Data Hygiene: A Challenge for Companies of All Sizes

Brittany Young’s experience raises an important query that should be addressed by companies of all sizes: how can we make sure that the marketing data we use is accurate and well-suited for campaigns?

The Value of Clean Data

Brittany emphasizes the importance of data cleanliness by stating that you need an outside tool and a person on your team who knows how to do all that data cleaning otherwise you’ll be troubled.

Data that is inaccurate or inconsistent can result in a number of issues, such as:

• Inadequate Targeting: Inaccurate data may cause efforts to target the incorrect audience, squander resources, and impede outcomes. Imagine sending cat food ads to people who clearly own a goldfish! 
• Inaccurate Attribution: Measuring the real efficacy of marketing initiatives is challenging when dealing with untrustworthy data. Clean data allows tracking of the customer journey and which points are driving the most sales or leads. 
• Customer Frustration: Customers may become frustrated as a result of irrelevant communications and a bad customer experience brought on by inconsistent data. An example is emailing someone consistently about a product they’ve already purchased. Relevancy is key for fostering customer relationships. 

Tools for Data Management

Brittany talks about cleaning and transforming data using data hygiene tools like Alteryx, before re-entering it into Marketo, their marketing automation system.

This emphasizes how crucial data management technologies are. These tools are useful for the following tasks:

• Error Detection and Correction: They can locate inconsistent data, duplicate entries, and missing values. Imagine trying to find a specific customer’s email that has various typos in various places in the database! Using such tools ensures the identification and correction of errors such as these. 
• Data Normalization: They can guarantee that all sources’ data formats are the same. When data comes from multiple sources, the formats can differ which is why consistency across all data points is key for easier analysis and utilization otherwise you’d end up with wrong results. 
• Enrichment: They can supplement current records with new data points. This can be for existing customer profiles such as demographic data, purchase history, website behavior, and more. These data points build a complete picture of customers that allows for targeted campaigns and personalized marketing strategies. 

A Challenge for All Company Sizes

While Brittany acknowledges that the complexities increase significantly for large enterprises, she emphasizes that all companies face some difficulty in managing lead data, no matter the industry. Even though the levels of difficulty may not be the same, the challenges are inevitable.

Poor data quality costs organizations an average of $12.9 million annually, as noted in Gartner’s 2020 research. Several key challenges make it difficult for organizations to tackle data quality issues effectively, including:

• Increasing Regulatory Demands: Regulations like GDPR and CCPA place strict controls on how companies manage personal data, holding them accountable for the data they collect and store. Understanding the nuances of Marketo GDPR compliance is essential for ensuring your data practices align with these regulations and mitigate potential risks.
• Data Inconsistency Across Sources: Gartner identifies data inconsistency as a top challenge, often stemming from data stored in isolated systems with overlaps, gaps, or contradictions. Without proper integration, standardizing data becomes a significant hurdle.
• Resource Limitations: Many organizations struggle to scale data quality initiatives due to a lack of skilled personnel, experience, or financial resources, even when there is a program in place in one department or data domain. Partnering with a Marketo Agency can help bridge these gaps by providing the expertise and resources needed to implement robust data management strategies and scale initiatives effectively.
• Absence of Ownership: Although business leaders recognize the importance of data quality, they often fail to view it as their responsibility or grasp how their domain’s data impacts broader business outcomes. Data quality requires a business-wide approach, with clear ownership and collaboration among stakeholders at every level.

Conclusion

Keeping marketing data clean is an ongoing battle for businesses of all sizes. However by utilizing data management tools and assigning a data champion, companies can ensure their campaigns reach the right audience with the right message while also maximizing campaign performance.