or redemption results from the A/B test without digging into the data.  Making sure all segments are driving the results is key.  Don't take for granted the customers that were randomly selected for each group ended up being totally random.  Ensure there was proper representation from each segment of the business and identify any other changes that could be tested based on different behaviors within the segments.

Data vigilance

As marketers, we should continue to explore new and better ways to harness the power of data, but we also must remain vigilant about becoming overly reliant on data.

Data can be a tremendous source of insight. Harness that. But don't pretend it's something more. And definitely don't put it in charge of your marketing team.

This reminds me when I was a product manager and we would receive these RFP's to determine if we were the right company to supply them with our product.  Sometimes the requirements were such that we wondered if the company wanted humans to continue to work for them.  I would comically refer to some of these as automated manager.  It seemed companies wanted to press a button and have a system do everything for them.  This is the trap Fishman is referring.  Humans have great insight.  Humans are the "art" in the equation to actionable outcomes.  This is equally important as the "science".

The Missing Connection Between Big Data and Great Insights for Data-Driven Marketers

Data-driven marketers today are wondering how they can gain insight from big data. The answer? The ability to change is the connection between big data and insight. Data-driven marketers today know that their roles are changing: 68% of marketers think that marketing has seen more changes in the last two years than it has in the past 50 years, according to a recent survey.  The changes are due to a renewed focus on customer experience within their jobs, and the need to use big data to improve that experience.

Customer Experience is the buzzword over the last 2 years, combine this with the other buzzword of "big data" and you can understand why 68% of marketers think marketing has changed so drastically the last couple of years.  I think what is causing all this change is how technology has shifted the paradigm of marketing.  

For many years marketers were able to call on plays from the same playbook and be very successful.  The technology was never really able to advance the playbook and very few companies were pushing the boundaries.  Today, marketing technology companies are driving the sea change, creating platforms which make creating authentic customer experiences possible on a large scale.  

Companies are having to tear up their playbook and turn their strategy on its head.  This goes well beyond just the marketing playbook.  Companies are having to start culture change throughout the organization as the customer experience goes well beyond just the marketing department.  As customers interact with all parts of organizations, there is little care of operational silos within companies.  

The biggest sea change is what Adobe refers to "marketing beyond marketing".  No longer can marketing leaders be focused on the message and bring in customers, only to wipe their hands after the customer starts engaging with the brand.  Marketers are learning they are the leaders of the customer experience renaissance.  Marketing is having to drive the experience of the customer throughout entire organizations, which is not a skill-set a traditional marketer has.  This change will be driving "big data" initiatives as marketers are learning to understand their customers in new and interesting ways.  

How CMOs Can Make Sure Their Companies Are Customer-Obsessed

CMOs are charged with making their companies customer-obsessed — so they can win in an age where customers are highly empowered. But the irony is that many marketing shops themselves are not customer-obsessed.

I am continually thinking about the customer-centric approach and who should own it in the organization.  The CMO is the obvious choice, however are they the best choice?  I have seen where organizations have a C-level position, something to the tune of Chief Customer Officer.  This is also thought because it ends up being another level in the organization, another potential touchpoint in the organization that has to bring different groups in the organization together around one common goal.  I think it comes down to having the right person.

Marketers are predisposed to think about the market first. So why are marketers not naturally predisposed to be customer-obsessed? The answer lies in gravity — the gravity of the P&L and the associated product, solution or service performance.

It's always about the customer.  Everything should come back to customer analytics.  I think Finance departments have too much power in some organizations where high-level KPI's are all about a product or a service.  The problem with these KPI's is they don't go far enough down to the "people" who are driving those metrics.  It is similar to fixing a symptom instead of the actual source of the problem.

For example, the company sells 1,000,000 widgets and they want to grow this by 3% in the next quarter.  This is the entire wrong approach to the problem.  Widgets don't grow by 3%.  3% more customers buy widgets in the quarter.  It is imperative to start with the customer because they are the ones that are purchasing these widgets.  So to grow those numbers, marketers need to embrace the customers to grow their numbers.

