The Hardest Part About Prospecting
The hardest part of prospecting is deciding who to prospect. The sad truth is that most salespeople prospect without ever thinking that through.
To some folks, it might seem like a no-brainer. You have your standard go-to in any type of organization — VP of Sales, CIO, etc. But what makes you pick that position or, more importantly, that organization? Do you have the hard data — from YOUR organization — to back up those choices? Does your acquisition cost increase or decrease relative to organization size? Do you have existing customers near their location?
You’re gonna need to put on your data scientist hat for this one, folks.
If you’re a startup with limited data, your choice is to either fly blind or use bland, industry-specific data. If you’re at least semi-established, you should have a wealth of information at your fingertips: your CRM! (Many of you will chuckle, as this will be a test of how well you utilize your CRM).
Are my current customers my ideal customers?
There are a series of questions that must be answered to establish some baselines for prospecting:
- What’s the average profile of my existing clients? What are the most common characteristics?
- What’s the profile of my BEST existing clients? Do they share common characteristics, if any?
- What commonalities do the best and the average client profiles share?
- Do either of these profiles match my ideal future client?
Obviously, you could rank these based on subjective feelings or you could pull existing data and weight it appropriately. The latter will clearly produce more sustainable results.
Characteristics to weight might include total company revenue, company size, sale size, total number of products bought, geographical location, number of contacts in company….the list goes on. Try to weight on a simple scale so formulating a compiled score isn’t too difficult — for example, 1–5 with .25 increments.
If this sounds like too much of a headache, let me include a shameless plug for my friends at Everstring, they’ll handle it (including prospect scoring) for you.
Here’s a sample of our existing clients scoring sheet:
In order to find our “average” client, we weighted client information by the following factors: median household income by zip code, state income rating, total number of cities >6000, state rating based on number of current customers, micro or metropolitan statistical area designation, and whether it was a city, county, special district (water, sewer, retirement boards).
Fortunately for me, BoardSync’s clients and prospects are in the public sector; information about the organizations and prospects is easily available, especially en masse, from services like Census.gov. We pulled information about income, population density, number of “bigger” cities (>6000), and how many existing customers all filtered by zip code, city, county, and state.
Yeah….it was a lot of data to crunch.
We had some interesting results with the correlation of best clients based on our weighting according to “ideal” characteristics and best clients based on the top 10 our CEO picked; there was no correlation at all. We were able to work our weighting formula to group at least half of the ten in the top half, but going beyond that seemed self-defeating.
Thus, we produced a formula that we could place on any prospect (city, county, or special district) that had the same data available in order to rank.
But what does any of this have to do with prospecting?
Outbound prospecting is a critical part of account-based marketing. — TOPO
One of the great attributes of account-based marketing is its efficiency when it comes to prospecting; there is a lot of initial work in creating a ranking system, but once you’ve created that and found a pool of prospects to rank, your sales team no longer has to worry about wasting time on junk leads.
Further, you’re able prioritize high and low value activities based on their respective rankings — everyone gets a touch, but some get more love. ABM also has a focus on organizational prospecting- ranking organizations is much easier than ranking individuals given the availability of data on most corporations.
Now that you’ve scored the prospects in your pool, you can begin mapping them out, but that’s a whole other post.
So, when was the last time you actually thought about why you’re picking those prospects?