Machine Learning Times
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Three Best Practices for Unilever’s Global Analytics Initiatives



This article from Morgan Vawter, Global Vice President of Data & Analytics at Unilever, serves as the foreword to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, by Eric Siegel.

Morgan will present a keynote at Machine Learning Week, June 4-7, 2024 in Phoenix, AZ – where registrants will receive a free copy of The AI Playbook.

There’s almost no business outcome that machine learning cannot help you improve today. From delivering a best-in- class customer and consumer experience to fueling productivity, increasing safety, optimizing operations, and improving your employee experience, ML can raise the bar on the metrics that matter across all. Its practical deployment represents the forefront of human progress: improving operations with science. But where do you start, and how do you ensure what you do start doesn’t end up in the dustbin?

Over the course of my career I’ve consulted with over thirty Fortune Global 500 companies on data and analytics, and led global data and analytics organizations at Caterpillar and Unilever. I’ve seen the highs and the lows, including analytics programs that generate tremendous value and competitive advantage, and those that never seem to leave the starting gate. In my experience, those companies or teams that struggle to embed analytics at scale typically suffer not because of imperfect analytics execution or ML models, but rather because of a gap in the other factors required for success.

As one example, while consulting, I worked with an analytics team at one of the world’s largest retailers on a program to improve marketing ROI. The in-house team had already developed an advanced media analytics model. They were flush with data, leveraging hundreds of millions of data points on marketing spend, response, products, stores, and other contributing factors. The team poured hours and hours into perfecting the model and fine-tuning it to highest possible levels of accuracy and then summarizing the output into a list of top insights for action. The day of the big presentation to marketing leadership arrived and the team presented the recommendations to improve ROI by making key changes to offline marketing spend. They looked to top marketing leadership for their reaction, expecting smiles, gratitude, praise, and appreciation. Instead, they were met with a mix of apathy and disbelief. The problem was that the team had missed crucial steps required to fully understand and incorporate stakeholder priorities, decision-making factors, and processes.

Contrast that with an experience I had leading an AI-powered portfolio optimization program at Unilever. Unilever is a global organization. The products are sold in over 25 million stores across 190 countries, with over 2.5 billion people using the products every day. Unilever’s brands include Dove, Knorr, Sunsilk, Hellmann’s, Axe, Ben & Jerry’s, Domestos, Suave, TRESemmé, and Magnum.

We saw an opportunity to make smarter and faster decisions by taking a global, data-driven approach to optimize our portfolio of products and reduce complexity—through a program we would later name Polaris. A sharper portfolio of products ultimately benefits consumers and retailers, optimizes our operations, and drives profitable growth for Unilever’s shareholders. Our team built an AI-powered capability and business process to analyze the entire product portfolio globally and recommend products to delist, grow, fix, and protect. The system leverages analytics to track the execution of those actions and drive accountability across thousands of individuals in the organization. We created and scaled Polaris globally in approximately two years, bringing together the best of machine and human intelligence, which empowered us to make more efficient and effective decisions and grow through simplification.

The path to get there wasn’t easy and there wasn’t a guidebook available to help us at the time. Fortunately for the reader, the steps outlined in Eric Siegel’s book The AI Playbook bring to life crucial best practices we followed in delivering a globally scaled initiative with lasting business impact. These include:

  1. Start with outcomes in mind and focus on delivering value incrementally. We started with a simple question: Could we increase the rate of decision making and execution to simplify the product portfolio—delivering savings while driving growth with our customers? Only after delivering on that scope and establishing that value did we expand to complete product portfolio optimization, including non-consumer facing simplification such as flagging specifications and ingredients to harmonize across products.
  2. Leverage empathy to overcome barriers to change. Consciously or unconsciously, we are all preprogrammed to resist change. To overcome this, the analytics team spent hundreds of hours with other teams across the business to understand how portfolio decisions were being taken currently—including marketing, sales, supply chain, finance, research and development, and retailers. By gaining an understanding of the pain points in the current processes, we were able to bring forward a compelling value proposition for stakeholders across levels and functions.
  3. Prepare the data so that it meets business needs. Only by anticipating early the differences in data availability, due to the global nature of our business, did the team succeed in scaling the capability across geographies. We recognized that we had to adapt to variations of data across markets—some of which were rich with retailer and third-party data illuminating shopper behavior patterns, while others held inconsistent point of sale and shopper information based on the route to market. A versatile data infrastructure and stringent data validation process were key to success.

These experiences have made me acutely aware of the many hurdles that must be overcome to deliver scaled value realization with ML. Innovating the enterprise with ML is revolutionary, and revolutions aren’t easy.

Many senior data leaders come to learn the same lessons, but only after years of experience and failed projects. Then after understanding it themselves, they still struggle to advocate for these success factors with their business counterparts. Without common understanding between business stakeholders and data leaders on the best practices for delivering data and analytics transformations, many projects fail to take off, struggle to scale, or ultimately don’t deliver on the business outcomes.

The industry needs a framework to better leverage ML for business results. The AI Playbook introduces bizML, which brings forward the best practices in a succinct and actionable way. Not only is the book a timely and much needed addition to the industry; it is also powerful in bringing AI down to earth, eschewing the hype, and making it tangible for all readers. This book is the driver’s manual for machine learning—every business and analytics professional should read it.

This article is excerpted from the book, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, with permission from the publisher, MIT Press.

About the Author

Morgan Vawter is the Global Vice President of Data & Analytics at Unilever. Formerly the Chief Analytics Director at Caterpillar and Data Management Practice Lead at Accenture, she has spent her career driving top and bottom line impact through digital, data and analytics, supporting more than 40 Global Fortune 500 companies. She works from a sense of purpose to activate the positive impact of exponential digital technologies, such as big data and AI, on business and our society.

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