I have spoken with many CMOs — across industries and geographies — and this common theme has emerged: Marketing’s relevance and performance is now predicated on putting the customer at the center of the universe. This is neither elective nor minor surgery. Most believe an overhaul — not a simple refinement — is needed to make marketing customer-obsessed and truly able to drive growth.

Changing to a customer-centric organization is a complete change in culture.  This does not happen overnight.  It takes a dedicated team with a singular focus many months to accomplish.  I once read to change a culture, a great organization with amazing focus will take 18 months.  There are not that many of these organizations out there.  The average is 4 years.  So organizations need to start their culture change today.  There is no time to waste.  The customer-obsessed organization will be the most successful in the new customer empowered buying dynamic.  

Business Intelligence vs Analytics vs Big Data vs Data Mining

To help you navigate the terrain of business data concepts, we’re going to give you a basic summary of what some of the most common terms refer to and how they relate to each other.  

This is a very good article on definitions in the data space.  So many times I hear executives talk about topics such as "big data", but they are really referring to analytics or data collection.  

Five Ways to Win with Data-Driven Marketing

Data-driven marketing has come to the forefront for companies that want to better engage their customers and prospects. With data-driven marketing, firms are able to gather, integrate, and assess data from a variety of internal and external sources to help enhance value.

Marketing automation starts with data.  In fact, in the digital age, almost all marketing initiatives start with data.  Companies who are data-driven have a distinct advantage over their competitors.  When a company is data-driven, they focus on their strengths, enhance their weaknesses and they don't obsess over their competition.  They have the data to understand how they can improve.

1. Determine what really makes customers tick. According to the DMA, data-driven marketing is about discerning what customers want and need and engineering the company to provide it: “The more firms can use data to develop a 360-degree, multi-channel view of what customers think and want, the more the customer will truly be king.” Through the use of both internal and external data, companies are learning how to “crown” their customers — truly understand what makes them tick, and then develop campaigns that engage them in the most effective manner possible.

This all comes with data analytics.  Understanding what drives your customers behaviors is step one to developing campaigns and offers.  Without an understanding of what your customers want, there is not an efficient way to determine what they would like from you.

2. Set baselines for campaign effectiveness. Data-driven marketing has effectively replaced the traditional “hit-or-miss” test component of the typical direct marketing campaign.

Baselines are a very important piece to understand when analyzing campaigns.  This is the beginning of the journey to understand the effectiveness of any changes that are made.  If an organization cannot answer what a particular program is bringing them, they should test the campaigns without the program and determine what, if any, the effectiveness of the program is bringing.  

3. Block out the “noise” and focus on what’s relevant. When assessing data over multi-year periods — and across different marketing channels — it’s not unusual for things to be extremely “busy” at the outset. There’s a lot of static and responses are all over the place. However, by using proven data-driven marketing techniques, you can start to pull out the relevant information, analyze it over time, pick up on traffic patterns, and drill down to specific marketing touch points (i.e., number of website hits that come in when a specific direct-response show airs).

This is a lot harder than it sounds.  Marketers are the kings of taking a piece of data and selling their story with it, even though it is just noise or a small sample of customers.  This is where the "art and science" approach is necessary.  Being able to combine data mining techniques with the business acumen is key to focusing on the relevance of the data.

4. Determine exactly how customers are responding.

Again, this is important to understand multi-channel marketing.  The ability to reach your customers on the right channel at the right time is only possible through data.  

5. Reach extremely targeted customer bases.

The promise of 1-to-1 marketing is arriving.  Be careful to shoot for this level of personalization, because it is very expensive and the pearl is not worth the dive for the majority of your database. However, being able to target your best customers in a very personal nature could help grow the business exponentially.  This takes extreme focus.  

