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How to Clean the Data Jamming Your Sales Funnel

By October 7, 2020December 28th, 2020No Comments

When we think about the fallout of bad data, we tend to think about lost accounts and revenue. However, there is a lot of work that goes into identifying potential opportunities, followed by even more work on nurturing and qualifying leads to prospects. Bad data can really put a dent into these efforts. This includes everything from the first contact, direct phone calls, scheduling meetings, and even identifying influencers. In this blog, we will take a closer look at the impact of bad data on these processes, understand the sources of this data, and finally provide some suggestions on how to improve your data quality and hygiene. 

How Does Bad Data Happen?

Regardless of whether you are in the B2B, B2C, or hybrid markets, the shelf life of the data in your Customer Relationship Management (CRM) systems is relatively short. For example, just last year, 1,160 CEOs left their jobs within the first nine months according to the recruitment firm Challenger, Gray, and Christmas. That’s a 13% increase from 2018! This is the highest turnover rate since 2002 when the company first started keeping track of such statistics. While this is just one obvious example, there are many more under-the-radar changes that happen every day.  People often move within companies, resulting in changes to email and business addresses, phone numbers, and job titles. Under the current environment where so many are working remotely, data decay has accelerated even further. So, it should not come as a surprise that a lot of companies are having trouble maintaining good data hygiene on leads, prospects, and even customers. 

Some examples of bad data include issues such as:

  • Duplicate data 
  • Missing fields
  • Data entered into the wrong field 
  • Inaccurate information
  • Typos, spelling errors, and alternate spelling variations 
  • Lack of data standardization and normalization

While the impact of these issues on your sales team is very tangible, the rest of your organization will also be affected. It just may take more time. 

Cost of Bad Data

A recent article about an insurance company with 54,000 employees serves as a great real-life example of the cost of bad data and, in particular, how duplicates can cause havoc. The problems began with conflicting data in their databases, but it really made an impression when they started experiencing issues with incorrect payments and mismatched vendor data. The problem was traced to a lack of standardized names for vendors (i.e., the same organization and contacts had misspelled or partial names entered). And when they queried the customer database, it was discovered that many of the records were duplicates. The employees of the insurance company had to manually sort through all these duplicates to identify which records should be eliminated and which should be kept (master record). And then they needed to determine if these customers actually paid their bill. 

This particular company had its employees do the deduping manually, an extremely time-consuming effort that also resulted in many disgruntled customers and revenue loss. Examples like this are not uncommon in many industries, which is why it is so important to be proactive with data hygiene in general and duplicates in particular. Manual deduplication is also impractical in many cases due to lack of resources or just the sheer size of the problem. This is why companies often turn to third-party apps to remove the duplicates or to prevent them in the first place. The alternative is just to keep re-doing the work manually.

Poor Prospect and Customer Relations

Bad data negatively impacts your prospecting efforts in many ways. First of all, your sales team will have to waste time researching basic information such as email addresses, phone numbers, job title, etc. Then we have to consider how much time is spent chasing prospects that are no longer potential customers. This can occur with duplicates since you can have multiple records, each containing bits and pieces of information that need to be reconstructed, with the result demonstrating that the prospect is not qualified. The other consideration is when data becomes stale. Old data can, amongst other things, demoralize a sales force. When so much effort is put into reaching a prospect, only to find out that they are no longer involved or helpful is a very frustrating experience.

To some degree, third-party providers can help to update and enrich your data. However, you must confirm that these organizations are reputable and authoritative. Buying data that is more outdated than what you have will set you back even further. At the same time, good data will result in a deeper insight into the current status of your customers and prospects and provide many opportunities for personalization. 

Lead Qualification Issues

Both junior and senior sales professionals understand that not all leads are quality leads. Of course, the best way to qualify a lead is to connect directly. Bad data hygiene and duplicate data are a big obstacle to doing this effectively. The last thing that you want to do is to contact the same person over and over again. It wastes the sales team’s time, but more importantly it often turns off the prospect. 

As a bad data real-life example, let’s take a look at Integrate, a SaaS platform for marketing automation. Between September 2016 and August 2017, they had 3.64 million leads. However, after doing some lead qualification work, they discovered that only 55% of those leads were actually good ones. It was also determined that duplicates were the biggest culprit accounting for one-third of their total leads or 1,217,769 total duplicates. While this is an extreme example, you can get a pretty clear idea of the type of chaos bad data can sow. The sooner you implement a data cleansing program, the better the results will be.

The Effect of Bad Data on Your Value Proposition

In order to create a value proposition for your lead, you will need to have certain information about their company and the problems they are experiencing. First, you need to understand why the prospect would buy from you. Then, you need to deliver quantifiable value and specific benefits based on accurate and timely data from inside your Salesforce (or other CRM) system. If the data is incorrect, your proposition to the customer is unlikely to meet their needs. The same holds true for all stages of the sales pipeline. Data quality is important throughout the entire sales process.

The Health of Your Sales Pipeline Depends on Effective Bad Data Management

The common theme we are noticing here is that significant problems with data quality make it more difficult to progress from one stage to the next of your sales cycle, and could put the entire process in jeopardy. In fact, as we saw from some of the examples mentioned above, you may be fooled by the quantity of the leads in your sales funnel, only to realize later that a significant percentage of those leads are simply duplicate records. In the manufacturing sector, quality assurance professionals use the phrase 1:10:100 to describe the accumulating costs of correcting a product as it moves through all of the various stages. 

The same concept can be applied to the costs companies will incur if they ignore their duplicates and other data quality issues. It costs $1 to scan the data for duplicates, $10 to fix each duplicate, and $100 per duplicate if nothing is done about it. If duplicates make their way through the pipeline and your sales professionals start devoting time to working with these records, you’re looking at wasted productivity hours that can get very costly, especially considering that there may be millions of records. It’s definitely better to get out ahead of the problem and resolve duplicates before they snowball into a huge chunk of your overall revenue. 

Trust DataGroomr to Turn Your Bad Data to Good Data

One of the biggest reasons DataGroomr is so effective is that it is easy to set up and does all of the work for you. DataGroomr uses machine learning to identify duplicates, meaning there is no need to set up any complicated rules. You will be able to customize the algorithm for duplicate detection based on your specific criteria and, over time, DataGroomr will learn to identify such duplicates in the future without being explicitly programmed to do so.

Try DataGroomr for yourself today with our free 14-day trial. There is no customization or setup required so you will be able to get started right away. DataGroomr will help you clean up your data, make it easier for your sales team to work with the data in Salesforce, and reduce the workload of your Salesforce admins as they will no longer need to waste time creating rules to catch every possible fuzzy duplicate!

Steven Pogrebivsky

Steve Pogrebivsky has founded multiple successful startups and is an expert in data and content management systems with over 25 years of experience. Previously, he co-founded and was the CEO of MetaVis Technologies, which built tools for Microsoft Office 365, Salesforce and other cloud-based information systems. MetaVis was acquired by Metalogix in 2015. Before MetaVis, Steve founded several other technology companies, including Stelex Corporation which provided compliance and technical solutions to FDA-regulated organizations.