Author: mep

When Simple Insight Tools are More Effective than Big Data

mep Analysis ,

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

Big Data, Small Data … Does Size Really Matter

mep Analysis

Talking about whether data is big or small or something in between is missing the mark.  If the data doesn’t help you start to move through the progression to information, to knowledge and then to wisdom and insight then the size of the data doesn’t matter.  If it doesn’t help to answer business questions in human terms then the size of the data doesn’t matter.  If the 1000’s of data points that are collected to predict the weather don’t lead to your knowing whether to take an umbrella to work with you today then the size of the data doesn’t matter.

Let’s take a step or two back.  Why do we even care about data?  We need to think like business people and ask the right questions for our business.  Once we have a business question or a purpose we can begin to consider the types of data and analytics that will lead us to a conclusion to move our business.  It’s about business and discipline, not data, not IT and not statistics.

I did a quick online search for “data,” mainly graphical, showing the path from data to wisdom and insight.  As expected there were dozens of representations.  I combined about 20 of these into the graphic below.  Although there are numerous horizontal axes, they are all generally consistent in their progression …

…from data (discrete elements, measured points of reality)

…to information (establishing links and relationships, or, answering who, what, when and where)

…to knowledge (establishing connections and patterns, forming a whole, or, answering how)

…to wisdom and insight (integrating the data and analytics with the context of the business situation, as well as, values and principles, or, answering why)

…in order to act.

Data_to_Wisdom

Balanced Systems

mep Systems

In today’s over-complex world, one way to success is to first start with a question, “What is important to our customers?”

A quick story can illustrate this.

A 5-year old is crying, frustrated she can’t tie her shoe.  You take care of it, dry her tears, problem solved… then she bursts into tears again.  With a little more digging, you learn she’s really mad at her brother.  She just happened to be fighting with her shoe at the same time.

Fixing the wrong problem is unfortunately a common thing.  A better approach is to take time for uncovering and understanding the needs and to clarify what you know about the challenge as well as what you don’t know.  This way you can target efforts and achieve results that matter faster and more effectively.

Data can always be gathered but are you measuring what is most easily measured (data that are in our legacy systems and data warehouses) rather than what is most meaningful?  Technologies are available to easily collect this data and they are improving.

So many companies have yet to figure out how to use their valuable small data but they are ‘leap frogging’ to big data without a second thought.  Today the concepts around big data are leading the use of big data.  The actual data are what’s available, most likely not collected with our business questions in mind, and we are trying to make it work.

There’s no question the new technologies will be used.  If we’re going to really capitalize on big data, we need to add human insight at machine scale. We will need some type of balanced systems that not only perform data analysis, but then also communicate the results that they find in a clear, concise narrative form.

Variance

mep Variance

Often, a company’s inside-out view of the business is based on average or mean-based measures of its recent past. Customers don’t judge companies on averages, they feel the variance in each transaction, each product shipped. The company needs to focus first on reducing process variation and then on improving the process capability.  Customers value consistent, predictable business processes that deliver world-class levels of quality.