Staffing is one of the most competitive industries in many parts of the world. In the U.S. alone, there are more than 20,000 agencies competing on 16 million hiring assignments.
In the course of a year, a typical U.S. staffing agency hires 80 people and the average hire is worth approximately $9,000. If temporary hires are excluded, the value of the average hire jumps significantly.
In a previous blog post we gave insights on how staffing companies should use buying signals to find better-quality prospects. These buying signals, such as leadership changes, funding rounds, market entries and new strategic initiatives, are also the key elements when using predictive analytics in staffing sales.
1. Combine Technographics with certain buying signals
So you've got some strong marketing automation experts in your talent database. Instead of only going after the large enterprises with big marketing spend in a certain geography, try narrowing down your target accounts based on their technology stack and a couple of buying signals. When a company using Marketo / HubSpot / Pardot raises a significant round of capital and hires a new VP of demand generation, odds are they're interested in all marketing automation experts who can join the team and hit the ground running in no time.
2. Combine historic data with financial buying signals
Despite being the buzzword of the decade, not all companies are interested in that big data guru that you have in your pipeline. In order to increase your probability of finding the perfect match, try approaching companies that have recently hired people with that area of expertise and have just announced another quarter with strong numbers.
3. Ask WHEN instead of just WHO
Another great way to find your next customer is to start with WHEN instead of WHO. Most companies need certain skills and expertise only at certain times, and the agencies who can approach them at the right time will collect most of their assignments. For example, when a SaaS company is expanding into a new country, they often need a lot of local knowledge. Instead of bombarding all SaaS companies with your value prop, try narrowing down your target group based on companies going through a certain stage in their life cycle.
Predictive analytics in sales is not about building a perfect algorithm, but about increasing the likelihood of your sales team to engage with customers who are having a need that you can solve.
When adding your own internal data into the mix, your predictive algorithms can be improved even more. What are the characteristics of the companies who have used your services in different segments? What events were happening with those companies right before they realized they needed external help from you? Predictive analytics in sales is not about building a perfect algorithm, but about increasing the likelihood of your B2B sales team to engage with customers who are having a need that you can solve.