The quality of your data is at the heart of your customer experiences. Good data enables you to provide personalized journeys fundamental for driving sales and developing brand loyalty. Bad data interferes with your ability to meet customer needs.

A recent study by Experian has found that bad data costs some businesses up to a quarter of their total revenue. So what is bad data and how can you get rid of it?

Defining bad data

Bad data – data that is incomplete or erroneous – can have an effect both internally impacting revenues and sales, but also externally with how the brand is perceived by customers and prospects.

With 92% of organizations reporting that they suffer from data quality issues in some form, what exactly causes bad data?

  • Data sources – Many brands fall into the trap of finding data sources that are high in quantity, but are not high in quality. This can call into question the validity and accuracy of the data, which will influence analytics and future business projections.
  • Duplicate data – Often caused by repeat submissions for a single account or lead, which happens as a result of incomplete data or poor database merging.
  • Poor design – Data capture forms are rarely implemented with well-thought out design, for example numerical fields that accept letters or a lack of automated validation to stop duplicate entries.

The impact of bad data on your business

Without good analysis and data management, the data that you collect will not enhance the customer experience, hindering your ability to improve revenue. This leads to:

  • A messy sales pipeline – Sales and marketing teams need relevant and accurate data to drive sales. Bad data directly impacts your ability to effectively sell and manage prospects and customers. If your data is inaccurate, you’ll end up with invalid leads.
  • Subpar customer journeys – Good data drives good customer experience. Customers want personalized journeys, but to deliver them brands have to rely on high quality data. Inconsistent and missing data about previous visits can lead to irrelevant retargeting. This can have a bearing on the consumers’ willingness to purchase and can drive them right into the hands of your competitors.
  • Stymieing of analytics and automation – Bad data undermines your analytics and business intelligence operations, causing marketers to make poor decisions. It can go on to hamper any future UX personalization improvements and any future marketing efforts, as the customer data collected will not be accurate.

Cleaning up bad data

To help improve customer satisfaction, acquisition and retention, it’s time to be more proactive at improving data quality:

  1. Understand the scope of your data quality problem – The first step is to determine the size of the data quality problem at every step of the business process by undertaking a data quality assessment. The International Data Management Association has produced an informative guide about what to consider when assessing data quality.
  2. Evaluate data that is over 1 year old – If you have had no interactions with a customer or prospect in over a year, it might be best to re-evaluate the usefulness and accuracy of that data.
  3. Build – don’t buy – Buying data is full of risks, the dataset may be outdated, inaccurate or full of prospects that might not be receptive to your products. You are more likely to get better data quality from building your own databases, a cost-benefit analysis of buying vs building will determine which is best for you.
  4. Consider outsourcing data quality management – As your datasets grow, your time and financial costs are likely to increase too. Outsourcing to professional data quality management experts can help save time and improve scalability sometimes at a fraction of the cost of doing it in-house.
  5. Align and integrate sales and marketing systems – This will centralize where your critical data lives, reduce the possibility of errors and improve the overall management of your currently disparate datasets.

Read the original post on the Decibel Insight blog

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