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

Explore all aspects of bad data – types of bad data, the impact of bad data, and learn about bad data management and how to fix bad data…

Published on: March 4, 2020 |


The impact of bad data on B2B Sales and 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?


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, verybad

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!


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 datadue 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.

Inaccurate Data

The primary purpose of collecting data is to gain insight into customer information, behavior, and spending patterns. Thus, if you wish to make informed, strategic business decisions based on this data, it is crucial for the data to be accurate. Incorrect data (data stored in the wrong location or under the wrong object head) and inaccurate data (data fields containing fake or wrong information) can spell disaster for any business. It will affect the effectiveness of your campaigns, rendering them useless. Reaching a data-driven decision based on bad data will only result in bad leads and subsequent losses.

Example of Inaccurate data: Your data entry operator enters in place of Notice how tough it is to locate the discrepancy?

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Inconsistent Data

Inconsistent or non-standardized data is a type of duplicate data wherein the same data sets are spread across different locations in your database. However, what differentiates it from duplicate data is that the values may indicate different entities but represent the same thing.

Example of Inconsistent data: In a situation where you wish to target the “Operations Manager” of each company, you segment your database and send out the emails. Wait, but what about those data values that are saved as “Op. Managers”, “O.M.” or even “Operation Manager”? Don’t they mean the same thing?

Incompatible Data

What happens when you switch from one marketing automation platform or database to another? You may end up with bad data!

Your data might get polluted as you carry out data transfer. Alternatively, it may get scrambled during the transition as a certain field may be mapped against an incorrect data object. According to Dun & Bradstreet, 41% of data managers believed that inconsistent data across technologies could damage the ROI on the software.

Too Much Data

Yes, having too much data could also give rise to bad data. Data hoarding could result in data pollution, which compounds into poor bad data management. Furthermore, it could also cause slower data exchange, inflated storage requirements, failure to keep within compliance limits, and inability to maintain data recency and accuracy.

As you may have seen above, good data can go stale due to a variety of reasons. The most unfortunate aspect of bad data is that it is unavoidable!

3. How Bad Data Can Sabotage Your B2B Sales and Marketing Efforts

The effect of bad dataon your B2B sales and marketing team, as discussed above, merely scratches the surface. In reality, the problem runs deeper. In this section, we will conduct an in-depth exploration of how bad data can dilute your sales and marketing efforts:

Impact of Bad Data in B2B Marketing

High Churn Rates

The term “churn rates” is defined as a percentage of email subscribers that leave your mailing list over a defined period of time. Typically, there are two types of churns:

  • Transparent churn: Includes spam complaints, hard bounces, and unsubscribes.
  • Opaque churn: Contains emails that end up in spam or list of subscribers who no longer open your mail or the inbox of the said email address.

Transparent churn rate amounts to 25 to 50%, while opaque churn rate touches about 10 to 25% annually. Thus, this churn rate depletes about 25 to 30% of the average mailing list per year. It is not rocket science: for your mailing list to grow, the growth rate should be higher than the churn rate. Otherwise, your email list is either stagnated or worse, shrinking!

Clearly, bad data could add to your churn rate woes, and consequently, wreak havoc with your email marketing efforts. So keep your bad data in check!

Email or Phone Call Misnomers

You have to turn on your charisma at the maximum setting if you don’t want to count as “that annoying marketer.” And how does one do that? By establishing genuine human-to-human connections.

However, addressing an email to Katherine in place of Catherine could rub her off the wrong way. (I mean, seriously, couldn’t they have looked up my name?) Similarly, you could mess up by not staying up to date on who has made it up the corporate ladder and bagged a senior managerial or director-level role. Imagine calling someone a “Marketing Specialist” when they have worked so hard to get the coveted promotion to “Senior Marketing Head”! (It is almost like they don’t respect my accomplishments!)

An outdated database could lead to embarrassing situations like these, and while they may seem insignificant at first glance, they could drive a lasting impact.

Missing the Target Audience

Anyone in marketing worth their salt would know that the trick to pulling off a successful marketing campaign is by striking the iron while it’s still hot. It basically means that you need to send out highly relevant email or brand messages to specific people at the most opportune time.

To be successful, you need to compare them to your buyer’s persona, identify any buying triggers, and create a custom marketing strategy. Hence, it is imperative first to study your target audience and approach them when the time is right.

