In the era of big data, business leaders sometimes focus too much on the quantity of data compiled and not enough on the quality.
Research from Dun & Bradstreet’s B2B Marketing Data Report 2016 has revealed gaping holes in B2B marketing databases. In a study of 695 million records in B2B companies’ databases, inaccuracies were found in over 70% of the records.
- 87% lacked revenue information
- 86% had no employee information
- 82% had no website information
- 77% were missing industry information
- 62% did not contain phone numbers
- 45% were missing contacts job titles
The following are some of the primary reasons that you should prioritise clean data over big data to optimise the value of your sales leads.
Focus on Selling
So much time is wasted in organisations looking for the right data.
When your sales reps are equipped with thorough, clean data, they can focus their time on converting prospects into buyers. In contrast, it takes time to work through the issues created by bad data.
The same can be said for marketing departments when they are trying to guide customers down the sales path, or creating customer loyalty programs.
Imagine a rep opening a contact profile in a database and realising that a digit is missing on the phone number or an important line is missing on an address.
These missing items impede the rep’s ability to optimise his workflow and begin the selling process. The distraction also takes away from your team member’s focus on optimising presentation and closing stages.
58% of Chief Marketing Officers (CMOs) say email marketing, search engine optimisation (SEO), search engine marketing (SEM), and mobile are the main areas that big data is having the largest impact on their marketing programs.
More Targeted Appointment Setting
Clean data is more useful in landing appointments with high-potential buyers. It is difficult for a person to make targeted prospecting calls when profiles are incomplete or inaccurate. A smaller amount of high-quality sales leads improves targeting capabilities.
With quality data, reps can better detect which contacts offer the right opportunities to sell the right solutions. Having in-depth information on B2B buyers is especially important, as your reps need the ability to tailor messages to specific interests.
In another study of 50,000 US and international marketing, sales and business professionals Ascend2 discovered that:
“35 percent of those surveyed said the biggest barrier to lead generation success is the lack of quality data.”
Save Time and Money
The efficiency with which reps can connect with top decision makers and sell is much lower with bad data. Instead of investing the majority of time preparing and delivering sales messages, reps are taking the time to sort through problematic prospect details.
With clean data, you eliminate wasted steps that cost your organisation significantly.
Better Results and Financial Performance
Most importantly, clean data gives your team the best opportunities to optimise conversion rates, selling cycle times and average deal sizes.
Think of this scenario as similar to a doctor going into a waiting room after reviewing a patient’s file. The more thorough and accurate the nurse’s notes, the greater the doctor’s ability to effectively and efficiently detect and resolve a patient’s health problem.
Better sales results drive optimised financial performance as well. It is easier to forecast sales accurately, which enables you to better align budgets with revenue projections.
Big data doesn’t do much good if all you have is a cesspool of problems. However, ample data that is clean and useful is of tremendous value to your team. Internal Results has expertise in data acquisition.
We maintain accurate data on over 61 million decision makers in more than 20 countries. Over 500,000 records are updated every month to ensure they are clean and accurate.
Whether you are looking at entering new markets, or geographical territories, contact us today to discuss why our expertise in clean data is a perfect match for the sales skills of your organisation.
Read more: Six Ways to Define Big Data