Posted by infotrellislauren on Monday, Dec 15, 2014 @ 1:34 PM
© Phil Date | Dreamstime Stock Photos
© Phil Date | Dreamstime Stock Photos
© Phil Date | Dreamstime Stock Photos

There’s a metaphor I like to use about public washrooms. Have you ever been in a public washroom where the toilet flushes automatically, the soap dispenses automatically, and the water turns on and off automatically, but then the drier is manual, and it seems really jarring and weird because you stick your hands under it expecting it to be automatic too and then nothing happens? That’s what’s going to happen to digital customer experiences and marketing best practices.

Let me elaborate.

Say it’s around the second week of December. I’m working on doing my Christmas shopping still, like many people are at this time of year. I open up an email from a large bookstore chain that I happen to have a loyalty card with – one of the few I actually use and carry around with me, and tolerate the promotional emails from. In the email is an offer that says “Got friends around the world? Check out with this coupon and we’ll ship to three different locations for free when you spend more than $100!”

For me, I’d be thinking: “Holy smokes, that’s perfect!! I have lots of friends around the world! I would love to be able to ship to three different places in one purchase! That’s so convenient!”

That might not be something that would excite you, but that’s why (although I’m not aware of it) I got this email and you didn’t. It’s tailored specifically to me because they know this is an extremely relevant offer that will motivate me to make a large purchase.

So I click to get the coupon and it takes me to a “gift suggestion” page. And somehow, it’s only showing me gifts and books that my friends and my family would like. It’s got science humour books, nerdy video game related books, and even suggests a book with big glossy pictures of cars for the two people on my list of ten loved ones that really dig cars. Me personally, I don’t like cars that much – but this isn’t a list tailored to me anymore, it’s tailored to the people I most care about and would likely spend more money on a gift for.

So here I am, sitting at my computer and thinking “WOW that is perfect for this person I care about, this one here is perfect for THAT person I care about, look at this I’m going to get all my shopping done in one afternoon,” and before I know it I have $250 of things in my basket.

It’s like a next-best-offer section, but super intelligently suggested.

How do they do that? They match my customer profile to my social media profiles, and they not only profile me, but they determine which of my friends I pay the most attention to and then they profile those friends. My activity and relationships on Facebook, Twitter, LinkedIn and other websites will all tell them who I most value of my friends. They can then match those friends of mine to their own internal customer records and provide me with the “next best offer” that would most apply to my friends based on their purchase history, without actually revealing that purchase history to me. If they don’t have a customer profile with the bookstore, they still have lots of data about their likes and interests from their social profiles that build a comprehensive idea of what kinds of books and other items they’d enjoy as gifts.

Now, this is the point that you think “That sounds kind of creepy.”

Yes! Extremely creepy!

But useful to the consumer.

Which is why you would label the suggestions “What’s hot right now!” The shopper can only assume the rest of the world has the same taste as all their friends, which isn’t that big of a stretch if they have a wide variety of relationships with a wide variety of people. By knowing when to make the personalization obvious and when to be more subtle about it, you reduce the chance of making your customers uncomfortable.

Ultimately, the consumer benefits because they get all the stuff they want and they don’t have to wade through products that are irrelevant to them, and the coupons or incentives they’re offered are always relevant or useful. It’s about making life easier for people. “If you could use magic to make shopping better in ways you don’t believe are actually possible, what would you change / improve?”

Now of course, it’s arguable that improving your marketing relevance is less about making it easier for people and more about making it easier to target consumers to spend more money. It really ought to be both, ideally. When the consumer benefits, the seller benefits – the idea being that if you give people a better experience, they reward you with loyalty.

So yes, your end goal is money – you are a business – but at the same time, you differentiate yourself from other businesses by recognizing that every person is unique, and giving them “special treatment” by using technology capable of instantly customizing the experience.

