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Exploring Factors Contributing to Data Quality Issues

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While some things are enjoyable when they’re a bit “dirty” – think of martinis, jokes, or chai lattes – there’s one aspect where dirt is far from desirable: data.

Contrary to its potential for fun, “dirty data” – referring to inaccurate or outdated data – poses a significant challenge for businesses, accounting for a substantial chunk of annual revenue (as much as 15-25%, according to Salesforce).

Consider this: a staggering 90% of contact information in an average CRM is incomplete, with 74% being outdated, and 25% consisting of duplicates. These statistics can significantly hinder go-to-market (GTM) efforts, impacting various critical functions such as prospecting, forecasting, closing deals, developing ideal customer profiles, audience segmentation, and lead routing.

Now, let’s delve into the various dimensions of “dirty data”:

  • Outdated Data: In the fast-paced business world, changes happen swiftly. CEOs change roles, startups emerge, and contact details undergo transformations. A B2B database, for instance, experiences a decay rate of 2.1% per month, leading to a quarter of contacts becoming outdated within a year.
  • Security Concerns: With the establishment of stringent data security and privacy laws, non-compliance can result in severe fines and damage to reputation. Staying ahead of compliance is crucial to maintaining clean and secure data.
  • Data Inconsistencies: Lack of standardization can turn your data into a chaotic mess, with multiple versions of the same elements across different records. Without a clear system, your CRM becomes unreliable.
  • Data Overload: Hoarding excessive, outdated data poses flexibility, efficiency, and security risks. Keeping your database sleek is integral to data hygiene.
  • Duplicate Entries: Information can be duplicated in various ways, creating confusion about which result to trust. This might involve a single contact appearing as an employee of two different companies or under two distinct job titles.
  • Incomplete Data: Missing details in a record complicate downstream processes. Lack of critical information, such as job titles, hampers segmentation and makes outreach efforts more challenging and time-consuming.

Impact of Dirty Data on GTM Efforts

Every facet of business relies on data in some form, but when it comes to CRM data, sales and marketing teams bear a disproportionate burden when grappling with the perils of dirty data.

Challenges for Marketing

  • Risk of Blocklisting: Email marketers dread the scenario of being caught in spam traps due to bad data on their lists. Hard bounces and emails sent to spam traps can lead to blocklisting, damaging a domain’s reputation or even resulting in suspension by an email service provider.
  • Disruption in the Buyer’s Journey: Marketing teams, despite being content generators, need precise data for targeted content delivery at different stages of the buyer’s journey. Dirty data can lead to misdirected content aimed at the wrong audience.
  • Compromised Buyer Personas: Inaccurate data undermines the effectiveness of messaging. Sales plays and content targeting specific buyer personas suffer when CRM information about personas is unreliable.

Sales-related Challenges

  • Time Wastage: Sales representatives waste valuable time on dead-end outreach efforts resulting from calling wrong numbers or emailing outdated accounts. Survey data indicates that 20% of monthly outreach calls by sales development representatives (SDRs) do not convert into sales, leading to approximately 45.5 wasted hours each month.
  • Negative Customer Experience: Dirty data contributes to issues such as misspelled names, undelivered messages, account mix-ups, and duplicate messages. These problems can alienate prospects and customers, leading to missed quotas, churn, and overall revenue loss.
  • Declining Morale: Consistently encountering wrong numbers or reaching the wrong contacts frustrates SDRs, impacting their morale. Given that many sales salaries are commission-based, difficulties in selling due to dirty data have a direct effect on morale.

Addressing the Challenge of Dirty Data

Fortunately, there are ways to clean up your data. Implementing data standards, establishing archive guidelines, and leveraging CRM data enrichment are effective strategies for maintaining a clean and efficient database.

  • Develop Data Standards: Standardizing practices contributes to cleaner data. Whether it’s specifying minimum characters for business addresses or enforcing specific information formatting, consistency leads to better data quality.
  • Archive Guidelines: Overcoming data hoarding and stale data involves setting standards for when and how to archive data. Keeping data fresh is essential for maintaining its cleanliness and relevance.
  • CRM Data Enrichment: Automated enrichment acts as a Roomba for your Salesforce account – an efficient way to clean up your database. CRM enrichment tools can swiftly turn dirty data into refreshed opportunities by adding details such as company name, location, industry, contact job title, seniority, department, and most importantly, up-to-date contact information.

Investing time and effort in maintaining data quality pays off. Companies that increase their data investment witness improved sales and marketing performance.

Key Insights

  • Dirty data encompasses incomplete, stale, duplicated, hoarded, insecure, inconsistent, or duplicated data.
  • Sales and marketing teams, along with up to 25% of annual revenue, bear the brunt of the negative impact of dirty data.
  • To counteract dirty data, implement standards around data entry and storage and consider investing in automated data enrichment.

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