The business world increasingly runs on data. Understanding customer demographics, personal preferences, and buying behaviors all help fuel a superior, customized customer experience. Companies know that to remain competitive, they must utilize data and offer customers a highly cultivated encounter that includes recommendations, convenience, and content that is all timely and targeted.
Every year, the urgency around data cleanup and enhancement is highlighted by the gradual decay that naturally occurs as organizations make changes and individuals move on to new positions, leave early to start a new career elsewhere, or are let go. Some experts estimate that the natural annual decay rate is around 10%; some believe it is even higher.
Everyone agrees that the exact rate of decay is often based on your target audience, industry, and job function. In other words, some industries experience a greater degree of change and attrition. And some job functions come with greater job security, while others experience more excessive change.
In 2020, data decay rules as we know them changed dramatically. COVID-19 is serving as a catalyst for an exponential decline in data quality. From unemployment and job changes to an upheaval in how and where employees work, no industry sector escaped change. COVID-19 is the reason data errors grow so significantly; in fact, since March 2020, errors doubled in some industry segments.
Even for employees that remain in their jobs, the setting of their work may have drastically changed. They may be reachable by cell phone, rather than through a corporate phone system, or they may be making decisions with a smaller budget or a different team of decision-makers. They may also be consuming content differently than they have in the past.
All these factors play into a need for accurate data. As companies push to retain a competitive edge in a struggling economy, the accuracy of their data will play an important role in helping them advance their new business development efforts including lead generation strategies.
The Cost of Bad Data
While there is a significant global toll on the economy resulting from bad data, the numbers may be more staggering when examined at the individual business level:
Inefficiency: This is the impact of a bad or wrong phone number, the wrong email or wrong demographic information. Harvard Business Review estimates that data professionals spend 50% of their time tracking down and correcting data errors.
A Drain on the Budget: Costs are higher to compensate for bad data than it is to maintain a clean database.
Poor Prospect and Customer Relationships: The loss of new sales opportunities, as well as lost opportunities with existing customers, are a side effect of incorrect data. The total revenue of a company may be negatively impacted by
as much as 25%.
Low Morale: From database managers to B2B sales reps, it is frustrating for employees to be forced to rely on inaccurate information. Database managers become weary of inaccuracies, while sales teams resent practices that deny them commission opportunities.
A Problem That Only Multiplies: This is just the beginning. Marketing departments make bad decisions based on incomplete or inaccurate data. Customers and leads react negatively to bad data, unsubscribing to emails that do not seem to apply to them or even complaining on social media about a company that seems out of touch with who they are.
Last Updated on November 20, 2023 by Ronen Ben-Dror