Using artificial intelligence to power innovation and revenue growth
The CPG landscape is changing fast—and those changes are making for some rough terrain at many large CPG companies. Macroeconomic influences like generational shifts, omni-channel media, retail fragmentation, urbanization, rise of niche brands and interest in health and wellness all contribute to this rapidly evolving environment. Multinational players accustomed to commanding billion-dollar revenues and sizeable share now see niche competitors and private labels, both in stores and online, inhibiting their growth. Meanwhile, consumers are looking for purchase decision information from Google search and social media like Twitter and Facebook, as well as influencer networks like Pinterest, thus reducing the influence of traditional media and brand advertising.
But help is at hand. The evolution of all these new data sources and the exponential enhancement of computing power has led to new capabilities that can not only handle big data, but also perform trillions of computations with it—at scale and speeds not possible with humans alone. Artificial intelligence (AI) has been applied in almost all major disciplines across industries. Now AI is driving revenue growth in the consumer packaged goods industry through innovation, improved forecasting and better in- store execution.
Artificial intelligence, an algorithmic system that replaces or directs a decision that previously would have required human intelligence, combines the speed and accuracy of computer models with the wisdom and ingenuity of people. Just like automation is the ability to reduce the need for or to enhance human physical labor, AI is the ability to augment and extend human mental effort. For example, automation can reduce the need for workers to put widgets into boxes. AI, on the other hand, would be a system designed to automatically drive a car or trade stocks.
One of the most exciting developments within AI is machine learning—the ability of machines to detect patterns from data and learn from
experience. Trained computer models can make predictions and determine the best decisions. The incorporation of a feedback loop can validate whether the machine’s decisions are correct or incorrect. This virtuous cycle not only makes machine learning models smarter with use, but it also enables them to adapt to changing conditions on their own.
One of the most exciting developments within AI is machine learning—the ability of machines to detect patterns from data and learn from experience. Trained computer models can make predictions and determine the best decisions. The incorporation of a feedback loop can validate whether the machine’s decisions are correct or incorrect. This virtuous cycle not only makes machine learning models smarter with use, but it also enables them to adapt to changing conditions on their own.
Advances in computing power and the availability of abundant data have led to the development of a class of machine learning algorithms called deep learning. Deep learning models learn features using layers of connected nodes (like neurons in a human brain). These models can discern highly complex patterns and learn skills such as facial recognition and driving cars. The uniqueness of deep learning is the ability of a machine to learn from a mistake just as humans do, and then apply corrective measures so the mistake is not repeated for the same set of parameters.
Artificial intelligence combines the speed and accuracy of computer models with the wisdom and ingenuity of people.
Just 20 years ago, CPG companies primarily had only syndicated data sources to utilize. Then came the evolution of shopper card data, which led to new entrants such as panel and loyalty card data providers.
However, within the past five years the amount of data sources has grown significantly to include new entrants such as online panel solutions as well as new services within social media. Now CPG companies can understand not only how their products are selling, but also the characteristics of the people who are buying their products and, even more importantly, what people are saying about their products on social media.
Big CPG companies must transform to stay relevant in this marketplace. Rather than relying on their traditional advantage of scale, they must become nimble to actively predict and shape what happens next. CPG companies must rapidly adapt their product, pricing, packaging and go-to-market strategies to anticipate and fulfill rapidly evolving consumer needs. These companies are compelled to spend more these days on R&D and M&A, and on creating independent startups to set up innovation platforms that serve consumers’ willingness to pay a premium for convenience, health and new products that meet their lifestyle needs.
However, these companies’ efforts to regain momentum are mostly falling short. Many large CPG companies continue to invest in outdated analytics solutions, ineffective marketing strategies, and lopsided relationships with their retail partners. And even though increased access to data should fuel smarter, more effective execution, few CPG companies are reaping the full benefits of big data to drive predictive and replicable business growth. The tools that large CPG companies have long relied on to understand consumers and execute operations no longer suffice. CPG companies are data-rich in silos but poor in unified insights. Legacy analytics solutions can’t keep pace with the speed of changing consumer behaviors.