Eventually the majority of companies will be capable of never, ever sending you something that is irrelevant to you. And then when that does happen, your reaction is likely to be, “Wow company, get it together.” To return to the bathroom metaphor, you’ve gone from three automated interactions to one unexpectedly manual one. Before, you’d never have thought about the dissonance, because you’d never been given a different experience. But once the ball gets rolling and you’ve gotten used to it, the experiences you thought of as normal will be bizarrely outmoded and stand out.

So the next time you’re pushing through a crowded mall or clicking through an online catalog trying to figure out what the heck to get for the people on your Christmas list, stop to imagine a better world in which the retailer has suggestions for you that are genuinely helpful and designed for you and the people you want to see smile this holiday season.

Big Data technology is actually making this kind of automated and sophisticated microsegmentation possible. Maybe you’ll even see it in action this time next year.

InfoTrellis is founded by a team of three architects who have been shaping the Information Management market space since 1999. At InfoTrellis we work on developing cutting edge technologies designed to tackle the new challenges facing the modern data-driven company, driving the creation of next-generation products for enabling targeted marketing, highly customized and individualized loyalty programs, deeply detailed competitive analysis, and enriched, automatically updating customer profiling. To learn more about InfoTrellis, visit our website at www.infotrellis.com or contact us directly at info@infotrellis.com

Topics: Big Data Customer Relationship Management Marketing Retail segmentation

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Posted by infotrellislauren on Thursday, Oct 24, 2013 @ 9:12 AM

 

With the show wrapped up and the recollections of many exciting conversations settling from a busy flutter into a determined simmer in the back of my mind, I thought I would sit down to try to write up my top ten takeaways from the DMA Show.

 

1. Everybody wants social CRM or social CIM in one form or another.

From the companies on exhibition to the narratives of the session presenters, the conversation kept coming back to the question of consolidating social data and other digital data (website clicks and the like) with the more traditional types of information about the customers. This is where everyone is going, and whether they’re gliding towards it full speed or staring at it over budgetary barriers with longing but unhopeful eyes, ultimately this seems to be an inevitable progress towards much more centralized and integrated data collection. I won’t be surprised if five years down the road social CRM is the industry standard with companies who have not adopted it slowly fading away.

 

2. Hardly anybody was using the phrase “big data”.

From a conference billed as “data-driven marketing” I expected to hear a lot more of this popular term, especially with Nate Silver as one of the keynote speakers. Instead it was a mix of “big data”, “social data”, “interaction data” and “unstructured data” – which I think I’m happy about, because it’s moving towards more self-explanatory naming conventions, even if they’re much narrower in scope than “big data”. I’m still waiting to see if someone coins a better way to refer to it.

Ultimately, marketers aren’t going around asking for “big data” – they’re asking about more concrete things like social, interaction, clickstream, next-best-offer. These are all things that are powered by “big data”, of course, but the allure and the message isn’t the data itself, it’s the end result.

 

3. Social Media monitoring without any context is now outdated.

Now that the industry is a few years down the path of collecting information from social platforms, we’re seeing a clear divide between the first generation tools that cropped up to meet that initial, experimental wave of adoption and the second generation tools that are being asked for now by marketing professionals. Social Media data without any context is now considered very thoroughly old-school – that is to say, nobody is impressed by a tool that can’t tell you more about the people talking. Where are they? Who are they? What do they mean to me? Are they existing customers? Loyalty program subscribers?

The greater the disconnect between the comments being harvested and the real customers they represent, the less value organizations see from collecting those comments. Companies are asking “why settle for a half-baked technology that only goes so far?” If you’re going to start using social media for customer service, for example, why would you use a social media response technology that isn’t at all integrated with your email and call center response technology? Information is meaningless without context.

