Why Bad Data Sabotages Your Sales

Data is the fuel that drives successful marketing, especially as the world becomes more customer-centric.

But not all data is created equal. There is a difference between good and bad data, and the costs for using bad data are higher than you might think.

Data and Marketing Association (DMA) predicts that this year and beyond there will be an increasing emphasis placed on the quality of data because B2B brands are finally starting to see the harsh truth: bad data hurts your bottom line.

Not only does it prevent marketing campaigns from performing, but bad data also costs sales reps their time and energy to fix. That’s additional lost value for every dollar spent.

For organizations that want to make the most out of their marketing campaigns without wasting valuable resources, having good data is essential.

But what makes data bad, exactly, and how do you know if you have it? More importantly, how do you fix it?

What is “Bad” Marketing Data?

Some might say that there is no such thing as good or bad data and that all data is just that: data. But bad data does exist.

Bad data is not the same as unsuccessful data, meaning that it isn’t classified as “bad” just because it fails to give you marketing insights; it’s “bad” because it’s inaccurate.

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Over time, leads change positions in their company, change their email addresses, or leave those companies altogether. Companies may also fold or merge into other companies.

Not only is this unhelpful for creating marketing campaigns, but this sort of bad data can actually harm your bottom line.

According to a study from Gartner, the average financial impact of bad data to organizations is $9.7 million per year, with Harvard Business Review (HBR) reporting that figure at nearly $3 trillion annually across all industries.

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For smaller businesses, Blue Sheep research found that smaller companies lose 6% of their revenue each year due to poor-quality data.

You shouldn’t assume that your business is immune, either. SiriusDecisions reports that between 10% to 25% of all B2B companies have critical errors in their marketing databases.

But the price tag isn’t all about money.

Time that could have otherwise be better spent nurturing leads and building customer relationships is spent updating databases or searching for new data. One study by DiscoverOrg found that sales teams lose about 550 hours each year updating old data.

HBR calls it the “hidden data factory.”

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In past studies, HBR found that up to 50% of employee time is wasted in these hidden data factories, hunting for data, finding and correcting errors, and searching for confirming sources.

With so much on the line, it’s vital that B2B businesses ensure their data is clean and accurate, without wasting employee time doing it.

How to Find Bad Marketing Data

So how do you know if you’re plagued by bad marketing data?

If any of the following sounds like your organization, you probably have bad data:

• Your databases aren’t regularly updated — If you haven’t scoured and scrubbed your CRM in years, the chances are high that you have inaccuracies somewhere.
• You don’t regularly use all the data you have — If you only rely on certain data, like names and emails, even though there is a lot more in your databases (job title, company name, phone number, etc.), then that data needs to be cleaned to ensure accuracy.
• You don’t have a system for collecting data from multiple sources — If someone can give you their email multiple times (sign up forms, event forms, etc.), but you don’t have a way to eliminate duplicates, you have bad data.
• You don’t know how to use your data for marketing insights — If you’re not sure how to use your data, you might eventually find you’ve collected the wrong types of data (you have too many phone numbers and not enough emails, for example). Missing data is also bad data.

Of course, just because you have bad data somewhere doesn’t mean it will pop up and say hello. Sometimes you have to root out that data, which means knowing what to look for.

Here are a few of the most common types of bad data you might encounter.

1. Duplicate contact records

When you have two or more of the same contact represented in your database, it not only creates clutter but can lead to confusion, especially if certain records contain different or incomplete information.

Ideally, your CMS or database should be able to double-check and remove duplicates, but if not, this process has to be done manually.

2. Incomplete profile fields

In some instances, data might come into your database already missing critical information. This is often due to user error — a contact doesn’t complete every step of the form, or the sales rep doesn’t fill out (or ask about) every field when interviewing a lead.

Missing or incomplete information is often the hardest to recover from, as it takes reaching out to that contact to retrieve the information.

Incomplete information means holes in your rep’s messaging, weaker outreach, and ultimately, less closed sales.

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3. Typos or inaccurate entries

Inaccurate data entries can happen when someone (either the contact or rep) inputs information incorrectly. If a prospect’s email address was “@businessname.com” but was entered as “@businessname.co,” then that email is considered bad data.

While it’s mostly up to the team or system collecting the data to ensure its accuracy, this type of bad data can, unfortunately, be difficult to avoid 100% of the time.

4. Incompatible software migration

In one survey, the biggest obstacles for receiving ROI on marketing data were inconsistent data across technologies and integrating technologies.

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In other cases, you may have complete and accurate records in one system, but lose data when it’s migrated over to another system.

Even importing data from an online form into a CRM may cause issues if those systems aren’t properly integrated. Spreadsheets, in particular, can cause problems when being imported.

Some of this bad data can be fixed fairly easily, but some of it requires more time-consuming cleaning processes which can eat into company time if not done properly.

How to Fix Bad Data

As they say, “The best defense is a good offense.”

Having a strong B2B data practice is important if you want to keep data clean, accessible and, most importantly, actionable across multiple departments, locations, channels or even brands.

