March 8, 2022

What Are Some Examples of Top Data-Driven Companies?

By Christina Bernard

Everyone’s doing it but does that mean that you should too? In this case, yes! The past two years have taught us that a strong technological business can make or break you in hard times. Companies everywhere began to realize that they did not have the infrastructure for sustaining or growing revenue. Transitioning to a data-driven company takes time and a strategic approach.

In this article, you will find examples of some really good data-driven companies who are getting it done. 

As a data nerd, I’m always excited to find great corporate case studies. Below you will find out how Disney, Starbucks, Adobe, and Zendesk are using data to drive revenue. 

Four Examples of Data-Driven Companies

Becoming more data-driven is a major goal for nearly every business but the reality is, few can obtain this elusive status. Listed below are four examples of companies that are truly data-driven. 


Data Streaming of streaming data has become more important as people binge watch tv shows. Disney+ has developed a data-driven approach to handle the problem of data silos. 

They could get information from different department silos to improve their delivery and recommendations but what they actually did was much better. They were able to increase data accessibility which drove the culture to become more data-driven. 

Through experimentation, they wanted to figure out how to make machine learning a first-class component in their processes. 

Their new approach ensures that everyone within the organization has access to the data they need when they need it. They developed a Streaming Data Platform that decouples the producers and consumers in the ecosystem to achieve data enablement. It’s all built on top of Amazon Kinesis Data Streams. 

They use the information gathered in their machine learning models such as fraud detection, personalization, and continue watching insights, and can have that information be easily accessible to relevant stakeholders in support, customer services, etc.

The idea is Ubiquity, Platform, and Culture.

The idea of ubiquity means that teams expose their dataset and provide a near real-time availability while still providing the appropriate framework for management and access. Information is enriched from other streams to make robust datasets. They built their own schema registry to make this possible, now that’s an investment!


So you want to open a store location? But where? Starbucks knows where because it uses a combination of location and social data to choose the best places to create a new location, based on customer profiles. 

In collaboration with Esri, a geographic information system (GIS) company, they developed an analytics process to help Starbucks best spend its resources. Taking into account factors such as demographic information, traffic flow information, etc. in potential new locations, Starbucks can confidently choose the best locations to expand into. 

Not only does it help with choosing locations but it optimizes for which product would best sell in a given area. Higher-priced items typically appear in more coffee-obsessed areas because customers are more willing to pay a premium. 

This means that all products are not carried at all locations. This is a great cost-saving measure to prevent the supply chain from having to send resources and to prevent stores from having to store resources that won’t be used. But also, pricing adjustments can be used to better optimize price in a particular location. 

Starbucks has been doing this type of analysis since 2014. They still lead the market in coffee shop chains because they stay ahead of the competition. Strategically expanding into the locations allows them to get the location to a left off profitability because the launch is optimized by the demographic it is serving.  


Not to be a complete nerd here but how many companies actively define a customer journey flow and map that against their marketing initiatives? Not many. 

It often makes marketing departments uncomfortable because they can no longer run broad-based campaigns. Looking at Adobe though, you can clearly see how a targeted customer journey has increased their target KPIs.

A customer journey helps companies create a more targeted customer experience for a particular customer profile, often leading to increased customer actions towards the desired purchase. Adobe constructed its customer journey around three core questions:  

  • How should we engage with our customers? 
  • How do we measure that engagement?
  • How do we know if that engagement is successful or not?

Every Data Science project is defined by a similar set of questions:

  • What is the simplest problem we’re trying to solve?
  • How do we measure it?
  • How do we define success?

Without those definitions, Data Science projects fail. After predefining these goals, Adobe sets up the rest of its marketing strategies. The digital transformation of Adobe took time but the benefits can be clearly seen. Adobe’s ‘Try to Buy and Beyond’ was developed out of this process. The retention strategy is embedded into the product and customer analytics that they predefined. 


Anytime I see the word ‘predictive’ I have to stop and listen because I want to know how they are using data. Zendesk’s sales process is a data scientist fantasy. They have standardized  their sales process with a point system to accurately forecast sales within two percent. That is so insane!

In their sales training, they allow for their sales team to score behaviors they consider key to the sales process. For example, there could be a category that says ‘Talk to a Competitor’. The score then could fall between 0-2. 0 means they haven’t talked to a competitor, 1 means they referenced talking to a competitor, and 2 means they mentioned the competitor by name. 

What does this have to do with predicting revenue? Everything!

When data collection isn’t standardized, you can’t build a predictive model that has accuracy. By standardizing inputs to their model (human-derived or not), they are increasing the predictability of revenue. They plug these points into their prediction model and can see what the landscape of their active sales looks like down to the date. 

That’s a data-driven company and they’re growing rapidly because of it. 


Becoming data-driven requires business acumen and expertise in data. Each of these companies took the time to create a strategy to work with their business goals. Talking with the right AI strategy team can get you headed in the right direction. 

Our AI strategy team would be excited to chat with you and can help answer your toughest questions. If you’re interested in becoming more data-driven, phData would love to help!

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