The Impact of Bad Data on B2B Sales & Marketing and How to Fix it

The stage is set; your B2B sales and marketing team has discovered a hot, new lead. Now, it is only a matter of contacting your POC who can endorse your product or service and get the nod on the purchase! It sounds like an easy sale, doesn’t it?

So you get on your phone, dial in the digits — only to connect with some other random employee of the company!!!

How often has this happened to you? If the answer to this question is “Often,” then you’ve got a “Bad Data” problem in your hands. Fortunately for you, you have come to the right place! In this post, we will discuss everything that there is revolving around bad data.

So, without further ado, let’s start with the basics: what is bad data?

What is Bad About “Bad Data”?

As cliche as it may sound, data is the fuel for successful B2B sales and marketing. So every bit of data regarding your customer or prospect must count to something fruitful, right?

Wrong.

The truth is that not all data is created equally. For example, kerosene and petrol are both fuels. But you can’t expect to run your car on kerosene!

Along the same lines, bad data is the kind of data that acts as an obstacle in the successful execution of your B2B sales and marketing campaigns. Bad data could be duplicate data, incomplete data, inaccurate data, or incorrect data.

According to Neil Patel, data quality is a factor of the following aspects:

• Availability: Does the organization have a database in the first place?
• Validity: Are these data values acceptable and valid?
• Consistency: Is the data based on the universal truth? Or are there variants available in different locations?
• Integrity: Is the relationship between the data set and the corresponding data entry accurate?
• Accuracy: Does the data value accurately portray and define the data properties for the object model?
• Relevance: Is the data appropriate or relevant for the data objective?

Naturally, your sales and marketing teams that are dealing with bad data will already be at a disadvantage right from the start. They will be unable to reach their target audience or meet their target goals. As a result, your team’s morale and productivity are more likely to take an irreversible hit. In most cases, bad data and subsequent bad leads can result in the systemic breakdown of your sales and marketing unit.

So, in a nutshell, bad data is very, very bad.

1. Bad Data in Figures

Do you believe that your business is unaffected by the debilitating effects of bad data? Consider a few statistics given below that highlight the prevalence and consequence of bad data in varying gravity and intensity:

• SiriusDecisions’ report, The Impact of Bad Data on Demand Generation, highlighted how 60% of marketers rated the overall health of their database as unreliable, which 80% admitted to having “risky” phone contact records.
• Furthermore, according to the Harvard Business Review, only a measly 16% of managers trust their data quality and utilize it while making important business decisions.

• In another survey, the Harvard Business Review discovered that only 3% of company data collection satisfied the “acceptable” range of error, while 50% of newly created data entries had critical errors.
• Kissmetrics discovered that about 60% of employees change their job titles or organizations every year. Also, 25% of email addresses go out of date annually.
• As per the Data Quality Index Report published by Integrate, nearly 40% of B2B sales leads contain inaccurate data.
• According to MarketingSherpa, about 25 to 30% of data in a database becomes inaccurate or loses relevance every year

• Your skilled sales and management teams may be wasting nearly 50% of their time handling mundane data quality tasks. This figure goes as high as 80% in the case of Data Scientists.
• Participants of What’s Working in Demand Generation report, published by DemandGen, reported that bad leads are the biggest challenges in B2B sales and marketing.
• A LeadJen Study brought to light that sales departments lose about 550 hours and USD 32,000 per salesperson due to bad prospect data.
• According to IBM and Harvard Business Review, bad data has cost the economy a whopping USD 3.1 trillion per year in the US alone!

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2. Different Types of Bad Data

As we stated previously, not all data is created alike. The same ideology extends even to bad data. Accordingly, bad data can be classified into the following categories:

• No Data

The worst and most common kind of bad data is no data at all! Some B2B companies may believe that data is unimportant in the initial stages. But the problem here is that data is the oil that will drive the growth of your organization!

• Outdated Data

Some things get better with age. Data is not one of them.

You come across an interesting case study or an industry report. However, its efficacy is diluted by the data that is several years old, making it irrelevant.

Typically, outdated data is a result of:

• Individuals changing roles or switching companies
• Re-branding or companies or mergers and acquisitions
• Shutting down of businesses
• Evolution of software or systems past their iterations

Given the highly dynamic nature of data in the business world, data decay is inevitable. Thus, in order to make effective decisions, companies must ensure that their data is fresh and up to date.

Example of Outdated data: Suppose you wish to contact a sales manager at XYZ Chemicals. Your database gives you information about Sam Smith and their contact information. However, when you connect with them, you get to know that Sam Smith has left the organization and has joined ABC Pharmaceuticals!

• Duplicate Data

Duplicate data is yet another cause of bad data. It could be a result of data migration, manual data entry, third-party connectors, data exchanges, and batch imports. Quite often, the commonly duplicated fields include Leads, Accounts, and Contacts.

Bad data due to duplication could lead to:

• Inflated storage clutter
• Inefficient data recovery and workflows
• Skewed data metrics and analytics
• Poor or incompatible software adoption due to data inaccessibility
• Dip in ROI on Marketing Automation Systems and CRM

Data redundancy and duplication in a data-driven environment is akin to injecting poison in a major vein. While you are using up space to save copies of the same cold lead, you may miss out on saving or capitalizing a mature one!

• Incomplete Data

Data records that are missing key data objects or fields may be classified as incomplete data. While it is not possible to collect every tiny bit of information, missing out on vital details that are necessary for B2B sales and marketing activities.

Certain processes, such as lead scoring, segmentation, and routing, depend on data fields to function and operate. Thus, having more data points can grant your team more flexibility in targeting leads.

Example of Incomplete data: Consider that your company is running a campaign for small businesses operating in California. Now, if there are certain companies that have their industry or location fields empty, you cannot locate them even if they do belong to your target audience. Thus, you miss out on revenue-generating opportunities.

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