The AdTech industry is constantly evolving. With heavy competition, the emergence of new technologies, and changes in user behavior, advertisers have to constantly adapt their advertising campaigns in line with the latest market requirements. This involves active monitoring of business-critical AdTech KPIs that impact customer experience and conversions.
Need for augmented analytics in the AdTech sector
With large volumes of streaming data from various sources, it is not easy to ingest and analyze complex user data for large-scale advertising campaigns. To detect issues quickly and to provide a unified brand experience across multiple advertising channels, marketers need to understand their users and buyer behavior in detail. Autonomous anomaly detection powered by artificial intelligence and machine learning provides advertisers with a bird’s eye view of all the customer behavior metrics in one place, making it easy to spot business-critical outliers in real-time and understand the correlation between various metrics. This speeds up the root cause analysis process, allowing advertisers to remediate incidents as they occur before it impacts end-users.
Whether it is a missed opportunity or a threat from anomalous bots, delayed data insights can hurt advertising companies badly in terms of revenue. AI-driven analytics enables AdTech companies to actively monitor and manage critical metrics while giving early insights into new customer and market trends. With accurate and timely insights, advertisers can focus their resources to engage the relevant audience and drive more conversions.
Real-time anomaly detection for AdTech analytics
Autonomous anomaly detection plays a critical role in process automation and content personalization in the AdTech sector. With augmented analytics, advertisers can proactively improve and personalize their marketing campaigns for a better ROI. Here are six ways in which AdTech companies can leverage autonomous anomaly detection:
1. Analyze marketing funnel
With real-time anomaly detection capabilities, advertisers can dynamically monitor and analyze their marketing funnel to gain a better understanding of the conversion path taken by users. By proactively monitoring metrics at a granular level including KPIs such as reach, impression, engagement, CTR, time spent by viewers on an Ad, etc, advertisers can gauge the effectiveness of various interactive Ad campaigns and formats. They can track multiple KPIs in real-time including site traffic, new visitors, bounce rate, page views per visit, average session duration, traffic source, newsletter subscribers, email open rate, blog traffic, display advertising click-through rate, affiliate performance rates, number and quality of product reviews, etc to identify issues and opportunities that impact user engagement.
2. Detect anomalous bots and Ad fraud that cause revenue loss
As per AdAge, advertisers lose 1$ out of every 3$ from their advertising spend to Ad fraud including fake traffic from anomalous bots. Advertisers can leverage anomaly detection to prevent revenue loss by detecting artificially induced impressions by non-human bots. By monitoring clicks for every Ad across regions and devices, advertisers can identify false impressions or clicks. By examining the correlation between anomalies in various metrics, AdTech companies can discover the root cause of issues such as IP address and user ids that behave anomalously. Anomaly detection alerts advertisers to any suspicious and fraudulent incidents in real-time, allowing them to take fast remedial action.
3. Monitor traffic to understand market trends
AI and ML technologies help monitor and analyze traffic to get early insights into business-critical trends. For instance, advertisers can track traffic from various partners across multiple customer segments to detect threats such as a decrease in traffic from a specific partner or a segment indicating a potential loss of customers to the competition. By automatically analyzing the changing market trends, advertisers can automate and optimize their campaigns to deliver the right message to the right customers on the right channel.
4. Prevent payment failures
Real-time anomaly detection also helps detect and fix root causes of issues that lead to payment failures such as credit card refusal errors across various dimensions including app versions, OS, payment gateways, and regions. Advertisers can leverage anomaly detection technology to identify any increase in the error rate of a given combination of metrics to alert IT teams in real-time.
5. Improve website performance
Further, advertisers can use AI-driven anomaly detection to automatically detect issues in website performance such as latency in loading webpages, changes in user response time, average time spent by users on the website across multiple channels, regions, OS, etc. They can find out in real-time if a software upgrade has to lead to bad customer experience in a specific combination of metrics. With better insights on website performance, advertisers can take quick actions to favorably influence new visitors.
6. Protect applications from threats
Advertisers can also monitor KPIs across the servers to identify anomalies for early detection of threats such as Malware or a dark web attack. They can use augmented analytics to track traffic and server utilization in real-time across the servers and regions.
With artificial intelligence and advanced analytics, AdTech companies can thus understand their audience better and deliver highly-personalized content that drives conversions.
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