Suppose you are a marketer at a wireless telecommunications company in Europe. Your company has a deep historical record of customer transactions, products purchased and billing history. Three months ago, your company also finished an implementation of an activity based costing project complemented with a profitability management application that now shows customer spend, margins and life-time value.

Deciding it’s high time to determine which of your customers are creating and potentially destroying value, you start analyzing a key segment of customers.

On one particular day, while finishing the last sip of your Monster energy drink, you’ve zeroed in on customer “Thomas Smith”.
The analysis shows that Mr. Smith is chronically late on his bills. He does pay but often late, and while you appreciate the incremental revenues associated with late charges, you also examine that he constantly uses your call center to ask mundane questions (bypassing the automated systems). You also see from your product analysis that he likes to switch phones frequently, bringing back product just before the thirty day exchange policy expires.

In the initial analysis, it appears Mr. Smith is costing your company a lot of money. Now, as a marketer, should you keep Mr. Smith as a customer, or gently pass him onto the competition?

What might be obvious is in fact a very complex decision, especially when you include social network analysis.

Leading edge companies are using Social Network Analysis to detect and interpret the patterns of social ties within a customer base. Authors Stanley Wasserman and Katherine Faust in their book “Social Network Analysis: Methods and Applications“, mention that SNA is “based on an assumption of the importance of relationships among interacting units. The social network encompasses theories, models, and applications that are expressed in terms of relational concepts or processes.”

In this particular marketing example, social network analysis can be used to determine the “importance” of Mr. Smith, especially in relation to other paying customers.

So let’s get back to the analysis on Thomas Smith. By adding call detail records to your data warehouse, and using social network analysis techniques, you now see that Mr. Smith is a “node” in a pretty complex network of customers. In fact, through a “Fab Five” campaign you concocted a year ago (where customers can call five friends for free in-network), you also see that Thomas is linked to five very profitable customers.

Since Mr. Smith is well connected to five very profitable customers, treating him poorly on his next customer visit, or jettisoning Mr. Smith altogether could lead to the defection of his five closest friends … customers on your network, and customers that pay their bills and produce positive cash flow.

Let us suppose you also had the divine prescience to add net promoter scoring to the mix. Now you notice that while Mr. Smith has trouble paying his bills, he is in fact a “promoter” of your company. He likes your friendly customer service representatives, and also is very generous in telling his friends about your willingness to “bend over backwards for him” to meet his changing needs.

With this analysis in hand, you determine that Mr. Smith isn’t a customer to jettison, and in fact, is probably one that deserves a closer look.

Before we get too caught up in the hype, social network analysis isn’t a savior to marketing decision making.

Social network analysis–done right–requires a lot of data from myriad sources. In the hypothetical marketing example above, simple call detail records are used, but to say–find a terrorist–the National Security Agency of the United States (NSA) would require call detail records, credit card transactions, car rental receipts, and many other digital markers. Even then, the output of the analysis isn’t always accurate.

Accurate data is a key factor in reliable results, but so too are the assumptions used in the model. Is Mr. Smith really that “important”? Should we assume that “associations” are actual close relationships? Should we also assume that if Mr. Smith leaves our company, his “Fab Five” connections will follow?

Social network analysis–hype or help? It’s up to you to decide.

Questions:
* It is well documented that Social Network Analysis (SNA) can be the source of “false positives”. Would you trust the output of SNA to make marketing decisions in your organization?
* Call detail records show Mr. Smith calls five contacts quite often. Does “activity” … in this instance a phone call–denote a “close relationship”?
* SNA can also be used to model employee connections in an organization–in effect to determine the “importance” of an employee? Good or bad idea?
* If you were the marketer in the situation above, what other tools might you use to determine if you should keep Mr. Smith as a customer?

Mathematics has left the ivory tower of academia for a marketing department near you. Are you ready for this massive paradigm shift?
Let’s be clear. I hate the phrase, “paradigm shift“. The words are close to meaningless due to overuse.

