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Past experience helps fuel current success

To truly understand what users would find most valuable, we called upon previous development experience. We analyzed how our previous product was used and left only the features that were utilized by 80% of the users. These features include: 
  • No-code data pipeline
  • Verticalized analytical experience
  • Data pipeline as API
  • Sync data across business systems
  • Discover data issues (before your CFO does)
  • Flexible and secure way to share data with partners
These features/products vary widely. They address different pains and have different users. We don’t want to guess which of the features is most important to users.

Patterns in usage


Four years of operation demonstrated the patterns in product usage, and some unexpected key patterns were uncovered during that time. We believed that automation of marketing operations would be the main usage pattern. In fact, it turned out that the key pattern was the usage of our program as a reliable source of truth across organization.

Let’s discuss all components of this usage pattern.
  • Reliability. Our clients told us about significant issues with supporting data integrity and accuracy.
  • Source of truth. Clients’ data is distributed among various SaaS products. For example, ad campaign data is in Facebook, visitor data is in Google Analytics, and payment data is in Stripe. acquired.io allows all of this data to be compiled in one common area, which acts as the common source of truth across the organization. 
  • Multi-function and multi-department. Our clients related how different company departments collect data in their own way and calculate metrics in their own way. This means the figures in reports do not agree, which leads to never-ending reconciliation and conflicts.

In this usage pattern, our program showed both positive and negative factors. 

Most important needs addressed


The most important need that our previous product was able to address for its clients is was to glue together data from numerous sources, so that it became possible to calculate ad campaign ROI at the most granular level.

The program combined data for costs and spends with forecasts by clients’ data scientists and delivered actionable figures.

By accomplishing these previously unmet needs, our previous product was able to bring success to our users. We used what we learned in this application to build an even more robust and unique solution with Conduit.