 

4. Marketers don’t just want to know what to say to customers – they’re starting to think about when to say it.

It was very obvious that the industry is evolving and evolving fast. One of the signs of this steady maturation is that conversations around social media marketing have gotten beyond the basic questions of “which channel” and “what to say” and started to get more detailed and granular. How do I know if this is the perfect opportunity to convert a competitor’s customer to my loyal customer? How can I tell if I’m about to lose a customer unless I respond immediately? When is the perfect time to send which message, and over which channel? Do I time my campaigns to be a simultaneous blast, or is it more effective to stagger them over a longer period?

The more details we start asking for, the more scientific the answers must become – and the more complex and sophisticated the marketing industry becomes. It’s really interesting to see the best practices that are emerging and developing in this time of dramatic change.

 

5. The better the tools get, the more there is to take into consideration.

What I mean by this is that as we see more amazing capabilities for collecting the data, especially digital data, the question becomes what to prioritize, how to consolidate it, and what to use it all for. Even immersed in the industry as I am, walking around the show floor made me realize what a glut of digital touchpoints marketers have to work with. Customer Interaction Management technology has exploded into a thousand different super cool and super specific directions, from mouseover heat maps to in-store cellphone based location tracking on where people stop in a store and how long they stop there. Google Glass type technology is even promising the ability to see what parts of a website catch someone’s attention and what parts get ignored by the consumer’s ever-flitting eyes. There is an immense number of ways to measure how people interact with a brand online and dozens of different channels, each with their own unique types of interactions.

As cool as this stuff is, what will be interesting going forward is seeing what gains traction the fastest and which technologies get folded together. How much touchpoint monitoring is micromanagement and how much is ignoring – even wasting – valuable information?  With how sharply focused some of these technologies are, I expect to see them consolidated not by the end users or the original architects but rather by bigger tech companies who see the potential of a valuable synergy and buy up these super specialized companies to add sophistication or new capabilities to their existing technologies. This is the strategy we’re seeing employed by companies like Google, SalesForce, IBM, etc. – acquisitions that lead to more bundled, wide-net technology offerings than anything a specialized startup has the ability to offer.

 

6. Marketing agencies are hungry for second generation social monitoring.

You might imagine that the early adopters of new technology will be the big businesses with the big budgets. I was actually quite interested to find that the most eager inquiries at the show about how we take our social data and match and consolidate it with internal customer data were actually coming from marketing agencies who work with these sorts of companies.

It’s possible this is linked to the demographics of the show, but I also think another possible reason for this is that marketing agencies are so sharply focused on marketing technology and so savvy with the many tools out there that they’re keen to give themselves an edge by knowing as much as they possibly can and uncovering the new tools with the greatest potential so that they can be the ones to introduce them to their clients.

 

7. Nobody is doing much in the way of actually matching the data between social understanding and traditional understanding of the customer.

In talking to these eager marketers and walking around the tradeshow floor with a pocket full of technical questions, we found out that nobody is doing the sort of matching we do, if they’re doing any matching at all. In fact, we found that a lot of organizations attacked the problem of integrating your social and digital touchpoint data with your internal customer data from the completely opposite direction. They started with a really cool UI that did lots of neat things, like identify the moment when a customer was at the peak of purchase intent or automate customer service responses that provided a range of reactions on social media depending on customer importance, and then worked backwards to put together something that would enable it.

The result is that a lot of people aren’t doing any matching, treating a Facebook account and a Twitter account and a LinkedIn account as three unique people even when they all belong to the same individual and having no idea which customer they are, if any. Some people are doing highly simplistic matching that is dependent on one or two pieces of information being the same, like an email address or a home phone numbers, and without that piece of information they’re helpless to make the connection.

(Our really cool matching technology doesn’t depend on a linking data point like an email or a name – if you want to know more about how we do it please feel free to send me an email and I’ll happily chat your ear off about it.)

 

8. There seems to be surprisingly little fear of or conversation around issues of data quality.

Maybe it’s because data quality just isn’t a very sexy topic, but I realized after the show that not a single person had expressed any sort of concern around rushing in to this “big data” stuff, trusting technology, and then ending up with a database full to the brim with garbage and bad analytics and faulty conclusions.

