You're investing in new marketing technology and making a serious effort to move away from outdated marketing practices. It's fantastic that you've recognized that the time for print brochures has passed, but fancy marketing tools in themselves are just tools. They’re not magic. 

“If we just invest in this expensive CRM, we’ll finally be able to control our customer data”

“We bought all these customer data lists, we can import them into our new tool and start personalized marketing”

Your SDRs all have their own data input workflows, each landing page is asking for different bits of data, in different orders, and a normalized database feels like something of a pipe dream—but you’re going to try automation anyway?

Do you hear yourself? Is it denial, or are you truly convinced that it will work?

Let's be honest. Your growing database is likely a mess. Worse—I’m sure much of it is unusable. You can't blame the technology for not functioning properly if you aren't giving it clean data to work with. 

It is not something to put off until later; this a data emergency. 

Want to check the health of your CRM data? Request your free data quality check.

The current state of data 

Let's talk about the current state of data. Too many companies wait until it’s far too late to pay attention to CRM data management. That’s fair, it’s a to-do that sounds more overwhelming than it will pay off. Especially if all you did before was sending out blanket B2B email marketing campaigns about upcoming trade shows or new product launches (did that work, really?).

But today, personalization is table stakes, and that requires some semblance of email list hygiene and data management.

Mailchimp report on the benefits of segmentation in email marketing


While you kept putting off managing customer data sets (or managing the way your team inputs them), suddenly, it’s all a disaster. How did it get to this point?

While it can seem like an impossible task, getting your data in order is a non-negotiable if you want to have any modern marketing success. Buying Hubspot and making your team use it is not enough. Data is king.

The impact of bad data

According to Gartner, poor data quality costs organizations an average of $15 million annually. Bad data will cost you. If you're putting bad data into expensive and advanced marketing tools, you're still going to get bad results. As the saying goes, garbage in, garbage out. 

Working with bad data means that any automation or machine learning tools you try to implement will be useless. You can keep uploading data into your CRM, but the reality is that bad data is worse than having no data at all. The impact of poor data quality goes beyond just financial implications. It also results in:

  • Wasted time 

  • Poor customer satisfaction 

  • Negative effects on customer retention/churn rate

  • Product delivery errors

  • Inaccurate reporting of campaign metrics

  • Failed marketing automation strategies

  • Missed opportunities for conversions and sales

  • High rate of unsubscribes

  • Getting flagged as spam

  • Bad reputation and negatively publicity on social media 

  • Loss of productivity

  • Loss of revenue

Your CRM dataset is a Frankenstein monster 

Looking at your contact lists, it's hard to imagine how they got so bad. Years of poor data management (or no data management at all) have likely turned your data into a complete jumbled mess. Let's break down how it ended up like this in the first place. 

Consider how you populate your CRM or datasets. If you have contact data coming in from various sources, it's hard to control what it looks like. Most likely, you’ve been filling your CRM from the following sources:

  • Online forms

  • Manual entries 

  • CRM migrations 

  • Mass uploads from Excel sheets

Collecting data via online forms, such as when people subscribe to your email list or register for webinars, means you have some control over the format. An innocent mistype, a stubborn keyboard letter, and human errors statistically happen more than convenient for your CRM. These kinds of errors are beyond the control of your team and cumulate.

You may also be uploading data to your CRM from excel data sheets. If you have multiple branches that are all collecting and contributing data to your CRM, things can escalate quickly. A lack of normalization across departments and regions can turn your data into something ugly, regardless of how good your own department’s practices are.

You could also have datasets in multiple CRMs, with different branches choosing to use different platforms. With so many CRMs out there, it's common that companies will use different CRMs or collect data in different ways. It is especially true when talking about the global market.

If you've got multiple branches uploading to a centralized database like HubSpot, then your list is bound to look like a disorganized mess. Most companies haven't figured out a way around it, and no one is ready to own it.

With so much data entering your CRM lists from different sources it looks like a total Frankenstein monster. It's so badly pieced together that you can't use it for anything. 

The main culprits of bad data in your CRM

graphic showing step by step what is needed for data integrity

Bad data includes these seven main culprits:

1. Missing data 

If you have empty fields in your data sets, you are missing opportunities for personalization. These fields are supposed to have data, but someone didn't fill them in. It could be pivotal details like their email, and without it, you can't even use the contact.

2. Inaccurate data 

Inaccurate data is anything that was entered incorrectly or not properly maintained. Inaccurate data includes:

  • Wrong contact details (names, emails, phone numbers, job titles, etc.)

  • Outdated information (old addresses, phone numbers, surnames)

  • Incomplete information  

3. Wrong field 

Extremely common in data sets is that information ends up in the wrong field. It could be an upload issue or due to someone accidentally entering details into the incorrect field. 

