The curse of data decay on sales prospecting

Sales | 2 minutes to read

30th Jun, 2021

Read this blog to discover deep AI & ML capabilities to smackdown data decay and boost sales & marketing ROI 


    • B2B Data decay festers in over 94% of enterprises

    • The market study states that enterprises face $3 trillion losses per year

    • In this blog, we will discover future-forward strategies to beat back the challenges of data decay productize B2B data, and drive net new business growth

    B2B Data decay is the biggest roadblock on the path to sales prospecting. It restrains enterprises from targeting net new revenue prospects and engaging the clients in an efficient way. A recent survey concluded that data decay festers across 94% of enterprises’ B2B data decays by 30-40% per year. Start-up data or the SMB segment decays by 70% annually. Enterprises realise that:

    • 66% people have changed their jobs
    • 20% of companies have shifted their corporate addresses
    • 18% telephone calls are not being answered

    What causes data decay? 

    B2B contact data falls from the multi-dimensional sources. It is complex and fast-changing. Enterprises generally lack the capabilities to continuously validate, filter, and improve it for sales prospecting. Here are some factors which cause data decay:

    • Missing data: Fields that are empty
    • Inaccurate or inappropriate data: Fields that have inaccurate information or inappropriate information
    • Duplicate data: Data that is entered several times in many other fields
    • Poor data: Gaps in data

    How bad is it for sales prospecting? 

    Data decay seriously dents sales prospecting. Bad contact data sneaks into marketing & revenue enablement processes and sucks out a profit in many ways. It cascades failure into the campaigns as they lead to product returns, misaddressed invoices, lost sales time, duplicate emails, wasted marketing materials, and email domain blacklisting.

    According to the 1-10-100 rule, developed by George Labovitz and Yu Sang Chang, it costs $1 to weed out Bad data, $10 to correct it, and $100 for restitution of losses.

    Gartner believes that poor data quality on businesses amounts to anywhere between $9.7 million and $14.2 million annually. At the macro level, bad data is estimated to cost the US more than $3 trillion per year.

    How do you eliminate data decay before it snowballs? 

    • One of the easiest methods to preventing data decay is switching from manual to automated digital tools for data maintenance, data cleaning and deduplication; and also use automated data enrichment and data refresh services which continuously append and correct decaying data.
    • Data cleaning and standardization (also known as normalization) is also essential to preventing decay, especially since all organizations use multiple sources of data – the accuracy of which can significantly vary depending on data quality checks that these sources conform to.
    • Marketers can prevent data decay by keeping their current audience engaged with content that helps gather updated contact information. Using opt-in or interactive content such as contests, gated whitepapers or webinars, or fun quizzes and polls, helps keep CRM data up-to-date and collect additional information on customers and prospects.
    • Some best practices for data-hygiene also incorporate real-time email verification in web registration forms or other points of email collection; implementing a double opt-in processthat prompts a new subscriber to click on the confirmation email sent to their inbox in order to officially join a mailing program. This ensures that an email is active and accurate; and even a “pre-deployment” practice of validating email or physical addresses before starting an email or mailing campaign.

    Attend this Webinar: The curse of data decay on sales prospecting

    In this webinar, marketing and data operations stakeholders from leading organizations like Microsoft, Markets & Markets along with Denave experts will deliberate about the ways to beat back the challenges of data decay. We will discuss the importance of targeted data, and its impact the bottom-line and missed opportunities, and digital automated technologies to alleviate these challenges.

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