Big data changing the coffee industry

How Big Data Has Changed the Coffee Industry

Big data is everywhere. From the finance sector to Hollywood, big data has made its mark in pretty much every industry. Today, big data even plays a role in an oft-overlooked part of your daily routine: that familiar morning cup of joe.

That’s right, big data is being used in the coffee industry and is revolutionizing the way coffee is produced and consumed.

The Case of Starbucks

Starbucks has over 25,000 stores across the world. With this volume, the brand has access to large amounts of data through its myriad customers. This data helps Starbucks make integral decisions, personalize marketing, and gauge which new products might succeed.

Big Data for Mapping Out New Locations

For instance, Starbucks uses Atlas, a mapping and business intelligence tool developed by Esri to determine where new stores should be located, an important factor when it comes to retail.

An article in Forbes states that this tool, “evaluates massive amounts of data, such as proximity to other Starbucks locations, demographics, traffic patterns and more, before recommending a new store location.” The tool is even able to predict any impact the new store might have on existing Starbucks stores in neighboring areas.

Big Data for Product Expansion

Secondly, Starbucks uses big data to decide which of its products should be available for purchase at grocery stores. Starbucks first collected data on how customers like their drinks based on in-store sales. This data was paired with intelligence about at-home coffee preferences.

Based on this, Starbucks was able to create its grocery store product lines. This is not a new phenomenon — Maryville University states that it is common for online retailers to use predictive sales analytics to drive consumer interest. But Starbucks is one of the first to integrate big data into coffee product expansion.

Consider the example of the widely available Starbucks K-Cups. Data insights showed that 43% of tea-drinking customers avoid sugar when buying tea. Similarly, 25% of consumers don't add milk to their iced coffee when drinking it at home.

This data was used to create two new unsweetened iced tea K-Cups, as well as an unsweetened and sweetened black iced coffee without milk or other flavorings.

“It’s a way for 'Starbucks' to expand a brand that’s already owning retail and bring it into consumer’s homes. So, they can almost double consumption because consumers are going to want to drink it when they go to the actual Starbucks locations and now they have the freedom to drink it at home.”

- Tammy Katz, CEO, and founder of Katz Marketing Solutions -

This sort of customization in products offered at grocery stores is a new trend and only possible thanks to big data. The data suggests that Starbucks does phenomenally well with its fall-inspired pumpkin spice-flavored products, so certain pumpkin spice products are now offered at grocery stores too.

These include: 

  • Pumpkin Spice Cafe Latte K-Cups
  • Instant Pumpkin Spice Latte Packets
  • Bottled Pumpkin Spice Frappuccinos
  • Bottled Iced Espresso with Pumpkin Spice Flavoring

The power of big data not only allows Starbucks to provide their customers with their favorite products at home, but it also offers ways to get customers to avoid other coffee brands. It’s extremely smart marketing, and the data-driven approach to production expansion is a win-win for both consumers and the brand.

But Is It Enough?

Starbucks sells about 4 billion cups of coffee annually. It stands to reason that these sales would produce a vast amount of data. Starbucks’ mobile app itself has more than 17 million users, and the reward program has about 13 million active users. These users alone create a gigantic amount of data that can be leveraged in various ways.

In fact, critics argue that Starbucks isn't doing nearly enough with the big data it has. So what more can be done with the overwhelming amounts of big data in the coffee industry? For one, it can be used to better coffee supply chain management all over the world.

Supply chain management is an integral part of any industry and is particularly important for coffee businesses. The journey from bean to cup is actually more complex than many give it credit for.

In fact, the coffee supply chain usually involves at least seven steps:

  • Growing
  • Harvesting
  • Hulling
  • Drying and Packing
  • Bulking
  • Blending
  • Roasting

This process is further extended by various intermediate steps involving exporters, global transporters, and retailers.

Storage and transportation are clearly important aspects of the coffee supply chain. The journey of coffee represents a vast interconnected global network, further highlighting the need for effective supply chain management. Improved supply chain management results in reduced cost effects, quality assurance, and higher efficiency — all three being vital components to facilitate the end product: a great cup of coffee.

Final Thoughts

Today, big data provides ways to better the coffee journey. When it comes to supply chains, big data is less about predicting how consumers will act; rather, it focuses on tracking how inventory moves and measuring supply and demand.

It is also used to improve production and delivery time, as well as identify problems in the supply chain that intermediaries aren’t always aware of. For the coffee journey, these insights can be revolutionary. Considering that coffee is moved across the globe, big data insights can provide effective solutions.

While Starbucks has started using big data in effective ways, there is still room for more improvements. From streamlining supply chain operations to personalizing the coffee experience, big data makes way for endless possibilities. Being a large well-established chain, Starbucks does have certain advantages when it comes to collecting and analyzing data.

However, smaller coffee shops can also benefit from the use of big data in their day-to-day operations. Emulating Starbucks’ data-driven approach is a good starting point for smaller coffee shops looking to leverage big data for their own success.

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