The Data Dilemma: Uncovering How Bad Data Impacts Your Marketing
Data is king and in the modern business world, it helps to inform numerous business decisions from product specifications to marketing strategies. With the power of data obtained from multiple sources – website analytics, surveys, social media platforms, online reviews, and direct customer feedback – organisations can gain valuable insights about their customer base that help them to better meet their needs.
However, not all data delivers the same value, and like so many things, it needs care and attention to ensure it delivers accurate and useful insights - and ultimately a good return on investment.
This is particularly important with marketing strategies, as poor data can mean failed marketing campaigns marred with missed opportunities, inaccurate results, wasted resources and wasted money.
If this sounds all too familiar and bad data is causing your marketing to fail, there’s no need to panic. In this blog post, we uncover the problems with bad data, as well as giving you actionable advice on how to turn bad data into good data.
The challenges of modern data processing
When we talk about bad data, it is usually referring to data sets that are inaccurate, outdated, or incomplete. Here are some of the challenges that lead to poor quality data:
Multiple data sources: from leads scribbled in a notebook and emails to website queries and automated lead generation, data is gathered from multiple sources, making it challenging to process and decipher the useful information. It also results in data silos, as data is often stored in isolation, in different formats and contains different information.
Overwhelming data volumes: the sheer volume of data can mean some businesses do not have the time or budget to implement the right processes to manage their data effectively.
Data input errors: typos or the wrong formatting is one of the most common causes for inaccurate data, which can mean unreliable information that leads to misinformed business decisions.
Outdated information: out of date customer data results in missed opportunities and wasting time reaching out to the wrong people at the wrong time.
Poor data validation: data needs to be validated and qualified to ensure you sift out the useless information to curate data sets that contain the most accurate and targeted data.
Lack of data governance: with data governance, consistency matters and not having structured policies and procedures in place for your data manager could be harming the quality of your data in the long run as well as impacting your compliance.
Duplicated data: duplicate entries can skew your data insights and mean you market to the same contact multiple times which can be a turn off for customers.
Matching errors: these occur when data entries that should be considered the same are treated as distinct because of inconsistent data formatting or duplicate entries. This can disrupt data integrity and cause inaccurate reporting.
The impacts bad data has on marketing strategies
To avoid the common pitfalls associated with bad data, you need to understand how it can negatively impact your marketing strategies.
Inaccurate targeting: marketing efforts rely heavily on targeting the right audience to maximise engagement and using bad data can lead to inaccurate audience segmentation. This wastes resources and dilutes the effectiveness of marketing campaigns.
Flawed decision-making: bad data leads to inaccurate reporting. When decisions are made based on inaccurate reporting this can impact results and effectiveness.
Poor personalisation: personalisation is key to driving customer engagement, but with incorrect or out of date information, you risk alienating or annoying customers
Wasted resources: marketing campaigns based on bad data can be a waste of resources and budget, as campaigns will be less effective and deliver a lower return on investment (ROI).
Low conversion rates: when marketing messages are irrelevant or mistargeted, customers are less likely to convert into paying customers.
Damaged reputation: targeting the wrong people or sending out irrelevant marketing content demonstrates a lack of diligence, which can damage your brand’s reputation, your emails marked as spam, and your domain blacklisted so emails don’t reach the recipient’s inbox.
Compliance risks: data privacy regulations such as GDPR and CCPA require businesses to manage customer data responsibly, so not keeping data up to date can lead to compliance risks.
How to overcome the bad data dilemma
Data can be a hugely powerful asset, and with a combination of proactive strategies and ongoing vigilance, you can effectively manage your bad data to ensure you uphold data quality and integrity:
Define data quality standards: establish clear criteria for what constitutes high-quality data within your organisation – such as accuracy, consistency, and timelines – and ensure these standards are communicated and adhered to by everyone involved in data collection and management.
Implement data validation processes: data management can be made easier with validation and auditing processes; this should include monitoring data metrics to identify anomalies promptly and ensure consistency.
Educate and train employees: employee training is key to ensuring your data is managed correctly as everyone will be working to the same standards and practices to uphold data integrity.
Standardise data entry: establish standardised formats and protocols for data entry to minimise errors and inconsistencies, as well as a single platform like a CRM for storing data.
Regularly cleanse and enrich data: frequently cleansing and enriching your data to remove duplicates, correct errors, and enhance its accuracy and completeness will ensure the data you use for marketing purposes delivers better results.
Ensure data security and privacy: implement robust security measures to protect data against unauthorised access, loss, or corruption and to ensure your organisation adheres to relevant data privacy regulations.
Invest in data quality management: leverage data management tools and practices to uphold data validation, cleansing, and enrichment processes, from data integration software with advanced matching algorithms and customer relationship management (CRM) systems to data management specialists.
Continuously improve processes: regularly review and refine your data management processes based on feedback, insights, and evolving business needs.
Summary
By implementing these strategies and fostering a culture of data quality, organisations can minimise the risk of bad data management and maximise the value of their data assets.
Overall, bad data undermines the effectiveness of marketing strategies, reduces ROI, and poses risks to a company's reputation and compliance status. Addressing data quality issues is essential for maximising the impact of marketing efforts, maintaining customer trust and ultimately, turning leads into business.