Bad Data is Bad News for Marketers

In recent years, marketers have had to juggle more priorities than ever before. And, as the COVID-19 pandemic has shown, they need to be able to quickly pivot their plans to ensure they are still reaching and engaging the right audiences as marketing and advertising budgets are slashed. Marketers can’t afford wasting time and money, especially as budget cuts continue to increase, but one surefire way to do that is if they rely on bad data.

When marketing departments rely on low-quality data, they risk diminishing the effectiveness and ROI on their campaigns, which can then compromise their ability to meet other goals, such as rolling out a new product or expanding their customer base. Despite this, the 2020 Nielsen Annual Marketing report, which surveyed more than 350 global marketers, found that data quality is shockingly not one of their highest priorities. Instead, marketers prioritize audience targeting, ad creative, and audience reach. But none of those efforts will hit their mark if marketers aren’t approaching them with the right information.

Throughout the year, but especially during periods of disruption, marketers need to ensure they’re leveraging analytics tools to capture the right data in order to hit their marketing objectives and meeting business goals.

The Benefits of Prioritizing High-Quality Data

For businesses looking to optimize their marketing spend, having the right data helps marketing departments make the most impactful decisions. Companies that focus on collecting and leveraging high-quality data as much as they do on targeting and reaching desired audiences will create a more effective and efficient marketing operation, thus leading to optimized spend, campaign effectiveness, and overall better results. And when marketers get better results, they increase their job security.

Considering the industry uncertainties caused by the COVID-19 pandemic, it’s more important than ever for them to leverage data and analytics where they can to successfully target the right audience at the right time. In fact, according to Nielsen research, having the right data, methodology, insights, and activation can lead to a 7x return on the cost of the analytics program itself, on average.

Marketers must also keep in mind that analytics programs in times of disruption like COVID-19 must be adaptable and take marketing and non-marketing factors into consideration. They need to understand the impacts to their brand both locally and nationally while foreshadowing potential disturbances to inventory levels, the supply chain, and consumer sentiments. Data allows for flexibility to overcome these influences.

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How to Improve Data Quality

According to the Nielsen report, most marketers make critical decisions based on their gut feelings, but what they should be basing these decisions on is data. Marketers can’t be misled or relaxed about the importance of data quality. Although some data problems may be out of a marketer’s direct control, marketing departments can take steps to improve data quality and the accuracy of their measurement, improving the effectiveness of their marketing efforts overall. These steps include:

1. Automating data collection when possible.

Typically, marketing decisions aren’t made in a vacuum. Factors such as product, creative, geography, and timing all play into them and as such, it’s useful for marketers to have current, comprehensive information on these areas so that they can devise informed strategies. However, manually collecting, consolidating, and standardizing data from myriad sources (e.g., point-of-sale, distribution, or media) risks wasting time and unknowingly making errors—which compromises the integrity of the information. With an automated data collection process, marketers can configure it so that they’re gathering a complete, contextualized data set that considers all the variables important to their unique brand. With time saved collecting data, marketers can then sooner analyze and action the insights it reveals it to improve campaign performance and spending effectiveness.

2. Reviewing and validating for accuracy.

In order to accurately measure data, marketers need to prioritize high-quality input. To get the most accurate and actionable insights, marketers need more than just automated data collection processes. They also need to create a process to consistently review and validate the data once it has been collected. This way, marketers can conduct in-depth reviews when they spot any anomalies or data that’s considered questionable.

3. Striving for granular-level data when possible.

Understanding why some customer interactions result in conversions and others don’t is what drives media optimization. If marketers can’t glean insights more specific than the channel level, they won’t understand what is – or is not – really driving impact. Granular data, such as information related to specific campaigns, tactics, products, or retailers, enables marketers to create better measurement models, and thus make better decisions. Highly aggregated data risks an underestimation of the true impact of marketing efforts, incorrect decisions, and wasted resources.

By improving data quality, marketers can make more strategic decisions about which campaigns and channels are the most effective to reach and engage target audiences, all while optimizing marketing spend.