With Monda, providers can easily build a data catalog, publish a public storefront, integrate with marketplaces, and manage demand - all from one place.
But while our mission was to make it easy for providers to share and monetize data, we discovered that the very first step, publishing their first product.
That delay caused longer wait times for leads, lower satisfaction scores (NPS -12), and reduced renewal rates. It also directly impacted our north star metric: GMV generated through the platform.
Alex, CEO at Alesco Data
They juggled product reviews, website checks, and setup calls - walking providers through every step and checking their progress.
What should’ve been a quick, intuitive process turned into a full-day grind.
A packed CSM calendar just to get one product live
Hotjar heatmaps, screen recordings, and usability tests showed users repeating actions and missing key information hidden in the interface.
Layout and usability issues increased the time to complete simple tasks, in some cases by more than double the expected duration.
Interviews revealed they didn’t understand which criteria drive product success or improve ranking. Confused by terminology and unclear steps, they struggled to create high-quality, SEO-optimized products independently.
Viktor, CEO at Success.ai
*Using the gathered information, I began solving the problem*
What if the interface could teach, guide, and optimize, instead of merely collecting inputs?
I focused on automating recurring steps and translating them into product features, empowering providers to create products independently and reducing the time spent scheduling and waiting for CSM calls.
Manual tasks that can be automated
Including the category, geographic coverage, and a unique selling point in product titles is essential for SEO and conversion. Previously, CSMs spent significant time reviewing and editing titles.
I proposed a rule-based solution that automatically generates a suggested title, using information already provided by users:
CSMs used to spend hours reviewing each provider’s website to suggest data categories.
I introduced an automated solution that analyzes uploaded data samples and recommends relevant categories from our taxonomy.
Combined with automation, this approach empowers them to create optimized listings, without depending on CSM support.
I added a sidebar explaining the purpose and impact of each field, with links to guides and the help center. This helps providers understand why the information matters while keeping guidance within their workflow.
Many providers struggled with terminology. To make the experience smoother, I added short, clear subheadings under each input explaining what the field means and what best practices to follow.
I replaced the multi-step wizard with a single-page form. Providers frequently returned to edit products, and jumping between tabs was time-consuming.
Usability data showed that 33% of providers selected only the USA as coverage, and another 33% selected all countries. To simplify this, I added pre-configured options, reducing manual effort and speeding up the process.
Previously, providers had to create a data dictionary twice - once for the data sample and again for the product. I automated this step, generating the data dictionary directly from uploaded samples.
*Shipping the feature to production*
By rethinking the experience from the provider’s perspective, we turned a frustrating checklist into a guided workflow that teaches, suggests, and rewards progress.
14 → 3 days
4 hours → 15 mins
+34
+5%
Our CSMs spent nearly four hours per provider manually reviewing datasets, suggesting categories, and validating product titles. Automating these steps didn’t replace people - it scaled their expertise, letting them focus on higher-value work and making every interaction more consistent and human.
Education works best when embedded where decisions are made, not hidden behind tooltips or external links. By adding contextual guidance, showing the impact of each step, and being transparent, we turned the product itself into a coach that teaches as users work.
Some tasks, like category recommendations or title suggestions, can use simple rules instead of complex AI. This lightweight approach improves UX while reducing technical overhead and third-party dependencies.
