How to Improve Sales Data Quality for Better Decision-making
Picture this.
You’ve just spent three weeks sending email campaigns and making cold calls to a sales prospect, receiving no answer to any of your efforts.
Before you move the lead over to closed/lost, you do a quick LinkedIn check.
And guess what? The prospect you had in your CRM doesn’t even work at the company any more!
This is just one example of how poor-quality sales data can lead to large amounts of wasted time, money, and effort. Improving sales data quality is critical for improving decision-making, driving strategic revenue planning, and, of course, closing sales.
In this article, we’ll provide actionable insights and strategies to help you improve bad-quality sales data.
But first, let’s examine what we mean by high-quality data for sales. There may be more aspects to data quality than you think!
Understanding sales data quality
When most people think about sales data quality, accuracy is what comes to mind.
That is, is the data correct?
Accurate B2B data is important, no doubt, but it’s not the only dimension we should consider when looking to improve poor data quality.
In our opinion, there are six dimensions of sales data quality worth working on:
- Accuracy. Is the data correct? Is the email address or phone number you have for a potential customer accurate?
- Completeness. Does every account have all of the correct data you’d expect to see, such as contact numbers and key roles?
- Consistency. Is your data formatted and stored in the same way across all accounts?
- Timeliness. Is the data you have easily accessible and kept up to date? This is especially critical as it relates to intent data.
- Relevance. How relevant is the data for your sales and marketing teams? How much of that information is useful in sales conversations?
- Uniqueness. Is your sales data absent of duplicates?
Common data quality issues
The first step in solving the problem of poor data quality is to understand what causes it.
These are some of the most common issues:
- Duplicate records - because the person (or tool!) that entered the data failed to check for an existing record.
- Data entry errors - spelling mistakes in names, phone numbers, and email marketing.
- Incomplete data - such as not including the prospect’s job title.
- Outdated information - like when the key prospect for a given account doesn’t even work at the company anymore.
- Unstructured and inconsistent data - which often happens with bad data quality integrations and a lack of data governance.
There are a number of causes behind these data quality issues.
Two of them are laziness and human error. Salespeople are notorious for wanting to work fast to close more deals, sometimes leading to corners being cut (like not checking for incorrect customer details or duplicate entries).
Lead lists bought from unscrupulous suppliers are another.
Then, of course, you’ve got data siloes that prevent knowledge sharing between teams, an absence of data audit and update processes, and the fact that as much as 30% of your sales data is out of date after 12 months.
All of this can have serious negative impacts on business outcomes and success. But how much of a problem is poor-quality data really in dollar terms?
One source estimates that the average rep loses around $32k in additional revenue due to bad sales data. For a medium-sized team, that can amount to around half a million dollars a year down the drain!
This potential revenue loss comes from misguided strategies, lost sales opportunities, and damaged customer satisfaction that leads to high churn rates.
Benefits of high-quality data
Enough about poor-quality data.
What happens when you flip the script and invest in improving your sales data quality?
Improved decision-making
Good quality sales data quite simply helps you make better, more informed decisions.
When your data is complete, for example, you can trust that you have an accurate picture of what your current opportunity looks like and what you should expect that to convert into in terms of revenue.
This means that business leaders can make more accurate forecasts and craft effective strategies for reinvesting that projected revenue.
And considering 39% of sales leaders say bad data hinders their ability to generate effective forecasts, there’s clearly a lot to be gained here.
Enhanced customer relationships
Bad-quality data can quickly ruin the customer experience.
Getting a prospect’s name or role wrong can be embarrassing, and even though it seems like a small factor, it does work to erode trust and rapport, two things that salespeople work tirelessly to build.
Accurate and timely data also serves to improve targeting, meaning your sales and marketing campaigns get in front of the right people at the right time.
Increased sales efficiency
Let’s face it:
Searching for accurate sales data or correcting errors in your CRM when you should really be on the phone making calls is a huge waste of time.
High-quality sales data boosts business performance and efficiency. How?
Eliminating that extra data correction work helps reps hit activity targets and close more deals.
6 strategies to improve sales data quality
Seems obvious that improving data quality standards in sales is a worthwhile investment, right?
So, how do you actually go about it?
Here are six important strategies for fixing low-quality data and ensuring any new data that finds its way into your systems is high-quality.
1. Use Cognism’s AI Search
Artificial intelligence is transforming the sales and marketing space.
More and more companies are using AI to manage the customer journey via intelligent chatbots and AI-powered outreach campaigns.
But AI has some powerful use cases that, instead of focusing on replacing human sales reps, can actually help reps work faster and more strategically.
In fact, a recent Salesforce report cited “improving sales data quality and accuracy” as one of the top three impacts of AI in the sales arena, alongside “understanding customer needs” and “personalisation for customers.”
As one of the leading suppliers of premium B2B sales data, we’ve seen firsthand how AI can improve data accuracy and speed up workflows.
Consider the typical process of searching for sales data.
Slow, manual list filtering is cumbersome and takes time out of reps’ days, meaning fewer calls are made.
