Using a database is a bit like baking: if you don’t have the right the ingredients or they are of poor quality, the cake will not taste good. This article explains, why companies can afford low quality data just as much as bakers can afford to use rotten eggs.
A low-quality database can mean many things: duplicate, incomplete or outdated data sets, unstructured data or data that cannot be shared because, for example, it is trapped in an Excel spreadsheet of an employee. Every successful digitization project needs the right data. Yes, every company has data but it only turns to gold, when it’s structured, updated and accurate.
So what exactly happens when a company works with a database that urgently needs a spring cleaning?
Customer segmentation does not succeed
Customer segmentation is a perfect way to deliver the right messages to the right contacts through the right channels, especially in campaigns and automated customer communications. However, if the necessary data is missing, not properly stored or cannot be combined, many contacts are excluded and therefore can’t be properly used.
Poorer campaign results
If an email campaign goes to 100 contacts and only 60 contacts have the right mail address stored, the campaign’s KPIs will look much worse than if all 100 contacts received their mail. If it’s a paid campaign or the campaign effort is particularly high, this can even cost money.
Data protection requirements such as the GDPR rely on a company having a complete overview of all personal data available at all times. However, if data is not stored correctly, it is more difficult to provide all information of a contact or – if necessary – to delete data completely.
Forecasts and reports are not accurate
Reports provide an overview of leads, customers and the general success of campaigns and are therefore crucial for corporate strategy. However, they only work if decisions are made based on correct and complete data records. If reports are generated with incorrect or insufficient data, this can sometimes have fatal consequences for corporate decisions.
Necessary insights are lost
Modern CRM and marketing automation systems in particular make it possible to connect links between contacts, for example to assign different e-mail addresses to one person, to bundle contacts that belong to one company/customer/transaction or to combine all communication channels of a contact. However, if the data is incorrect or outdated, this can result in wrong communication strategies and/or dead ends.
AI applications can’t reach their potential
Artificial intelligence is helpful in many different areas, for example to make recommendations based on probabilities. But AI only works with accurate and current data. Unstructured data, for example, cannot be used and inaccurately/outdated data leads to incorrect results.
Employees waste time on inaccurate data
Every data record that has to be checked manually and every contact that has to be asked individually for more up-to-date information costs time. And this time adds up especially when the errors in a database have grown over time.
Individual customer approach does not succeed
Personalization in marketing, sales and service is directly related to existing customer data. If it is incomplete, automated processes cannot run as desired and even individual conversations can be „off“. Instead of with „Hello Mrs. Müller“, the customer is greeted with a generic „Hello“ in her e-mails or the sales employee must ask Mrs. Müller, which business unit she belongs to before he can make an actual offer. This of course creates a bad customer experience, increases the bounce rate and damages customer loyalty.
Opportunities get lost
The right message at the right time on the right channel can turn a lead into an opportunity. If data is missing or is difficult to structure, many processes cannot be set in motion effectively. Opportunities are lost because, for example, the follow-up did not take place, took to long or was not relevant/personalized enough.
Unclean, outdated, duplicate or inaccurate data records can break a system. Processes come to a halt, reports and AI applications lose accuracy, employees lose time reviewing information, and the overall customer experience suffers. All of this ultimately results in fewer leads, fewer opportunities and fewer customer conversions. In short, mismatched data means less revenue (and higher costs).
Is your database healthy and ready to use? Find out with the Data Quality Assessment.
https://www.ec4u.com/ec4u-blog/wp-content/uploads/sites/3/2020/08/Chaos_AdobeStock_147128744-1.jpg270710Juliane Waackhttps://www.ec4u.com/ec4u-blog/wp-content/uploads/sites/3/2016/02/Logo-ohne-Schriftzug.pngJuliane Waack2020-08-18 09:00:372020-08-12 16:33:1310 Disadvantages of working with a low-quality database