Recommendation and Conclusion 5

5) CPG Companies Need Data and Analytics to Support an Innovation-driven Culture

This new exponential era the industry is entering will require a culture of experimentation, trial and error, and other methods that support a company’s ability to remain agile, fluid and adaptable to the pace of change.  A strong versus weak data and analytical maturity is one of the key differentiators between a CPG company that will successfully navigate through this new era of innovation, versus falling victim to the exponential pace of change.

As described in the previous sections of this report, the emerging era of Wave 2 and Wave 3 innovation will usher in both new opportunities, as well as new threats to the industry. Even for the more analytically mature competitors this will likely not be business-as-usual. The changes necessary will require a significant cultural shift in order to align the organization, its resources, and its decision-making processes with the new rapid pace of change and innovation.

This new era requires companies to become highly agile, fluid, and adaptable – requiring a culture that promotes experimentation and trial and error. In addition, CPG companies should not embark alone on this journey; they should start looking for partnerships with the emerging ecosystems of innovators and collaborators. 

A culture of Innovation

In this new era, CPG winners will think differently. The notions of “organizational alignment” and “getting everyone on board” may be a hindrance to keeping up with the required pace of progress in an era of rapid innovation. Companies will need to rethink their internal models in order to stay contemporary and remain relevant to their consumers. For some organizations, this may require bold moves in which specialized teams within the organization are given special permission outside of the normal corporate culture and constructs of the organization.

For example, Walmart launched @WalMartLabs in April of 2011, which today operates in a start-up style model within Silicon Valley, 1,849 miles from the corporation’s Bentonville, AR, headquarters.  (See “Walmart’s New High-Tech Labs: You're Not in Arkansas Anymore - The way we shop is changing at lightning speed, and the world’s largest retailer knows it needs to keep pace.” – MIT Technology Review, By Jessica Leber on October 16, 2012).

“Big data tools largely developed to handle processing of the immense amounts of data being generated by the consumer web allow us within @WalmartLabs to improve the online customer shopping experience.  These Big Data tools, in conjunction with the appropriate machine learning and information retrieval methodologies, can profoundly improve the eCommerce shopping experience… The targeting team within @WalmartLabs ingests just about every clickable action on Walmart.com: What individuals buy online and in stores, trends on Twitter, local weather deviations, and other local external events such as the San Francisco Giants winning the World Series.  We capture these events and intelligently tease out meaningful patterns so our millions of Walmart.com customers have a shopping experience that is individually personalized…. Our Big Data tools help us personalize the shopping experience, and our psychological analysis helps us to dissect even deeper meaning behind patterns in the data.  We apply behavioral economics to find clarity behind both the rational and irrational behavior shoppers exhibit.” (link)

In January 2012, Walmart Labs experimented with theGet on the Shelf contest that “challenged inventors, tinkerers, thinkers, marketers and everyday Joes to submit their products for sale on Walmart’s shelves. Walmart shoppers watched over 4,000 product videos — some intriguing, some wacky, some inspiring — and voted for the products they liked best. The result? A totally new way for products to earn a place on Walmart’s shelves, courtesy of our savvy shoppers.” (see link).

These lab models emerging in CPG and retail are used to not only incubate new innovations, but simultaneously used to harness Big Data to drive new insights that lead to developing the next innovation (see sidebar). This is an example of The Formula for Growth as described in Recommendation and Conclusion #2.

For many organizations these new models are about more than just incubating innovations, they are also a means for fostering a new culture of innovation.

Because the pace of innovation can make it challenging for CPG companies to continuously separate the hype from reality, they need processes to identify, assess, filter, and prioritize which innovations they should be adopting.  To do this requires building competencies around new methods of experimentation and trial-and-error.  These methods require managing data assets and driving real-time analytics as part of proof-of-concepts and pilots for assessing new innovations.

Characteristics and traits

Using Big Data and analytics, CPG companies will be able to navigate the pace of change more efficiently, anticipate future trends more proactively, and demonstrate agility, fluidity, and adaptability.  For example, consider the fluidity of retail pricing during this most recent holiday season. Prices were reportedly changing on the Web hourly in some instances (see “Retail price wars online have entered a new era of speed and precision”). E-commerce pricing data (which is Big Data) from across the web is made instantly available for retailers to leverage in their pricing strategies through data aggregator innovations. This concept is even developing consumer applications commonly called pricing aggregators that help consumers optimize the price of the products on their shopping list through apps like MySupermarket in the UK (http://www.mysupermarket.co.uk).

As CPG companies embark on this journey, they will need to remove their traditional fear of failure. In order to succeed in an era of rapid innovation, some level of failure is required – in fact, it is part of the learning process. Consider the following use-case of experimentation which features a crowd sourcing proof of concept that Deloitte conducted with a major national retailer.

 

Expanded ecosystem

CPG companies require support as they enter into this new era of innovation. Surrounding themselves with a new ecosystem of innovators and collaborators can increase chances for success. Procter & Gamble, for example, has had an executive embedded for the past three years at Silicon Valley-based Shopkick (www.Shopkick.com) to advise on their popular mobile shopping app (see link).

There are also new crowd-funding innovation models being used to fund new products. These models can be excellent sources for CPG companies to locate potentially new and exciting young brands.  For example General Mills is participating in crowd-funding platform CircleUp (www.Circleup.com) to remain aware of forthcoming products within CPG categories (see link).

The CPG industry will also need to think differently about sharing data. In the past, the CPG industry has been rather guarded with its data assets. However, in an era of Big Data, the industry will need to explore new opportunities to be more open with data and other intellectual property. In order to tap into the evolving number of external innovators, the industry will likely explore new concepts of data sharing, like emerging "data markets" that allow CPG companies to maintain and exchange their data – including product, and places / location– for other sources of data they find useful, such as social “likes,” reviews, and more (see www.factual.com and see link).


Lastly, the mentality of “not invented here” will need to change. Strategies should be defined for harnessing outside innovation and expertise. For example, in October of 2012 Mondelēz International launched Mobile Futures to work with and fund outside innovators, or start-ups and entrepreneurs, with the goal of becoming the top mobile marketer in the world (see press release).

Another CPG example is the Pepsi10 program announced by PepsiCo this past September, which explains the brand’s willingness to invite collaboration: “The next phase of its digital incubator program, which is setting up shop in Brazil with an open call for entrepreneurs and university students in the country to submit ideas…. It's looking to emerging markets for innovation and the big ideas in technology across four categories: business sustainability, entertainment, mobile and retail.” (see related article).

As the competitive divide widens between CPG companies with analytics capabilities and those without, the ability to use decision-making capabilities to improve performance remains a key differentiator. CPG companies failing to develop competencies around big data and analytics will be unable to capitalize on the pace of technological and digital innovation. By embedding analytics strategically and tactically within the business, new competitive abilities will evolve, successfully fueling the growth of these more nimble organizations and the CPG industry as a whole.