As marketers prepare to launch their 2019 campaigns, they should make sure that a complementary data hygiene plan is in place, and certainly AccuList USA data services stand ready to aid in ensuring the quality, up-to-date, enriched data essential for achieving marketing results.
Why Does Clean Data Matter?
Marketers don’t want to join the 88% of U.S. companies whose bottom lines are hurt by dirty data, based on Experian research. The top areas impacted by poor data practices are marketing (66% of companies) and lead generation (80% of companies), according to DemandGen. Dirty data leads to poor targeting and ROI for marketers, reduced revenue from customer acquisition and retention, wasted company resources and misdirected strategy. To avoid that fate, marketers need a plan to regularly fix any customer and prospect data that is incorrect, inaccurate, incomplete, incorrectly formatted, duplicated, or irrelevant, plus to enrich the database via appending of relevant but missing customer parameters.
Developing a Data Cleansing Strategy
Pete Thompson, founder of DataIsBeauty.com, has put together a useful primer for developing a data hygiene plan. Start with the basics: Decide what data is important for business decisions and estimate the ROI of data quality improvement. Then review existing data processes: types of data captured, where it comes from and how is it captured, the standards for data quality, how errors and issues are detected and resolved, etc. Other questions include the main sources of errors, methods for validating and standardizing data, methods for appending or combining multiple sources, automation used if any, accountability for data quality, and measurement of data ROI.
Key Elements of a Data Hygiene Plan
Without going into detail, the basic steps of the data plan will start with creating uniform data standards, preferably applied at the point of data capture. Then develop a data validation process, applied either when data is captured or, if that is not possible, at regular intervals for data already entered. After data has been standardized and validated, you can append missing fields by cross referencing with multiple data sources. Streamline the process through automation tools and scripts, saving time and money and reducing human errors. However, while it may be tempting to start with automation, Thompson cautions against putting the cart before the horse; success requires having data standards and a proven validation process in place before automating. And then set up a monitoring system of the hygiene process, whether automated or not, via random test samples and back testing, and implement periodic checks.
For regular monitoring, or overall scrubbing without an automated regimen, experts suggest a quarterly hygiene review for databases of 100,000 records or more, and semi-annual cleaning for smaller databases. Based on our own years in the data business, we think the best advice from Thompson and other experts is to enlist the services of data processing pros when hygiene is due!
Check out more details from Thompson’s data hygiene plan.