However, if your database is tainted, your marketing team will end up wasting their time and resources approaching the incorrect target audience.

4. Impact of Bad Data in B2B Sales

Sales Inefficiencies

Bad data is bound to affect the performance of your sales and marketing teams. The primary reason for the inefficiency lies in their inability to contact target audiences at the right time. For example, they push for a sale when they make a data-based assumption that the prospect is ready for it. However, due to bad data, this assumption may emerge as faulty, which brings the entire process to square one!

Further, every time your B2B sales team dials an incorrect phone number or messages a dud email address, they are wasting their time! This time could have been spent on making sales or nurturing prospects. Therefore, it is a no-brainer that bad data can negatively impact their productivity, which is basically a loss in revenue.

Higher Maintenance Costs

Think of bad data as an incompetent employee. Now that you have a rotten apple in your team, you must dedicate time and resources to hone their skills, train them, and teach them the tricks of the trade. You may also have to mitigate the damage caused by the rookie’s mistake. In some instances, they may be the dead weight that is slowing the entire team down.

Similarly, once you have bad data polluting your database, it is time to get your hands dirty and get it all cleaned up. The manual process of refining data

Furthermore, every department will have to accommodate bad data errors in their day-to-day functions, which makes it even more expensive and time-consuming. However, these manual corrections may not be reflected in the centralized data source. This cumulative effect of bad data, called Hidden Data Factory, can result in a snowballing effect, which, when unchecked, could transform into an avalanche!

Unfulfilling Customer Relationships

Once again, B2B sales are all about building long-term customer relationships. However, bad data can result in lost deals, customer churn, improper strategies, and poor communication. Communication gaffes like misspelled names, incorrect salutations, change in account details, contact mix-ups, undelivered messages, duplicate communications, etc. can hamper with the customer experience. And while it may seem insignificant, a subpar customer relationship arising out of poor communication can affect nearly 25% of your total revenue!

Further, not having access to accurate data can lead to sales goal misalignment and ill-informed decisions that affect sales strategies. Naturally, a poor sales strategy will result in the loss of resources and missed sales opportunities.

Dip in B2B Sales Team Morale

For salespeople, who make their earnings through a commission in sales, having to deal with poor data can be rather frustrating. They are basically relying on bad data or inaccurate information, over which they have no control. Moreover, it adds to the challenging nature of their already high-pressure job. In some cases, they may take it upon themselves to clean the database, which renders them unproductive.

Therefore, the all-round difficulty of their job can bring down the sales team’s morale!

Poor Brand Image and Reputation

An often overlooked impact of bad data relates to negative brand perception. Interestingly, it is also the most hard-hitting consideration given that issues like poor customer experience, communication errors, and lack of personalization can result in lead reduction as subscribers opt-out of your mailing list. At the same time, a high bounce rate could get your IP blacklisted, and spam complaints can even get your domain blocked! Data duplication could result in sending multiple emails to the same address, which is rather annoying.

All in all, every kind of bad data can unfavorably affect your sales and marketing campaign in a dramatic fashion.



5. Steps to Clean Bad Data and Best Data Hygiene Practices

Now that you understand the full impact of bad data on B2B sales and marketing, it is time to remedy it. Here are a few tips that can help you with bad data management:

Identify Bad Data

Before you begin, ask yourself the following questions:

  • Do you trust your data and data quality?
  • Does the data quality standards satisfy your organizational requirements?
  • Do you have a system or a process that can keep your data updated?
  • Do you have a universal source of data? Does the data repository sync with other systems and sources as used by your staff?
  • Does your B2B sales team have access to updated data so that they can spend more time selling than researching?

If your answer to any of the above questions is “No,” then it is time for you to conduct a health check on your data.

Your first priority should be to identify the bad data plaguing your organization’s database. For your convenience, here is an easy checklist on where to look and how to identify bad data:

  • The first place to hunt for bad data starts with your existing database. Check when it was last updated, and if it has been over a year, a chunk of it must have already gone bad.
  • Next, check your data sources. If the incoming data is not sorted before it is stored, it may result in data duplication.
  • Now, match up your existing database with your data requirements. If you have an excess of data, then it may have to clean it up and streamline it.
  • Finally, sort your data based on various fields and parameters. Locate the data with missing values as it is bad data.