I know I would be a bit afraid of that, and to be honest I was expecting it to be something that the slow-to-believe crowd would bring up as one of their reasons to hesitate. Instead it seems like that’s less on their mind than the danger of getting zero value out of investing in big data technologies. Maybe the thought of worse than zero value – active harm – is just too much to fathom. I just hope data quality governance isn’t a question that organizations are forgetting entirely.

 

9. Business cases for big data are increasingly – almost overwhelmingly – abundant.

Another stumbling block for people wondering about this risk of “zero value” is that the use cases are all over the map. On the one hand, it’s amazing how many ways you could improve an organization using social media data alone. On the other hand, if you rattle off a laundry list of ways the technology could help, it can become overwhelming – not just for the end user, but for the technology vendors themselves. We met some organizations that had technology similar to ours but had focused on a very cool but completely different business case in how they focused their product development, and it shaped us into very different solutions when compared side by side.

So here’s the question: do we want to show organizations a shallow glance at thirty different business cases for big data, or is it better to focus in on two or three in great detail? Is it riskier to specialize, gambling that there will be enough demand within the niche you’ve decided to cater to, or is it riskier to be a generalist without one compelling message?

There’s also the question for the marketing executives looking to invest in new technologies, too – are they looking for a magical solution that has an impact on every part of their business, or should they prioritize and go for something specific to one particular business goal or pain point?

 

10. Using big data technology effectively is a challenge of goal-setting and imagination.

“What does the typical big data and social CRM client look like?” I got asked this particular question a few times, and the more I think about it the more convinced I’ve become that the two unifying traits of companies that do big data and do it well are the ones that have:

  1. A very specific result or business goal in mind before they start work, and
  2. The creativity to imagine how big data technology can accomplish this result

Big data is amazing because it has heaps of potential – but that’s exactly what makes it tricky. The last thing you want to do is fall into the ‘technology for the sake of technology’ hype. The teams that have accomplished incredibly impressive business results using big data are not the ones with the most money or the biggest projects; they’re the ones asking questions like “what if we could…?” and “can I apply this to…?” and “imagine if we tried…?”

Start with a business problem, and then apply a little theoretical whimsy – it might surprise you how much today’s technology is capable of. Success is just a matter of how cleverly we apply that technology.

Topics: Big Data Big Data Quality Industry Marketing social media Twitter

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Posted by infotrellislauren on Tuesday, Jul 16, 2013 @ 11:40 AM

Everybody, it seems, is getting onto the social media bandwagon. You can’t get far into any discussion about information management or marketing without it coming up, and it’s fascinating to see the emerging best practices and strategies behind social media products and consulting groups.

Here are five lessons from over a decade of working with Master Data Management, a much older piece of data-wrangling technology, that will serve any marketing or IT professional well as they navigate the social media technology landscape.

 

1. Huge Investments are a Tough Sell

I’m going to assume if you’re reading this that you see value in social media marketing, or else you see the potential for value. If you’re looking to leverage social media for your organization at a scale and level of sophistication higher than a summer intern firing off tweets now and then under the corporate handle, you’re going to have to actually spend money – and in an organization, that can be easier said than done.

Master Data Management teaches a very simple lesson on the subject of talking to your executives about a wonderful, intangible solution that will surely provide ROI if they can find it in themselves to approve the needed budget. The lesson is this: the bigger the price tag, the harder time you’ll have convincing a major decision maker it’s a necessary or worthwhile investment.

Often with MDM the more it’ll cost to implement, the more fantastic of an impact it will have on the data within the business. With social media, that’s a little harder to prove. It doesn’t help that there are more “social media marketing solutions” out there than you can shake a stick (or a corporate credit card) at.