Legacy analytics solutions can’t keep pace with the speed of changing consumer behaviors.
Fortunately, modern day computing power coupled with vast data sources across many disciplines (syndicated, loyalty, social media, omni-channel) provides the potential to unleash the power of AI in changing the fabric of the CPG industry. In fact, AI approaches are proving to generate unprecedented ROI. Research suggests that AI could potentially increase overall profitability rates by an average of 38 percent by 20351.
To stay ahead of the lightning speed shifts in today’s retail and CPG landscape, businesses will need technology that makes them smarter, swifter and ready to seize the future.
Here are three key ways AI can help big CPG companies innovate quickly and execute accurately through CPG innovation, more precise forecasting and superior in-store execution.
AI combines the power of big data with the subtlety and discernment of human intelligence. By analyzing actual behavioral data instead of surveys—what consumers do versus what they say they’ll do—AI and machine learning power a predictive approach, illuminating future opportunities so companies can execute strategies to capitalize on consumer demand.
Understanding demand is crucial for any company’s growth. Yet most firms still rely on rudimentary data: If product X sold more than product Y, X must be more in demand and deserves a better spot on the shelf.
What’s missing from this equation? The consumer. To truly gauge demand, understanding how the consumer perceives a product is essential. Does the product stand out from its competitors? Do ingredients and claims pass muster? Will a shopper actually place the product in his or her basket? This type of information defines the unmet needs for consumers. And if a niche player develops an attribute that a similar product from an established brand lacks, then a vulnerable demand for the established brand will be identified.
Adding to this, CPGs need to identify the predictors of consumer needs. Where the consumers are at (home, work), who the consumers are with (family, friends, colleagues) and the day and time (morning, afternoon, evening, weekend, weekday) are clear indicators of needs and consumption habits and patterns. Consumers’ collective need for
unmet and vulnerable demand, along with the predictors of need, quantifies the opportunity for CPGs to architect the shape of demand for consumers.
With the availability of all these data sources on new product introductions, POS transactions, loyalty data, consumer demographics, social media chatter on lifestyle trends, and omni- channel data, it is possible to further fine tune and generate leading indicators of consumer demand. These are key inputs for the CPG innovation pipeline.
The operationalization of this consumer demand occurs through three types of innovation: brand refresh, category reframe, and business breakthrough.
Brand refresh means taking an existing product and tweaking attributes such as flavors or colors—for example, creating cherry flavored cola. Category reframe is taking something that already exists in the marketplace and creating the capability to move it to a different paradigm or consumer need, such as a baked potato chip that is healthier than a traditional fried potato chip. Business breakthrough is defined as developing completely new ingredients, new manufacturing techniques, new packaging materials, etc., where the innovation is around white space, with consumers willing to pay more for quality. Two good examples are whitening toothpaste strips and laundry detergent pods.
1. How AI Boosts Industry Profits and Innovation, Accenture, 2017
AI-driven consumer insights are also opening exciting new frontiers in forecasting. While tradi- tional statistical forecasting methods have been focused on predicting demand at a SKU-location level, AI with deep learning capabilities is enabling CPGs to understand, anticipate and shape buy- ing behavior at a household and shopper level. The AI approach to forecasting leverages data sets across retailers, channels and insights about customer engagement, brand preference, price sensitivity and lifestyle choices. It marries histori- cal data, such as sales from past promotions, with granular forward-looking elasticity models that are far more effective at predicting lift than traditional elasticity models. AI enables exponentially more on-target forecasting, thanks to its deep learning ca- pabilities. In fact, early studies suggest AI forecasts reduce traditional statistical forecasting error by 25 to 50 percent2.
More precise forecasts and predictive insights about lift can be combined with cost-benefit trade-off models to radically improve the ef- fectiveness of trade tactics and drive profitable growth. CPGs can reap many benefits both ex- ternally and internally such as improved in-stock rates, more effective targeted promotions— which can yield
stronger partnerships with retailers to more efficient manufacturing schedules. An AI-enabled forecast allows CPG companies to create a defensible plan for various retailers during negotiation sessions.
