Why does your company need anomaly detection?
1. Anomaly detection for marketing performance
For marketers, every dollar spent, impression, click and conversion is precious. Traditional approaches to reviewing and improving marketing performance takes days to weeks to react to issues, leaving your business to spend money on marketing channels that don’t generate maximum returns, and leave revenue on the table that could otherwise be captured.
Often, performance and analytics teams look at last week’s (or month, or quarter) performance, to understand which campaigns were able to drive conversions, clicks and impressions. Traditionally delivered using dashboards, these static analyses often arrive to late for the information to be actionable.
As an example – if a campaign suddenly stops generating conversions, would you like to know within hours, or at the end of the working week? In the latter case, at least several days of conversions (and their associated revenue) has already been lost.
Anomaly detection solves this problem by highlighting the problem as soon as it occurs – so humans can take action – for example, this is traced back to an configuration change (e.g. tags, website), the relevant teams can be contacted to rectify these problems. If it’s related to a paid campaign, the team can focus on specific ones to ensure the right audiences are targeted.
2. Anomaly detection for sales performance
Website checkout funnel
In eCommerce, customers have come to expect a smooth flow from visit to purchase completion – but problems can occur in every step of the journey in between. Failing to address these issues as they (often inevitably) arise costs the business revenue.
Reports on eCommerce funnel activity are often shared for review weekly – but an interruption of even a day can be extremely costly in terms of a drop in sales – especially if the problem is across multiple areas of a website.
As an example – after an application update, if a certain payment gateway is not working for users from specific countries and is not picked up for a week, this could result in tens of thousands of dollars in lost revenue.
Detecting this type of early on with anomaly detection and activating the response team will significantly limit the impact of these events, and return cash into the business rapidly.
3. Anomaly detection for user experience
For consumer-facing businesses, an error-free experience is crucial – be it in content streaming, service provision (think Gmail), or social media. Users rightly feel out of pocket if the service they’ve paid for doesn’t function as expected!
As an example, a content-streaming website focusing on live sports depends on subscriptions to their web, mobile and tablet applications to view sports events, as they happen. An issue with the sign-in module that prevents users from logging in grew from a small problem to an extremely significant one as a major soccer match was streamed, resulting in hundreds of thousands of complaints and an avalanche of subscription cancellations & refunds.
With anomaly detection, leading indicators for this would highlight the unusual behaviour ahead of it becoming a P1, critical issue, and allowed the team to make changes ahead of the important event, retaining the confidence of its customers to deliver on its core offering (and their subscription revenue with it, too!).
Understanding the different anomaly detection methods
In data-poor businesses, anomaly detection is generally manual and performed solely by humans. With only a small number of high-level metrics to track, these are managed by members of the analytics team using data extracts from operational systems.
As businesses grow and becomes data-rich, a new problem arises in the form of scalability. Tracking now needs be more granular – for example, tracking “overall” sales used to be sufficient, but now monitoring sales split by category and country is now considered essential, reflecting the organisation’s success.
With hundreds, thousands or even millions of metrics to keep on top of, humans struggle to deal with the deluge of information, and dealing with this new complexity becomes both time-consuming and expensive- after all, there’s only so much budget for analysts!
Business Intelligence have tried to satisfy this need with dashboards – but businesses find themselves with hundreds of dashboards, each with reams of graphs, and still reliant on the human operator to check through and assess these dashboards – simply not feasible!
An automated anomaly detection method differs from these historical approaches in three key ways:
- Processing happens in the background to check for unusual behaviours – a task that a computer is uniquely well-adapted for
- Only (metrics with) anomalies are shown to users – reducing the cognitive load and allowing them to tell the signal from the noise
Make it easy to share these anomalies (and the insights associated with them) with other users – rallying teams around an event, whether good or bad!
Conclusion: Getting started with anomaly detection
This overview has provided a high-level explanation of
- What anomaly detection is
- How it works, and some of the underlying techniques
- Where it might be applicable within your business
- Why it’s important for data-rich businesses and alternative approaches
With many companies having embarked on a journey centralising and collating their data —now is a great time to use that data to gain insights that will improve your business outcomes.
If you’re ready to try out anomaly detection on your data, sign up for a free demo/trial!