Don't Stress Over Big Data; Master What You've Got

With all the current obsessions over massive amounts of social, mobile, local and search data coming over the horizon, it’s not unusual for brand marketers to lose focus on the present analytic opportunities already emerging from more conventional data sources.

Yes, Big Data is coming, but there are a great many opportunities here now.

Your customer-facing organization already has access to more complex and varied data sources than ever before. Mastering syndicated data and item movement was just the start. Today your category experts must fold in shopper insights from loyalty, baskets, demand, and activation.

More retailers are beginning to widen their sights to capture shopper movement, dwell times and conversions. All can be tremendously valuable for category and promotion planning and performance evaluation.

Depending upon the category and the retail account, today’s practitioners may look well beyond traditional sources such as syndicated sales data, household panel data, and competitive data available from Nielsen and IRI. In addition, they are trying to master:

• Shopper leakage data, both cross-outlet and cross-channel, now coming into common use.
• Item movement data flowing from sources such as RetailLink.
• Shopper behavioral data from a variety of sources like dunnhumby, CVS, and various other frequent shopper programs.
• Shopper tracking and activation data now coming on line from a variety of new sources.

With so much more to analyze and understand, leading brand marketers are responding by expanding organizational expertise and establishing centers of excellence. The opportunity lies in putting this knowhow to work at the account level – and not only in front of the top 10 or 15 retailers. How do you turn so much complexity into persuasive support for your brand?

Speed to Insights
Mastering these wide ranging data sources and extracting the valuable insights that they carry requires a combination of expertise among groups such as category management and shopper insights and a process that eliminates redundancy. Ad hoc analytics are insufficient – even for talented analysts in the largest and best equipped account teams. Today the facts define the deal, and getting to the relevant, actionable facts first can make the difference in category leadership.

Leaders in the CPG industry are taking a three-part discipline that uses intuitive analytic tools to bring clarity and speed to the data analytics used every day at all account levels. The best are learning to:

• Elevate the caliber and sophistication of data analyses. They work faster and with more data types to reveal powerful, persuasive insights.
• Automate routine fact-gathering and analytics to save time and improve consistency. Their precious intellectual resources are freed to focus on breakthrough insights.
• Propagate best practice analytics that originate with the data experts. They communicate them across the customer-facing organization with a few intuitive clicks.

What do we mean by “elevate”? These are fact-hungry times in the fast-moving consumer goods business. The performance differences between winners and losers within a category can be quite subtle. Category planning rules of thumb are giving way to superior insights drawn from more data sources on a more rapid time scale.

In the present Shopper Marketing era, retailers expect their manufacturer partners to focus harder on shopper insights. They want greater granularity and segmentation in analytics and planning. Where consumer segmentation was traditionally product centered, now the demand comes for analytics that are relevant to retailer’s view of their shopper segments.

This degree of sophistication can be harder to deliver on a continuous basis. Not all account team analysts possess the necessary skill level, and relying solely upon the data experts at headquarters can create a bottleneck that puts account-facing teams at a disadvantage.

That’s why it’s desirable to “automate” to the extent possible the reports, scorecards and presentation formats for key data sources – both traditional and cutting edge. This makes routine analytics and insights accessible to your business development pros. They identify a format that tells the story, select the data sources and parameters, and produce presentations at a consistent level of quality.

Automated presentation development shortens preparation time and saves hours from your key analysts. It lets your account-facing teams negotiate with confidence, because the facts are known, and what-if questions are easily answered on the fly.

Count on your most talented data experts to develop new, more sophisticated ways to parse, combine and interpret multiple data sources. The organization can “propagate” their knowhow by embedding formats for various analyses in the presentation tool kit used by the account teams. New analytics don’t need to be reinvented each time a new data source is tapped, but each visual is still dynamic and customizable. Parameters are selected by the user with a few intuitive clicks and the story is built in minutes – in charts and descriptive text.

Deep, Fast, Wide
Elevate – automate – propagate is a framework for superior fact-based category planning and negotiation.

