Data Quality Best Practices for Effective Sales Lead Management

Data Quality Best Practices for Effective Sales Lead Management

Every company has its way of managing leads. Some business owners use basic tools like Google Sheets or writing everything down. You should update this approach - when possible -  but it can work for startups, mom-and-pop shops, or side hustlers. Larger companies, on the other hand, rely on lead management systems or customer relationship management (CRM) software. 

The latter option is more effective because it allows you to collect, analyze, update, and manage customer data at scale. However, sourcing high-quality data can be challenging, regardless of industry or business size. About half of all newly created data records have one or more critical errors, and only 3% of data meets basic quality standards.

In today’s digital age, salespeople need to determine where their customers come from, what they expect from a brand, and what drives them to buy. Marketing and sales teams use this information to generate, nurture, and qualify leads. The outcome depends on data quality, among other factors. 

What Is Data Quality and Why Does It Matter?

Most business professionals make decisions and plan things out based on data. The same goes for salespeople, who need accurate insights into their target audience and their market. First, however, you’ll need more data.

An organization can have massive amounts of data, but that's not necessarily helpful. Data quality matters most, defined as the overall accuracy, reliability, completeness, relevance, and consistency of the data you're working with. 

For example, sales reps need accurate data to identify and categorize leads. These data points may be related to customer behavior, demographics, firmographics, etc. 

If this information is inaccurate or incomplete, it becomes challenging to identify qualified leads, resulting in wasted time and resources. Your sales team might target the wrong people instead of focusing on those most likely to convert. 

High-quality data can lead to better decision-making, increased customer satisfaction, and more effective marketing campaigns. In addition, it lets enterprises personalize their communications, boosting brand awareness and customer loyalty. 

In a 2022 survey, 56% of consumers expected personalized offers from the brands they love. Not surprisingly, about half of the companies that get personalization right see higher engagement and conversion rates. But again, getting such results without access to accurate and reliable data is pretty much impossible.

How to Improve Data Quality for Better Lead Management 

Bad data costs companies about 15% to 25% of their revenue. Yet, most businesses rely on inaccurate, incomplete, or outdated data. 

Imagine the following scenario: You sign up for a company's newsletter to stay up-to-date with the latest offers. Let's say you're a 30-year-old single man without children.

Yet, the emails from that particular brand feature special deals on summer dresses, toys, or children's clothes. 

Now think about how you'd feel about it. You'd lose trust in that brand and unsubscribe from the newsletter. Even if you remain subscribed, you'll stop reading their emails after a while.

That's just one example of what can happen when companies rely on bad data. Not only do they risk losing customers and sales, but their reputation may suffer, too. 

High-quality data can boost your sales outreach and personalization efforts, resulting in more sales. Over time, it can reduce customer churn rates, increase brand awareness, and drive business growth.

With that in mind, here's what you can do to improve data quality for better lead management.

Define Your Sales Goals and KPIs 

First, define your sales goals and key performance indicators (KPIs) or the data points you want to track. 

The latter may include:

  • Cost per acquisition

  • Lead conversion rate

  • Average revenue per customer

  • Repeat customer rate 

  • Conversions by channel

  • Customer lifetime value

  • Return on investment (ROI)

  • Email open rate

  • Email deliverability rate

  • Bounce rate

  • Click-through rate

  • Sales cycle length

For example, tracking your email deliverability rate and similar metrics makes sense if you reach out to prospects or customers via email. This way, you can see who opened and read your message, their actions, and so on. Then, analyze the results and decide on the next steps. 

If your messages go unanswered, use an email checker and email verification tool to see what's wrong. This practice can help you identify inactive or invalid contacts so you can remove them from your list. For example, those people might have changed their email addresses or marked your email as spam. 

Also, make sure you track the right metrics for your goals. For instance, sales reps who use their websites for lead generation should monitor traffic sources, conversion rates, click-through rates, goal completions, and other KPIs. 

Centralize Your Data

Next, use a CRM system, Amazon S3, or other apps to bring all your data in one place. This practice makes sharing reports, data sets, and other information easier across departments while preventing redundancies. 

