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".

How Not To Use Marketing Automation

Normally I would never suggest not using a marketing automation for anything, but it is a funny title so I'll let this slide.  I would even argue that bad marketing automation is better than no marketing automation, but not by much.

Generic Broadcasting – The time that you save with marketing automation should be used to not only improve your content in the first place, but also to personalise through segmentation. Consumers in all market places are becoming more and more sophisticated, and can spot poorly executed marketing automation. And their perception is likely to be that you don’t care about the communication.

This is the first step of marketing automation.  So many times the implementation strategy of the marketers installing the new system is to do what they are currently doing, but in a new fancy tool.  I think this is an okay step if the desired outcome is to QA the output to make sure all the data is correctly hooked up.  Other than that, marketers should have an understanding of what the new capabilities of the tool they have purchased and at least start with a few general segments to make sure there are some differentiation in the messaging.

My advice is to bring in a group that has experience in the tool who focuses on the strategy behind utilizing the tool to help build a roadmap.  It's okay to start broad, in fact I recommend it.  But don't tart from scratch.  Start implementing the "low hanging fruit" opportunities in your business right away.  This will be your baseline and then you start to grow from there.

Being A Spammer – Automated emails are a great way of engaging with recipients who have shown an interest in your email, but you should still spend time focusing on the quality of your communication. Avoid the usual spam trigger words and don’t go sending an email to thousands of people all at the same time. Marketing automation can increase the risk of spam, but a good email provider will help you with this.

All the good Email Service Providers (ESP) will provide services to assist you in "warming up" your domain to the Internet Service Providers (ISP).  This is a necessary first step to make sure everyone can see your emails when you send them (deliverability %).  

However, this does not mean your job is over.  If you decide automation will allow you to send emails to your customers everyday with messages which do not resonate with most of them, you will quickly be flagged as spam.  If this happens too many times, the ISP's will block your emails.  When I started at one property, Yahoo was blocking all the emails and the deliverability rating was in the high 60% range.  It took a long time to get unblocked, so make sure your content is relevant and you stop sending to customers that are not opening your email.

Bad Time Automating – Automated communications are tricky: you’re writing them at a time where the context of how the communication will be received isn’t known. Most of the time, this is absolutely fine as you are only scheduling a few hours ahead, but beware of shifting events. 

Most of the time your emails will not be "set it and forget it".  You may run with an automated email blast for customers that signed up today with an offer to engage further, and that is fine in most cases.  In a lot of the cases the automation is used to increase the segmentation capabilities, not to create a generic email blast to all your customers over and over again.  

If you run into this problem of timing, then forget about scheduling too far in advance.  Take your time and make sure the message is relevant to the customer at the time the email is sent.  This will save you from looking like someone that doesn't understand the customer at all.  That is the worst thing that could happen.

Communicating Constantly – With marketing automation, communications with your audience should become a lot easier. But don’t get carried away. If it is easier, then the temptation will be to communicate more often, but this is as off-putting for a recipient as communicating poorly. It can also have a detrimental effect on the size of your audience.

The quickest way to being marked as spam or unsubscribed is to over communicate through email.  Just because its easier to do, doesn't mean you should.  Make sure you are communicating a little more than your customer is engaging with your brand.  Its okay to communicate everyday if your customer is buying something everyday, but this is usually not the case.  If your customer purchases a product once a month, maybe every other week is a good cadence to start.  Remember, the beauty of a marketing automation tool is your customers don't have to all be on the same communication cadence, they can be on their own, as long a you have enough content to make that strategy make sense.

Send And Forget – One of the objectives of most communications is to elicit a response. Whether that is an open from an email, a click on an advert or a reply / share from a social media post. So when you are automating, you should always have a process in place for monitoring their impact – you should be able to set this up as an email or smart phone notification. Ignoring this can result in recipients not talking (positively or negatively) to anyone, something to avoid at all costs.

As I said above, this strategy can be detrimental to having a marketing automation tool.  Never send without analyzing the results.  All marketing automation campaigns are living and breathing entities, they need to be changed and enhanced constantly, because as you change behaviors the communication cadence and the offers need to change with it.  There are also segments of customers in the campaign who are not getting what you are throwing out, so constantly look for opportunities to enhance the campaigns taking these customers into account.  Analyzing is the most important step of the process.

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.

Yelp is Looking For Buyers

In reading the daily update on Stratechery by Ben Thompson, which I highly recommend, he discusses Yelp being on the market.  Yelp is definitely underperforming compared to other social network advertising platforms.  Their revenue is very small, $377 million in 2014 and the growth is not as large as it needs to be for a corporation the size of Yelp.  

What strikes me as interesting in the case of Yelp is their strategy.  Yesterday I commented on an article about strategy and how to assess if your strategy is valid.  I believe Yelp has a strategy that is destined to fail.  Yelp is running the same strategy as the market leaders, which is as an advertising platform.  

The issue I see with their strategy is it is not differentiated.  In fact, their offering is worse than the market leaders when it comes to their ad product.  One may argue their product is differentiated because if a customer is searching for example Mexican Restaurants, then as a Mexican Restaurant it can't get more targeted than an ad for someone looking for that kind of food in a small geographical area.  The problem with this is customers aren't looking for ads on Yelp, they are looking for advice.  An ad is the opposite of what they desire.

 I frequently hear in the tech community that Twitter doesn't understand its product.  They want the product to be something other than what it is.  I fear Yelp may be in the same boat.  Yelp is an aggregator of reviews, they are the trusted source of "where should I eat".  That trust comes from customers reviews.  

elp has the opportunity to differentiate their business.  Their strategy should be the opposite of the strengths of Google and Facebook.  

