Less Download, More Action.
CHALLENGE
Maars Drinkware was spending 35 hours/week just acquiring and preparing Amazon data, even after investing thousands in custom scripts. Their time-consuming process limited the amount of data they could manage every week and the scope and accuracy of their forecasts.
SOLUTION
By switching to Reason Automation, they saved over $60,000/year on data processing while eliminating manual downloads. Combining DTC sales data from their website with daily ASIN-level Amazon data, Maars’ new forecasts improved manufacturer order accuracy. Within twelve months, Maars’ Amazon in-stock rates improved from 77% to 95%.
How Maars Drinkware saved $60K/year with a $5K/year data investment
then improved in-stock rates from 77% to 95% in 12 months.
ORIGINAL PROCESS
WITH REASON
Analysts reclaimed:
— 15 hours/week on downloads
— 20 hours/week on database maintenance
From 77% to 95% in-stock.
What will analysts do, freed from manual downloads? At Maars, the speed and granularity of Reason’s data helped them build better forecasting and ordering systems.
When your container lead time is 6+ months, demand forecast accuracy is critical. By combining their direct-to-customer sales data with daily ASIN-level orders from Amazon, Maars’ new forecasts drove a sustained, nearly 20% in-stock improvement within 12 months.
MAARS OUT-OF-STOCK IMPROVEMENT
Using Reason’s granular order and sales data, Maars analysts built better forecasting models with their reclaimed time.
“We have specific reports we want to build and ways we want to review the data.”
— PETER LIEW, MANAGING PARTNER
What was your primary data challenge?
Our processes were very manual. They consisted of downloading and uploading reports from Seller Central and our ERP, so there was always a lag because we had to wait for our data person to finish the reports before they could be accessed. We had someone specifically devoted to that, and that's a big chunk of what they were doing. They were probably spending a good two to three hours daily, so it was probably close to 10-20 hours per week that this person was spending on this data collection task. And that's despite the fact that this person had really good scripts to clean the data.
Another challenge is that this method limited the reports we could look at. Because we approached the process manually, we had to prioritize which data we would grab while leaving lower-priority data by the wayside because we just didn’t have the time to gather it all. This limited the creativity of our analysts.
What solutions did you try before Reason?
We evaluated tools that offered very basic sales reporting that only showed summary views without an easy way to drill down into ASIN-level granularity. This made period-over-period comparisons, and performance over time comparisons difficult to assess.
Furthermore, reporting was locked into their user interfaces. To perform complex analyses such as combining Amazon with our ERP and DTC channels, we needed to export data which incrementally wasn’t that different from working directly out of Seller Central. We also tried Grow.com, which had a lot of the capabilities we were looking for, but they weren't specific to Amazon data. Also, their data was built off of an API that limited the datasets we wanted.
All in all, other software options didn't have the flexibility we were looking for to create our own reporting system. We have specific reports we want to build and ways we want to review the data, and so we needed a provider that gives us that flexibility. Reason Automation is that provider.
How did Reason help solve your problem?
Reason Automation was exactly the solution we were looking for. They helped our company in three key ways:
1. They gave us open access to our data.
This is important because it allowed us to feed the data into tools we were using, such as Microsoft Excel. This also allowed us to connect and combine multiple tables of data into reports, giving us control over how we managed our business in a way that other platforms couldn't.
2. They helped us free up labor for more important tasks.
Reason’s solution replaced all of the manual processes we were using. We were spending 10-20 hours of valuable work time on maintaining this system, and we were able to redirect that labor to growing our business and solving more difficult data problems.
3. They saved us thousands of dollars.
We were spending lots of money on writing scripts, setting up databases, and maintaining the whole system. Reason’s solution did all of that work for us at a monthly price that was far lower than we were spending to do it all ourselves.
