Each new customer gets a heat score:
The people in red definitely won’t buy. No need to contact them.
The people in green definitely will buy. No need to contact them.
The people in yellow a tad close to just not buying. Definitely CONTACT THEM!
Your team can now focus on a smaller group of people who are "on the fence" about purchasing. By contacting them over the phone or sending a gift, you have an extraordinarily large chance of getting these people to buy.
This predictive modeling is best for SaaS businesses
- A “SaaS” business just hosts software somewhere else and lets you use it when you need it.
- You pay Google Docs for Business $10/month to use their service.
- You pay The New York Times $9.99/month to read their content.
- You pay Adobe $20/month to use Photoshop.
These are all “SaaS” (Software As A Service) businesses.
The way they get their customers is by giving away their services for free at first... and then converting people into paying customers.
This is how they bring in revenue. For example, Adobe will give you a free 30 day trial of Photoshop. If by the end you like it a lot, and use it a lot, you'll still want it. So you'll want to pay $20 per month.
Numbers add up nicely:
|Price per user per month
|Price per user per year
|Yearly revenue from 5,000 users
As you can see... it’s definitely worth it to “tempt” someone with a free trial!
You want more people. So you will lure them with a free bribe.
A quick EXAMPLE of how user scoring gives tremendous advantage:
Let's say 5,000 new people per month signup for your free trial.
End of the month you count how many people subscribed. You're very lucky and 10% signed up. That's 500 people paying for your service EVERY MONTH! Money!!
Only problem is... during this month you have no idea which of those 10% of people will be signing up. And you cannot follow up with a phone call, send them bottle of beer...
What if you could call 10 people/day/person with a sales team of three (= 600 people per month). Who would you call?
What if you could tell RIGHT AWAY who these people should be?
So your 3 guys making calls can call the same 600 people, but have an 80% success rate, and ALREADY KNOW that person is a super-likely to buy.
That’s 80% more conversion with the exact same sales team.
User Scoring Case Study with Moovly
Moovly is a video SaaS company (check the full story here), and we implemented DataStories User Scoring on their new customers.
Moovly was getting around 1,500 free leads per day. That's around 45,000 free leads per MONTH. It's nearly impossible to have personal contact with all these people.
With DataStories, they can now identify just 1,000 people per month who are EXTREMELY LIKELY (89% score) to buy.
The Moovly sales team narrowed down their prospects from 45,000/month to just 1,000/month because of our User Scoring algorithms. These algorithms analyze hundreds of thousands of different factors that a human can't possibly analyze.