AI Has Turned Lumbering Consumer Goods Giants Into Entrepreneurs.
But Start-Ups Can Still Win.
By Nik Larsen
President of The Stable
Every day, entrepreneurs are chomping at the bit to start companies that break new ground and take it to the Big Guys. After all, entrepreneurs, by sheer definition, see things others don’t and then seize the opportunity. However, before you cash in the 401K and start pitching to angel investors or private equity, realize that the playing field has changed.
The days of feisty start-ups catching the corporate giants napping is being challenged by the corporate giants themselves, thanks in large part to artificial intelligence (AI). This is especially true in consumer goods, which historically aren’t born from a technology base. But don’t despair. Savvy start-ups and early-stage companies can still compete and win. If they act like the very companies they’re trying to sneak up on.
Ah, What’s Artificial Intelligence Again?
Artificial Intelligence (AI) has its modern roots in the 1940s with the invention of a programmable digital computer, which led to the formalization of AI as an academic field of study at Dartmouth in 1956. In short, AI is the ability to replicate and simulate human intelligence in machines in a fraction of the time.
It’s often confused with machine learning and deep learning, the building blocks of AI. Venture capitalist Frank Chen summarizes the difference among the three as, Machine learning supplies data to a computer and uses statistical protocols to learn how to progressively get better at achieving tasks. Deep learning, a type of machine learning, runs inputs through a neural-network architecture to dig ‘deep’ into its learning to make connections and weigh input for best results.
“Artificial Intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machines learning techniques.” — Frank Chen
AI systems ingest data, adapt and react to that data in their environment, project or predict multiple steps into the future, and then continue to learn. The speed of AI is the difference between one doctor making a diagnosis every 10 minutes compared to one million diagnoses every 10 minutes.
AI is typically divided into two broad categories:
1. Narrow AI (‘Weak’ AI) — Focuses on performing single tasks extremely well but under more constraints and limitations that basic human intelligence. Examples include:
• Google Search,
• IBM Watson winning Jeopardy
• Self-driving cars
2. Artificial General Intelligence (AGI, ‘Strong’ AI) — A machine with general, human intelligence to solve any problem. AGI doesn’t currently exist. It’s the Holy Grail of AI and the stuff of late-night sci-fi movies with robots over-running and controlling humanity (think Westworld). Theoretically, AGI could perform any task a human could and a lot a human couldn’t. AGI would combine human-like flexible thinking and reasoning with instant recall with nanosecond number crunching.
AI Creates A New Paradigm
Successful entrepreneurs have typically been the ones to beat large companies to the punch to disrupt categories across a range of business fronts, including…
• Fresh, new niche products;
• Disruptive distribution methodologies;
• Innovative pricing strategies; and/or
• Creating unique marketing techniques
However, thanks in large part to Big Data and artificial intelligence (‘Narrow’ AI), a new paradigm has arisen that compresses virtually every element of the go-to-market process, especially trend analysis, product development and testing, and marketing launch.
Not only does AI compress the process, but it also helps short-circuit layers of bureaucracy inherent in large corporations that can slow down decisions and water down innovation. Disruptive large company is no longer an oxymoron.
As a testament to this paradigm shift, many once-lumbering CPG giants, such as Conagra Brands, McCormick, and Nestlé, are using AI to anticipate, not just keep up with, changing consumer tastes in a fraction of the time it used to take.
Conagra used AI-enabled platforms to build and analyze data from social media channels, quick-serve restaurants, and retail food purchases to identify trends to develop new products and line extensions. This led to the identification of frozen food ‘Flexitarains’ — Millennials with mostly plant-based diets who also occasionally eat meat. The result? Healthy Choice Power Bowls (grain-free, low-carb meals with meat, vegetables, and riced cauliflower) were launched in June 2019. Thanks to AI, Conagra went from recognizing a trend, to product development, to launch in a matter of months vs. years.
Power Bowls have been so successful that Conagra has expanded the line to include wraps and breakfast bowls, and thus turning a niche product into an entire category, something every entrepreneur strives for.
Says Conagra CIO, Mindy Simon, “We’ve reduced the time pretty dramatically in terms of being
able to see the behaviors in the market and being able to execute on them.”
Similarly, 130 year-old spice maker McCormick partnered with IBM to use AI to analyze 40 years of data on consumers, taste palates and product attributes to use cumin to enhance its pizza flavoring spice. Not to be outdone, food-giant Nestlé is combining DNA testing and meal analysis to collect consumer data on diet and health to help develop and launch new products under their ‘Wellness Ambassador’ program in Japan.
And in the personal transport business, Lyft is using AI to ascertain bike-sharing travel and usage patterns in order to rebalance their citywide distribution of bikes multiple times a day. This gives Lyft a distinct competitive advantage vs. less sophisticated bike-sharing companies to ensure Lyft bikes are where they’re needed most.
Entrepreneur To-Do List
For the past several years, large corporations have stumbled all over themselves to act small, like an entrepreneur. Mission accomplished. Now in the age of AI, it’s time for entrepreneurs, especially those in non-tech industries like consumer goods, to start acting more like large corporations in order to launch, compete and succeed.
