Blog: Analytics

A core value of customer engagement is to market based on thorough understanding of customer segments and how they differ from one another. In theory, this makes sense, but in actual marketing practice, carrying on communication with customers is always a challenge. In our daily lives, we have many different channels to communicate with a friend when we want to find out his/her opinion about something. But, how can we reach out to half a million people and get to know how they think about a 'bigger' concept - a company product, an innovative service, a marketing promotion plan, or a company recruiting tactic?

As online activities continue to embed themselves deeper into the consumers' everyday lives, companies' efforts to capitalize on the latest online trends has literally become a high-speed chase. The online terrain often transforms quicker than a company can execute a strategy, but digital leaders still find a way to be nimble and alert enough to adapt (and profit from) the latest online climate.      

With the final quarter of 2011 setting in, lofty corporate ambitions for the next year are in motion. But planning for 2012 based on 2011 fads will likely mean toppling right into the biggest caveat of the web: surefire transformation. Decisions must be based on insights looking forward in addition to data looking backward (A great way to blend both approaches is through the use of predictive analytics). As you begin shaping next years' strategies and formulating projections, look out for the following trends in 2012:

Recently, there has been a flurry of articles in serious forums about the promise of BIG DATA. The Aspen Institute  authored a report on the Promise and Perils of Big Data and McKinsey Quarterly ran an article on the Challenge and Opportunity of Big Data.   There is even a conference for data scientists, a newly found species.  The most recent issue of Fortune calls data science the "hottest new gig in tech".    

The media world has been and continues to undergo dramatic shifts as content continues to be delivered and consumed in myriad ways.   A recent McKinsey Quarterly paper highlights the dramatic increase in the intensity with which people use digital devices and platforms. Approximately 50% of online consumers are now advanced users of smart phones, social networks and other tools such as tablets, up from 32% just two years ago. 

MIT did.  The Massachusetts Institute of Technology recently won a contest presented by The Defense Advanced Research Projects Agency (DARPA).  The contest was developed to test the powers of social networking.  The challenge was to find the coordinates of 10 balloons randomly placed throughout the United States.  Sounds massive?  Well it only took a team from MIT less than 9 hours to successfully complete this daunting task.  How did they do it?  They used the power of the internet.  They posted a link on their site informing people to sign up and help find these wonderful balloons.  To encourage participants they offered an incentive.  Money!  Since the prize from DARPA was a nice $40K for the winner, the MIT team was able to effectively tease internet users to work for them.  In the end MIT offered up the entire $40K to the participants; but if this were a business they could have walked away with a substantial ROI.

I was reading the recent research from Center for Media Research and it was interesting to note that 8% of Internet Users account for 85% of all Clicks. The results indicate that the number of people who click on display ads in a month has fallen from 32% of Internet users in July 2007 to only 16% in March 2009, with an even smaller core of people (representing 8% of the Internet user base) accounting for 85% of all clicks.

Last month, the National Hockey League announced that four teams will be participating in an "overlay" advertising experiment during the 2009-2010 regular season. The expansion to four teams comes after a trial run last year by the MSG network on a series of New York Rangers games. The trial conducted by MSG Network was deemed successful in terms of generating additional advertising revenue both for the network as well as the team. Given the economic challenges facing the NHL (and other sports leagues in general) it is not surprising to see the experiment expanded to four teams this season.

Recently, during a company presentation at CSG's Quaero, a senior director talked briefly about down selling. We often hear about cross-selling and up-selling but very seldom do organizations talk about down selling. So, what is it and why is it relevant to marketing professionals?

There are two ways to think about it.  Downselling could mean that you:

I was surfing the web the other day looking for some deals and entered into a sweepstakes, just for kicks. In the process, I inadvertently signed up for some educational deals and also provided them with my number (read the fine print, boy) . Within ten minutes (if that) of clicking the submit button, I received a call from a call center asking if I wanted any brochures or advice regarding higher education. I was amazed at the quick turnaround time from entering the information on the website, updating the database to triggering an event at the call center.

