Consumer needs, tastes, and buying habits shift constantly. Since their inception, consumer packaged goods (CPG) companies have faced the challenge of moving quickly enough to match their products to consumer demands. However, this pace of change is accelerating, primarily due to the rise of the millennial shopper, a growing demographic soon to exceed 50% of the total U.S. population. For CPG companies that are able to harvest and glean insights through big data analytics in retail, there is hope. They’ll be able to better understand rapidly evolving consumer tastes and preferences, develop products that consumers want to buy, and tailor targeted marketing strategies and distribution models.
CPG companies depend on brick-and-mortar retailers and syndicated market data providers for the information that drives their market moves. But expanding retail channels, digital shopping platforms, and social communities combined with direct access to consumer data, and the use of big data analytics, is changing how businesses can now make decisions. By moving past relying solely on sample market data, and typically stale historic information, toward a broader 360-degree view of consumers with closer-to-real-time insights, they can form a more complete picture of their consumers. This leads to better understanding, predicting, and even influencing when, how, and what they like to buy.
CPG companies routinely post dismal growth numbers. IRI reports that even large CPG companies with $5.5 billion or more in sales had average negative growth of .5 percent. Midsize and small companies did slightly better, with average positive growth of .1 percent and 3.1 percent, respectively. The story of CPG companies and big data is playing out like many other mature, slow-growth industries that are facing disruption. Those low-growth numbers are beginning to have an impact on the pace of which businesses approach big data analytics, and the use of consumer, product, inventory, and sales information to drive their revenue.
There are key areas where using big data analytics in retail forecasting can help CPG companies improve their growth and market share prospects. These include:
New product development: CPG companies regularly launch new products or extensions to existing lines. To improve the dismal new product failure rate, which is as high as 85 percent according to Nielsen, they can use big data analytics to better understand consumer tastes and preferences, and align rollout strategies with retail channels that offer the most promising return on investment (ROI). As millennials continue to become a larger consumer base, businesses have to think about this new set of buyers. Younger buyers place more weight on product sourcing, social responsibility, and protecting the environment. They also place more value on social connections, and spend time interacting directly with brands as a way to both register complaints and praise actions.
Another consideration for CPG companies is private labelling. As production lines begin to slow down due to shrinking retail store sales, an alternative for CPG could be working more closely with retail customers who are actively seeking distinctive products to differentiate themselves. CPG companies can bring premium product development and marketing expertise to retailers that traditional private label suppliers simply cannot.
Retail/e-tail channel exploration: CPG companies are already working with traditional retailers like Walmart, Kroger, and Target that have successfully expanded their brick-and-mortar presence into robust, digital channels with effective marketing and personalization capabilities. They are also turning to e-tailers like Amazon, Boxed, and Jet. E-tailers, in particular, understand the role data plays in fine-tuning consumer interactions, and are often willing to share granular levels of browsing and purchase behavior insights with CPG companies to fine-tune marketing strategies and help grow sales. Direct-to-consumer sales that cut out the retailer/e-tailer altogether are also part of channel disruption.
For some CPG companies—particularly those that sell unique items that aren’t found in your typical grocery store—direct-to-consumer (DTC) sales provide another viable channel and an even better source of consumer data. CPG companies are also leveraging their DTC platforms, in some cases, to engage consumers in the product development process, even providing consumers the ability to create personalized “blends” with ingredients that are tailored to their specific tastes and preferences.
Smarter marketing spending: CPG companies spend hundreds of millions of dollars every year on advertising and brand awareness to reach consumers. Marketing mix modeling is a common technique used by businesses leveraging big data analytics, often requiring data from across the spectrum of marketing and advertising channels. To best understand advertising effectiveness, businesses that employ this technique depend on big data and advanced analytics to maximize their return on marketing investment (ROMI). As CPG companies continue to look for the most effective use of marketing budgets, the ability to continually improve ROMI with an evolving target market and changing retail channels is becoming more challenging and perhaps more important.
Consumers generate data every time they browse, discuss, or purchase a product. Businesses continue to test and learn by harvesting multistructured data sets and extracting insights to more effectively and efficiently micro-target product promotions, campaigns, and new products. CPG companies continue to reallocate market development funds (MDF) away from mass marketing and retail circulars and more toward digital channels where ad spend can more effectively be measured, targeted, and adjusted in real time. Ultimately, those advertising budgets are being shifted to reach consumers where they live: their digital devices.
As the market shifts, it’s important for CPG businesses to be aware that a dynamic, experimental strategy is the key to thriving. Experiments lay the groundwork for success in big data analytics in retail. According to Brent Biddulph, general manager for retail and consumer goods solutions at Hortonworks, “If you’re not already experimenting with alternative retail channels, you need to be. Big data analytics is not about getting an immediate answer. Ask questions, put yourself in difficult situations, but be willing to test, learn, and fail quickly. You’ll learn which insights matter, and you’ll build up a strong data repository and insights that you can keep coming back to and building success from.” With that said, without a nuanced understanding of how big data insights can help you connect with consumers effectively and efficiently, you risk falling behind.
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