Loyalty

Yelp has a loyal customer base, however they do not take advantage of this.  Their product has not really changed much since its inception, especially in mobile.  With the advent of technologies, such as beacons, it surprises me that Yelp hasn't taken advantage of its loyal base and struck up deals with local businesses to do a loyalty program with Yelp.  Businesses rely on having great Yelp reviews and this can be parlayed into some kind of loyalty program with a beacon backbone that would identify if a customer was at the business and how much was spent using new location aware technologies.

Recommendation Engine

Because of the amount of data Yelp has it is surprising they haven't developed a more intuitive recommendation engine.  I am always looking for places that I would enjoy and it would be nice if an app told me where I should go and what I should order or what services I should buy.  Yelp is in such a unique position to deliver this.  

I believe they have the ability to enhance their product by allowing customers to rate something without writing a review.  This is something that doesn't have to count to the external rating of the restaurant, but as a means to gather likes of an individual.  This is easy and more customers would rate the businesses in turn.  They can then use this information to have the ultimate "lookalike" recommendation engine.  This is far more powerful than anything Google or Facebook can do.

Targeted Ads and Data

With this lookalike system in place, Yelp can then sell back to the businesses in the form of ads and data.  Since they will have information on all the buyers who are interacting with Yelp, not just the people who take the time to write a review, Yelp can then sell all the information about the customers back to the businesses for a fee.  The ads can then become more targeted because advertisers can get on the home screen of the app with a customer that is highly likely to enjoy the businesses offerings.  As customers see the recommendations are more accurate and they enjoy the businesses experiences, they will end up buying more items through Yelp advertising because of the accuracy.  This will drive higher ad prices for Yelp and bigger returns for the business as they can attract customers that will become more loyal.

I would love the opportunity to innovate at Yelp.  They are in such a unique position to do something different, but they are building the exact same monetary offerings as their competition.  The problem is they don't have the scale.  Just like Twitter, they have to be better and more accurate with their advertising.  This will drive advertising dollars their way because it is more efficient spend and that is what advertisers are looking to achieve.

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.

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.  

Retention is King

There are too many companies asking, “How do we acquire more users?” that should instead be asking “How do we get better at keeping the users we already have?”.

Its easy when approaching the problem of growth to think that you just need to get more users, after all that seems to be the very definition of growth. However, if you take a step back though and think about growth as the maximization of user-weeks over time, it quickly becomes apparent that focusing on retention has a much larger effect than topline growth. This is also much more of a sustainable growth mindset. Rapid user growth followed by rapid user attrition is an indicator of unsustainable growth. Strong retention of users over time is a good indicator of product-market fit, something you’re hopefully looking to achieve anyway.

Retention is the place I start everywhere I go.  Building a strong retention program is the key to success for any business.  There's the old "It's much cheaper to keep a customer happy than find new ones" saying, but it goes beyond that.  If one thinks about it logically, the bigger base of loyalty business that is retained, the more money one will make.  Retained/loyal customers have many advantages over new or dormant ones.  

Customers in retention campaigns have a well-defined pattern of behavior

These customers are perfect for targeted promotions, cross-sells and upsells.  Because of the purchasing and communication interaction behavior stored from these customers, tailoring offers specific to the needs of customers is the easiest way to convert into sales.  The less that is known about a customer, the more shotgun approach is taken and less likely to obtain real revenue.

Customers in retention campaigns have less expensive communication channels

Because the customer is known, the communication with the customer is much cheaper on a converted basis.  Even through the direct mail channel, which can be as high as $3-4 per piece depending on how elaborate it may be, the conversion rate is much higher on this type of communication.  Most communication in this channel can be near free, with email and push notifications through apps.

On the other side, acquiring new customers is very expensive.  Even if going completely online, the conversion rates are so small compared to the cost per click or action, that it makes the customer acquisition cost upside down for 2 - 3 purchases for many companies.  If the business needs to go traditional advertising routes, now the cost becomes staggering.  

Retention customers bring in the most revenue

While this varies from business to business, I doubt you will find many longterm successful organizations that don't have this phenomena.  The loyal customer is the bread and butter for the business and can be relied upon to grow revenue.  Within retention campaigns there are customers of all different types and understanding the loyal customer that can spend more money is the best opportunity for profit growth.  

It may seem counterintuitive to look for growth in your loyal customer base, but I have always thought of it like this.  The more customers that I can have in the active customer base, the more opportunity I have for growth.  Acquisition rarely can go away and there should always be a plan to acquire more customers, but that cost should decrease as the business matures.  For a very mature business, this cost should be as low as possible.  

A simple way to illustrate this is 

New Customers + Retained Customers + Reactivated Customers = Active Customer Base.

So if the business can acquire at a consistent base, lets call this 1 million customers per year and retain the majority of their customers, lets call this 10 million customers, then they can grow their active customer base by close to 1 million per year.  Now if those customers are retained and a new million come in, the growth lies in increasing the retention customers.  Otherwise, it costs too much to try to double your acquired customers, especially the more mature the company is.  Try to focus on retention first, it is truly the King.

Data is the First Step to Marketing Automation

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

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

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

Understand the problems to be solved

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

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

Define success for the campaigns

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

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

 The expectations for campaign execution times

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

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

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

Big Data: How Netflix Uses It to Drive Business Success

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

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

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

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

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

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

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

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

Building credibility for your analytics team—and why it matters

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

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

Start Small

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

• What are the most engaging features of your product?

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

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

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

Know your audience

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

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

Don’t be a House

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

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

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

Be Transparent

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

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

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

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

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

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

Turn Your Data Into Smart Data

Great insights from Scott Houchin regarding data.

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

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

Collection

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

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

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

Strategy

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

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

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

Alignment

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

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

To Benefit From Big Data, Resist The Three False Promises

From Forbes.com:

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

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

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

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

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

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

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

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

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

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

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

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