Having everything on one platform allows sales and marketing teams to clean, manage, update, and analyze more efficiently. It also enables executives to develop a solid framework for data governance, leading to improved compliance and transparency. 

Other benefits may include:

  • Build a single source of truth

  • Increase data security 

  • Improve collaboration and knowledge sharing

  • Streamline data analysis and reporting

  • Enhance data governance and control

  • Cut operational costs (e.g., the cost of managing multiple data storage systems)

  • Break down data silos, which can result in better decision making 

Centralizing your data gives you a complete view of your business and customers. 

All the information you need will be at your fingertips, not scattered across multiple platforms and apps. 

Keep Your Data Clean 

A recent survey conducted on 500 data experts found 77% had data quality issues. In addition, more than 90% of respondents agreed bad data affects business performance. 

Nowadays, people relocate to different cities or countries, change jobs, or switch careers in the blink of an eye. Therefore, your information about your clients may need updating. Misspelled names, duplicate entries, and other inconsistencies can hinder your efforts to generate leads, resulting in wasted resources. 

The only way to prevent these issues is to clean your data regularly. You'll need a couple of tools, such as OpenRefine, ZoomInfo OperationsOS, or Trifacta, but you could also use an Excel worksheet—especially for small databases. 

First, determine which tools you want to use. After that, remove duplicate contacts and incomplete entries. Finally, identify and correct typos, inconsistent abbreviations, or other structural errors. 

This process is more complex than it seems, but you only have to do it once a year. 

Standardize Data Entry

All the measures above will only be worthwhile if you have clear guidelines for data entry. For example, everyone in your organization should use the same measurement units, abbreviations, acronyms, etc. 

Let's say one person uses the word "Master’s" when creating a new entry for a prospect. Another person uses "Master," "M.A" (Master of Arts), or "M.S" (Master of Science). 

In this example, "Master" and "Master's" or "MA" may appear as separate categories in your database, which can lead to poor targeting and audience segmentation. 

Go one step further and train your sales teams on data management. Ensure they understand bad data's impact on their efforts and show them how to maintain data accuracy. 

You may also use this 7-step blueprint for managing sales leads to show your team how to capture and leverage B2B data. It describes the steps needed to gather lead intelligence, build a standardized lead scoring system, nurture prospects, and more.

Implement Data Quality Metrics 

Managers must also define data quality metrics and then track those metrics to identify areas of improvement. 

For example, measuring your data against a reference data set, such as the U.S. Securities and Exchange Commission or private B2B databases, can help you determine its accuracy and completeness. You'll also want to measure data consistency, integrity, timeliness, and other attributes. 

Still trying to figure out where to start? 

Let's see a few examples of data quality metrics:

  • Number of empty values

  • The ratio of data to errors

  • Email bounce rate

  • Data lineage

  • Maximum time lag

  • Missing data points

These numbers may change every few months, so it's essential to perform regular data audits. 

You'll have to analyze data patterns, look for anomalies, and take corrective actions to ensure data accuracy. 

Supercharge Your Lead Management with Quality Data

Access to high-quality data is crucial at every stage of the sales process. This aspect alone can impact your efforts to attract, nurture, and convert leads. 

For example, you need accurate data on potential leads when writing cold emails. The better you know your customers, the higher your chances of building lasting relationships. You'll know what to say to draw their attention, how to keep them engaged, and how to address the challenges they face. 

High-quality data can also streamline decision-making and reduce marketing costs. Your sales teams will be better able to score leads and focus their efforts in the right direction. Plus, they'll find it easier to collaborate with other departments, which can increase efficiency across the board.

Maintaining data quality is an ongoing process that should be at the top of your priority list. For starters, assess and clean your existing data. Next, set clear KPIs based on your sales goals, leverage B2B databases to enrich your data, and switch to a centralized platform to keep things neat and organized. 

Meanwhile, establish clear guidelines for data entry and perform regular audits. Next, train your teams on the best practices for data management, and be clear about who's responsible for what. Last, review and refine your data quality processes regularly to prevent inconsistencies.

Want sales triggers on all your top accounts?

Sign up to try Detective for yourself right now.