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Analytic best practices: Overcoming information overload

There are more ways than ever before to efficiently analyze information. All of this readily available information can be overwhelming, but using it wisely can provide robust, actionable guidance. The following paragraphs attempt to provide some guidance on navigating all of this available information.

·         First and foremost, know the question(s) you are trying to answer. Sample design, survey design and how the data is analyzed, all should tie-back to the question.

·         Understand your sample plan– know to whom your findings do and do not generalize.

·         Do your homework before designing the survey. This will help to ensure that important measures are not excluded.

o   There are a myriad of available statistics to help assess the goodness of the model and variables measured. The findings from these techniques can be used to guide survey design moving forward.

·         Use all pieces of available information to answer the question- survey results- both closed and open-ended measures, behavioral data, voice of the customer, competitive data and information, historical data, news and industry information, etc.

o   Using all available information helps to provide a robust answer to the question, but keep this in-mind- some findings may appear to be inconsistent. These apparent inconsistencies can be another valuable source of information (assuming no errors have been made).

·         Understand the level of data being used- is it being analyzed at an individual customer level, or is the data being used at an aggregate level (e.g., by account, by call center, by region, etc.). 

o   Post-data collection is also the time to re-evaluate sample representativeness- are there customer non-response issues, how do the sample distributions align with the population distributions, etc.

·         Use the most appropriate statistics to effectively and efficiently answer questions.

o   Rather than just comparing performance scores, statistical testing can be used to narrow down where statistically meaningful differences exist between scores.

o   Multivariate analytic techniques can further help to direct focus on the measures that most effectively distinguish or help in understanding customer attitudes and behaviors.

o   Do not assume that if you do not see patterns in the data, that none exist. Sometimes the failure to see patterns in the data is a result of not appropriately segmenting the data. As a follow-up to customer segmentation, profiling techniques can be applied for further understanding and description of customer segments.

·         Answering the question often leads to new questions being asked. A single program cannot/should not be designed to answer all questions.

o   Tracking the questions and answers over time, and analyzing how these things have evolved, can again, lead to further insights. Review this process periodically to adapt to the ever-changing business world.

It can be very easy to become lost in the vast amount of available data and analysis. Having a clear understanding of the question(s) to be answered is the first step in not getting overwhelmed. Who to sample, how to analyze the data, etc., should always build from that question. Answers to questions often lead to more questions, and the process starts all-over again…

Amy Heleine
Director, Marketing Sciences

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