If your executive doesn’t have time for your technobabble pitch for a million dollar overhaul, try wiggling your foot into the door by starting small without a lot of commitments. For MDM, that’s a proof-of-concept, and there’s no reason that can’t be applied to social media marketing. Consider starting off with something that is subscription based (my more IT-minded colleagues would refer to this as “software as a service” or “SaaS”) to give your management the confidence that if they aren’t seeing returns, they can just turn off the subscription and stop spending money on it.

 

A high level dashboard application is an ideal place to start.

 

This is your social media marketing proof-of-concept – if your initial test run gets you great results, that’s a good sign that your organization is part of an industry that stands to really benefit from a bigger, more expensive social media based project. Maybe even something that involves the term “big data”, but let’s not run before we walk.

 

2. Consolidated Records Mean More Accurate Information

This is the core premise of Master Data Management as an information management principle: you want there to be one copy of an important record that consolidates information from all its sources in the organization, containing only the most up to date and accurate data. It’s a simple but powerful idea, the philosophy of combining multiple copies of the same thing so that you only have one trustworthy copy, and then actively preventing new duplicates from cropping up.

The same thing applies to social media, especially when we’re talking about the users as actual human beings and not as individual accounts across multiple channels. Face it, we’re not interested in social media as an abstract concept – we’re there for the people using it.

(Which is why I love to cite this actual exchange between an older gentleman of a CEO and his marketing manager that goes something like: “I don’t get Twitter. I don’t use it, I don’t want to use it, I don’t personally know anybody that does use it, and I think it’s stupid.” “I agree. I honestly think it’s stupid too – but that doesn’t change the fact that 90% of our customer base uses it, and that’s why we need to pay attention to it.”)

So we’re there for the people – why on earth would we approach gathering and visualizing metrics and data on user accounts instead of people? Should we treat the Facebook, Pinterest, Twitter, LinkedIn and Tumblr account of one individual as having the weight of five individual voices?

What you really want to be looking for is a solution that matches and combines users across multiple channels. This isn’t quite the same process that it would be as part of MDM – this is new ground here that needs to be broken, and if you want to figure out that a Facebook user is the same person as a Twitter user, you need to be a little more creative than just checking to see if they have the same name.

With access to less traditional data (like a phone number or an address) it takes a bit of new technology combined with new approaches to match social media accounts accurately. I won’t bother getting into the details here, but suffice to say it’s something that today’s technology has the ability to do and a couple of companies are actually offering it. It seems perfectly logical to me that if you’re going to seriously use social media, especially in any sort of decision making process, you need to have a consolidated view of each user instead of a mishmash of unattributed accounts, which would, without a doubt, skew your numbers one way or another.

I’m going to briefly mention that if you want to take it a step above and beyond for even more insight into your customers, you can further consolidate that data by matching it to your internal records – Joe B in your client database is Joe B on Facebook and JoeTweet on Twitter, for example – but this is a much more ambitious project.

 

3. Data Quality is Not Just An IT Concern

Master Data Management is intended to bring greater value to an organization’s data by making it more accurate and trustworthy. Whether or not that actually happens very strongly depends on the quality of the data to begin with. As they say, “garbage in, garbage out,” and that’s even more true of social media marketing solutions. If you thought the quality of data in your organization was sorry to behold, I have a startling fact for you: the internet is full of garbage data. Absolutely overflowing with it. Not just things that are incorrect, but also things that are irrelevant.

If you’re going to get facts from social media, you’d better start taking data quality seriously – and make sure whatever solution you use is built by someone who takes it even more seriously. Let me give you an example.

Suppose you’re a retailer who sells Gucci products. You have a simple social media solution, a nice little application that gives you sentiment analysis and aggregate scores. You investigate how your different brands are doing and, to your shock, find that Gucci has a horrible sentiment rating. People are talking about the brand and boy are they unhappy.

You do some quick mental math and determine that it must be related to the promotion you just did around a new Gucci product. The customers must hate the product, or the promotion itself. You hurriedly show your CEO and she tells you to pull the ads.