As data analytics keep surpassing new high-water marks, well prepared CPG marketers must be prepared to navigate an ever-widening flow of data sources to gain the best possible grasp of the data that matter. They will tap more information types than their competition. They will automate fact-gathering, and make the findings rapidly actionable. They will empower their non-experts by propagating analytic expertise through systematic processes.

Today’s winning expertise goes beyond mastery of traditional category management data sources. Leaders understand it is a core competency to be able to access and interpret data from any source that is relevant to driving category growth and brand success.

Big Data will likely be part of the equation when it gets here. Right now, however, it’s smart business for brands to master what’s right at hand.

By Zel Bianco, CEO, Interactive Edge

ABOUT THE AUTHOR
Zel Bianco is President, CEO and founder of Interactive Edge www.interactiveedge.com, an industry leader in data analysis and presentation software for the Consumer Goods industry. Zel has maintained long term relationships with his clients including Dr Pepper Snapple Group, Georgia-Pacific, Hormel, Mars, Nestle, Newell Rubbermaid and many more.
Zel has been a frequent speaker on topics of interest to the industry, including sales force automation, category management and business intelligence. Zel is a featured BrainTrust panelist on RetailWire, whose comments have been published in Forbes and other business and industry publications. Zel and Interactive Edge are Corporate members of the Category Management Association and the DePaul University Sales Leadership and Category Management Board of Advisors.

http://consumergoods.edgl.com/news/Don-t-Stress-Over-Big-Data;-Master-What-You-ve-Got88389

ConAgra Looks to Big Data for Sales Growth

You already know grocery and big box stores are collecting data on your shopping habits — what you buy, when, how often, and for how much.

But behind the scenes, Omaha's ConAgra Foods and other consumer product manufacturers that sell to these stores have become even bigger and more sophisticated players in the “big data” game.

They're now borrowing shopper-specific data directly from retailers and crunching it in new ways to better understand consumers and respond to their needs. That's in addition to how they use internal data to forecast demand and increase sales, and use aggregate retail data to plan promotions and strengthen supply chains.

“It's a gold mine of insights and knowledge that nobody has tapped into in a big way,” said Bob Nolan, ConAgra's vice president of customer insights and analytics.

ConAgra, behind brands such as Hunt's, Orville Redenbacher and Marie Callender's, is now studying individual shopper habits at several major U.S. grocery retailers and big box stores. The retailers don't share a shopper's name, demographics or financial information, but do provide all the purchase information associated with a particular customer number.

Now, ConAgra can see how often Shopper 1234 comes into a store, how often she buys a certain product and what products she tends to buy together — pasta and tomato sauce, for example, or seemingly unrelated items, like tomato sauce and diapers. ConAgra can learn how loyal the shopper is to a certain brand, and what makes her switch among brands. And the firm can sort shopper habits by individual store location, seeing differences among neighborhoods.

Now well into its 2014 fiscal year, ConAgra is intently focused on sales growth after a disappointing 46 percent decline in first-quarter profits.

The solution will involve being more competitive on price and investing more in promotions, CEO Gary Rodkin told analysts last week.

Data analytics can help the company target exactly where promotions and discounts are working.

“We need to bend the trends on our market share. It is a market share gain. It's category by category, customer by customer. We've got smarter analytics, and we've got to put them to better use,” Rodkin said.

Consumer goods manufacturers that use data analytics to understand shoppers outperform competitors that don't, according to Progressive Grocer, citing two IBM studies published in July.

“In an increasingly competitive marketplace, the ability to detect subtle shifts that were previously indiscernible is imperative,” found one study, by the IBM Center for Applied Insights and Kantar Retail.

That's because consumers today are not only bargain-conscious, but they're also no longer limited to a few retailers. Consumers, empowered by technology, have more choices — not just the neighborhood grocery, but also the warehouse club, the dollar store, the pharmacy or Amazon.

“The best way of winning their business is not to try managing them: it's to listen to them, understand them and serve them as discrete individuals,” authors of the IBM/Kantar study found. “That requires considerable analytical horsepower, though, and two-thirds of consumer products companies don't have enough.”

