Starbucks is one of the world’s most popular coffee brands. Holding the top spot in its sector for many years in a row, the beloved coffee brand has secured customer loyalty through constant adaptation and innovation. What is driving innovation at American multinational today? Big data and data analytics.
So much so, that some have even suggested that Starbucks has transformed from a coffee business into a data technology company.
They use location, demographic, customer behaviour and consumer trends data to forecast and predict the success and future performance of stores they plan to open in different countries around the world. In doing so, they will quickly find out if they’ve set their sights on opening a store in a location that is likely to be unprofitable.
Similarly, they also use customer data to better engage with the clients; providing personalised offerings based on the consumer’s preferences and buying patterns.
The secret ingredient
Starbucks’ retail transformation story showcases why data analytics is a real game changer for retailers who are looking to stay relevant in an increasingly customer-centric, digital world. And note that we’re not just talking about data. With so many sources of data – from social media and consumer search patterns to online traffic and foot traffic in stores – one can easily become consumed with collecting and storing all of this information.
But if you have all the information in the world and don’t analyse it and draw real, practical insights from it, you won’t achieve retail transformation. “Customer analytics and insights must be focused on being both understandable and actionable,” says Deloitte.
Data science transforming retail
In the age of digital age, data has become its own currency. If retailers want to cash in on the trend they need to:
- Harness the right information.
- Use this data to build prediction models and drive future strategy.
- Leverage the information to better understand the industry and market.
- Evaluate legacy systems to determine if they are effective at supporting data mining.
- Train employees in to understand new processes.
- Build loyalty programmes that speak to and target high value customers.
- Plan and improve production management and supply chain.
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