Sales Pipeline

The Real Cost of Bad Data and Steps to Improve Data Quality

You built an email list of your target audience , hired the best email marketer to craft the best email message. You also purchased a good email marketing tool to measure the effectiveness of your email campaign.

As you hit the send button, you expected a lot of leads and enquiries. But something else happened there:

  • More than half of those emails that were delivered ended up in the junk folder.
  • Some of the readers hit the unsubscribe button to never hear from you again.
  • Lead qualification criteria like job title, company size, location or industry don’t match.
  • The contact lists were already used by your competitors.
  • Your business depends on how efficiently and effectively you manage data. Yet 97% of organisations say that they experience contact data errors .

    What’s more shocking is that 57% of the survey respondents admitted that data quality issues are only found when reported by employees and customers.

    data quality issues

    What Causes Data Errors?

    There are several reasons that lead to poor data quality.

    Most often it starts with data acquisition.

    In a hurry to generate a lead database, businesses depend on scraped contact lists available on sale without realizing those are inaccurate before it’s too late.

    As a result of purchasing readymade data from data banks or bulk database suppliers, lead database turns stale and unusable.

    While data generation is of prime importance to build a successful lead and sales funnel , accuracy of your data is at stake, and with it your reputation and revenue.

    So what’s the real cost of unverified, bad data? How does it affect your business? And most importantly how to ensure your data is clean, usable, and sales-qualified?

    Let’s find out.

    The Actual Cost of Bad Bata and Impact it Has on Your Business

    IBM says, bad data costs companies $3 trillion annually. While a Gartner survey found that most of the companies surveyed are losing an estimated $ 14.2 million annually.

    The financial cost of inaccurate data is obvious, but it has far-reaching consequences like:

    1. Loss of Productivity, Time and Resources

    In sales, time is money. But each time your sales team dials a wrong number or sends an email to an invalid email address, they’re wasting time.

    42% of sales representatives struggle to get accurate, up-to-date data before making a call, which makes it more difficult for them to do their jobs effectively.

    By ensuring data hygiene, sales teams can move through their call lists more quickly and use their time talking to qualified prospects instead of trying to figure out if they have the right contact information.

    2. Inaccurate Data Ruins Targeted Campaigns

    The quality of your data plays a major role in how your prospects feel about your company. Inaccurate data can make them want to abandon purchases. Inaccurate data can also confuse them if they are targeted incorrectly by your sales team.

    Good data is essential for providing personalized journeys and driving sales.

    Bad data, on the other hand, interferes with your ability to meet customers’ needs and drives them away.

    3. Negative Impact on Email Marketing

    If you don’t prioritise data hygiene, you’re likely to see negative impacts on your email campaigns like:

  • Unsubscribes
  • Email bounces
  • Increased spam reports
  • Lower click-through-rate

  • 4. Incorrect Buyer Personas

    Sending the right message to the right person at the right time is crucial when attempting to generate product or brand interest.

    But with inaccurate prospect data, it can be challenging to deliver personalized messages to your prospects because of faulty buyer personas created based on incorrect information.

    Worse is you might be sending messages to the wrong group of people who aren't interested in what you're selling.

    5. Incorrect Business Strategy and Forecasting

    We all know that inaccurate data is an obstacle to success. And when leaders and managers use bad data to forecast sales expectations, it can lead to incorrect goal setting and inaccurate business decisions.

    How to Improve Data Quality

    Improving data quality is a multi-step process that requires patience, skill, experience, and an eye to capture errors.

    Here are some of the ways to improve your data quality effectively and efficiently:

    1. Improve Data Acquisition Process

    Buying contact lists is no longer an option. Don’t buy data that generates little engagement and zero sales.

    data quality issues
    “The aroma and flavour of freshly brewed coffee is always better. Same applies to your leads database. When you stop buying contact data and instead, generate fresh database customised to your marketing goals, the results are always better”
    - Ashish Gupta

    Instead invest in generating a well-researched, human-verified, fresh data database that has the highest potential of turning into leads and sales.

    Also ensure that your database is GDPR-compliant and double-opt in. It will help you avoid legal complications that may arise from targeting people who didn’t sign up to receive communications from your company.

    2. Manage Database Decay

    Data decay is a major concern for businesses.

  • Unsubscribes
  • Gartner’s report shows that it occurs at about 3% per month globally.
  • According to HubSpot, email marketing data decays at the rate 22.5% annually.
  • B2B data erodes at the rate of 70.3% per year.
  • What does this mean for your business?

    Data decay negatively impacts lead generation and sales process. Each year some portion of your lead database turns unusable; and when you pass that on to your marketing and sales team, they waste their time and energy contacting people that exist no more.

    To tackle this, you need to analyse, evaluate and verify your CRM data and contact lists periodically and get rid of bad data that hurts the sales pipeline. And this leads to our next point.

    3. Regularly Cleanse Lead Database

    Data cleansing is the process of identifying and correcting inaccurate records from a dataset, table, or database.

    A spelling error in an email address or missing numeric digit in a phone number can also make it unusable.

    To keep your lead database free from errors, you need to identify and remove.bad data that is no longer valid, incorrect or incomplete.

    4. Append Your Data

    While cleaning your data, it’s equally important to look for missing information and add it to your database, a process known as data appending.

    Take the time to review your data collection process, and make sure to follow best practices for sorting data.

    And if it sounds all too complicated, hire StatByte for data appending services.

    Our team, skilled in data management and data hygiene, will cleanse, verify, and append your B2B database.

    Or better yet, let us generate fresh, bespoke, GDPR-compliant and double-opt in B2B data so that you can generate more leads and close more sales without having to worry about data quality.

    Author Bio:

    Mou Mukherjee

    Mou Mukherjee is a content marketer and digital marketer with a keen interest in learning more about the ever-evolving digital demography and helping businesses survive and thrive Twitter and Facebook.