ANALYTICS
SECTION ONE
SECTION TWO
Generating Growth Opportunities
Through Effective Use of Big Data

By Ash Patel

Big Data is here to stay. However, the term is often misunderstood like so many other business trends. Understanding exactly what Big Data is and how to use it opens up exciting opportunities to achieve significant improvements in revenue and inventory management.

Here’s an example: A frozen food manufacturer was able to identify specific types of products that shoppers at the retail stores frequent most. The most attractive shoppers were targeted using shopper segmentation data. Linking their demographic profiles, media consumption habits and retailer preferences, the manufacturer was able to develop high-impact advertising that appealed to shoppers’ lifestyle choices, delivered the message through their preferred media channels, and promoted at specific retail stores where they shopped. This resulted in faster growth and more effective use of marketing funds.

Successful applications of Big Data have several things in common:
  • Securing highly granular data: For example, store-level data or individual shopper data.
  • Collecting different types of inter-related data: The most interesting applications of Big Data come from the integration and correlation of different types of highly granular data; for example, household demographic data at the zip-code level and store-level sales data.
  • Applying analytic techniques that work with incomplete data sets: Data collected from multiple sources often have holes and may include bad data sets.
  • Thinking creatively: Big Data can address issues that previous analytic solutions could not. Marketers should think broadly about interesting applications that drive value.  

Clearing the Hype about Big Data
Technology analyst firm Gartner offers a clear definition of Big Data: “High-volume velocity and variety of data/information assets, structured and unstructured, that…can be analyzed in real-time with innovative and cost-effective analytical techniques and technologies…for enhanced insight and decision making.”

To put the amount of data available today into perspective, Americans create 2.5 million gigabytes of data each day, which is projected to grow 40% every year.

To illustrate one of the broad application areas of Big Data, compare the information manufacturers had in the past versus what is available today through multiple online, mobile and social sources of Big Data:

The Small Data of the past:
  • Urban dweller
  • 18-35 years old
  • Mid-to-upper income
  • Ethnic minority
  • Financial services professional

Compare that to the Big Data of today:
  • Currently in Manhattan near 55th and 7th Ave.
  • Sprint $326 on clothes/shoes on Saturday
  • Returns 40% of what she buys online
  • Upset with recent experience at United Airlines; let ‘em have it on Facebook and Twitter
  • Never clicks on banner ads
  • Makes lists for everything - ZipList for food shopping and EverNote for social  functions
  • Reliant on friends for technology ideas, but not afraid ty try something new
  • Excited for Happy Hour tomorrow!

The ability to capture more data on consumers from disparate sources can significantly improve a manufacturer or retailer’s understanding of a target shopper’s attitudes and behaviors.

Demonstrating the Power of Big Data
To illustrate how big data can lead to enhanced growth generation, below is a scenario confection manufacturers and retailers often face:
























Big Data applications can yield answers to a wide range of questions that arise in disciplines ranging from consumer and shopper activation to retail execution.

Now let’s look at a shopper activation case study using hyper-local targeting and digital marketing. A powdered drink mix manufacturer was able to generate a 5.5%  dollar sales increase and expand household penetration by 11%, achieving a $2.65 return for $1 of advertising spend.

The company’s marketing team achieved this by first collecting online data such as websites visited, media consumed, coupons downloaded and social media activity.   They also integrated consumer panel data including purchase behavior, attitudes, shopping behavior and demographic information.  Finally, they included point-of-sale (POS) data by store. With this information in hand, marketers analyzed category development (CDI) and brand development (BCI) indices of the company’s product and competition using panel and POS data. This identified households where the drink mix was underdeveloped relative to the category. The team then constructed look-alike modeling to identify target consumers on the digital partner’s database.

As a result of the analysis, the marketing team was able to recommend households to target for a new digital marketing campaign. This recommendation was based on actual sales and consumer buying behavior and mapped to the digital campaign partner’s database.

The Next Wave of Big Data Value
Big Data is in its third wave of evolution. In the first wave, decision makers were able to secure large amounts of data, but lacked the analytical tools to wring out critical insights. In the second phase, large amounts of data and improved analytical solutions enabled marketers to gain value insights, but information only focused on past activities. In the current phase, new analytics algorithms enable marketers to both predict future trends and prescribe activities marketers should take - an especially exciting development.

As the volume of data continues to grow and analytic solutions continue to improve, the ability for manufacturers and retailers to activate consumers effectively is limited only by marketers’ imaginations. Those that apply Big Data most effectively will enjoy sustained improvements in profitability and inventory management.

Strategies for Moving Forward
For marketing teams eager to better leverage Big Data, it’s critical to understand manufacturer or retailers’ current strategic priorities, and current marketing processes where decision support would be helpful. While the approach for each Big Data application should reflect the specific issue to address, a few common steps are below: 
  • Brainstorm potential big data applications designed to address strategic priorities
  • Secure the data sources necessary to conduct the project (these should include both internal and external sources)
  • Identify the analytic techniques required
  • Develop the application and then pilot it to test its functionality, quality of results and ROI
  • Deploy the application to address the business issue(s) at hand.

While this process may sound daunting, harnessing Big Data can be much simpler than it sounds. There is much expertise available on the market that can help manufacturers take advantage of Big Data and drive value in any one of the CPG applications discussed above.


Ash Patel is the Chief Information Officer of Information Resources, Inc. For more information: www.iriworldwide.com.



Click on the LinkedIn logo to join the new Shopper Technology Institute Discussion Group
SECTION THREE