Anna helps you to uncover insights that you might otherwise have missed, either because it would be impossible to analyse all factors manually, or simply because you didn’t know where to look!
As you upload data to Anna, she scans your data to find unexpected changes. If found, Anna surfaces insights that may require further investigation in cards. These insights are automatically surfaced in Discover and Missions.
What types of insights does Anna look for?
Anna looks for Anomalies and Outliers.
Anomalies are values that sit outside the expected range. Anna detects anomalies by calculating the expected result (based on historic trends) in comparison to the actual result. This comparison factors in previous trends and considers seasonality. To detect anomalies, we use algorithms such as STL, S-ARIMA, ARIMA, Random forest and PCA.
Anna only searches for anomalies on a monthly basis and will only search for anomalies if you upload more than seven months of data.
Example: Sales for Cosmetics tend to decrease between June and July each year. Between June and July 2019, sales for Cosmetics increased by 6.52%. Anna considers this behaviour to be an anomaly, as the actual result (an increase in sales) differs from the expected result (a decline in sales).
Read more here on the different types of anomalies that Anna can surface out.
Outliers are values that have experienced large growth or decline when compared to their peers for the same period. For example, comparing the growth of different departments within an organisation, or even individual performance in a team. This helps you to benchmark performance so you can spot elements that have experienced unusually high or low growth.
Anna detects outliers by calculating the average range of growth across a segment in your data (e.g. the average growth of sales for all departments in an organisation). Anna considers an outlier to be a value (e.g. a department) that sits outside this average range of growth. We use a number of algorithms to detect outliers, for example, the interquartile range (IQR).
In contrast to anomalies, Anna can detect outliers on as little as two periods of data (2 days, 2 weeks or 2 months). In combination, anomalies and outliers gives you a full view of unexpected changes across different time periods.
Example: In this example, the growth range for all departments was -11% to 50%. Seafood department is considered an outlier because its growth of -33% puts it outside the overall average range.
If you have any questions, please contact us at email@example.com.
Getting Insights From Hyper Anna