How often have you called or emailed a prospect and received the response, “They no longer work here… They left five years ago.”
You hang up, and then maybe curse your CRM or lead database as useless. But what do you do next?
Do you update the contact information or just move on to the next name on your list?
And do you ever stop and think about how former sales reps probably ran into the same issue but did nothing about it, thereby leaving you and all the future generations of the company’s sales reps in the dark?
“Show some integrity!” you might think. Data integrity, to be exact.
Today, a clean database is critical to your success as a sales team. Good, accurate data allows you to spot sales opportunities, make better decisions, and spend more time reaching out to people who actually work at your target company.
So in this post, we will go over how to keep your database neat and tidy so that you can unlock the ROI hidden within.
What Is Data Hygiene?
Data hygiene is defined as the collective processes conducted to maintain the cleanliness of data.
Data is considered clean when it is free of errors such as typos, duplicate records, or outdated information.
Of course, anyone who tracks data will come to a point where their data has some of these errors. That is where data cleaning comes in.
Data cleaning is the process of finding and correcting any issues within a data set. It can be as simple as updating your client’s phone number in the CRM and as complex as writing an algorithm that finds and corrects every numerical error in your data set.
But no matter the scale of effort, data cleaning is worth it.
That is because you use data every day to make decisions. And you want correct information to inform those decisions.
If a gold digger in the Gold Rush heard from his pal that gold was red, he would spend a year taking incorrect action and come back with wheelbarrows of clay.
Here is an example of using clean data to make a decision.
If you have accurate data in your CRM relating to past prospect meetings, you can use it to locate the prospects who are most likely to buy your solution.
Perhaps those are accounts that were “closed/lost” in the last year. You figure that if they considered buying once, they might again.
So, you try giving each of them a call, bring up something about the past sales conversation, and see if a sale is possible.
If your data lacked that information, you might have spent your time calling a random cold prospect with whom your team has never spoken. Therefore, your chances of a sale would be less likely.
Characteristics of Clean, Quality Data
Quality, clean data is, first and foremost, accurate. You want the system that contains your data to be a source of true information about whatever you are tracking.
Here are some other characteristics of clean, quality data:
Complete
Your staff can draw more insightful and accurate conclusions from data that paints the entire picture. A larger sample size leads to more accurate conclusions.
Let’s illustrate how.
Say your sales team tracked every single reason (1000 in total) a prospect has given for not buying your solution—the price is too high, the product lacks integration with another software, the sales rep did not curse enough, etc.
You can analyze those results and draw accurate conclusions about your product or selling strategy. Perhaps you will find that 250 out of those 1000 cited “lacking a specific integration” as a reason. Now you know what to do next about your project.
If, instead, you documented only 100 out of those 1000 instances, you might find that 10 of 100 gave “lack of software integration” as a reason.
This will tell you that only 10% of all clients find it a problem. But, as you saw, this is inaccurate. It should be 25%, not 10%. Why did this happen?
Because the other 240 complaints about the “lack of integration” were hidden among the 900 instances you did not track.
Therefore, your results weren’t telling the whole truth.
And since 10% is much smaller than 25%, and therefore much less deserving of attention, this inaccuracy could lead you to make poor product choices going forward.
Consistent
There should be uniformity between similar records in your system. Data entry rules for record names and fields make it easier to report on the data.
For example, if you wanted to pull every account in New York City, but when entering the data, some sales reps type NYC while others type New York, you might accidentally exclude some of the accounts.
This will give you an inaccurate view of the situation and result in data filtering problems. To prevent this, make sure your reps are entering data in standardized ways.
Unique
Duplicate records misrepresent information in reports and cause trouble for those working on the backend of the data set. They can also cause your sales reps to miss out on details about an account or lead.
For example, say you have two accounts in your CRM for the same company—Farms Are Us and Farms R Us.
Some reps will put their data concerning phone calls, emails, and other interactions into one of them, while other reps will put theirs into the other.
This will prohibit any future sales rep from seeing the whole history of the account. To avoid confusion, you should make sure all your records are dedicated and unique.