The ability to predict what will happen and, more importantly, execute accordingly generates a big boost to performance. Predictive models fuel smarter, more accurate planning, which leads to more effective trade spend and ultimately a greater ROI on every dollar spent. AI adds a forward-looking spin to consumer insights that enables manufacturers to shape demand rather than only respond to it.
Early adopters of AI promotion planning tools are already seeing the payoff. Recently, two large grocery retailers tested the capability against their existing forecasting models. Both found that the promotion planning AI tool generated significantly better predictions than their
current methodologies did, generally predicting promotional sales 4 times more accurately3.
AI technologies such as machine learning for image recognition and augmented reality are being leveraged to improve in-store execution.
Take implementation and compliance, key concerns for CPG companies and retail partners alike. CPGs need confidence that retail partners are meeting agreed-upon distribution, pricing, shelving and merchandising metrics. Retailers need to quickly identify and correct compliance issues, including relevant assortment, right position on shelf, correct pricing and POS, as quickly as possible across their store estate.
Traditionally, CPG field teams have carried out manual audits, which given most have only 30 minutes per store, took up the majority of their time. It also led to an inaccurate, costly and time-consuming process, leading to frustration between CPGs and retailers. A number of CPGs then looked to image recognition to make data
capture more efficient. Early technologies used picture capture, and while this was a step forward in efficiency, the level of item recognition was very inconsistent and often cost prohibitive.
By incorporating AI and leveraging video streaming image recognition into their processes, retailers and CPGs can obtain a comprehensive understanding of compliance issues, from the warehouse to the shelf to the shopping cart in real time. Retailers enjoy less risk of human error and reduced pressure on store employees because the automated, user- friendly nature of AI solutions makes tracking and monitoring simple for store associates, while CPGs can evaluate in real time that their trade dollars have been optimized. They know when distribution, pricing, shelving and merchandising strategies are being executed as intended, and exceptions can be identified and corrected rapidly.
Deep insights for innovation, improved forecasts and air-tight execution are just the beginning. AI and machine learning will revolutionize how CPG companies and retailers do business. They enable strong collaboration between partners, smarter and more impactful execution, and greater return on every trade dollar spent.
Watch for emerging AI technologies to further bolster accuracy and security, especially as physical objects increasingly come infused with a built-in digital fingerprint. This concept, known as the Internet of Things (IoT), allows companies to track where a product goes, along with how and by whom it’s used. And just as a human fingerprint can’t be falsified, the digital fingerprint can be made essentially tamper proof, ensuring sound and accurate data.
Blockchain networks are another promising technology for CPGs to leverage. Blockchains are tamper-proof shared ledgers. They record and store rich transactional data that can be integrated into a shared yet secure system. AI, when used with IoT and blockchain technology within digital networks, could completely transform the consumer value chain. When CPGs and retailers collaborate within a blockchain network, not only can they benefit from having shared access to a broader scope of high-quality data in real time, but the network’s machine learning capabilities allow both entities to reap predictive insights that can optimize execution. AI solutions on blockchain networks enhance security and make everyone smarter because their predictive power increases as more data drives more learning. Together, IoT, AI and blockchain networks will create a digital ecosystem that will get smarter as it grows.
Times have been tough for large CPG companies, and the landscape will grow only more competitive in the years ahead. To survive and thrive, CPG companies will need to rethink their entire approach to insights, marketing, trade and execution. They’ll need to break down data silos and shake free of old strategies and solutions, make decisions grounded in more accurate forecasting and invest in more effective trade spend. And they’ll need to shift from a short-term,
reactive stance to one that is proactive, intelligent and forward looking. Therefore, as more and more CPG companies embrace AI, their tools will get smarter—and by extension, their users—resulting in growth and efficiencies in the turbulent marketplace. The future belongs to businesses that marry the promise of big data and AI with the power of human intelligence.
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