Remember my friend Sue, the self-proclaimed big data "Stuper User"? Well, as you may recall, Sue's company was interested in customer data analytics and the ability to extract insights for their marketing campaigns. The problem is, there is a shortage of people with the necessary skills to provide those insights.
This is not surprising given that data science is only now coming into its own. Harvard Business Review called the Data Scientist the "sexiest job of the 21st century" but not just anyone can be a Data Scientist: as Joel greenhouse writes in the Huffington Post, statistical know-how is the foundation of the profession.
This is what differentiates an analyst from the scientist: the analyst will run the queries and statistical tests, but the scientist will design experiments and tease out those oh-so-valuable insights that everyone is talking about. And this, folks, is why it is so difficult to find an individual who:
- Understands your business and industry;
- Has the necessary statistical background;
- Is technically savvy enough to understand how databases work;
- Can write the programs used to test hypotheses (such as SAS programming language, R, Erlang, etc.);
- Is able to craft simple and coherent reports that are actionable.
Educational institutions are falling over themselves to create/capitalize on master's level certificate and graduate degree programs in data science, business intelligence, or business analytics, etc. And they're apparently not cheap! They range from $10,000 to $60,000 and anywhere from 10 months to a couple of years in duration. Time will tell if these programs are graduating data scientists or analysts.
In the meantime, Sue's company will continue to search for someone who can fill its need for customer insight in a market that has a shortage of available candidates.
Many businesses have some form of loyalty program, be it a member card or maybe just a punch card for the 10th coffee free. That's great: customers love free stuff, you love repeat business. But, you're leaving value on the table.
The basic premise of a loyalty program is that customers are rewarded for their continued, well, loyalty. That is, the more that customers buy from you, the more perks they get, even if it's only a coffee after the n-th coffee purchased. This relationship between customer and business is pervasive; from where I'm sitting, this is probably one of the more interesting use cases for data analytics and one of the tools that businesses can use to maximise their revenue per patron.
Every loyalty program has to have some tracking mechanism. Corner coffee shops typically use cards on which customers accumulate stamps towards that proverbial free coffee. More advanced retail loyalty programs use member cards with mag stripes.
The coffee card doesn't give the business much information other than that the customer bought a specified number of coffees (not even what kind of coffee!). Not very valuable information, but it's low-tech and cheap. What would have been more interesting to the business is to have known that that customer had also occasionally bought a scone with their coffee. This would allow the cashier to upsell that scone whereas the customer might not have otherwise ordered it. There's that value left on the table.
Big box retailers may also be leaving value on the table. Let's take a real-life example: the TJX group has loyalty programs that are honored in Winners, Marshall's, and Homesense stores in Canada and, according to the TJXStyle+ web site, this loyalty program offers its users:
- Early shopping access to stores
- Advance tips on fresh arrivals
- Special offers
- 30 day returns
- The chance to enter exclusive member only contests
Interesting. At least that one line about special offers is interesting insofar that it implies that customers receive some incentive and/or that there is some sort of tracking. But, since there is no e-commerce implemented on the Winners, Marshall's, or Homesense web sites (nor is it implemented on its US cousins' sites such as TJMaxx), we are led to believe that customer purchase tracking happens at the cash register. Customers who sign up for the TJXStyle+ card or email updates on each of these sites only have to provide very basic demographic information limiting the segmentation that the vendor can perform. This forces the vendors to market to a larger segment instead of engaging customers in one-to-one marketing interactions. (Caveat: TJMaxx offers a TJX Rewards Credit Card in the US but it is not clear how they use the transaction data to promote to customers. Given how the TJXStyle+ program is implemented, the assumption is that the credit card is being used in a similar manner reducing the vendors' ability to reach individual customers.)
The point here is that, regardless of your loyalty program, if you don't capture transaction data about individual customers, and if you can't analyse it through the lens of demographic segmentation using analytic tools, you're losing opportunities to upsell, cross sell, promote, incent, and generally provide a better shopping experience while maximizing the revenue per patron. So, your opportunity cost is the unrealised sales.
As Big Data eases itself into the mainstream, more and more companies will be using user information to uncover and access hidden value in an effort to increase revenues.
All the while, we are asking ourselves (or should be, if we're not) more and more questions such as:
- At what point does big data become intrusive to individuals?
- What does the law say about the use and sharing of personal information?
- To whom does metainformation derived from this personal information belong? Does it belong to the individual or the business that created/collected it?
- What are the ethics of using personal information of minors and subsequent marketing to them?
- What are the applicable laws?
- Why is it that everyone wants in on big data?
What's of concern are statements such as those made by Equifax's CIO David Webb: "We know more about you than you would care for us to know." That's pretty scary. How does that impact minors? And, what are the ethics of monitoring kids for suitability as credit consumers, for example?
Disney has reportedly spent $1 billion on their MagicBand (or MyMagic+, it's not clear what it's actually called because of conflicting reports) RFID bracelet program making the happiest place on earth one heck of a data generating machine. The bracelets are tied into many of the company's systems (POS, ticketing, resorts, character interactions, etc.) allowing Disney to personalise the experience that much more once its fully implemented. Of course, as a customer you can opt out of providing information to Disney and identify yourself as a number linked to an account. Regarding children, "Disney is aware of potential privacy concerns, especially regarding children," but no action plan has been detailed to my knowledge.