What you didn’t know, and what your keyword based social media monitoring application didn’t know, is that there is a rap artist who goes by Gucci Mane whose fans tweet quite prolifically with reference to his name and an astonishing bouquet of language that the sentiment analysis algorithms determined to be highly negative.

Your customers are, in fact, pretty happy with Gucci and the most recent promotion, but the relevant data was drowned out and wildly skewed by a simple factor like a recording artist with a name in common. This wasn’t a question of “the data was wrong” – the data was accurate, it was just irrelevant, and the ability to distinguish between the two requires technology built on a foundation of data quality governance.

If you’re going to use social media data, especially when you’re using it as a measure for the success of a marketing campaign and subsequently the allocation of marketing budget, make sure you’re paying attention to data quality. Don’t veer away in alarm or boredom from terms like data governance just because they aren’t as sexy as SEO or content marketing or 360 view of the customer – train yourself to actively seek the references to data quality as part of the decision making process around a social media strategy.

 

4. Don’t Let Someone Else Define Your Business Rules

One of the most time consuming aspects of preparing for a Master Data Management implementation is sitting down to define your business rules. There is no one definition of the customer and no one definition of a product. These are complex issues that depend heavily on the unique needs and goals of an organization, and don’t let anybody try to tell you otherwise.

To that end, social media marketing demands the same level of complexity. If you’re building a social media strategy, you absolutely need to be thinking about those business rules and definitions. How do you define a suspect? A prospect? A customer? What makes someone important and worth targeting to you? Is it more important to you to have fifty potential leads or five leads that are defined by very specific requirements for qualification?

Every organization will be different, and a good social media solution takes that into account. Be wary of a piece of software or a consulting company that has a set of pre-established business rules that aren’t easily customizable or – even worse – are completely set in stone. If an outside company tries to tell you what your company’s priorities are and applies that same strategy to every single one of their clients, thank them for their time and look elsewhere.

Also steer clear of a solution that oversimplifies things. If you’re looking to social media opinion leaders as high value targets, you want to know how they’re defining that person as an opinion leader. Are they using one metric, like Klout score or number of followers? Are they using five? Would they be willing to give more emphasis to one over the other if your company places more value on, say, number of retweets than on number of likes?

Good solutions come preconfigured at a logical setting that is based on best practices and past client success – but are also flexible and able to match themselves to your unique business definitions and strategy as much as possible.

 

5. Data Silos Are Lost Opportunities

Finally, I want to talk about data silos. I’m going to expand on this term for those of you reading this who are marketing people like me and not necessarily information management junkies (although I confess the people who are both combined in one are always a delight to talk to). A data silo generally refers to situations in which the different lines of business hoard their databases and don’t like to share their information throughout the entire organization. This can be a huge problem for Master Data Management adoption, because of course the point is to make it so that everyone is using the same data, but it’s also a problem for social media marketing.

Social media data, first of all, is not just marketing data. Your sales teams will undoubtedly have uses for it in terms of account handling, and your product development teams, if you have them, will be interested in learning more about what customers actively crave from the market, and heck, your customer service division almost certainly can make use of an application that instantaneously warns them when people are dissatisfied.

The fact is, if you want to prove that gathering this data is useful, don’t hoard it all to yourself. Share that data around and let people play with it. Creativity – and creative ways to use data – happens when people think about things in ways they don’t normally think about them. Traditionally social media has been relegated to marketing, but it doesn’t have to be.

An ideal social media solution, even one of those affordable subscription-based ones I’ve been talking about, presents the data in an accessible, easily shared format. The good ones come with both a high level dashboard in business terms that even a CEO who thinks Twitter is stupid can log into and gain insight from and also the ability to drill down and export raw data so that the people who want to do complex and unique number crunching have that ability without the restraints of the program itself.

