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Analytics Magazine

Telecommunications Marketing: From business intelligence to analytics

January/February 2011


The journey from what to who, when and why.

Talha OmerBy Talha Omer

The other day, my analytical team at a major telecommunications company and I were presenting an all-new behavioral segmentation to one of our senior executives. Seeing the first slide, the executive jumped out of his seat in excitement and exclaimed, “Some of these insights took me years to uncover after repeated trial and error and hundreds of BI (business intelligence) reports.”

Using a cell phone to make a voice call leaves behind a significant amount of data. The cell phone provider knows every person you called, how long you talked, what time you called and whether your call was successful or if it was dropped. When you use your cell phone, the cell phone provider knows where you are, where you make most of you calls from, which promotion you are responding to, how many times you have bought before and so on. In many cases, a cell phone is the only thing people carry with them at all times.

For a BI analyst trying to make sense of all of this data, the task becomes manageable thanks to hundreds of attractive looking tools that instantly create reports presenting data in every conceivable slice, graph, table, pivot or dump imaginable.

However, no matter what tool you use, the best that traditional BI can help you understand is what happened. It cannot, no matter how much you torture the data, tell you when something happened, who will it happen to or why something happened. This is precisely the reason that advanced analytics is so important; it’s the difference between the 99 percent of effort that typically yields very little insight, and the 1 percent that provides a window into the mind of a customer.

Borrowing an analogy from the popular MasterCard commercial, the distinction can also be seen as:

  • Data: petabytes
  • Reports: terabytes
  • Excel: gigabytes
  • PowerPoint: megabytes
  • Analytics: bytes
  • One business decision based on analytics: priceless

Analytics is not always easy – petabytes of data everywhere and a priceless insight difficult to find. Creating terabytes of reports, engineering gigabytes of excel sheets by writing killer macros and presenting them in flashy slides to top management in the hope that all this effort will tell you something and result in action rarely occurs.

On the other hand, combining the what with the when, who and why will provide a company with a long-term strategic competitive advantage.

Many different methodologies answer the when, who and why questions including:

  • Predictive analysis (to answer the when question – identify customers that will take a certain action, e.g. churn)
  • Descriptive analysis (to answer the who question – profile customers, e.g. segmentation)
  • Qualitative analysis (to answer the why question– identify the reasons for the when and who, e.g. surveying)

If you are new to this world, the last bulleted item – the why question – is a great way to start your foray because it is easy to implement and full of insights that will be very action-oriented.

Though traditional BI has several benefits and is easy to implement, it is confined in its ability to predict, identify and give a full customer view, e.g. predicting customer lifetime, identifying triggers for churn, measuring the influence of social network on your customer’s decisions or suggesting the next best offer.

If we measure any of the following it is likely that we live in the world of BI:

  • daily revenue
  • churn rate
  • daily unique subscriptions
  • top international calling destinations
  • number of new customers

We are now heading toward a world where we don’t simply measure the what question but rather do a rigorous analysis of the what question in order to determine the when, who and why questions such as customers’ primary purposes and product/service affinities.

No matter how you slice and dice the data using traditional BI methodologies and tools, analytics is always big step ahead of it. Analytics requires a business- and data-driven exploratory process. Any attempt to limit either of these dimensions will inhibit its effectiveness.

Absence of thorough analysis can lead to frail conclusions and incorrect decision-making. The data for the analysis must be pulled from a number of subject areas: voice, sms (short message service, the text communication service component of phone, Web or mobile communication systems), mms (multimedia messaging service, a standard way to send messages that include multimedia content to and from mobile phones), bill payments, etc. Simply sending all customers a special discount to remain loyal isn’t an effective use of marketing funds.

Consider the example of a telecommunication company. For marketing analysts to optimize their campaigns, they need to take advantage of analytics techniques such as segmentation analysis to draw a line within their consumer base. By knowing what each segment of customer wants, marketing and campaign management experts can optimize the campaigns for maximum efficiency. Hitting the loyal customer with the wrong offer may put his loyalty at risk.

Telecommunication companies are interested in identifying their most profitable customers and rewarding them. A company needs a 360-degree view of its customers’ spending and behavioral patterns across all properties. Two customers with the same revenue may not necessarily be similar in profit, and each customer’s future lifetime with the company may vary. Applying advanced analytical techniques such as linear time variant would help identify the most profitable and loyal customers over their lifetime, which is of utmost importance to strategists these days.

When analysis is done in an isolated fashion it also leads to the types of mistakes that drive customers away. One company sent out campaigns based on the analysis done on a handful of variables. This was sub-optimal since it did not depict the “whole” behavior of a customer. Segmentation and profiling across more than 1,500 variables was necessary to depict the true persona of the customer. The result was an increase in marketing return on investment.

We live in a world of analytics when we realize that every piece of data we look at drives action that adds the value we are trying to achieve for our customers. Without analytics, BI alone can no longer keep a business competitive – only answering the what question is no longer sufficient. The world of analytics that answers the when, who and why questions takes time to move into, but once you get comfortable in it you will have achieved a long-term strategic advantage.

Talha Omer is an analytics professional and researcher. He currently serves as an analyst at a major telecommunication company. He holds a Master’s degree from Cornell University in operations research. He can be reached at


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