Data is a vast and nebulous subject that nonetheless can make or break a company. When companies generate and unify good data, they can create actionable insights and optimize processes for a number of teams within the organization. But when data is poor or unused or unusable, the business can miss out on revenue potential, business efficiency (including marketing efficiency) and customer experience.

Data isn’t useful unless available, identifiable and understandable. This is where an AI-driven customer data platform (CDP) comes in. CDPs take in data from a wide variety of sources and use that data to help businesses achieve digital transformation.

What is the business case for a CDP? Depending on where your organization is with digital transformation, it could range from advertising efficiency to personalized customer journey, supply chain optimization and even product development. By putting your data to strategic use, your company can make better decisions, which in turn drives performance and profitability.

Centralize Your Data

Nearly all organizations struggle with integrating data from multiple sources — 83 percent of respondents identified this as a challenge according to a recent study. (1) The ability to centralize data and insights is a key factor in making the business case for CDPs.

While data is frequently generated from multiple sources — both first and third party, on- and offline — data needs to be centralized to be useful and actionable for functions across the organization. Otherwise, companies run the risk of making business decisions based on incomplete pictures of their customers.

Now That the Data Is Centralized, What’s Next?

For marketers, the top use case for a CDP is its ability to unify data from many different sources to provide a single unified customer profile. (2) But how do you make the data work to its maximum potential? Often, implementing a CDP with disparate data sources is a complex and technical process and businesses might not know how to take full advantage of their CDP.

Treasure Data has solved this situation with Treasure Boxes — a set of ready-to-use templates that anticipate the most popular CDP use cases and accelerate the rate of CDP implementation by making it easy for organizations to adopt the more advanced use cases such as Machine Learning-based predictive modeling. By using templates, Treasure Data customers avoid duplicating efforts and can significantly speed up their time-to-value.

Making Data-Driven Decisions Leads To Better Performance

CDPs evolved out of a need for a single, holistic customer view. Original tried-and-true use cases involved omnichannel marketing and personalization for the customer experience. With Machine Learning, CDPs can deliver high-value capabilities such as multi-touch attribution and real-time next best action recommendation. These are just a few examples of what Treasure Boxes are designed to solve.

Learning Opportunities

The ability to personalize campaigns to individuals is evolving. By analyzing consumer buying habits within a CDP, companies can tailor their messaging to the individual in an effort to nudge a sale. If your company already uses a machine learning engine or has a data science team, those teams can still take advantage of Treasure Boxes’ templated solutions to easily integrate existing machine learning and data science toolkits with the Treasure Data CDP.

The Data Deluge Is Coming — and Most Companies Aren’t Prepared

The proliferation of channels customers are engaging with brands means information is coming at companies from every conceivable direction, both on and offline. Research suggests that most companies aren’t prepared for this flood of data. For example, IT and business managers predict that by 2025 their organizations will have nearly 5 times the amount of data than what they currently generate and receive. In that same study, 14 percent were aware of the increase in data volume at their organization but weren’t yet thinking about its implications, while 22 percent weren’t even preparing for the volume increase. (3)

Platforms like Treasure Data’s CDP can handle large volumes of data. For example, one customer was able to unify 200 data sources and 80 billion data records while collecting more than 8 million new transactions daily when they first implemented Treasure Data’s CDP. The ability to handle and unify such large volumes of data can help companies make the connections they need and maximize revenue potential by leveraging their data acumen.


Data is already valuable to businesses and will only become more valuable over time. Companies need the aggregation power and insights a CDP provides to use that data for actionable insights. Without the ability to access and unify disparate data, companies may be missing out on crucial sales and customer interactions.

Knowing how a CDP can best work for your business is also critical. While CDP implementation may be a complex process, vendors like Treasure Data aim to make the process as easy as possible. Their schemaless data ingestion architecture and templated solutions for advanced capabilities means you can more quickly apply common CDP use cases to your data, allowing you to optimize your digital transformation.

Learn how Treasure Data can deliver actionable insights with your customers’ data at

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