Closing the Loop on Marketing Automation

Marketing Automation is starting to come into the mainstream, but many companies are not using the toolset to create amazing interactions with their customers.  I know many brands that have sophisticated toolsets and it is used to show me points and my name.  I get the same exact email that all of their customers get with my name attached, this is not an amazing interaction.

Data and Analytics as the Foundation

Seems logical, right? You would be amazed at how many brands are still working through “We don't know how to get our transactional point-of-sale integrated with our demographic and third-party purchase data.” Solid data management and extract, transform, load processes form the foundation from which a solid enterprise marketing platform is built.

Data is the backbone of any marketing automation solution.  This may be why the technology isn't as pervasive as it should be.  Getting all of the data into one location in a consumable format for marketers is not an easy task.  This is the first step in the marketing automation journey.  Starting with the data will increase the chance to have amazing interactions with your guest.  The more knowledge about the customer, the more customized a communication can be and this is what delights the customer.

Integrate, Orchestrate and Optimize

This is a large category, but an important one as far as customer engagement is concerned.

First up is integration. Integrate marketing programs across channels — leverage insights from outbound marketing programs to better serve customers on inbound channels and vice versa. With consumers switching channels as frequently as they do today, this is imperative.

Marketing automation tools today can currently run many inbound tasks.  This is especially true when sub second response is not necessary.  When giving the customer an option to to click on a button to serve up an offer or promotion, use the marketing automation tool to serve up the offer.  This way it ensures the customer is seeing the same offer they saw in an email you sent yesterday.

Orchestrate campaigns and their offers so that the timing and sequence, as well as the channel delivered, make sense based on individual consumer preferences. 

So many brands are tied to their own timing of communication, not the customers.  For instance, we alway send our bi-weekly communication on every other Tuesday.  This makes it easy for the marketer, however this does not take into account the customer.  

All customers should be on their own timeline.  Marketing automation tools are very sophisticated and can handle this type of philosophy.  Planning the interactions with customers based on their behavior will result in much higher response.  This is the type of delightful interaction customers expect.

Optimization is the final step in the execution phase. Make sure you use analytically based optimization across all channels to avoid over-contact and saturation of consumers. Consumers are only annoyed by receiving an email offer for a product or service that they just signed up for last week during an inbound contact center conversation.

Be sure to optimize constantly.  Marketing automation campaigns are living and breathing entities.  They are never finished and there is always money to be found in optimizing the programs.  Optimization goes much further than over contacting the customer, just as bad is not contacting the customer at the time they want to purchase.  Even worse is offering the customer something they would never be interested in, especially if they have been your customer for an extended period of time.  Tiffany, I already bought my wife the diamond, stop telling me about how amazing it is, you had me at hello.

The last step is to close the loop in order to perform truly integrated marketing. Take the information you learn from the delivery of both inbound and outbound offers: Did a customer open an email, respond to a social message or accept a verbal offer delivered via the contact center? If so, what effect does that have on downstream marketing efforts?

It all comes back to constantly learning.  The more your customer interacts with the brand, the more they tell you about themselves.  I am not the biggest fan of over surveying the customer and when asked why that is, I say its because I survey my customer all the time.  I send them outbound communications with call to actions and if they reply, they are telling me what is more important, voting with their wallet.  If they don't reply, they are telling me they don't appreciate this offer, or maybe it is this time, etc.  Learn from these interactions and enhance your campaigns.

Remember, Data + Insight = Action.  Always be looking for actionable data on your customers and using that in your marketing automation programs.

What does it mean to be a data-driven marketing success in 2015?

Ian Michiels writes for mycustomer.com:

Micro segmentation over 1:1 personalisation

Even when data is readily available to inform highly targeted engagement, someone actually has to produce the creative and copy to trigger the engagement.

