Month: May 2016

Data Analytics is About Finding Facts

mep Analysis

Data analytics is about finding facts. What we do with those facts is comparable to what we do with any other tool. I have a good hammer and a great screwdriver in my tool chest. They don’t make me a carpenter. The best pots and pans won’t make me a chef either.

A manager who gets a data report showing that he or she needs more sales reps can’t blame the data if he or she then hires poor sales reps. Another manager might ignore the portions of an analyst’s report that don’t fit his or her own preconceived notions and biases. And, there are bound to be managers who blindly follow the data without really testing the findings for validity and scalability.

These are human failings that could lead us to blame the data when, in fact, they are the mistakes that people make. It’s not about the data; it’s often about the people who are not strong quantitatively.

As more companies embark on big data analytics strategies, have mercy on the good data that ends up in the hands of bad managers. Some of these projects will fail in grand fashion. Some will soar to mediocrity. Some will be home runs. Business is still populated by fallible humans, at least for today.

When Simple Insight Tools are More Effective than Big Data

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According to Gartner and Forrester analysts (*) there is a battle raging across the categories – a battle for the hearts and minds of the consumer through customer experience … Before we charge blindly toward harnessing big data driven by technological innovation, we should pause and consider some of the more traditional methodologies that allow companies to optimize satisfaction on existing or new products with accuracy, speed and cost efficiencies, even providing the Holy Grail in ROI.

While big data measures what happened in the past, conjoint analysis and trade-off methodologies help companies build an accurate understanding of the future commercial appeal of a new product or package. New dashboard technologies can be a fast and easy way for marketers to judge satisfaction vs. investment before execution – research that gives foresight and not big data-based hindsight. These methods can also provide insights into how to adjust packages and communications to maximize the uplift for specific demographics.

It can be easy to look to big data-driven, tech-based innovations as a panacea for a brand’s customer experience woes. By using a more traditional methodology marketing researchers can learn more about the customer, even before a purchase. While big data shouldn’t be overlooked as a tool for collecting real-time data for satisfaction levels, customer experience really begins before the purchase is made. By relying completely on big data, brands can’t dig into how a new product will appeal or perform prior to launching. It’s through well-tested and reliable traditional methodology that businesses and marketers will gain the foresight needed to make the right decisions on how to optimize customer satisfaction.

(*) Quirk’s Marketing Research

Article ID: 20160526-1     Published: May 2016     Author: Tim Glowa

http://www.quirks.com/articles/2016/20160526-1.aspx