In another post 'Netflix: What were they thinking?' I summarized how Netflix used online data, a prize and the analytic community to increase revenue. 

In this blog I would like to focus on the biggest barrier in turning online behaviors into actionable and optimized customer interactions throughout all channels.  Let's start by breaking down the components of online analytics:

Upon my arrival home from a recent business trip, I was greeted with three different catalogs from the same well-known outdoor clothing and apparel company. While each catalog focused on a slightly different area of interest what really got me thinking was simply the volume of catalogs I received in this instance coupled with the fact that I receive catalogs from this same  retailer every two or three weeks year-round. My first thought involved the production and mailing costs this retailer must be incurring in sending me catalogs as frequently as they do.

See a previous post for a summary of the competition - 'Netflix: What were they thinking?

It seems that after 3 years a team has won the prize.  The winning solution was submitted on June 26^th, and still must be verified, but all indications are that this solution has broken through a barrier many thought unattainable.  The winning solution predicts customers' movie interests 10%+ better than Netflix's internal system.  That does not sounds like much (10%) but given that Netflix had already squeezed about as much information out of their data as possible, and additional data sources could not be used in the competition, this was a colossal task.   

1. Making ads more interesting (Google) -- Three months ago Google announced the launch of their version of behavioral targeting. It's called "interest-based advertising". Three important features of Google's approach are: transparency, choice, and control. Don't miss the Google Ads Preferences Manager, which willl show you the interest categories associated with your browser.
2. Behavioral Targeting Legislation Hearings (DMNews) -- The US Congress is holding hearings to evaluate whether behavioral targeting ought to be regulated.
3. Onsite Behavioral Targeting (Amadesa) -- Amadesa's behavioral targeting engine illustrates the current state of the art in online behavioral targeting for anonymous visitors.

Whether marketing to consumers or Healthcare Practitioners (HCP's), many pharmaceutical companies have changed the way they segment.  HCP segmentation, for example, is no longer driven exclusively by specialty type and rearview mirror volume metrics such as NRx and TRx.  Instead most companies are becoming savvier and incorporating marketing and analytic best practices to build robust customer profiles and segmentation schemes that are based on customer need and value, interest, attitudes, and behaviors.  Integrating customer data from multiple data sources, appending records with commercially available data (census, demographic, interest/lifestyle, etc.) and surveying prospective and current customers to determine attitudes about the disease, treatment programs and tools, and the brand are all part of developing an effective data strategy to be able to tag customers to segments.  The challenge, however, is that newly created segments don't always manifest themselves to customer records that already reside in the database.  So how can you make your segmentation actionable and realize the benefits? 

1. New England vs. Silicon Valley (Kevin Hillstrom) -- Key quote: "There may be fundamental differences in the way your customers shop, based solely on geography."
2. The Prediction Lover's Handbook (Thomas H. Davenport and Jeanne G. Harris) -- Definition, use cases, strengths, weaknesses, and prospects for the following techniques:
   

In 1899 the Economist Thorstein Veblen coined the terms "Conspicuous Consumption" and "Pecuniary Emulation" in his classic text The Theory of the Leisure Class.  In this work, Veblen evaluated the behavioral characteristics of the super-rich and the middle-class of his day and developed a new school of economic thought, Institutionalism, which still holds relevance today. Veblen posited that people make purchase decisions not necessarily based upon the usefulness (utility) of the products purchased but instead based upon the perceived status such products bring. As a result, "Conspicuous Consumption" refers to purchases made primarily to demonstrate one's wealth and status to others as opposed to purchases made out of necessity.

In October 2006, Netflix announced that they will pay anyone $1Million if they can improve the accuracy of their customers' suggested interest rankings (http://www.netflixprize.com/). Why? Netflix realized that if they could improve their accuracy by 10% it would be worth well over $1Million annually to the company through improving communication relevance through well targeted online movie suggestions.