4. Non-conforming data

Many organizations don't set data normalization standards, and therefore data ends up non-conforming. Formats are wrong, and there is no cohesiveness across how data is entered into the system. Here are some examples of how the same piece of data can look without normalization.

Date

28/10/2020

28.10.2020

October 28, 2020

Telephone number

+1-555-555-5555

5555555555

1(555)-555-5555

Name

Mr. John Smith

J.Smith

John F. Smith

5. No documented workflow

If every salesperson has a different workflow, or none documented at all, the data entry process will vary from person to person. That data handed off to someone else is useless if you don’t know what it means.

6. Duplicate data 

Duplicate data is whenever a single account, lead, or contact has multiple records in your database. 

7. Poor data entry/human error 

Poor data entry includes typos, variations in spelling (such as U.S. vs. U.K. English), misspelled names, incorrect format, transpositions, etc.

If your data is bad, your automation tools are useless

CRM software sounds like the end-all solution, but unfortunately, it doesn't work that way. If you spend money on automation tools but haven't changed your data management process, then you still won’t be able to do anything with that data. A new CRM won’t change anything.

For these advanced data analyzing tools, data quality is vital, and your Frankenstein list will not do. There is no opportunity for customer segmentation in your CRM or any type of personalization if your data is patched together in a way that not even a detective could figure out.

Check out our post on data cleaning steps and techniques to make any CRM powerful.

The cost of bad data 

So, how much is bad data actually costing you? According to IBM, poor data quality costs the U.S. economy a whopping 3.1 trillion per year, but where are you losing money? You're uploading lists of 2500 contacts at a time, and CRMs like HubSpot charge you a base pricing rate of $3200 plus more based on how many contacts you have. So, if half of your contact data is bad, then you're wasting a lot of money. 

If a salesperson calls the wrong number, it both wastes their time and means they can't make a sale. Inaccurate data wastes approximately 27.3% of sales reps' time, which averages at about 546 hours of time wasted a year. If you pay your sales reps the average $20/hour, that’s $10k lost per year, per sales rep. And you said cleaning your data can wait?

Beyond the money that you're spending on CRM software and tools, and the money lost on wasted employee hours, there is also the money that you aren't making as a result of bad data. Not only does lack of personalization mean prospects will flag you as spam, but inaccurate details mean you can't even reach them in the first place. 

Does looking at your #CRM data stress you out? The best CRM software in the world can’t help you if you don’t clean up your data act. This post looks at the state of your CRM, and how it became a breeding ground for #BadData:

Click to Tweet

How to fix bad data 

Okay, now the scary part is over.

The good news is that there’s hope. You can clean your data, after all. And it doesn’t need to be an overwhelming task.

It's entirely possible to clean up your data act and optimize your data management process. You can get rid of your current Frankenstein setup and begin working with a clean list of contact with a few key steps. 

Visual listing the difference between data enrichment vs. data cleansing

1. Identify the main sources of bad data 

Where is the majority of your bad data coming from? Identifying the main sources will allow you to adjust your processes accordingly. Recurring bad data can come from:

  • Online forms which keep allowing duplicates
  • Unclear fields, where people are continuously inputting incorrect information 
  • Specific branches of your company that keep uploading messy Excel spreadsheets 
  • CRM migration that always makes a mess of your data

2. Stop collection of bad data

Prevent the collection of bad data at its source. Data normalization is the best way for you to keep your data clean. There needs to be a cohesive approach to data normalization for large manufacturing companies with branches in different places. For some reason, it's overlooked, and everyone continues to input their data their own way. Put one person in charge of normalizing data entry and CRM strategy across the company. You can thank us later.

Implement strict data normalization processes and formatting across all your branches and CRMs. Ensure that each branch understands precisely how to input data and adheres to these policies. Also, make any fields in online forms ultra-specific to encourage users to enter their details correctly. Include descriptions for each field in a master sheet.

3. Cleanse your data

The reality is that your data will never be perfect. Bad data will always slip through the cracks, but cleaning your data regularly will reduce the number of inaccuracies in your data sets. CRM data cleansing will detect and correct bad data entries by replacing, deleting, or modifying them according to standards you set. 

You don't need to do this manually, as there are some fantastic data cleansing tools that you can use to automate the process. Some data cleansing tool providers even offer data enrichment, so not only is your data clean, but you'll also have more to work with. There are some key differences between data enrichment vs. data cleansing, but both will help you get the most from your data. 

Takeaway

If you don't solve this data emergency fast it's problematic. The longer you wait to improve your data management, the messier it will become. This Frankenstein monster is out of control, and until you clean up your data act, you won't be able to move forward.

Want to check the health of your CRM data? Request your free data quality check.

Maeva

Maeva - November 10, 2020

Maeva Cifuentes is a B2B content marketer for tech scaleups and the founder of Flying Cat Marketing. She helps SaaS companies build trust and authority with useful, on-brand and original content.