When it comes to marketing, data is everything. It provides insights into customer behavior, demographics, preferences, and interests. But, what happens when the data is inaccurate? That's where the phrase "garbage in, garbage out" comes in. If the data entered into a system is flawed, the output will be equally flawed. Luckily LBDigital is here to explain the importance of accurate data in marketing and spell out how to avoid the pitfalls of garbage in, garbage out.

What is Garbage In, Garbage Out?

Garbage in, garbage out, or GIGO for short, is a concept that describes the relationship between input and output. In the context of marketing, if the data input into a marketing campaign is inaccurate or incomplete, the results will be equally inaccurate.

For example, if a marketing campaign targets a specific age group, but the data used to identify the target audience is inaccurate, potential customers outside of the target age group may not receive the campaign. This can lead to missed opportunities and wasted resources.

What is the upshot of using poor quality data?

The consequences of inaccurate data in marketing campaigns can be significant. It’s generally accepted that approximately 50% of B2B contact data becomes outdated each year. According to a study by IBM, poor data quality costs US businesses an estimated $3.1 trillion each year. The study found that inaccurate data can lead to poor decision-making and lost revenue.

Independent data validator, Truthset has conducted several studies on how inaccuracy in demographic data is affecting the performance of consumer marketing campaigns. For example, they found that 39% of gender targeting budget is misspent. As they conclude. “with that limited increase in accuracy, you’re almost better off not spending money on audience targeting at all!”

How can marketers avoid the effects of inaccurate data?

In short, data accuracy must be a priority throughout the marketing process. This requires the use of reliable data sources, robust data management systems, and ongoing data analysis and validation.

Reliable data sources are crucial in ensuring data accuracy. Using data from credible data sources enables marketers to gain insights into customer behavior, demographics, preferences, and interests. It is also important to ensure that data sources are regularly updated to ensure the accuracy and relevance of the data.

This is where LBDigital comes in. Quarter after quarter our data outperforms other leading providers across key demographics in data analyses conducted by Truthset. According to Kathryn Barnitt, Head of Data Science, Truthset: “LBDigital continues to blow us away with the quality of their data. Their demographics rank tops in accuracy compared with other data providers.”

Robust data management systems are also critical in maintaining data accuracy. This requires the use of sophisticated tools and technologies to capture, store, and analyze data. A robust data management system should also include data validation and cleansing procedures to identify and correct any inaccuracies.

Ongoing data analysis and validation are essential in ensuring data accuracy. This is also true when using third-party data in your marketing campaigns. LBDigital believes in a test and learn approach and, as part of our data consultancy service, we work with our clients to tweak their data segments, helping us to improve their scale and conversion rate.

What about identity graphs and data models?

Identity graphs are a powerful tool that allow marketers to connect data points from various sources to create a comprehensive profile of a consumer and deploy people-based marketing strategies across devices. In addition to targeting at scale, an identity graph enables marketers to implement true frequency capping across devices as well as correctly record which channel led to the generation of a specific action from the user.

However, the quality of the data used in identity graphs is crucial for their effectiveness. If you use poor quality data an identity graph, this could distort the profile of the consumers in your audience, resulting in poor campaign performance and inaccurate attribution.

The same can be said for data models. If you want to create a lookalike model based on an initial dataset but that dataset is inaccurate, the resulting lookalike audience will look nothing like your target audience. In fact the flaws from the dataset will be amplified in the lookalike model.

What benefits will marketers gain from prioritizing data accuracy?

The benefits of accurate data are numerous. Accurate data can lead to increased ROI, improved customer satisfaction, and stronger customer relationships. By using accurate data, marketers can identify potential customers who may be interested in a product or service, resulting in a higher ROI.

We recently worked with M+R and one of their non-profit clients on a donor acquisition campaign. Thanks to our accurate data segments, we increased their reach by 24% on Facebook and reduced their cost per donation by $184.

Change “Garbage in, Garbage Out” to “Quality In, Quality Out”

Accurate data is essential for the success of marketing campaigns. Inaccurate data can lead to missed opportunities, wasted resources, and decreased ROI. By prioritizing data accuracy, marketers can achieve better results, generate higher revenue, and build stronger customer relationships. If you’re ready to choose “quality in, quality out” for your next marketing campaign, speak to LBDigital to discuss your data needs.