Starbucks Blends the Perfect Coffee Using Data Analytics
Starbucks <- "Data is essential to our coffee brewing process"
From which coffee product you would like to take, how you order your coffee and where you would like to sit as you sip your coffee, data analytics has been the core ingredient fueling the perfect blend of Starbucks’ growth. Data and analytics add the perfect flavour to attracting coffee drinkers to the coffeehouse giant’s 35,000 stores.
The magical beans roasted by the coffeehouse aimed at revolutionizing customer experience are shipped from the world of big data, artificial intelligence and data analytics.
The subtle rebranding from not just a coffeehouse, but a data company is displayed by revolutionary data analytics products and utilization of big data and artificial intelligence in business making decisions and operational efficiency linked to customer experience, sales, marketing initiatives, operations and inventory management.
The article will focus on two core facets:
- Understanding Starbucks’ business model
- Highlight significant big data and artificial intelligence tools impacting Starbucks’ business model.
Starbucks Business Model
Starbucks is an American Multinational Corporation that hosts a chain of coffee outlets and roaster reserves. It is a global brand focusing on being a roaster, marketer and retailer of specialty coffee with operations in approximately 83 markets globally. The operations of the global coffee brand are focused on three core segments: Americas, Europe, Middle East, and Africa (EMEA); and Channel Development. The product portfolio of the corporation entails coffee, hand-crafted beverages and fresh food.
The essential building blocks of Starbucks’ business model are highlighted below
Key Partners
Starbucks’ key partners include suppliers of coffee and equipment manufacturers. Coffee farmers supply coffee beans to Starbucks. Equipment manufacturers provide coffee and tea brewing equipment to be used by the company when preparing different beverages.
Key Activities
The core activities undertaken by the corporation include: production, marketing, research and development, innovation and supply chain management.
Key Resources
A distribution network encompassing a network of farmer support centres ensures the supply of good quality coffee and store partners who serve their product offerings to its target consumers. A stable brand heritage portfolio consisting of product brands such as Starbucks Coffee, Seattle’s Best Coffee, Tazo Tea, and Evolution Fresh form part of the company’s core resources. A skilled workforce geared towards customer experience ensures the company achieves its business goals.
Value Propositions
Premium quality products serve as Starbucks’ unfair advantage in the coffee market. The company focuses on delivering quality products at premium prices synced with its creative and appealing ambience derived from consuming coffee in its stores. This enhances positive customer experiences. Convenience is depicted via mobile application shopping options for clients. Cutting-edge innovation is infused in their products and customer experience strategies.
Customer Relationships
Starbucks’ strategy of writing customers’ names on their cups enhances customer experience and brand loyalty. The company’s Gold Card Reward program ensures consumers stick to Starbucks as they get rewards each time they purchase coffee using their mobile application account.
Customer Segment
The company generally targets anyone who loves coffee. Demographically, their core audience entails adults aged 25–44 years and young adults aged 18–24 years.
Channels
The main points of contact between the company and customers are company-owned stores, licensed stores and e-commerce platforms.
Cost Structure
The main cost drivers are production expenses, administration, operation and marketing costs, and occupancy costs.
Revenue Structure
The company makes money via the sale of its brand products. Company-owned stores generate 80% of total sales.
Significant Big Data and Artificial Intelligence Tools Impacting Starbucks’ Business Model
The impact of data analytics and data transformation strategies adopted by Starbucks can be viewed from an insight-based value creation model lens. An insight-based value-creation model is a business value-creation process that transforms data into vital insights influencing positive business results. The process of creating an insight-based value creation model entails generating and collecting data, extracting insights from data and transforming insights into action.
Various big data and data analytics tools and processes adopted by Starbucks aligned to insight-based value creation include:
1. Selecting New Store Locations
As in real estate planning, location is crucial in determining how quickly units sell to attract tenants and homeowners. The same concept applies in the n quick service industry in determining the right location for a new store.
Starbucks utilizes location intelligence (Atlas, a mapping tool and business intelligence platform) provided by a location-analytics company called Esri in determining where new stores should be located. The platform analyzes massive amounts of data linked to integral variables like population, income levels, traffic, competitor presence, and proximity to other Starbucks locations, and recommends a strategic location for a new store.
Impact of Location Intelligence on Real Estate Decisions
- The company saw a 26% surge in revenue from 2016–2019.
- Data intelligence insights derived from location, demographics, buying behaviour, behaviour trends and traffic pattern provide input for forecasting the success and performance of new coffee shops.
- Data insights influence product expansion strategies such as types of products to sell in company-owned grocery stores.
2. Starbucks Rewards and Mobile App
The perfect marriage between Starbucks Rewards Program and Mobile App can be described as the north star of Starbucks. Marketing, financial and operational excellence all continuously derive insights from the duo.
Starbucks’ loyalty rewards program enables customers to accumulate points (stars) for each purchase they make via mobile application. Customers can later use accumulated points to purchase coffee at discounts and other specific products.
The mobile application provides personalized experiences and promotions to customers based on purchase history, community preferences, local store inventory, location, and weather patterns. Personalized recommendations are achieved by merging the physical interactions of customers with digital interactions via a digital flywheel program, a cloud-based artificial intelligence engine that’s able to recommend food and drink items to customers.
Impact of Starbucks Reward Program and Mobile App
· Personalized experiences result in an increase in purchases, number of mobile app users and brand equity; positive metrics driving Starbucks growth
· As of 2020, the rewards program had 19.3 million members generating about 50% of the total revenue earned by the company.
- Personalized promotions and marketing initiatives.
- The mobile app has more than 17 million active users.
- Data collection centres. The volume of data collected from members of the Reward Program and Mobile App provides enough fodder for optimizing Starbucks’ competitive advantage.
3. Updating and Optimizing the Menu
Dynamic menus are launched based on insights of personalized experiences derived from online channels, rewards program members and in-store experiences. Special limited-menu offerings are influenced by data insights. Changes in the weather, and events happening at a specific time influence offering of certain products. Case in point, when Memphis, Tennessee, was experiencing a heat wave, Starbucks launched a local Frappuccino promotion to entice customers.
Impact of Menu Updates
- Upselling popular products thus increasing sales margins
- Acquiring a new customer base by offering new types of products to new clients.
Just like Starbucks, companies seeking to increase the stability of their unfair advantage should automate important processes. Personalization of customer experiences is a never-ending cycle being leveraged by established firms. Exploiting big data analytics helps strengthen a venture’s core competitive advantages.
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