Qualifying, coding and ranking your leads so the hottest opportunities are the first to get distributed to your sales teams is vital to your company’s sales and marketing success. That’s a given.
Yet, what many companies fail to understand is that data quality is core to an effective lead scoring system.
A SiriusDecisions study, “The Impact of Bad Data on Demand Creation”, shows that following best practices in data quality generates a 66 percent increase in revenue.
The study summary cited in a CRM Magazine article, “Looking to Score”, offers some key insights for B2B organizations, such as:
- 10 to 25 percent of customer and prospect records contain critical data errors.
- The amount of data doubles every 12 to 18 months.
- It takes $1 to verify a record as it is entered, $10 to cleanse and de-dupe it, and $100 if nothing is done, as the ramifications of the mistakes are felt over and over again.
- From inquiry to marketing-qualified lead: A data strategy that solves conflicts at the source can lead to a 25 percent increase in converting inquiries to marketing-qualified leads.
- From marketing-qualified lead to sales-accepted lead: Unifying the data, whether into one database or by using technology for virtual integration, can lead to a 12.5 percent uplift in conversion rates to the next stage.
- From sales-accepted lead to sales-qualified lead: Clean data can reduce by 5 percent the time spent conducting the kind of additional research that precedes initial contact with a prospect.
- From sales-qualified lead to close: Given that the average field-marketing function spends no more than 10 percent of its budget in support of this final conversion, accurate data is a must for applying the right tools and resources to the right audience at the right stage of the buying cycle.
Jonathan Block, author of the report and senior director of research at SiriusDecisions, writes, “A best-in-class data strategy is shared by marketing and sales, and is focused on quality from [inquiry] to close. Although it is a job that consumes both money and time, paying more attention to data quality is not only worth it, it is something that your organization simply can’t afford not to do.
I couldn’t agree more. Have you reviewed the quality of your data lately?

