Why Dirty Data Costs to B2B Companies?

Dirty data of B2B companies consists of incomplete information,expired data, duplicate records. CRM is a perishable asset.  Constant attention is needed to maintain healthy CRM data. Customer contact information, job titles, keep on changing due to various reasons. Company contact information changes due to collaboration shut down and more. Duplicate records can give your misleading analytical reports. Tackling incomplete records is crucial for your marketing activities. For example if your CRM collects data from conference list which consists of name , address then this data can't be used for email campaigns due to lack of information regarding the email ids.  To make your CRM database free from dirty data is very crucial to effectively run your marketing campaigns. Dirty data can costs to your company via the impact it can have on business processes and marketing campaigns. According to B2B marketing  Dirty data costs companies 15% of their renewable revenue every year. 

Dirty data can create following problems for B2B comapnies

Higher campaign running costs

Due to expired records present in your data your campaign running cost can increase in terms of wasted postage or telephone calls for messages that are never delivered. 

Spoils your brand image

Having duplicate records in your CRM database can lead to sending duplicate messages or calls to clients who have multiple contact records in your database. This upsets your customers because he receives multiple messages from single contact which provokes him to treat your messages as spam. This spoils your brand reputation and hugely affects your marketing campaigns.

Increases message failures

Dirty data consists of wrong data and typographical errors. Having such data in your CRM can lead to increase in message failures due to faulty data. Faulty data leads to sending messages to wrong person. Messages might never reach to potential person. Increased message failures makes it difficult to calculate ROI and other success metrics. 

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