However, in this rare instance, where the world of mathematics is invading the marketing kingdom, it makes sense to emphasize a new way of thinking that is radically changing the way marketers do business.

Marketers have always wanted to know more about customers–after all, better segmentation and targeting of a customer base helps improve marketing ROI and ultimately increases satisfaction as customers are not bombarded with irrelevant offers.

Fortunately for marketers, advances in technology (both applications and infrastructure) have made it easier to capture, manage and analyze data so as to piece together a more complete picture of customer behavior and of enterprise operations.

Case in point, an article from Business Week, published in 2006 titled, “Math Will Rock Your World“, highlights companies such as Google, Aetna, Harrah’s and others that are using mathematics via analytical applications to sort out “swelling oceans of data” and mine data for insights to better understand customers.

While arguably a bit dated, the Business Week article showcases how companies are using customer data to build profiles and formulate models of both customers and employees that they believe will allow them to simulate and predict how to, “sell us things, steer us clear of diseases, and ramp up (employee) productivity.”

Other examples in the article show how companies are using advanced algorithms to make sense of unstructured data (emails, documents, call center notes), and optimize online advertising campaigns through the refinement and selection of keywords for search.

Using mathematics to better understand customers is serious business–just ask Netflix. According to a recent Wired magazine article, this online movie rental company is offering a $1 million dollar prize to any one person or team that can improve its current movie recommendation algorithm.

By opening access to one of the largest data sets available of online behavior–100 million customer movie ratings–Netflix is ultimately hoping to “crowdsource” improvements to Cinematch, its engine that essentially recommends, “If you liked this movie, you’ll also like this one.”

The Wired article details how different teams from across the globe have attempted to help Netflix improve its recommendation algorithm, and how difficult a challenge this is turning out to be. The contest is two years running and still a 10% predictive improvement remains elusive.

However, even minor improvements to Cinematch have thus far helped Netflix utilize more of its DVD inventory and improved customer loyalty as subscribers find movies of interest that perhaps they might have previously overlooked.

In another article, “Guessing the Online Customer’s Next Want”, Barney’s New York is mentioned as a company that’s seen dramatic marketing ROI improvement from using sophisticated analytical applications based on complex mathematics.
Through the use of technology, Barney’s is able to collect and analyze the online behavior of its customers and then craft smarter and more appropriate responses to interested audiences.

For example, the article notes, “An e-mail message announcing sales might go to those Web site visitors who had purchased certain products or types of products in the past, but who had done so only when the items were on sale. In the simplest terms, if someone buys only when something is on sale, but never buys anything in December, then the e-mail sale flier might not be sent to that customer in December.”

Just as in the early 1980s, when the financial industry was upended by the flight of quants from academia to Wall Street, marketers are starting to reap the brainpower of mathematicians, physicists and others as they codify their expertise and knowledge into sophisticated information technology systems and analytical applications. These innovative systems are helping marketers leverage information to better connect with customers and drive the business forward.

Paradigm shift? Absolutely. The world of marketing will likely never be the same again.
Questions:
* Are you seeing these trends in your particular industry? If so, how so?
* The companies mentioned above are starting to treat data as one of their most valuable assets. Is your company on that path?
* Are you concerned with the potential “dark side” of simulating and modeling customer behavior–i.e. privacy issues?
* What skill sets will marketers need in the future to be able to compete in this new world of mathematics and marketing?
I’d love to hear from you!

Forbes reports that the typical BofA mortgage customer will eventually have an astonishing six BofA products (credit card, debit card, checking account, savings account, and online bill paying). That’s in stark contrast to the bank’s non-mortgage customer who typically has only a checking account and an ATM.
WOW!!! How powerful is that “connect the dots” insight!! It’s a huge strategic directive by which other BofA customer-centric initiatives such as new customer prospecting, customer experience programs, cross-selling tactics etc. can be mobilized.
OK, I admit that my small-town North Carolina roots naturally make me proud of BofA’s savvy customer management moves. ( I grew up just 30 miles away from the bank’s Charlotte, N.C. headquarters.) Can’t help but crow a little.