Customer attention is overwhelmed. And with ever-increasing avenues for engagement and a constant flood of businesses trying to engage, the way for companies to stand out and drive loyalty and growth is by getting back to the basics of good customer service: personalized experiences. Personalization at scale is tricky, but the companies doing it well are doing it with real-time customer data and then syncing that data to their data warehouses.
However, just as important as having that customer data is how you act on it—and how you keep acting on it. Like your customers, personalization is a living thing. It requires continuous iteration and experimentation to match the ebb and flow of customer interest. Even something as simple as a seasonal sale needs to change and evolve with, well, the seasons.
So how do you deliver personalized experiments effectively with the data in your data warehouse? A warehouse-native tool may seem like the best answer for data teams—but based on the experiences of many organizations, it just doesn’t have the speed to keep up with your customers the way a warehouse-connected platform like can.
Warehouse-native takes longer to start learning
For effective personalization, companies need to test and iterate quickly. Every day that passes carries the risk of losing more customers to a lack of interest—or to a competitor. While warehouse-native experiment solutions excel in data governance and security, the blunt truth is that they’re slow.
That’s not necessarily because of their data processing speeds (some can run at a moderate 15-minutes-per-question pace). The bigger issue is the organizational workflow needed to run warehouse-native experimentation and the bottlenecks inherent in it.
- Managed setup: Warehouse-native experiments are complex and need to have all their key components defined and implemented by data teams. Experiment requests need to be accurate and on point to avoid setup issues.
- Team capacity: The data team can only set up so many experiments at once. They have to shift to a support ticket system, and other teams have to wait in line.
- Testing backlog: Continued demand and ad hoc requests pile up, testing delays get longer, and data team burnout gets worse.
And to iterate, you'd have to go through that process all over again. Add in the higher costs from all that querying, compute, and storage, and you have a solution that doesn’t just reduce the agility of your innovation—it also reduces the agility of your budgets.
Some might think that warehouse-native experimentation is still worth it for the robust control it offers. But it turns out, there’s a faster option that doesn’t sacrifice data governance, security, and compliance.
Warehouse-connected experimentation: combining speed and accuracy
A warehouse-connected experimentation platform like Amplitude uses a hybrid approach to experimentation: It integrates with your data warehouse use and preserve your data, but it’s operated from a separate, self-service interface. That makes it fast and robust.
- Self-service: Because it has a simpler user interface that doesn’t require SQL skills, any team can set up an experiment, and they can do so concurrently. The experimentation bottleneck is gone.
- Guardrails: Purpose-built integration lets data teams still avoid data drift and compliance issues through , , , and capabilities.
- Scaling: More teams are able to run tests, while data teams are able to focus better on high-impact strategic analysis. And since results sync to your warehouse, they’re available to the rest of your organization, from product to marketing to data.
A warehouse-connected solution supercharges your speed of innovation—and your ability to personalize experiences. It’s allowed companies like Evaneos to explore user journeys, formulate hypotheses, and successfully convert their customers in one place.
Why speed matters for personalization: Evaneos
is a tour company whose whole business model is personalization: Their tagline is “Tailor-made tours and travel.” However, their product teams used to struggle to personalize their own in-app experiences. Coordinating experiments and analysis meant juggling SQL queries, disconnected tools, and slow, manual workflows that reduced their confidence and speed with personalization.
With Amplitude, teams no longer need to rely on SQL and data teams to set up and analyze test results. Product managers can launch a test, monitor results in real time, and get alerts when something needs attention, all without waiting on analysts to do the heavy lifting.
When the team noticed that bookings in one region were underperforming, they could instantly start to explore the differences in performance in one platform. They hypothesized that cultural differences were affecting how users responded to the CTA without having to wait 15-20 minutes to answer one question using SQL.
They validated that insight through interviews and A/B tests, then localized the messaging. The result: a 2x increase in clickthrough rate and a 20% lift in conversions—a meaningful win in a high-consideration industry like travel.
The ability to quickly test and personalize wasn’t just helpful—it was essential. Evaneos could move faster, stay relevant to users based on their needs, and drive engagement at scale.
Getting personal faster with experimentation
Personalized experiences communicate that you value your customer and their experience to keep them coming back. It’s a virtuous cycle that drives growth.
Engaging in that cycle is active. It isn’t enough to just collect information about your customer and store it in a data warehouse. You need to question, explore, and refine the experiences you give your customers through experimentation.
Doing that at a speed to match your customers takes a warehouse-connected experiment platform like Amplitude. And fortunately, that speed doesn’t come at the cost of data accuracy, or the higher total cost of ownership with warehouse-native solutions. It’s an advanced technology that lets you get back to the basics of connecting with your customers.