Data-driven Marketing vs. Conventional Marketing

Chapter 5

Business Intelligence Fundamentals (CLTV & OLAP)

Business intelligence (BI) as mentioned in previous chapter, is a fundamental contributor to a data-oriented organization. Given the intertwined relationships between a data-oriented firm and a data-driven marketing framework, a functional and effective BI system is a critical topic.

This function integrates and relates two data sources retrieved from internal company sources (such as financial and operational) and external data derived from the market or the industry that the firm operates.

A functional BI framework can support an organization in a data-driven decision making, in an ample spectrum, from operational to strategic imperatives.

BI may comprise different range of tools or platforms, from simple frequent reports or an interactive dashboard to more complex featured mechanisms such as data mining, process mining, complex event processing, benchmarking, perspective and predictive analysis.

CLTV (Customer Lifetime Value analysis)

This attribute is an insightful strategic based need, encountered in a BI, to figure out the overall benefit that a segment of customers brings over their total engagement and touchpoints with the organization, calculated from a simple formula:

As broached previously, CLTV is a perfect case derived from internal and external integrated data analysis, which would be impossible to reach from a singular data set.

Given the simple formula of the CLTV, the elements of customer value (may interpreted as profitability as well) and average Customer Life Span are not quite simple and straight forward items to calculate. These are multifactorial items requiring valid customer data collection, financial and operational exercises and proper analysis tools.

Example:

In 2005, United Airlines (Previous Continental Airlines) received the Gartner Business Intelligence award, across all industries, thank to a value based and data driven actions and marketing journeys.  

What were the differentiative parameters of United Airlines?

First of all, since the delays and some other errors might be unavoidable in airline industry, this airline initiated a service recovery program by designing an action outline, executed within 12 hours of a misconduct or an error, expressing apologies and a proportionate compensation.

This program evidently needed a timely report of customers data and the events (both internally and externally routed, such as flight attendant misbehavior or an airport operation related issue) that might cause discomforts. So, a proper BI, regardless of the technological requirements has supported this firm to benefit form a recovery plan, minimizing the churn rates.

As a proceeding step, the operation team of this airline, initiated an exercise to revise the traditional customer ranking methodology, which used to be based on flown mileages. This decision for revising, came from the customer value re-calculations, as the first element of CLTV metric which indicated prominent inaccuracy in the customer ranking method.

When the factors such as type of purchased ticket and the cost to serve the different segments of customers were embedded into the analysis reports from the BI, the picture altered dramatically in a way that the “silver” and “Platinum” ranked customers swapped their positions.

This example is only highlighting the very primitive impacts and benefits of a BI, and a data-oriented approach to customers data. With today’s cutting-edge means, tools and analytical platforms, much more insightful and advanced assessments have been made possible.

(Example Source: Data-driven Marketing – 15 metrics, by Mark Jeffery) 

OLAP

So far, the strategic aspect of a BI function was discussed, but what would be the technical requirements?

Online Analytical Processing (OLAP) is a category of software or operating system that provides the necessary grounds for data analysts to reach and interpret information from multiple databases, simultaneously, while allows users to have an understanding of several integrated data sets, from different point of views. In addition, it enables performing dedicated functions to handle analytical queries much faster.

Given that OLAP data can be simply pre-calculated and pre-aggregated, this technology has facilitated the analytics and accelerated reports extractions, resulting more powerful BI backgrounds.  

An OLAP system, typically consists of following components:

• Data Source: Raw data, in standard format and usually not augmented or for OLAP queries.
• OALP Database: Where data is stored for analysis, after transformation and preparations of the initial data set.
• OAL Cube: The core of the OALP concept, is based on a OLAP cube, which consists of measures of numeric facts, classified by dimensions.

An OLAP cube example:

Conclusion

The basic technical requirements for OLAP implementation and rationales behind with examples, will be discussed in upcoming chapters, focusing in BI. 

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