Just like all areas of your working life, your customer relationship management (CRM) software can become cluttered and inefficient. It can result in a lack of organization and can lead to everything from a drop in productivity to an inability to operate with agility, all of which can affect your bottom line.
Studies suggest that 30% of data becomes outdated each year, so it’s paramount you keep on top of changes. After all, if a contact has a new email address or changes their job title, you’ll want to be confident your database is reflecting these changes.
Functioning with a clean CRM is vital to ensure the marketing department is performing at its best and helping to reach business goals. A standard process should be put in place for regular data cleansing so that simple steps can be taken to keep on top of the issue.
Hygiene is an important aspect of life and ignoring it can have terrible consequences. In the same way, good data hygiene is crucial to a successful business. It’s important and a good practice to keep on top of your company data, but cleaning it is just as vital. Where errors occur, any results involving the data will be problematic and, when you’re relying on that information to influence important business decisions, it’s safe to say there could be severe consequences.
Cleaning is also known as cleansing or wrangling. Essentially, it refers to validating the data you’ve already collected. However, it’s not just a case of removing inconsistencies or errors, but actually involves identifying ‘rogue data’. This includes anything that’s formatted incorrectly, incomplete, irrelevant, inaccurate or corrupted.
The purpose of data cleaning, as mentioned above, is to avoid making uninformed decisions later on. To help keep the reliability of your analytics and insights consistent, an overarching aim of data cleaning is to keep as much of the dataset intact as possible.
In today’s marketing landscape, it’s inevitable that there will be an accumulation of errors in your data over time, due to a number of inherent factors. These include:
Not inputting data in the right format or in the correct place within the CRM after each interaction can lead to bigger problems. This can make optimizing marketing campaigns impossible and limit the success of your operations.
As much as 70% of data in a CRM ‘goes bad’ each year, according to research by Salesforce. Due to the ramifications of obsolete, inconsistent and irrelevant data for a business, it’s crucial for any successful organization to undergo a CRM data cleanup. Here are three compelling reasons why you should implement this in your business.
A very common issue with CRM databases, HubSpot forecasted data duplication rates to be as high as 30% for businesses in need of a CRM data cleanup. This can occur for a variety of reasons. Most commonly, a particular lead might sign up using different emails or postal addresses, therefore creating two or more separate records in the CRM. Alternatively, an individual could enter the same data in different formats. This is really common with phone numbers and your CRM system won’t realize it’s the same person without data cleaning.
Many of us will have been the causeing of missing data, although probably not on purpose. Imagine you’re filling out a contact form and forget to input a specific field, such as your name. That business now has your details, but no idea what to call you. When this type of incident occurs irregularly, it’s not a big problem. However, if a pattern occurs on a larger scale, the time and resources needed to address the issue make missing data an incredibly painful task to resolve.
Like many things in life, the older your data is, the more likely it is to have become irrelevant or obsolete. Your CRM could show you that you have 5,000 leads in the Chicago region, but what it can’t tell you is that 2,000 of those leads no longer live in that area. Without conducting a CRM data cleanup, your information is simply incorrect and any business decisions you make using it are likely to be unsuccessful, or at least less impactful than when you have a clear picture of your data.