 

Shown above: Social Cue™, the InfoTrellis social media solution

 

It’s important to have a good balance of goal-oriented strategy – never go into social media without a plan or a purpose – and openness to innovation. It’s even more important to be working with an application that accommodates both.

 ———–

InfoTrellis is a premier consulting company in the MDM and Big Data space that is actively involved in the information management community and constantly striving to improve the value of CRM and Big Data to their customers. To learn more about Social Cue™, our social media SaaS offering, contact the InfoTrellis team directly at demo@infotrellis.com to schedule a product demonstration.

Topics: allsight Big Data data governance Data Quality Marketing Master Data Management mdm Social Cue social media Social Media Marketing

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Posted by infotrellislauren on Monday, May 6, 2013 @ 11:18 AM

The availability of Big Data is changing the way companies interact with the people who make up their customer base, and changing it rapidly. Some of these changes are ones we’ve seen in an embryonic form for many years as CRM systems try to better collect and share information about customers and web analytics provide new tools for excitedly trying to measure marketing metrics. Organizations that wanted to target women over thirty learned to place ads in magazines with readerships that reflected that intended audience. Toy companies learned to book ad space on the TV channels with the most colorful cartoons. The bright minds behind political campaigns learned to identify swing voters and target the publications they read, the channels they watched, and the radio stations they listened to. We constantly surge forward in the levels of sophistication we can apply to targeting the people we want to receive our message.

 

The problem with trying to do this with high levels of accuracy has always been us. Our brains aren’t capable of quickly sorting through huge amounts of information about people and then using little bits of knowledge to accurately categorize them. It just isn’t possible – not in any reasonable amount of time. The moment computers get involved, though, the process becomes a lot more feasible. First customer segmentation becomes possible: with what limited information a company can collect on their suspects, prospects and customers, they can group them up and market more effectively by tailoring their message and their offering to the general characteristics of that group. This isn’t too bad of a model when it comes to business-to-business, but when the end consumer comes into play and insists on being an individual, things get more complicated.

 

Our traditional understanding of the customer has always been incredibly limited by either quantity or quality. Hundreds of years ago a merchant might know his or her customers with intimate detail through personal interaction – and some small business owners still do. They could craft custom sales offers on the spot simply by knowing the customer well. “Hey, George, I haven’t seen you in a few weeks. New baby must really be sucking up your time. Hey, you know what I bet you need. Some good coffee. You look tired. Tell you what, I just got some new stock in of this really good coffee, strong delicious stuff. Let me throw in a little sample of it free with your usual order.”

 

That’s the most powerful kind of marketing, and what I would call “data-driven marketing” – it just so happens that all that intimate customer data is stored inside our shopkeep’s brain, and not in a database somewhere. The problem with this scenario is that the shopkeep can only remember this level of information about so many different people. With twenty or so regulars, that’s not a problem – as many as a hundred, if shopkeep is a smart guy. The more customers he has to try to remember, though, the less detail and intimacy he’s able to retain about them, and the harder it is to treat them as a friend and accurately anticipate what their needs and desires will be. You either have to compromise on the quantity of the data (remember only a few people in high detail) or on the quality of the data (remember lots of people without any meaningful detail).

 

These days, when a single organization may have billions of individual customers, companies have no choice but to lean towards quantity. They’ve started to grasp more at that ‘personal touch’ they once had in their humble roots as a Mr. Hooper-esque friend and advisor, but it isn’t easy. Even with computers to collect all this data, often the best they can do is to divide their ten million customers up into primitive groupings based on a high-level categorization like age or income bracket – which we’re starting to recognize aren’t really very meaningful classifiers for targeting marketing messages. Marketing departments often don’t have the manpower to do more than that very simple segmentation, though, because at the end of the day a human, not a computer, has to make the call about how to divide up these groups and define the various markets. As we’ve established, the capacity of the human brain is incredibly limited.