The value and promise of big data is tangible. Will efforts to use our personal information and data to help us part company with our money succeed? Or will there be a backlash from a "No-Loyalty" activist group to fight against this money grab? Time will tell.
I read Tomasz Tunguz's blog on LinkedIn about blog reach over the weekend. In his post, he discusses some shortcomings of blog analytics. in his post, he laments the unavailability of analytics tools that can segment readers into categories (such as entrepreneur or sales person, for example) and the content they viewed and whether it resonated or not. He goes on to point out that LinkedIn is best suited for such analytics as it manages personal information for all of its members and hosts the blogs themselves.
Of course I run analytics on this blog, but it doesn't really give me the depth of information Tunguz is talking about. Sure, I can see where readers came from, what they read, how long it took them to read it (or not), and whether they visited any other pages during their browse through my blog. To be able to get deeper information would mean to leverage information in provenance from various databases (variety), to analyse a potentially large volume of information (volume), and all being created and analyzed in near real time (velocity). Sounds a lot like big data, huh? Maybe for sites with a wider audience like on LinkedIn, GigaOm, or The Huffington Post.
As Tunguz says, such a tool for regular bloggers does not (yet?) exist. There would be a requirement for users to identify themselves--raising the potential for privacy concerns-- and the challenge of obtaining relevant information about their background to segment them though there are several social media tools that offer APIs that can help in that respect. We could also infer engagement by the amount of time the reader spends reading a blog and, at least for first time visitors, how many additional blog posts they read.
Interesting as it is, this level of analytics is not available to bloggers such as myself. If anyone is developing such a tool, sign me up for the alpha and beta tests. If one already exists, then this post is a moot point and please let me know the name of the tool.
By using Facebook "Likes", researchers at Cambridge University were able to build astonishingly accurate and detailed profiles of people ... from their politics, to emotional stability, to their parents' marital status. CBC.ca/TheCurrent
Three hours after I [Joel Stein] gave my name and e-mail address to Michael Fertik, the CEO
of Reputation.com, he called me back and read my Social Security number to me.
"We had it a couple of hours ago," he said. "I was just too busy to
call." Time Magazine: "Data Mining: How Companies Now Know Everything About You"
We know more about you than you would care for us to know. Equifax CIO Dave Webb--CIO.com
Scary. The age of Big Data has begun, and we're only now starting to realize that our digital footprint is much more important than we know. The question is, how important is it, and to whom is it important. Privacy laws say they are important to us, but with every user agreement we accept without reading the terms and conditions, we are giving our personal information away, as a currency of sorts, in exchange for some "free" service. And someone, somewhere, is making money off of us.
This is the kind of stuff that advertisers and marketers drool over. Their holy grail, so to speak. To be able to target ads and specifically target customers to such a degree is crazy. Well, crazy to me, but perfect for advertisers and marketers because they can adress customer preference and customize the advertising/marketing so that the user experience is favorable. Ever see ads that are out of context? I once had an ad for bananas on my blog.. I have no idea how or why this happened to this day. Now imagine that the ad is for a new book by your favorite author. Or an upcoming movie, or education, or some other product or service that you are likely to buy based on your digital footprint.
We're talking long tail here and appealing to customer preferences. But, all of this makes me wonder:
- How well do targeted ads really do?
- Will users just keep spending on products that are being advertised to them specifically?
- What is the threshold? At what point do we stop buying and the ads become ineffective?
- Will we all go bankrupt because we buy everything that matches our preferences?
OK, maybe that last one is a bit nonsensical. At least I hope it is! But the questions remain. In their quest to maximise profits, are companies doing so at the expense of their customers? Well, yes because we're paying them because they offer a product or service that we really want. Rather, are we being duped to buy more than we need or outside of our normal buying habits by targeted advertising?
Maybe it's a question of being aware of what we need vs. what we want as consumers and knowing that advertising is increasingly targeted and customized.
Caveat emptor.
"It's all about making Big Data accessible." That's what an acquaintance recently told me during a conversation about Big Data. This individual, we'll call her Sue for argument's sake, qualifies herself as a "stuper user"; that is, an individual who knows a lot about Marketing, is competent with marketing analytics, but only knows enough about cloud computing and Big Data to know that she needs it. Just like many organizations today.
So, as a matter of interest, I'm going to write a few posts to chronicle Sue's exploits with Big Data. It should be interesting to watch her learning curve and how she adapts and takes advantage of the tools and outputs available to her.
Realistically, it really is about making Big Data accessible. Up until now, it has largely been the domain of academics and very large, often financial, organisations such as Equifax. Netflix is one of the exceptions to this statement and surely a shining star in the new cloud/data driven business model that will probably become the norm sometime over the next decade or so. There are, however, many companies building a market by simplifying big data and helping others take advantage of this newly discovered tool which should help to push Big Data into the mainstream in the next few years.
As an aside, I'm going to build this blog, as I did with The Case for Cloud, by making it a channel for business and governance related posts and information. There are enough people and companies talking about the tech, so I'll leave them to it.
