Are you a Starbucks customer? If so, the coffee chain is analyzing your purchases to create a personalized experience for you – and to get you to spend more money. To do this, they have created what they call the digital flywheel program which analyses 900 million weekly transactions, taking into account customer purchases, store locations, meteorological data, inventory data, and more. The coffee giant is leveraging this approach to predict and drive sales.

I don’t like the term AI, but they are using next level stuff here to micro target you with personalized offers based on your preferences and to get you to engage more closely with the brand. Starbucks has cracked the code here when it comes to integrating data analytics and planning.

They are successful not only because of the data and technology they have; they’re successful because of their people and their processes. I talked to Brian Nagy, Senior Demand Planning Manager at Starbucks, who is driving next level planning at the coffee chain and is at the forefront of their analytics and planning efforts. We talked about AI, demand sensing, demand shaping, and how they overcome the same planning challenges we all face. Here are the highlights of that conversation.

How COVID has caused fundamental shifts in consumer behavior

“One of the things we are looking at is the impact of COVID in terms of demographic change. We’ve seen massive population shifts in the last 3 three years – there’s been a mass movement of people leaving places like New York and moving to Florida and people from California moving to Texas. We’re trying to get ahead of that and make sure that our footprint’s there and getting ahead of our competitors. Having that information about demographic shifts is hugely powerful.”


On scenario planning for strategic planning/budgeting

We prioritize end-to-end capabilities and being able to assess all those outcomes, what we would call either strategic planning or budgeting. Companies tend to do this manually once a year, looking holistically at their business figuring out what their strategic direction looks like. We’re doing it with a lot of directional input and projecting trends forward. Those trends may or may not make sense but we need to know what is actually driving those plans. So being able to integrate some of this information like pricing scenarios and internal and external data to look at risks and opportunities is important.

“If you have a revenue or margin target you can input that and theoretically find the different paths of getting there within your mix, pricing, and customer base, and with regional approaches and different promotional strategies. It’s that capability to really dial in on how to optimize the business and get everyone understanding the risks and opportunities. That’s what S&OP and IBP is all about so it’s really just about getting a tool to get us there.

On the planning tools of the future

“What comes to my mind is an 80s stereo with a thousand different knobs. We want most of this to be AI and machine learning facilitated and with the capability to play with those dials and levers, whether it’s demographic data, different pricing alternatives, external data. We want to look at different things that can impact your business whether that be marketing strategies, promotional strategies, what you bring to the table from an innovation standpoint and being able to run those scenarios seamlessly and quickly.

“Obviously, this all starts with the demand plan but then it needs to go the whole way through the stream of the supply chain so we can think about things like warehousing strategies, ocean freight availability and costs etc. to the point where you have really robust contingencies in place.

“We’ve seen ocean freights just go through the roof with COVID – in a case like that what kind of scenarios have you thought about as a business? We need these things in place to steer a different direction if required. Planning tools should help facilitate those types of things just by asking “What if we go this route? What if we produce domestically versus importing?” That’s really the value of integrating a true end-to-end type capability; being able to assess the entire way through and make decisions as a group.”

On Starbucks overcoming the continuous challenges of planning

“I have been a manager for eight years now and the same principles always seem to work as far as having really robust exception management, having the right tools to understand the business, and getting the right data. 

“Sometimes you gotta be scrappy and pull it together but being clever and thinking of different ways to problem solve is important. Getting away from shipment data is something that’s been necessary because shipment history for most businesses over the last three years has been relatively worthless as an input. So just trying to come up with creative ways, looking more at POS data so we can get real-time signals of where customers are going where they’re headed.

“But largely it’s the same principles of forecasting that have always worked: the right exceptions, having the right training, and getting the team up to speed and just having Demand Planners know what to do and when they need to do it. Then the rest kind of takes care of itself.

 “The only other thing I’d say is being more vocal in the business and calling these trends out as we’re seeing them. I’ve seen demand planning emerge in a lot of different business settings since COVID. It’s been an unfortunate event for the world obviously but I think we’ve seen a lot of benefit on the demand planning side as more business leaders have recognized the value of it and are more willing to listen to what we have to say.”

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Post-COVID, brand royalty has dissolved, meaning that companies must work harder to retain them and that requires personalized experiences. If you’re not talking directly to the consumer the way they want to be interacted with and on the right platforms, you may lose them. Underlying this is predictive analytics for demand sensing and demand shaping which on the one hand helps us understand behaviours and on the other allows us to micro target customers in ways that get them in store and maximize spend.

 It’s not just about coffee or retail either, demand sensing and shaping applies to all industries. For further information on this, there is a chapter about Starbucks’ demand sending and shaping approach in my book Predictive Analytics For Business Forecasting & Planning.