A customer data platform example is useful when you can see the customer problem, the data work behind the solution and the result the brand reports. A list of impressive metrics without that context won't tell you what your team needs to build.
These six published Amperity customer stories show different ways to put connected customer data to work. They are examples to investigate, not forecasts of the results another organization will achieve.
Key Takeaways
Start with the business decision each customer needed to improve, rather than treating a CDP as an end in itself.
A shared customer profile can support different jobs, including segmentation, loyalty growth and fan engagement.
Warehouse connections and identity resolution address different parts of the workflow.
Customer-reported outcomes apply to the implementation described; they are not universal platform benchmarks.
Use the examples to define a test using your own data, destinations and success criteria.
Brooks: a shared runner profile for marketing
Brooks Running wanted to understand runners across a fragmented technology stack. Its published customer story describes creating a Customer 360 used for analytics, segmentation and marketing tests.
The story reports a 260% increase in paid-search click-through rate and a 128% increase in return on ad spend. Those are separate reported measures, not an identity-accuracy score.
The evaluation lesson is to follow the profile into a specific audience and test. Ask which customer information changes the audience and how the team measures its response.
Wyndham: loyalty and non-loyalty guest audiences
Wyndham needed a usable view of guests to support direct bookings and loyalty growth. Its customer story describes bringing together profiles for loyalty and non-loyalty guests, making customer insights accessible and supporting more specific audiences.
The story reports that information previously taking weeks to access could be obtained in minutes. That is a customer access-to-insight example, not a guarantee that every source or workflow updates in minutes.
For your evaluation, distinguish time spent obtaining data from time spent defining a segment, delivering it and measuring bookings.
Catalyst Brands: value-based media audiences
The Catalyst Brands story connects fragmented online and offline data with more informed first-party audiences. It reports 5x ROAS using predicted customer lifetime value for segmentation.
The useful mechanism is the relationship between the profile, the prediction and audience selection. A predicted value is an input to a marketing decision, not a promise about an individual customer's future spending.
Ask how your team would compare a value-based audience with its existing approach. Keep eligibility, spend and measurement rules visible when interpreting a result.
New Look: finding omnichannel customer value
New Look's published implementation combines Databricks and Amperity Bridge with identity resolution. The story reports that 71% of its top customers shop across multiple channels and that an Amperity-built audience boosted ROAS by 50%.
Those findings answer different questions: the first describes customer behavior, while the second describes a reported marketing outcome. Neither should be presented as a platform-wide performance expectation.
The evaluation lesson is to inspect how identity changes the view of customer value before deciding what audience or campaign to run.
Virgin Atlantic: connected travel histories
The Virgin Atlantic story describes connecting booking, loyalty and complaints data using Databricks and Amperity. It reports more than two million unified profiles and more than five use cases live in under 90 days.
That is a documented implementation example. It doesn't establish an implementation schedule for a different airline or source environment.
For your own planning, name the source owners, identify the customer relationships the use case needs and define what a usable profile must contain.
Seattle Seahawks: a broader view of fan engagement
The Seattle Seahawks story describes connecting ticketing and other engagement touchpoints into shared fan context. The team reports reaching 10–20% more fans on game day and uncovering 5,000 previously unknown fans.
The useful lesson is that ticketing activity alone wasn't the whole customer relationship. Connecting more touchpoints gave teams a broader basis for decisions.
When evaluating a similar workflow, define what it means to recognize a fan, preserve the underlying evidence and separate profile coverage from engagement outcomes.
Use customer examples to design your own evaluation
Choose the example closest to your business decision, then work backward to the sources, identity relationships, profile fields and destination behavior it requires. Compare that work with your operating capacity.
Our build-versus-buy framework can help clarify ownership. The guide to identity resolution techniques explains matching approaches, while the cost of poor customer data quality helps frame operational rework.
Explore our customer profile capabilities, then ask for a demonstration that follows your own customer data through one complete business workflow.