Streamline Your Documentation Methods

Next, you need to organize your sales and marketing teams to ensure that they are not the ones introducing bad data to your database. Hence, start by mapping out the marketing funnel and the corresponding flow. Create a step-by-step process on where the data comes from, who gains access to it, what procedures to follow while vetting and storing it, and so on. Most importantly, align this workflow with your sales team for better conversion.



By outlining a clearly defined process, you are ensuring that low-quality data does not make its way into your database. With regards to data collection, here are two recommendations:

  • Streamline Manual Entry: Manual data entry leaves a significant margin for clerical errors. However, it is also not feasible to completely eliminate it either. In a situation where a single letter could determine the fate of the deliverability of an email, companies must work towards a centralized record creation policy for checks and balances on each entry.
  • Update Your Forms: Another source of gathering customer lead information comes through forms available on websites and landing pages. As your website develops, it can be easy to gloss over an old form available on some forgotten web page. As a result, you have varying data coming from different sources. Counter it by standardizing your forms across all channels.

Use a Lead Generation Platform

The most effective way to overcome human errors is by introducing automation. Thus, your business must invest in the right set of software and tools can help eliminate bad data. A tool that can fill in the missing gaps, verify the entries, and match and remove duplicate entries will play a crucial role in putting in the elbow grease and keeping your resources free to target the tasks at hand.

However, the tool that you choose must be compatible with your existing digital infrastructure to prevent any data pollution arising out of migration or miscommunication. These applications are often available in the form of SaaS, which makes it highly scalable. Hence, pick something that meets not only your current requirements but also something that you can scale up or scale down depending on the circumstances.

Automate Bad Data Management

Eliminating bad data is not a one-time activity. If you are in the long run, you need to make it a habit to review your data and conduct regular data audits. Additionally, you must also evaluate your data collection processes and sources to plug in any leakages through which bad data may be seeping into your database.

Do bear in mind that as your database expands and your business grows, it will start getting progressively difficult to maintain data hygiene. It will get tough to keep up with the dynamic information related to your leads, and once you lose your grip, the bad data will start to trickle in. B2B sales intelligence solutions such as Clodura can automate this task for your company.

Only Collect Data That is Required

With the volume of big data pumping through the networks, it is easy to get swayed and overshoot your requirements. However, storing much more data than you require can also emerge as a headache.

For starters, you may need a larger database to store all the information. Unfortunately, most of this data will not qualify as usable! As a consequence, the unusable data will only end up as defunct bad data. Next, the information overload will make it difficult for you to dig through bad data and locate the good data. Finally, managing this data will be not only difficult but also time-consuming and a burden to your resources.

Therefore, it is essential to maintain a lean and organized database to keep it succinct yet impactful.

6. How to Measure Data Quality – 4Ts of Data Quality Measurement


To make the best use of your data, you need to have the latest and current information on your leads. Thus, trending and up to date information will aid you in your quest for achieving your business goals.


Having a database that is riddled with typos and clerical mistakes will only let your efforts go in vain. Trueness relates to data purity and correctness with regards to new and existing data. It ensures that your messages find their way to the intended recipient.


Data inconsistencies can hurt lead scoring, routing, and reporting. In this aspect, your data must be consistent and normalized to ensure tenability.


Incomplete data can make it tough to target leads with missing information. However, long forms can deter the visitor from signing up. Thus, data needs to be thorough in the sense that it covers all the basics to carry out granular segmentation without scaring away the lead.

With this 4T approach, bad data does not stand a chance to show up in your database. Not now, and neither in the future!

Final Thoughts

The message is simple – Bad data breeds bad leads.

What makes bad data even more potent is its ability to mutate and spread like cancer. If left unattended, bad data could cost your business its life! Having a unified and integrated sales data platform like Clodura can free up your valuable human resources. As a result, your sales and marketing teams can then focus on adding value to the organization, meeting prospects, and closing more deals.

Divyaprasad Pande is Marketing Director of Clodura.AI He has more than 12 years of experience in marketing, having worked in various leadership roles for various companies. He is passionate about driving business growth and success through strategic marketing initiatives to increase brand awareness, generate leads, and support the sales team in achieving revenue goals. With a strong background in marketing and experience in the tech industry.

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