Today, we’re proud to announce that you can now use Apteo to statistically analyze your KPIs with our simple and intuitive new analytics components. We’ve added two new tools to help you measure the impact of changes on your metrics:
Our customers have a lot of data, but there’s a huge shortage of data scientists today. Our goal is to make it easy for anyone to leverage their data to make better business decisions, and these new components make it easy for anyone to get a quick answer to whether or not the changes they see in their key metrics are statistically significant or due to random chance.
With our analytics components, you can now connect your data and analyze it in just a few minutes. Both the before/after analyzer and the comparison of averages calculators will help statistically measure the average value of a key metric, but each is designed for a different purpose.
You can easily configure both components so that they analyze your data in its current format. They’ll do the hard work of running statistical calculations and will provide you with results in a graphical, intuitive format.
The before/after analyzer lets you quickly determine if a metric has changed after a particular point in time - either as a specific date or as a relative date that’s stored in a column in your dataset. You can use it whenever you need to determine if a change you’ve implemented resulted in significantly better or worse performance.
We’ve created a public workspace that shows you how this works. In the transactions data source, you’ll see a list of sales amounts and timestamps. In this dataset, we’ve imagined a scenario where a company sends a gift card to users on June 1st, and wants to analyze whether sales increased significantly 1 month after that date. On the homepage, the first component shows you that the average transaction amount was around $89 after June 1st, compared to around $56 before June 1st, and that this increase was statistically significant.
The comparison of averages calculator lets you quickly determine whether the average of two different datasets is statistically significant. In the same example workspace from above, we’ve created a dataset of hypothetical sales data from 2018 and 2019 and used our calculator to analyze whether the increase in sales over the year was statistically significant. The results on the second chart on the page shows that the results are statistically significant.
We’ve got several great statistical and predictive analytics features planned for the future, and would love to understand what’s most useful for you as we continue to build out our platform. If you’ve got an interesting analytics use case that you’d like to see supported in our platform, feel free to contact us, we’d love to hear from you. And if you’d like to stay up-to-date with the latest analytics news and information, feel free to subscribe to our newsletter.