Cost of Dirty (Bad) Data
In sales, dirty data hinders your ability to make data-driven decisions, which are integral for gaining an edge over your competition.
When your data is dirty, you might draw the wrong conclusions and, as a result, take the wrong actions. You might call the wrong numbers when prospecting, write to the wrong email address when following-up, or use the wrong name when addressing a client.
Dirty data can waste your time, frustrate you indefinitely, and lose you sales. Not to mention, it can damage your relationship with clients.
1. Wasted Time and Money
According to Gartner’s research, “the average financial impact of bad data quality on organizations is $9.7 million per year.”
Another research found that this poor data quality costs businesses at least 30% of their revenue.
Although the financial costs of bad data depend on your company’s reliance on data, it is safe to say that bad data costs you money.
The main way it can hurt your revenue is by influencing you to confidently enact ineffective sales and marketing strategies, assured they would work. This certainty of success can make it hard to turn back on a failed plan, causing you to continue accruing losses.
With bad data, you also might miss out on opportunities. For instance, perhaps there is a segment of the market that really needs your product, but this is hidden from you in your mangled, confusing data.
Finances aside, bad data can also waste your time. It can make some low-potential leads look good, causing your sales team to spend precious time reaching out to leads who probably won’t buy your solution.
The opportunity cost here hurts too. If the lead data had been cleaner, your reps could have been calling on quality leads and creating more opportunities.
2. Higher Churn Rates
Poor data can make you look foolish. Even worse, it can cause you to make mistakes that make clients think you don’t care about them.
You could accidentally call them by the wrong name or talk to them about job responsibilities that are completely out of their scope of work. Consistently making errors like these could lead to higher churn rates among your clients.
When building and maintaining relationships in sales, it pays to know a lot about your clients and to demonstrate that knowledge to them.
Television is misleading when it shows the best sales professionals asking, “How are the wife and kids?” Top reps don’t do that.
They refer to the spouse and children by their names, thereby showing the client that they value the relationship.
The costs of a bad memory for names can be eliminated by a good CRM. It doesn’t matter if a sales rep memorized the information or not, as long as they took the time to dig up the information before the meeting.
3. Mismatched Emails and Disengaged Audiences
If you partake in any email marketing or nurture campaigns to keep your audiences engaged with your brand, you want to be sure you are sending those emails to the right addresses.
Bad data can cause you to do the opposite.
If your data consists of wrong email addresses, tons of your well-written emails could be flying straight into a black hole.
And even if you are sending to the right email addresses, your database might not have the right information about the recipient, hindering your ability to personalize messages.
For instance, you might have set up a system that sends certain emails to certain titles. If your system doesn’t know a subscriber’s job title, it might send them every single email instead of the ones specifically catering to roles like theirs.
And this lack of personalization leads to disengagement among your audience. Even worse, it annoys them and causes them to unsubscribe or swear off your brand forever.
93% of consumers say they receive marketing communications that aren’t relevant to their position, and a large majority (as many as 90%) of those consumers report finding such communication annoying.
Don’t let this happen.
Use clean data to personalize relevant messages to your audience. Automatically send them a gift card on their birthday. Address them by their correct name. Most importantly, send VPs of Marketing emails that VPs of Marketing actually want in their inbox.
Related: Best Email Subject Lines for Sales
4. Lower Email Deliverability and Open Rates
There is nothing more annoying than spending 10 minutes writing up a personalized, persuasive email only to see it bounce. Sadly, this happens to a lot of salespeople.
As many as 67% of brands report problems with email deliverability and bounce rates due to poor data quality.
On top of that, 28% of brands report hits to customer service because of invalid email addresses and an inability to effectively connect with customers.
Poor data quality is especially frustrating when it prevents sales reps from connecting with leads. During prospecting, reps don’t want to search for email addresses or be forced to guess.
If that happens, reps will lose their train of thought, and the work will start to seem tedious.