Traditional search functionality generally requires an exact match between your search term and a data point. Not exactly helpful when you’re looking for “CEOs working for software companies”
That’s why we built AI Search.
With our AI Search functionality, you can write or say what you’re looking for in plain terms (like the sentence above), and our AI engine surfaces accurate results to help you find what you’re after.
Cognism AI Search features three routes to finding high-quality sales data:
- Text-to-command search. Type that ideal target list into the AI Search bar and you’ll get all the relevant results. For example “Show me Marketing Directors working at engineering companies”.
- Direct prospect or account search. Have a specific person or account in mind? Type them in to get their data.
- Voice search. Try prospecting in your native language to get results quicker than ever.
Even if your database is in English, you can search in French or German and still pull up relevant results. Perfect for distributed workforces where English may not be a rep’s first language.
By using AI Search in your hunt for customer data, you can build more accurate target lists, identify targets 74% faster, and get back to those tasks that move the revenue needle forward.
Watch this video to see how AI Search works 👇
2. Implement data governance
Getting serious about B2B data quality requires the implementation of both a data governance framework and data stewardship roles.
Let’s explain what both of those are and why they matter.
Data governance framework
This is a structured approach to managing data, ensuring quality, consistency, and security.
Basically, it’s the formalised answer to the question, “How are we going to manage sales data?”
Your data quality framework should include:
- Policies, such as guidelines for data access and usage.
- Processes, such as how you’ll obtain your data and enter it into your system.
- Metrics, such as how you’ll measure accuracy and what your expectations are as to how much data can be inaccurate at any given moment.
- Tools, such as what software solutions you’ll use to store data and how you’ll integrate the different tools.
- People, as in who is involved in maintaining data quality (your data stewards).
Data stewardship roles
These are individuals or teams who are responsible for overseeing data quality within specific departments or domains.
Basically, they are the answer to the question, “Who on our team is responsible for data quality?”
Knowing who is responsible for what aspects of data management - such as whose job it is to audit and update inaccurate insights - helps improve accountability. It also avoids the whole “I thought you were doing that” situation!
An important role of data stewards is to play a proactive role in identifying quality issues and resolving them, as well as working across departments to share knowledge and break down data siloes. Your focus must be on continuous improvement across the entire organisation.
3. Conduct regular data audits
Your data governance policies should outline how often you should conduct data audits and what they should involve.
Routine data audits help you identify potential issues with your data and rectify them.
In an ideal world, you’ll simply pinpoint what data is outdated and needs replacing.
In most real-world cases, however, you’ll also discover incomplete data and duplicate contacts. You’ll want to investigate the cause behind this (such as a bad data integration or sloppy data entry) and put a plan into action to prevent the same issue from occurring in the future.
4. Standardise data entry processes
Consistency isn’t one of the most obvious data quality challenges, but it’s still worth your attention.
Inconsistent data slows reps down. When you’re dealing with dozens of prospects a day, it’s helpful to know that you’ll find the same data in the same place and present it in the same way. When data is inconsistent, it makes it harder to digest, which means more time is wasted between calls.
Inconsistency also leads to errors, something we want to avoid at all costs.
There’s an easy fix here:
Create and distribute standardised protocols for data entry. Outline the fields that must be filled in when entering new data into your CRM, as well as what you expect to see in each field.
Yes, you want to get granular here. Specify, for example, when names or titles should be capitalised or how direct dials should be entered (with or without a prefix).
5. Integrate data sources
Sales data can come from a number of sources.
You might buy a lead list, receive sales intent data from a third-party supplier, or get data directly from the prospect when they fill in a form or sign up for a demo.
An underrated data quality solution is to integrate your data sources or audit the accuracy and effectiveness of your existing integrations.
Pay special attention to similar data that may come from different sources. Opt for “overwrite existing” automation rules over “create new”; this way, you’ll avoid duplicates and reduce data redundancies.
6. Train and educate staff
You can have as many policies and written procedures in place as you like. If your employees aren’t following them, your data quality won’t improve.
That’s why our final strategy for improving sales data quality is to train and educate your staff on the importance of data quality.
A few methods to implement here include:
- Training department leaders in best practices and having them run sessions with their team (to minimise the headcount in each meeting and allow the manager to make the conversation role-relevant).
- Develop video content on the importance of data security that employees can constantly refer back to.
- Create a Slack channel to highlight good examples of data quality management in practice.
How does AI Search improve sales data?
AI Search automatically generates the most relevant search experiences based on data within the platform. Users send prompts (a natural language input) to return their list of results.
In Cognism AI Search, this looks like individual prospects and lists of people or target accounts based on the criteria the users enter. AI Search helps to improve sales data quality by identifying the most relevant data based on the prompt given.
Improve your sales data quality with Cognism
Sales data quality is a critical, yet often overlooked, component in driving revenue success.
Higher-quality data leads to better business decisions, more accurate forecasts, and less time wasted fixing mistakes.
By working on the strategies we’ve discussed above, from implementing data governance frameworks to leveraging Cognism’s AI Search functionality, you’ll create a more sales-friendly data environment and let your reps get back to what they do best:
Selling!