I was on a panel at an Adobe event late last year when the topic of 1-to-1 marketing came up.  I have always been a huge advocate of trying to get as close as you can to 1-to-1 marketing, but that comes with a caveat.  The cost to get to the elusive everyone is individualized is massive.  When I say as close as you can, what I mean is start from the top of your customer list (not by alphabetical order, but by some worth and frequency or potential worth metric) and work as far down that list as you can to create 1-to-1 marketing for your best customers.  The other customers you want to have as many segments as makes sense, but always allow the data to drive those segmentation decisions,  

Automating up-sell and cross-sell campaigns

Marketing is the only function in the business that actively communicates across the entire spectrum of the customer lifecycle, from the inquiry to a loyal customer. That raises two very interesting questions that data-driven marketing has answers for:

• Should marketing own the customer lifecycle?

• How should marketing allocate time, budget, and effort across the customer lifecycle?

As I commented on recently in my article Retention is King, retention's the first place I start when implementing a marketing automation program.  The customer lifecycle should be owned by marketing.  Marketing has all the tools to automate the communications in the relationship and target based on behavioral and demographic data.  When it comes to the question of time allocation, make sure the retention programs are dialed in.  They will never be finished and you will always be tweaking, but then you can move on to acquisition and reactivation.  It is much easier to cross-sell or up-sell a loyal customer than it is to acquire a new one.

A/B testing on landing pages and email campaigns

According to the 2014 Gleanster Marketing Resource Management report, only 60% of small and mid-size firms conduct A/B tests on email, landing pages, and website properties. It’s actually shocking to learn how much you really don’t know about your customers when you run A/B tests on creative and copy.

In sales they say "ABC", Always Be Closing.  In marketing automation and data driven businesses we should say "ABT", Always Be Testing.  The caveat to this saying is there needs to be an understanding of a baseline first.  So if you are implementing a new program, let it run for a bit (unless it is a total disaster), use analytics to look for opportunities and test those opportunities.  Don't just test for the sake of testing, always let the data drive the opportunities and then test the hypothesis.

Machine learning is your best friend

One consistent theme that keeps coming up in our advisory sessions is that marketers want help in data analysis. Thanks to advances in computing power, data analysis that previously took days can now be done in seconds and often in the cloud. Machine learning applies rules to data sets and looks for correlations between data. Does this do the job of a marketer? Heck no! What machine learning does for marketing is help isolate trends that should be investigated further. Marketers still need the context about customers and products to translate those correlations in the data into action.

As I said just above, let the data drive your testing.  Machine learning and data mining techniques can uncover insights within your data that the human eye could never perceive just by looking.  Many marketers want a predictive modeling tool to spit out an answer as to what they should do and just go do it.  If that were the case, why do we need the marketer?  It is important to make sure to understand what the outputs of these tools provide and test their findings.  Without the business acumen, the output could be very flawed.  Don't jump to a conclusion, use the insight to form hypothesis about your customers and test away.  Remember as I wrote before, Data + Insight = Action.

Gartner Predicts Three Big Data Trends for Business Intelligence

Always good to see what the researchers are predicting for the future.  This is an interesting take on big data.  It focuses on an outcome of big data and then from a business perspective, what will happen to big data.

No. 1: By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.

Very interesting.  Most successful products take a human process and automates the process to increase efficiency.  I'm sure this prediction is a slam dunk as businesses will use massive data to help enhance current products and processes.

No. 2: By 2017, more than 30% of enterprise access to broadly based big data will be via intermediary data broker services, serving context to business decisions.

This is another no brainer.  As companies like Experian and Acxiom make it easier to access their data, more and more companies will begin utilizing this data to make better decisions about their customers.  This is something that I believe in greatly.  The more data to enhance marketing campaigns, the better equipped marketers are to change the behaviors of their most valuable, or better yet, their most potentially valuable customers.  

No. 3 By 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT.

The Internet of Things will be very interesting when it comes to data.  How companies use data about customers behaviors in their own house with the items they use will be a touchy topic in the coming years.  If companies can prove they are using the data to make the customers lives better, it will be a smash hit.  If they are becoming creepy with the data, then the IofT will never reach its full potential.  

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.  

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.  

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.  

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.