Alberta Business Marketing » Databases / Analytics

Customer Insights and the Qualitative and Quantitative Mix

Elizabeth Harvey at CMA — Wed, 03 Mar 2010 14:00:00 +0000

Word of mouth is likely the oldest form of advertising and traditionally one that has been nearly impossible to target and measure. But that is changing, and changing quickly. In addition to web analytics and third party audience measurement data, there is an increasing wealth of information available for organizations to measure and mine. Consumer feedback sites, social networks, blogs as well as on-site tools all provide a wealth of information that companies can use for product and service improvement. With these opportunities come new challenges, as success is a measure of more than just numbers and percentages.

The eMetrics Marketing Optimization Summit (April 6 – 9) is a good place to go to really understand how far eMetrics has come. One of the panel presentations, that includes Lisa Lloyd of Microsoft (who will also be wearing her CMA hat) will address this very issue.

On a related panel, named Predictive Analytics and Digital Marketing - Paul Tyndall of RBC (also wearing his CMA hat), will be discussing how RBC and other marketers are utilizing predictive modeling in the online space.

Full disclosure – CMA is one of the association sponsors of the Summit.

.... if you are a member of CMA, you can save an additional 15% off the regular attendee rate by using discount code CMAPARTNER15 when registering for the conference.

Elizabeth Harvey, Manager of Councils and Self Regulatory Programs, CMA

Marketers, Beware the Decimal Point

CMA on behalf of Paul Tyndall — Wed, 10 Feb 2010 14:00:00 +0000

In CMA’s Weekly Watching Brief February 5th edition (accessible to CMA members), there was reference to a study from the US-based CMO council regarding the value of loyalty programs. I found this posting very interesting for many reasons, but mostly because it illustrates how easy it is to potentially mislead people, whether intentionally or not, by including a few choice numbers. In the classic 1950s book called “How to Lie with Statistics”, the author Darrell Huff describes how easy it is to prove whatever point you want by choosing which numbers to present and how to present them.

In the case of this posting, I am referring to its fairly rash generalization regarding loyalty and reward programs. There are probably as many different types of loyalty and rewards programs as there are published studies about them. Loyalty programs could be something large and complex, or as simple as a frequent coffee-buyer card from your local shop. To state that 61% of marketers believe that the consumers who take part in these programs are their best and most profitable customers demonstrates such an oversimplification as to make this statistic practically meaningless. How did the survey respondents choose to define loyalty program or best customer and which ones were included, or excluded? There are no consistent definitions of these concepts and I have rarely met a marketer who has actually pursued a data-driven assessment of their own program to find this statistic to be true. It depends on so many factors including the type of products or services being offered, the competitive context, the types of rewards being offered and the types of consumer behaviours required to earn these rewards. Depending on how a program is set up, its heaviest users could actually be the least profitable customers.

Too often marketers are willing to turn over any quantitative assessment of marketing initiatives to the data geeks or finance and take the answer at face value, without questioning the results (unless of course they are positive). There are usually many ways to skin the proverbial cat, including such things as definitions of test and control cells, definitions of success and what costs are included in profitability calculations. And depending on how these various factors are defined you could come up with very different results. Since these calculations are used to support decisions about potentially significant major marketing investments, you need to be completely confident in how these calculations were done and what was, or wasn’t, included. I strongly encourage marketers to get more involved in the analysis and understand the definitions being used, how the results are calculated and what other factors could influence the outcomes instead of simply going along with an answer because it was calculated to 6 decimal places.

By Paul Tyndall, Senior Manager, Predictive Modelling & Segmentation at RBC. Paul is also a member of CMA’s Marketing Technology and Database Intelligence Council.

Getting Started with Your Data

CMA on behalf of Richard Boire — Wed, 09 Sep 2009 14:00:00 +0000

I recently wrote an article that attempts to depict how one would get started in developing a measurement framework within an organization. While I acknowledge that there are a number of marketing/business issues that need to be addressed when beginning such a process, the primary focus of my article deals with data issues. My philosophy in tackling data analytics projects is, first recognize what you want to do from a business perspective, and then identify a data strategy that accomplishes those business goals. At the heart of any analytics exercise, it is all about the data and what can be used to meaningfully solve business problems.

As practitioners, compromises are sometimes made in terms of the data that is both accessible and available for analysis. Yet I would argue that even if compromise is necessary, it is still better than doing nothing.

I would certainly invite your comments concerning experiences in building measurement framework solutions that are clearly sub-optimal given a limited data environment.

Posted by Richard Boire, Partner, Boire Filler Group - also Chair of CMA’s Marketing Technology and Database Intelligence Council