Here are a few data management principles that can help set you up for success.

1. Know what bad data looks like

Again, bad data is any data that is missing, incomplete, inaccurate or otherwise unhelpful to your team.

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Wrong email addresses and phone numbers are just the start. You might also notice:

• Blank data fields
• Duplicate data (even if it’s accurate)
• Not enough data (low-quality data)

A lack of data can also be considered “bad data,” so if you’re not getting what you need, you should have a strategy for getting more of it. Keeping in mind, of course, that all data will go bad over time.

2. Keep your team organized

Sadly, people can be one of the biggest roadblocks to maintaining good data.

Whether it’s a contact or lead giving you a misspelled email, or a sales rep entering in the wrong phone number on accident, user error can wreak havoc on your data.

Before you tackle your database, you have to ensure that your team is organized in a way to prevent bad data from entering your system in the first place.

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This may mean mapping out your marketing workflows — where your data is coming from, who has access to it, and what procedures they should follow — before you do anything else.

3. Choose a CRM that manages bad data

You also want to create opportunities for CRMs or other software to fill in the gaps.

Choosing the right marketing CRM is important, of course, but it’s equally important to make sure that CRM integrates well with other software or tools you’re using.

Sure, Salesforce integrates with practically everything. But it’s also expensive and a pain to use for most small teams.

If you use Gmail all day, just use a simple Gmail-integrated CRM. It doesn’t even have to cost you a lot. SalesMate has a free one to test out.

4. Automate data wherever possible

Most clerical errors occur because data is being entered into the system manually.

Automating manual steps such as moving figures and summations can reduce bad data significantly.

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You can (and should) automate everything from the initial contact information collection through CRM update and follow-up replies. That way, no friction exists in between each step and the odds of clerical errors will fall dramatically.

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Automating other parts of your lead generation process, like using online forms or collecting emails with tools that integrate directly with your CRM (and are designed to do so), can also reduce the odds of clerical errors.

When things are automated, bad data is often eliminated before it ever touches your database.

5. Only collect the data you absolutely need

More data doesn’t equal better data. Most B2B companies have more data than they know what to do with, which can make it even harder to spot missing or incomplete data until it’s too late.

The easiest thing to do is, first, clean out your database of any unnecessary data. This means removing duds and duplicates or incomplete and outdated contact records.

Second, get rid of any fields that you don’t absolutely need.

If you don’t plan on reaching out to people via direct marketing strategies like postcards or mailers, for example, don’t bother collecting addresses. Streamline your data to focus on the marketing strategies that really make an impact.

6. Perform regular data audits

If you’re not regularly updating your records for accuracy, you’re going to have more bad data, which means your campaigns will never be as successful as they could be.

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These audits should ideally review how data is collected and how the collected data is being used. Specific points for data audits include:

• Reviewing data collection forms and eliminating any unnecessary information.
• Creating consistent practices across all of your data touch points, like websites, ads, social media, and so on.
• Using tools provided by your CRM (or outside tools and applications) to automate the data cleaning process.
• Adding expiratory dates to all of your data, to signal when another audit or update should occur.
• Reaching out to contacts to update stale records and noting any changes (email addresses, job changes, etc.) in your records (this process can be automated in some cases).
• Centralizing your database so no information is left “out there” and that each data silo makes its way there.
• Creating a marketing taxonomy for your campaigns so that all information is categorized and classified appropriately in your database.
• Making sure your team understands what to do with data and how (and when) to handle data discrepancies.

Regular audits will help ensure that your marketing efforts (and your bottom line) aren’t ruined by bad data.

7. Hire someone to clean and audit your data

Sometimes doing it yourself or even with a small team isn’t always the best option. It can be easy to look at streams of bad data without noticing any glaring inaccuracies.

Or, you might notice too many and feel like you’re not sure where to start.

There’s no shortage of both free and paid tools that are designed to help you achieve optimal data cleanliness, however. And many might already be a part of your existing CRM software (like Salesforce) or other analytical tools.

But if your tools or your team can’t get the job done, considering hiring someone who can help you clean your data. Gmass offers the following five-step checklist that almost anyone can follow to find email addresses:

1. Start with LinkedIn searches
2. Try the LinkedIn Sales Navigator Chrome extension (formerly Rapportive)
3. Try the ContactOut app
4. Ask Norbert to help
5. Subscribe to your contact’s company newsletter

Bad data will cost your business significantly more than the costs of a cleaning solution.

Conclusion

Bad data hurts your bottom line.

Not only will it cost you in terms of revenue — losses in the millions to even trillions across all industries — but also time, something that you can never get back.

Think of it this way: would you rather have your dedicated sales and marketing team, whom you pay for their top talent, spend their time talking to qualified leads, or would you rather have them chasing down email addresses?

Hopefully, you’d rather have them talking to qualified leads. But to make sure that happens, you need to ditch the bad data that keeps them from doing their jobs.

Read more: What is Bad Data and its Side-Effects?