Citation Professional Solutions are one of the UK and Ireland’s largest providers of HR, employment law and ISO services.
They employ 55 business development consultants, who are tasked with setting appointments for their sales team over the phone.
Until recently, Citation sourced lists of companies from a data provider and applied two hard filters – sector (SIC code) and number of employees – to come up with a shortlist of who to call.
Meanwhile, their business development consultants were often discovering good prospects through their own research that weren’t showing up in the datasets. On further investigation, it was discovered that Citation’s own filters were causing these companies to be excluded.
- Uncover the true size of Citation’s addressable market
- Identify the companies within it (without filtering out good prospects)
- Improve efficiency by prioritising those companies according to their likelihood to purchase
To start with, we took Citation’s historical prospecting data and used it to identify the traits common to the prospects who had gone on to become their best customers. This information was then fed into a model which identified thousands of net-new prospects that shared those traits – and ranked them according to number and strength of the shared traits.
The model proved highly effective:
- It uncovered 268,000 companies, previously unknown to Citation, that were likely to be in-market to buy from them based on their traits
- It identified 65,000 businesses Citation had previously been targeting, that shared few traits with their best customers, and were therefore highly unlikely to convert
When Citation started calling the new in-market companies, and stopped calling the businesses that were unlikely to convert, the results were outstanding.
One in three completed calls resulted in a positive outcome and Citation achieved a tenfold return in revenue on the cost of GrowthIntel within two months.