In the modern marketing climate, data is key. Now, this shouldnâ€™t be news to you. Weâ€™re living in an age where more data is generated in a 24-hour period than ever before. A recent study by software company Domo highlights the staggering statistics behind the trend, finding that over 2.5 quintillion bytes of data are created on a daily basis, and that by 2020, itâ€™s estimated that 1.7MB of data will be produced every second for every person on earth. The numbers are astounding.
Apply this back to digital marketing today, and there are simply no excuses for not knowing who exactly your customers are and what exactly they want. A great plethora of tools exist in the marketplace today that SEM practitioners can utilize to capture a whole range of data sets at various stages across the sales funnel and customer journey. The days of mass, aimless broadcasting have long been a thing of the past, making way for focused campaigns backed by data-driven insights that target specific audiences at dedicated times.
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The Importance of Quality Paid Search Data
While all this vast quantity of data available at the fingertips of every marketer represents untold opportunity, it does bring with it certain challenges. Chief among them, data quality and anomaly detection; pains that affect nearly every organization running SEM programs. With inaccurate or inconsistent data comes misguided decision-making. Put simply, low-fidelity data could lead you away from things that really move the needle and/or place you in a position under which youâ€™re working with an imprecise interpretation of customer interest. The end result is business outcomes falling short of peak potential and, possibly significant, wasted spend.
Unfortunately, detecting anomalies and identifying quality issues is incredibly difficult as they are often hidden from our purview in sub-segments of data. Uncovering and assessing these data irregularities is one of the fundamental requirements to advanced PPC bidding optimization, fuelling accurate forecasting and ensuring the most efficient performance for your paid advertising initiatives. For a long time, this was plainly impossible with a high degree of accuracy due to the architectural limitations of available technologies that failed to conceive of the complexities of modern SEM.
The landscape has changed, though, with automated ad management point solutions solutions coming to the fore and empowering marketers with the tools they need to detect data anomalies at deeper levels of granularity than previously possible. QuanticMind is one example; a solution leveraging the latest advances in Data Science – including machine learning algorithms, Bayesian modeling, predictive performance methodology, and natural language processing – to optimize SEM performance toward specific business goals. One major part of QuanticMindâ€™s breakthrough technology is a sophisticated anomaly detection functionality.
So, how does it work?
The Modern Solution for Anomaly Detection
To ensure bids only based upon accurate data are pushed to the publishers, the QuanticMind solution employs multiple anomaly detection techniques to check for outliers or other unexpected behaviors that may indicate an issue with the data. The means, on a daily basis, all key metrics (cost, revenue, clicks, cost-per-click, etc.) are compared for that day against what was forecasted. In the event a significant difference is surfaced, a flag is called out as a potential outlier. If an outlier is detected, an alert is generated and bidding will not be updated based on the potentially inaccurate or deviant data. Once this issue is resolved or corrected, bidding can be resumed.
This anomaly detection capability within QuanticMind is only possible because of the way the platformâ€™s foundational infrastructure is designed and built. By utilizing distributed cloud computing, all the data isnâ€™t stored in one computer but rather in a way that makes retrieval, reporting, and calculation both lightning-fast and scalable. Here is an example to illustrate the power of this technology: if five servers are recording pixel hits for load balancing, but one server is failing and not being monitored, then 20% of the data being pushed back for calculation and optimization is unavailable. The calculations, therefore, will all be rendered inaccurate. With QuanticMind, everything can be pulled and monitored in one place despite being distributed across a cloud network.
A notable degree of performance benefits over other solutions is driven from the infrastructure developed for consistency, accuracy, and completeness of data available for optimization calculation and reporting. In the final analysis, even the best algorithms working from incomplete data will still produce poor results.
Uncovering poor data will always be a significant pain for digital marketing professionals, preventing the types of analysis that can ultimately lead to increased revenue. Where legacy platforms fell – and continue to fall – short in this department, modern technological solutions like QuanticMind have paved the way for a new era of paid search bidding that solves these problems by using multiple anomaly detection steps.