Many of our customers use Heron's capabilities to display back insights and analytics to the businesses that are their users. The key outcomes we help our customers achieve are:
- Allow users to see spend by merchant or by category
- Help users with revenue and runway calculations
- Give users an overview over their cash-based P&L, for example to monitor spend on certain categories
This helps our customers drive retention and engagement for their users.
If you have specific requirements for low latency, we are able to process your transactions in a priority queue to ensure we deliver the latency needed. Please contact your Heron representative to find out more.
Most customers that use Heron for this use case begin by enriching their transaction feed first. To get started, please follow the Enrich Business Bank Data section of the Beautiful Transactions section first
After following the tutorial on how to enrich business bank data and make the transaction feed look beautiful, you should have enriched transaction data for a given company.
- A standard use case would be to show your customer all spend per merchant. To do this, sum all spend for a given
heron_idfor a merchant.
- You can also build comparative statistics and recommendations on top: For example, for a customer with a given revenue, are they spending relatively more or less than others on a payroll provider?
- You can display back categories directly, or use the category labels to calculate items like a company's P&L or historical revenue.
- You may want to use confidences to only show back transactions that are categorised with high accuracy. If the annotator on the category label is
reconciled, your category model has been fine-tuned to have confidence values that map directly to accuracy. This means that for a category label with a confidence of
0.9, we’d expect the label to be accurate 90% of the time.
- You can use these confidences to display back only labels with a certain probability of being correct, depending on your use case.
- If the annotator on the category label is either
predicted, your model is not fine-tuned for reliable confidences. If you still want to use confidences, please contact Heron.