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Industries are leveraging extensive data services for competitive advantage, using high-volume, fast-incoming, and exceedingly diverse data to keep connected with consumers. Data provides insights enterprises need to stay relevant, but the vast volumes of it presents challenges around storage requirements, collection compliance, and time investments.
This article presents five use cases for enterprises to take advantage of big data to gain insights into customer behavior and improve their product and services offerings.
1. Procter & Gamble (P&G)
P&G has been in business around the world for almost two centuries. One reason it keeps its doors open is by making timeless products—the company uses big data to remain at the forefront of consumer demand by knowing what to keep on store shelves. P&G collaborated with Microsoft and its Azure suite to maximize the power of artificial intelligence (AI) to provide these much-needed insights.
Chief Data and Analytics Officer Guy Peri said the company’s multi-cloud big data solution is “an elaborately planned disruption” that starts and stops its business strategies as real-time data funnels into its databases. This data gives P&G the ability to be proactive as it identifies trends and immediately reacts when unexpected changes occur.
“In order for P&G to understand and best serve consumers,” Peri said, “we need to drive a data-enabled culture and operationalize algorithms into every major business decision.”
When companies get as large as P&G, data fragmentation is inevitable. One company branch has a separate data set, for example, while another across the globe has exclusive information that would benefit the greater whole. P&G’s big data strategy focused on connecting these silos to enhance its data analytics plan multifold.
Combining data stores was step one. Then the company chose three challenges for data to address:
- Create a more resilient supply chain
- Improve retail execution
- Design and deliver products with improved packaging
Big data can show how well suppliers perform, enhancing the strength of B2B relationships and encouraging supply chain diversification if specific products or parts receive frequent delays. Seeing what P&G can and cannot obtain from third-party suppliers can inform the company’s retail execution knowledge and determine, for example, whether it’s worth finding a supplier for a product that doesn’t sell well.
Finally, constant data flow allows P&G to constantly innovate existing products, whether for improving its cost-effectiveness from an internal point-of-view or for quality on the customer side.
Industry: Consumer goods
Big data product: Azure Synapse Analytics
Outcomes:
- Connect segmented data silos
- Respond in real-time to product adjustments and improvements
- Keep top-selling products on shelves
2. Starbucks
With 90 million coffee purchases and 25,000 global storefronts connected to apps and store tech, Starbucks has a wealth of information to guide marketing and sales decisions. The company’s primary resource is its proprietary app, which connects customers to the reward program and catalogs every purchase and store visit. By learning, for example, how frequently customers make to-go orders ahead of time via the app rather than making impromptu drive-through purchases provides valuable insights about customer preferences and behavior.
Starbucks is able to gain these insights because of its innovative Digital Flywheel strategy, which melds digital and in-person customer experiences to improve personalization, ordering, rewards, and purchasing.
As the app collects customer data, AI and big data combine to provide special offers for customers based on their favorite treats. Coupons and discounts relevant to their buying experience means they’re more likely to make subsequent purchases. By identifying trends—similar behaviors among millions of customers—Starbucks can also make decisions about which menu items are working and which are not.
For the company to continue to grow and change, it needs to keep customers interested in trying new things. The Starbucks app will recommend related drinks based on customer tastes, much as the data-driven Netflix does with subscribers’ viewing choices, expanding their interest in a broader array of products. These suggestions are tailored based on location-specific availability and are smart enough to recommend hot drinks on colder days by connecting with local weather services. The deeper the commitment to a wider variety of menu items, the more loyal the customer remains.
Looking at the big picture, Starbucks uses the collected personal data to inform campaigns on social media and shift the brand’s goals to initiatives that keep profitability sky-high. As Starbucks’ Chief Strategy Officer, Matt Ryan, explained, “This fundamental modernization of our technology stack will replace legacy rewards and ordering functionality with the new scalable cloud-based platform for rewards and ordering, improved customer data organization, and tighter integration with store-based operating systems, including inventory and production management.”
Industry: Food and beverage
Big data product: Starbucks rewards-connected app
Outcomes:
- Personalized customer rewards offers
- Thorough transaction cataloging
- Streamlined audience research for campaigns and menus
3. Spotify
Music streaming platforms are ubiquitous nowadays, but something sets Spotify apart from the competition—in 2017, the music megagiant acquired AI music company Niland and blockchain company Mediachain to enhance big data strategies.
The company has always embraced big data to keep listeners coming back for more. Spotify has information on every second of a listener’s music taste and habits, and the platform has hyper-specific metadata that separates songs into genres and bundles them into Daily Mixes that continue to expose listeners to new music.
Spotify uses programming languages like Java and Apache alongside big data to make connections between how long users play each song, what devices they play them on, and if they play them from a shuffled playlist or preselected album. This lets the company know how to adjust settings and toggles so users stay listening without interruption within the artists and genres in which they’re most likely to get invested.
