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.

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.

 

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.  

5 Mistakes You're Making That Are Killing Your Marketing Campaigns

In a past article from Juntae DeLane, he brings up very good succinct points about pitfalls of marketing campaigns.

1. Lack of Audience Understanding

Having a greater understanding of your audience should be the first step when developing a campaign strategy. Some entrepreneurs will produce evergreen campaigns with no specific targets hoping that new targets will emerge. Some may see a practical benefit in doing so; however, why run two campaigns to accomplish one task? Your marketing campaign will be optimized by doing research beforehand so you can make an impactful and relevant introduction to your brand.

The key to digital marketing is knowing your audience.  The more information you have about your customer the better and when using marketing automation tools, it is important to utilize this knowledge.  It is easy to lump as many individuals together and call them segments, however the more individualized your campaigns can become, the better experience the customer will have interacting with your brand or product.  

2. No Strategy

Many marketers get confused when talking about strategies and tactics.  A tactic is how you are going to do something, the strategy is what you are going to do.  They must work in tandem.  Many times marketers start with the tactics, "we are going to send an email to all of our customers who abandon a cart".  Why are you doing this?  You have to start with the strategy of "increase our sales from all parts of the funnel" to reach the tactic.  Otherwise, how do you know the goal?  The goal may be simplified in this case, but so many times a marketing plan is not strategic, it is a list of tactics the company is going to employ.  

Having an overarching strategy will help guide decision making.  Just because you can do something doesn't mean you should.  Focus is the key and understanding the strategy assists in that focus.  

3. Too Much Sales Pitch

I think another way to think about this is understand your customers are not stupid.  They know when they are seeing content from your company they are being sold something.  They want to understand why they need something, how will this make my life better, will I feel satisfaction with this purchase.  By trying to convince them to buy leads to buyers remorse.  The ultimate goal is to create loyal customers that will return again and again to purchase. 

4. No Tracking or Data

With all the tracking services out there, you should be able to easily track your campaign efficacy. From Google Analytics to KISS Metrics you can establish a tracking dashboard at virtually no cost.

However, what will kill your marketing campaign is if you identify the incorrect metrics.

I don't see this too much, most everyone is tracking some kind of performance.  I believe in the comment from above, what are the key metrics that drive the business.  If number of sales is your key metric, this can come at a loss because the amount of money invested to drive those increased sales is more than the revenue being generated.  Be careful to choose your metrics wisely.

5. Too Much Branding

I think everyone believes in increasing brand loyalty is key to a successful business, but this goes so much deeper than pushing the brand.  Brand loyalty comes from consistency, delivering the promise of the brand and always putting the customer first.  These don't come from a catchy slogan or advertising, this comes from hard work to deliver the best customer experiences.  The brand is all aspects of the transaction, from the customer service agent answering the phone to the ways in which a mobile app enhances the buying experience.  

6 Ways Mobile Marketing Automation Boosts App Engagement And Monetization

"These are a few of my favorite things", mobile apps and marketing automation.  A perfect marriage.  Mobile is the channel of the future and marketing automation can enhance it to improve monetization no matter what the business model.  This article is focused on the freemium business, but I believe it applies to everyone who has a mobile app.  Marketers should start thinking of mobile as a channel instead of a business unit, then the understanding of marketing automation and true omni-channel marketing can come to fruition.

1) Understand users’ behaviors

For any type of campaign to succeed, developers must first understand their users’ behaviors and the motivations behind them.

What price point is likely to get a certain user to make a purchase? Which items or services are they most likely to pay for? What is most likely to trigger their first purchase? Their second? Their tenth? What kinds of rewards (free coins, extra lives, unlocked content) do they want most? How likely are they to refer a friend? Why or why not? Which features do they use most often, and what new features would they most like to see?

This is marketing automation at its finest.  Taking the users behavior and trying to drive additional behavior or change the current behavior if possible.  Using mobile as a channel allows for the ultimate in timeliness.  Most people have their mobile device on them all the time, so being able to communicate and knowing it will reach your intended target immediately makes mobile the best channel for marketers.  Targeting the offer and the message is just icing on the cake.

2) Build advanced user segments

Not all users are created equal. They must be treated as individuals, and in order to do that at scale, developers have to divide their user base into distinct segments.

Is there any other way to build segments?  Start small and grow your segments.  There are numerous ways to skin this cat, but segments should be grown out of analytics.  Don't segment customers by gender if males and females behave exactly the same.  Segments are built from knowledge of behavior that is different from the rest of the group.  That's how new segments are born and they are different for every business.

3) Set up custom messages and campaigns

Once cohorts are created, developers can start targeting those groups with custom messages and campaigns.

Segments are built for customizing offers and messages.  If this is not going to happen, then there is really not a need to identify the segment other then for analytical purposes.  The reason these customers stood out from the rest is they were different, so make sure they receive different messaging and offers.

4) Deliver messages during contextually relevant moments

The next step in perfecting a mobile marketing automation strategy is to pick the right moments to serve campaigns.

The right offer to the right person at the right time.  This has always been the direct marketers mantra.  Timing is very important in marketing.  In this context, the discussion is when to serve up an app in a game, but this applies to all marketers.  I bought an engagement ring at Tiffany's for my soon to be wife.  I received weekly emails after that purchase advertising the engagement ring.  This would have been the optimal opportunity to sell me a wedding band, both male and female.  

5) Select the right channel

In-app messages aren’t the only way to promote campaigns.

In the context of marketing for freemium games this is always a tough one, but for regular brick and mortar businesses, this brings home the point I started the article with, mobile is a channel.  Sometimes it will not be the right channel. For instance, as a hotel mobile is a great channel for marketing offers while the customer is at the property.  For when they are at home, they don't need to see there is a free cocktail waiting for them at the bar, bad channel and timing.  A lot of times, email is the preferred channel and mobile is used for more contextually aware needs.  But test that theory.

6) Track, measure, and optimize

The final step, as with any campaign, is to continually improve upon your results.

This is the best final step there is, because without it there is no way to really enhance the campaigns.  Be sure to capture all the relevant data and be able to access it through a BI tool that can represent data visually.  This will allow for greater insight to the data.  Once hitting a wall with the BI tool, then advanced analytics can come into play in the form of data mining and predictive analytics, but there will be plenty of segments created without those tools.  Remember, marketing automation campaigns are living and breathing.  They are never finished, so constantly be looking for that next great segment.

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.  

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.     

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.  

Southwest Airlines Making an Impact in Marketing Automation

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

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

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

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

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

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.

Business Intelligence for the Other 80 Percent

Ted Cuzzillo writes for Information-Technology:

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

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

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

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

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

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

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

Social Media: Stop It With Pointless Metrics

From Martin McDonald:

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

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

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

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

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

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

Measuring Social Media Effectively

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

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

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

7 Limitations Of Big Data In Marketing Analytics

Anum Basir writes:

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

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

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

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

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

1. User Data Is Fundamentally Biased

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

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

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

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

2. User-Level Execution Only Exists In Select Channels

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

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

3. User-Level Results Cannot Be Presented Directly

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

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

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

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

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

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

5. User Data Is Not Suited For Producing Learnings

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

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

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

6. User-Level Data Is Subject To More Noise

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

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

7. User Data Is Not Easily Accessible Or Transferable

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

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