Instead, you want the email address right in front of you, so that all you worry about is whether they take the call to action, not whether the email finds them.
5.Potential to Get Blacklisted
A blacklist is often defined as “a list of individuals or organizations that are penalized because they are believed to engage in unethical business activities.”
Inclusion on a blacklist can negatively impact a company’s ability to sell or market goods.
One way to get blacklisted is by contacting (calling or emailing) a prospect who told your company that they wish to no longer be contacted.
The SEC states that you must listen to such requests and put them on your do not call list.
It is one thing to say you won’t email them again. The hard part is remembering not to.
A good CRM can help with this. On most CRMs, there is a little checkbox next to a field that states, “Do not contact” or “Do not call”. But this means you have to remember to fill in this data.
If you don’t, you might forget their demand and continue spamming them with emails. If they get angry enough, they could report you, leading to an investigation and a potential blacklisting.
Now, getting blacklisted is very rare, but it can happen. So it’s best to eliminate the possibility entirely by keeping your data clean.
Data Hygiene Best Practices
To keep your data clean, create protocols for entering data, and make sure you communicate them to your team.
That way, whenever an employee goes into the database to add or change any data, they are doing it correctly.
Here are some data hygiene best practices for your team to follow during their daily routines, along with some easy ways to clean up any already dirty data.
1. Handle Missing Data
Missing values in your data can contaminate the data set and lead to inaccurate reporting results.
Therefore, it is critical that you spot any missing data values (names, addresses, phone numbers, or any other empty fields) and fill them in.
A great way to locate the missing data fields is by using a code that tells your system there are empty values. This will allow you to run reports and highlight all of the accounts or leads that are missing specific values.
For instance, you can tell your team to type “0” for any empty numerical fields, like revenue won or sq. ft managed.
Then you can run a report to pull all the accounts with revenue won = 0. A lucky team member can find the correct inputs for each missing value.
And if it’s a verbal field, like a name or a title, you can write “missing” or “NA”.
The biggest takeaway here is to simply make sure that there is consistency in the way each member of your team labels missing data values.
Pro Tip: Salespeople often forget to document their meeting notes in their CRMs. This type of missing data hurts other members of their team, both future and current.
For example, an Account Executive might lose a sale because of a certain competitor.
If they fail to record this reason in their CRM, a Sales Development Representative trying to re-open the account a year later will lack the intel they need to succeed.
So, even though your CRM doesn’t usually contain fields for this type of complex information, ask your team to document these interactions.
2. Remove Duplicate or Irrelevant Values
Duplicates and irrelevant values distort your reporting results and make it difficult to find what you are looking for in your database.
For instance, if you have two leads who share the same details but have different phone numbers, you won’t know which contact to call. That can waste serious time if it happens more than once.
Even worse, if you look at the less reliable lead record, you might miss out on critical information such as a summary of the last meeting with the lead. This could have helped you win the sale.
You should create a best practices template that outlines what to do if your team members encounter a duplicate or irrelevant value. This could be merging two accounts into one or deleting the irrelevant value.
The protocol will differ for each situation, but it’s best to have your team following the same set of steps.
Removing duplicates and irrelevant values comes with an added bonus. The data validation process (see tip #7) will be much easier.
3. Standardize Formats
Write up a list of standard formats for each type of data your team routinely enters into your database. This will make reporting more accurate, as it will include every piece of necessary data, instead of excluding data that was written differently.
Your first step is to review the data being used by your team (dates, phone numbers, postal codes, etc). Once you know the main classes of data, write up a document that instructs your team on data entry.
Don’t be afraid to summon the prestigious magazine editor within. The stricter the rules, the better.
The document could look like variations of the following:
- Phone Numbers: Write 834-222-XXXX
- Address: Example – 24 Old Ghosts Highway, Victory, FL, 00021
- First Name: Capitalize the first letter.
Once you have outlined your formatting rules, put the document somewhere easily accessible.
That way, whenever someone is confused about how to write a piece of data, they can refer to the document.
