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Are Marketers Bad at Numbers?

Are Marketers Bad at Numbers

Consumer information is immensely valuable to marketers, yet marketing is increasingly plagued by the issue of poor-quality data. This can prove costly, draining organizations of an average of $12.9 million annually. Beyond its significant dent in the return on advertising spend (ROAS), subpar data quality also hampers a brand’s strategic and decision-making prowess.

But what’s the root of this problem? Companies collect and analyze data to refine their operations, from determining the most effective website design for customers to deciding which product or service features might elevate sales. However, Wharton research indicates that 57% of marketers are misinterpreting the data, potentially leading to misguided conclusions.

This raises a critical question: Are marketers inherently poor at analyzing data?

Is this a plausible argument when any marketer worth their salt is likely buried in spreadsheets daily, relying on data to guide their next move? The crux of the issue lies in deciphering the true desires behind consumer requests because often, what people ask for and what they genuinely want can be worlds apart. At times, consumer demands may even be unattainable.

Another factor to consider is that marketing data is notoriously complex and not as straightforward as a simple transactional opportunity. The challenge primarily revolves around the precision and effectiveness of the data at a marketer’s disposal. Remarkably, about ninety-something percent of marketers are grappling with inappropriate datasets or struggling to make meaningful connections with the data they have. Consequently, the fortunate 5% to 10% who can effectively harness their data, are surging ahead in the market.

So, it’s not necessarily that marketers are inept with numbers. It could be that they’re simply analyzing the wrong datasets.

It could be argued that marketers also struggle with prioritizing investment to secure the right data. A significant factor here is the intense pressure they face, including budget reductions and the overwhelming demand to generate sales pipelines above all else. Ironically, this lack of investment in proper data infrastructure means marketers are often unable to conclusively demonstrate their contribution to creating a sales pipeline.

Improving Data Quality: A Marketer’s Guide

  • Assess Your Data 

Regular scrutiny of data sets is essential to reducing poor data and spotting typical indicators of data fraud. For example, businesses should be wary of any email or IP address associated with excessive connections—like more than four IP addresses or six emails. While these indicators don’t necessarily confirm fraudulent data, they are strong signs to watch out for.

  • Enhance Identity Resolution Capabilities

Understanding the red flags of bad data should lead marketers to enhance their internal systems, particularly identity resolution tools.

Identity resolution integrates various identifiers across devices and channels, providing a comprehensive view of each customer. Brands rely on these tools to connect all consumer interactions, both digital and physical. This capability is instrumental in reducing errors in device identification caused by user mistakes, owing to its proficiency in accurately linking customer data.

  • Leverage Machine Learning

Forrester Consulting points out that marketing teams devote up to 32 percent of their time to managing data quality. Maximizing the efficiency of data-related tasks is crucial. Machine learning aids in scanning vast amounts of data for accuracy and automating decisions without human intervention. Implementing machine learning for systematic data entry and validation can drastically cut down the volume of bad data generated by user errors and outdated information.

In Conclusion

While achieving flawless data quality may be unattainable for most companies, marketers need to approach their data meticulously and consistently.

This involves diligently examining data sets for possible fraud indicators, bolstering identity resolution systems, and embracing machine learning to enhance the efficiency of data management and validation processes.

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