Revenue and Cost Monitoring
Bulletproof your
revenue streams
Anodot continuously monitors your cost and
payment data ecosystems to surface potential issues
and catch missing revenue in real time.
payment data ecosystems to surface potential issues
and catch missing revenue in real time.
/* $( */ hbspt.cta.load(2238292, 'd0b8ce85-f091-45ac-94f6-801e0dd5736c', {"region":"na1"}); Your revenue is made up of a complex
ecosystem
Revenue streams are complex and fragmented, and can be highly susceptible to changes in ad performance, payment gateways and purchase behavior. The dynamic revenue ecosystem is affected by billions of daily events across segments, products, and payment providers. Whether your revenue sources are micro-transactions, subscriptions, partners, or affiliates—acute incidents or chronic glitches can quickly result in massive bleeds to your bottom line.
Internet Business Revenue Ecosystem
Revenue streams are complex and fragmented, and can be highly susceptible to changes in ad performance, payment gateways and purchase behavior. The dynamic revenue ecosystem is affected by billions of daily events across segments, products, and payment providers. Whether your revenue sources are micro-transactions, subscriptions, partners, or affiliates—acute incidents or chronic glitches can quickly result in massive bleeds to your bottom line.
Revenue data is too volatile for
static thresholds
Revenue data is made up of billions of upstream and downstream events, and is highly influenced by human behavior—making it extremely volatile, seasonal, and contextual. Sampling rate is irregular and relationship between metrics is unknown. Manual monitoring and static thresholding is irrelevant for the dynamic nature of revenue data.
Monitor the data that directly influences your revenue
Monitor the data that directly influences your revenue
To identify impactful issues as soon as they appear, revenue monitoring requires 100% coverage of revenue-related metrics in real time, autonomous learning of metric behavior and seasonality, and full metric correlation for root cause analysis. Since metric behavior is volatile and erratic static thresholds can’t work, as can be seen in this example of a peak hour drop in credit card transaction success rate. As this is an acceptable drop for off-hours payment gateways, a manual threshold would have missed it altogether, resulting in significant losses for the company.
AppNexus
Major cost and ROI benefits
Our clients are very happy that we’re giving a more detailed service and are able to solve problems that used to either go undetected or take days to solve. We solve them in a matter of hours.
Travis Johnson
VP Engineering