Additionally, it helps Spotify promote its small and large music partners and promotions—without substantial data, how would the company know who to market an up-and-coming artist to? It also gives artists a way to create something resembling personal communications with fans. The Spotify Wrapped feature presents each listener with a year-in-review based on the last 12 months of big data, and artists can use this information to offer their top listeners early announcements for tours or discounts on merchandise.
Industry: Music
Big data products: Python and Apache Hadoop, among others
Outcomes:
- Experiences like Spotify Wrapped and Discover Weekly
- Increased fan/artist engagement through personalized communications
- Curated playlist recommendations
4. Marriott
Since the COVID-19 pandemic, the hospitality industry has been experiencing a never-before-seen boom. Isolation made people lust for travel. The best way for companies like Marriott to stay in touch with customers is through big data. There is significant nuance in dictating pricing based on peak times and demand while considering economic situations like inflation. They must maintain processing customer feedback from survey data to make changes in their chains, and must stay in touch with local events to know how to manage expectations given major musical tours or yearly festivals that bring spikes in tourism.
Marriott’s digital services department oversees incoming data to ensure accurate and sensible reservation experiences. How are cancellation trends impacting availability for customers? How fair is dynamic pricing, and will it increase or decrease bookings? What are customer burdens between booking and checking in that cause resistance from subsequent reservations?
The company collects this information from several silos, like booking platforms and apps, but the primary source is the Marriott Bonvoy platform. It uses big data processing to provide members with tailored rewards experiences, and uses AI integration to reactively adjust against competitors to remain the best choice for travelers.
The app allows customers to receive a next-level, contactless hotel experience with such features as:
- Mobile check-in with facial recognition
- Alerts when the room is ready
- Digital keys to prevent lost or demagnetized entry
- Connectivity with in-hotel Amazon Echos
- Customer feedback submissions
- Room service or cleaning requests
Industry: Hospitality
Big data product: Marriott Bonvoy platform
Outcomes:
- Dynamic, predictive room pricing
- Suggestions for tech-integrated lodging experiences
- Increased awareness of consumer booking and cancellation motivations
5. Mint
Mint is one of the most popular personal finance management tools on the market, with its functional, easy-to-learn user interface and a suite of functionalities. Because it offers a money management service that customers use regardless of their spending behaviors, Mint does not use big data to determine customer spending habits.
Instead, the fast-growing company analyzes spending categories on customer-input and bank-derived data to educate customers rather than to inform its internal business decisions. Mint’s data analysis processes can also detect issues like fraud, adding to the value of the services it provides to customers.
With 13 million customer accounts and billions of transactions, Mint has created an incredible picture of the world’s financial wellness about economic trends. It’s plain to see how these add up—Mint uses big data to help customers see snapshots of their money progress and net worth, but as a byproduct, it became a collage of spending trends that reflect modern history, giving the company information about debt balances, business employee payment records, financial goals, investment portfolios, spending categories, and bank statements.
This led Mint to implement the large language model (LLM) AI-powered operating system GenOS. GenOS has a few primary components that all circle, generating fast, accurate financial assistance for everything from taxes to budgeting while keeping data safe. The power behind generative AI gives Mint a distinctive perspective. Instead of providing advice based on historical user data, the company can deliver fresh, attentive responses based on the specific customer.
Industry: Fintech
Big data product: Intuit GenOS
Outcomes:
- Snapshots of national spending behavior
- Data-based financial advice
- Actionable customer insights
Business Intelligence (BI) Tools to Help With Big Data
Big data is its own industry with a hand in nearly every other industry. For companies to make products informed by big data, they need BI tools to drive digital transformation and data discovery. Ideally, BI tools integrate with open-source frameworks like Hadoop, Apache’s collection of open-source software utilities that makes it possible to use a network of computers to process massive amounts of data. Here are a few of the most popular BI tools to help with big data.
Datameer
Datameer models data in its user-friendly interface, merging data sets, analytics, and other company system assets into a single dashboard. The appeal of Datameer is its scalability and interactivity, making it easy to train employees even without coding or analytics experience.
QlikSense
QlikSense has a unique touchscreen interface that works well for companies with users who work on the go. Its AI-powered search and conversational analytics feature is specific to Qlik and gives companies a new way to communicate with their databases to reveal even more insights than if they had just performed regular oversight.
Zoho Analytics
Zoho Analytics provides visualizations while syncing to other connected devices and easily integrated programs. Syncing is a crucial aspect of these programs, especially when data silos remain fragmented. It collects information to form cohesive reports and provides it all in one crisp dashboard.
Bottom Line: Big Data in Practice
Companies that use big data prove they want to remain top-ranking. Because they rely on technologies, they make more personalized customer experiences and more accurate decisions on where to take their companies because the data tells them the best road.
Whether to explain national purchasing decisions or provide novel song recommendations, companies do everything in big data’s power to keep consumers engaged in an information-loaded world. If they succeed, they will see their highest revenue in history while streamlining and bolstering their business structure.
Read next: Top 23 Big Data Companies