Today, the goal of entrepreneurs should be to compress the go-to-market process with greater certainty by using AI-enabled tools, platforms and data. After all, AI is all about clearly seeing the road ahead and speeding through the course with as few pit stops as possible.
1. Think Like a Technology Company — Most consumer goods companies are born out of a non-technical bent. However, in order to develop winning products and succeed, all companies, regardless of industry, need to have a technology-first mentality. For instance, rather than thinking of it as a consumer goods company with technology bolted on, think of it as a technology company in the consumer goods business.
Even traditional breweries are thinking like tech companies, much to the chagrin of craft brewers. With a Silicon Valley outpost dubbed ‘Beer Garage’, AB InBev is using AI and machine learning to improve the quality and flavor of its existing products, quickly launch emerging beverage styles, improve real-time product stocking and to determine distributor creditworthiness.
Another example in a historically low-tech industry is start-up Better Mortgage, which has turned the mortgage business on its head. Better Mortgage thinks of itself as a technology company in the mortgage business vs. the other way around. They use AI-powered insights and implementation strategies to deliver mortgages to Millennials in a fraction of the time of conventional mortgage companies.
2. Build An Insights-Oriented Technology Team — Most non-technical start-ups have a founder/CEO and CFO. But a technology-first business foundation also requires a CTO, CMO and CIO to work seamlessly as an insights-driven technology team to develop product and go to market faster with greater chance for success. As a start-up or growth-stage company, it may be necessary to use outside resources to bring AI-empowered insights to the company’s forefront. Depending upon the industry, there are numerous AI-enabled platforms to choose from, including stalwarts like IBM, Oracle, and Salesforce, to vertical entities like Tastewise (CPG).
3. Fund Success — Yes, CTOs, CMOs and CIOs don’t come cheap. But this executive trio is fast becoming an imperative for success. Keep this in mind when seeking funding and considering equity partners. The chance for success greatly increases with raising enough money to fund a technology-first, insights-driven management team. Savvy investors know this, and thus are willing to invest in such companies in order to enhance their own return on investment.
4. Don’t Skimp on Marketing — One of the biggest mistakes start-ups make, especially in the consumer goods business, is underfunding marketing. On average, CPG companies spend 24% of their budget on marketing. Thus, consumer goods start-ups also need to invest 20–30% on marketing, especially up front, to build their brand, gain trial and repeat customers, and expand sales. Allow for a sufficient marketing budget in the business plan and funding needs.
5. Explore AI-Empowered Advertising — AI-enabled advertising tools and insights can enhance the likelihood of success and return on ad spend (ROAS) by marrying the right product, to the right audience, with the right message, in the right media, in the right place, at the right time. For instance, Sephora’s Color IQ program uses AI to scan shoppers’ skin to provide a customized foundation and concealer shade recommendations. And Pinterest Lens uses AI to allow users to point their smartphone camera to anything they want to discover, explore or buy. In fact, Pinterest has licensed the technology to Target to give shoppers a direct in-store buying experience.
Even ad creation and media buying use AI. Dynamically generated, customized digital ads are served to individuals in an instant based upon their data footprint, search behavior, and purchase patterns. Programmatic media buying uses AI-enabled algorithms, similar to real-time stock trading, to bid for and win the optimal digital media ad placement for a company’s specific customer. Thus, with the breadth, depth, and complexity of AI digital marketing, it’s important to work with a full-service marketing firm that agnostically deploys a range of viable ad solutions in order to quickly test, learn, and optimize the quickest path to success.
6. Don’t Rule Out Retail — A lot of consumer start-ups automatically default to Direct To Consumer (D2C). The belief is that it’s less expensive and the best way to quickly grow customer count and top-line revenue. But according to the National Retail Federation, online still only accounts for 15% of the $3.7 trillion in U.S. retail spending. Thus, being exclusively on-line may be a mistake vs. adopting an omni-channel approach. This is especially true for everyday consumer products that people are used to buying while shopping for other items at retail.
For instance, Harry’s razors, quip oral care, Native deodorant, and Cora feminine-hygiene products have all had success expanding into retailers such as Target and Wal-Mart to help expand their customer base and become profitable after years of selling exclusively online. AI-empowered data, analytics, and insights can help determine the best channel and shopper-marketing strategies as well. In fact, AI will govern 85% of retail consumer interactions in some form in 2020, according to Gartner.
The days of catching the large, lazy laggards sleeping on the job are gone, thanks in large part to AI. But entrepreneurs and growth-stage companies can still compete and win by asking a question they probably never thought they’d ask…
What would the Big Guys do?
2) Wall Street Journal, ‘CIO Helps Turn Food Trends Into Products’, July 22, 2019, Sara Castellanos.
3) Tinuiti, ’10 Jaw-Dropping Examples of Artificial Intelligence in Retail’, January 29, 2019, Tara Johnson.
4)Builtin, ‘What is Artificial Intelligence and How Does it Work?’
5) Forbes, “The Amazing Ways The Brewers of Budweiser Are Using Artificial Intelligence To Transform The Beer Industry”, September 9, 2019, Bernard Marr.