 

Computers, however, are getting smarter. With the steady advance of Machine Learning and Natural Language Processing technologies, our wonderful little robot assistants are becoming more and more adept at identifying significant patterns without our direct intervention and helping us to see pathways for smarter, more targeted marketing and sales efforts. Some of what is being accomplished with a handful of very clever algorithms and well-built platforms is beyond impressive – it’s stuff that seems pulled right out of a science fiction novel. Walmart figured out that people buy more Pop Tarts when they know a hurricane is coming and took advantage of this to drive dramatic sales boosts by having the right product in the right place at the right time. Target can use innocuous purchase data to deduce pregnancy. MIT has put together an analytics piece that can supposedly determine a person’s sexual orientation.

 

So having grappled in the last few decades with the sheer immensity of the number of customers they need to try to remember (nevermind trying to pick out important information about), organizations at last have the technology to deal with all of this data. With a “brain” capable of handling the three Vs (volume, variety, velocity), they can start working on getting back to that cheerful, familiar shopkeep status. The sooner they can say, “Hey George, been a while since you were last at Walmart, how’s the baby? Bet you’d like some coffee. How about a personalized coupon for half-off on this new coffee from your favorite coffee brand sent right to your phone?” the better.

 

Most sales and marketing people would call this practice “microsegmentation”, but the more I dive into the motivation behind using big data for sales and marketing purposes, the more I think this term is missing the point. The notion of “microsegmentation” just sounds like nitpicking over customer segmentation for the sole reason that we can do it. Technology gets smaller and more compact, so obviously standard segmentation will go the way of the SD card and get all micro on us, because that’s just how it goes.

 

What we seem to be forgetting is that the language we use shapes us and shapes the way we think about things, and this language is completely overlooking the whole point of what we’re trying to do. The end goal isn’t to make our defined markets smaller and more specific. The end goal is to get back to a point where we interact with our customers like valued individuals again. It isn’t segmentation for segmentation’s sake – there’s a reason for doing all this, and the reason is to have stronger customer relationships, deeper brand loyalty, more effective customer service, and more trustworthy and accurate recommendations to the customer. We want to group our customers in ways that give them what they actually want from us – and not what we assume they probably want based on something arbitrary like what year they were born or whether they use the public washroom door with the pants or the door with the dress.

 

For that reason, I’m rejecting the term “microsegmentation” for what we’re trying to do with Big Data analytics. Instead, I’m calling it “anthrosegmentation” (from Greek “anthropos”, meaning “man”): the principle of highly tailored sales and marketing campaigns with the ultimate goal of treating customers like individual human beings rather than faceless members of a crowd. Anthrosegmentation is about using technology to offer highly customized, individualized interactions with customers and patients, rather than painting them all with the same brush.

 

Anthrosegmentation is the confident and excited reply to that basic demand of every patient, customer, and citizen: treat me like a human being. This is why Big Data is a big deal to retailers, governments, financial institutes and every company that runs the gamut from corner store to corporation – they finally can get back to the highly personalized and tailored customer experience. We’re moving away from clunky, outdated modes of thought that were stunted in growth by the limitations of our data and our technology. As these limitations are rapidly overcome, we need to remember that keeping up with technology doesn’t just mean inventing new words – it means inventing new ways of thinking about what we’re doing, and never losing sight of why we’re doing it.

 

Big Data presents a multitude of opportunities for improving and innovating around how we do business, and customer segmentation is just a subsection of that opportunity. For an overview of some of the exciting use cases we’ve seen so far, stay tuned for our upcoming article.

 

InfoTrellis is a premier consulting and product development company in the information management industry. With our deep heritage in Master Data Management, we bring rigorous data quality best practices to our Big Data products and solutions. Visit  our website or contact us directly to learn more about our Social Cue™ and Human Profile™ Big Data solutions or to schedule a product demo.

Topics: Big Data Big Data Analytics Big Data Quality CRM Customer Loyalty Customer Relationship Management Machine Learning Marketing Natural Language Processing Retail social media

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