4. Avoid Typos
Computers, unlike humans, are usually incapable of identifying words as misspelled. They can’t tell that “Kentcky” was probably meant to be “Kentucky”.
So any miswritten words will avoid notice from any reporting algorithms, causing skewed results.
Not to mention, you don’t want to misspell a client’s name. The next time you call her, you might not be sure you have the right person.
So, instruct your team to avoid any typos and to fix any they come across.
Most CRMs allow you to create dropdown lists for certain fields, which makes it impossible to misspell anything in those fields.
Consider using this functionality for your most important or regularly reported on data fields, such as type of company or reason for churn.
5. Clean Data in Small Chunks
If you try to tackle your data hygiene in one large scale project, it might become overwhelming.
You will invest a lot of labor and time into it, which could leave your team frustrated and exhausted.
So, it is best to take small steps.
Also, enact preventative care. Include daily cleanliness actions into each team members’ routine. It is the difference between brushing your teeth twice a day and going through a root canal operation.
If you consistently clean your data in small chunks, you will never have to do a big overhaul of your data. When you do find a problem, take care of it quickly so that it doesn’t escalate.
Investigate the root cause, and employ the help of the right stakeholders to fix it. That could be your IT department or your data operations team.
6. Prioritize Data Sets by Business Value
To best manage your time and resources, you want to prioritize fixing up the data that directly affects your revenue-generating activities.
That way, you won’t get bogged down in data-cleansing tasks that don’t add much value to your bottom line.
For instance, you might prioritize updating contact information for large accounts. Your salespeople can reach decision-makers during their outreach, which can increase your chances of closing a big deal.
On the other hand, you might put off low-value data cleaning activities such as filling in missing values that are of low importance. That could be a company’s county or zip code.
If you sell through an inside sales model, those details don’t really matter until you close the deal. And by that time, you can just ask the new client for these details.
Here are some key data points to prioritize:
- Lead Names.
- Contact Information.
- Lead Titles.
- Account History – emails, phone conversations, meeting notes.
- Prospect’s Current Product/Service – note any competitors.
- Company Name.
Make sure the above are correctly written in your databases. With this information intact and accurate, you can generate more sales for your business.
7. Validate the Accuracy of Data
Business development professionals rely on the accuracy of lead data for their outreach efforts. Phone calls to incorrect numbers and emails to non-existent addresses are huge time wasters for a sales rep.
Data accuracy also enables sales professionals to forecast sales and make predictions.
So, it is critical that you validate the accuracy of your data — especially your lead data. Most sales managers do this data validation through a process called cross-referencing.
They compare their data to data in a trusted database and throw out any data points that differ. This is a useful technique for when you are assembling lead lists and want to make sure their contact information is correct.
You can also limit the possibility of inaccurate data entering your base. Do this by incentivizing your team to practice good data hygiene.
Some companies do this by creating a data hygiene performance category in their sales dashboards. Others have taken such extremes as linking KPIs and compensation to data hygiene.
Automate: Use Soleadify B2B Database 🏆
Manually inspecting and validating data is often tedious. Setting up algorithms to clean your data can require a lot of programming effort and money.
These difficulties are why a lot of salespeople are now automating their data hygiene duties with database tools.
Even better, they are automating the acquisition of data—especially lead data. They figure that if they get the correct data in their initial pull, there won’t be much to fix later on.
And they are right.
One of the most amazing tools for this is Soleadify, a B2B database built for quality lead generation. Its data is accurate and GDPR compliant, meaning its data pass certain standards and is publicly available.
Soleadify enables salespeople to generate lists of best-fit leads, along with their correct phone numbers, email addresses, titles, and more.
With a tool like Soleadify, you can spend more time directly interacting with potential buyers and less time searching the web for decision-makers and validating their contact information.
Conclusion
Sharing intel is critical to the success of your sales team. You want everyone to understand as much as possible about potential customers and current clients so that you know how to best win them over or keep them happy.
That said, keep your databases updated and clean. And use the knowledge housed within to book more meetings, win more sales, and lengthen client relationships.