To boost its own horsepower, ConAgra made a big investment in its data analytics capabilities starting in early 2012. The firm hired Nolan, a former PepsiCo. executive, for a newly created position. Under his management, ConAgra added a new business function called customer analytics to the same department that houses two existing areas: shopper insights, a group started in 2006 that studies shopper behavior and needs, and category leadership, where the firm works with retailers and other manufacturers to improve the selection and display of various products. The group employs about 125 people.

Under the new customer analytics area, ConAgra has hired a dozen new employees, recruiting from other consumer goods companies and market research firms. Hiring is competitive as retailers and other manufacturers are also stepping up hiring of data analysts.

“These are different skills than we would have hired for in the past,” Nolan said. Some of the employees work in ConAgra offices, while others are deployed directly to retailers' corporate headquarters.

The firm has also leaned on its IT department to expand its in-house data center to handle the additional terabytes of information now coming its way.

With the new data enabling it to drill down to shopper-level habits, ConAgra can put a finer point on work it is already doing to understand the shopper needs and emotions that drive decisions.

Walmart also has amped up its capabilities. Even though the retailer doesn't use a loyalty card program outside of Sam's Club, it has a unique ability to correlate geography with purchase information because it has so many outlets, including Supercenters, Sam's Club, Neighborhood Markets and its online store, CEO Bill Simon said in September at a Goldman Sachs Global Retailing Conference.

“We think that gives us a competitive advantage that others would really struggle to get to,” he said.

A recent Deloitte Consulting analysis of manufacturers' use of big data analytics found that most of these firms lag in developing their analytical maturity, even though their competitive advantage depends on it. The Grocery Manufacturers Association, which sponsored the study, this fall will host its first conference designed to help retailers and manufacturers take a shared approach.

What ConAgra will do with retailers' data depends in part on what its retail customer wants out of the partnership. ConAgra declined to name its data-sharing partners but said the first retailer to share data in February wanted to better understand how people shopped in its frozen food aisles. Frozen food sales are stalled industrywide, and both retailers and manufacturers like ConAgra are eager to see that change.

One early finding was that people who buy one kind of single-serving frozen food tend to buy several kinds of single-serving frozen food. ConAgra could suggest to the retailer that it group smaller portions together, instead of stocking single-serve pizzas by family-size pizzas, for example.

That strategy might work well in one store but not in another, depending on demographics, and any suggestion to change displays would have to be easy for the retailer to execute, Nolan said. “You can't make things more complicated.”

The data may also reveal other shopping “affinities,” for example that people buying pizzas also love buffalo wings, and the retailer might decide to produce a buffalo-flavor pizza, said Christopher Durham, a private brand consultant based in Omaha.

By sharing and studying the data, he said, retailers can use it to inform private brand product development, another area where ConAgra could benefit considering its acquisition this year of private-label manufacturer Ralcorp.

“With retailers, it's not about big data, it's about big answers,” Durham said. “You can have piles and piles of data, but if there's nothing actionable coming out of it, it doesn't matter.”

It may seem strange that a grocery chain or big-box retailer would give up its information, for free, to a supplier. And strange, too, that a supplier would, for free, work with the numbers and offer advice on how the store could improve sales. Historically, retailers were reluctant to share this information, fearing that manufacturers might give the data to a competitor, or use it themselves to enter the market.

But sharing is becoming more widespread as the value of studying the data becomes clear.

Nolan said sharing information takes a sensitive approach. While ConAgra is the one crunching the numbers, the focus has to be on mutual benefit, not just what ConAgra can gain. “It's almost like being a consultant. We want to become the indispensable partner to our customers.”

Then, if ConAgra foods fit into a larger sales plan, he'll talk about how his canned tomatoes or frozen pastas can help the store.

“If they do a better job of managing their frozen department, and get more people down the aisle, ConAgra will get our share of it,” Nolan said.

Contact the writer: Barbara Soderlin

barbara.soderlin@owh.com    |   402-444-1336

Barbara Soderlin covers food safety, ConAgra, technology and employment/unemployment issues.