At Perfilan we are passionate about measuring and exhaustive data analysis, which always provides us with a way to improve processes. For this reason, by obtaining appointment and sales data from the sales team, we can cross-reference the information with our system, performing Machine Learning analysis of the patterns and characteristics of these people, constantly improving the Lead Scoring algorithm .
All of this makes the information we provide to our clients increasingly more accurate, and below we present four very interesting examples of how Lead Scoring allows you to improve the sales process:
Notes: We omit the names of the developments Telegram Number Data to respect the privacy of our clients. The presentation of the information varies since each client monitors it independently.
Case A
Product: Premium Residential Lots
Location: Veracruz
In this case, crossing the sales information with the Lead Score generated by the platform, we found that indeed, the vast majority of sales were carried out by prospects with a rating above 60, approximately double that of those with a rating between 33 and 60 , and more than 10 times that of prospects rated less than 33 .
Because of this, the sales team has a better chance of closing a sale if they focus on following up on prospects with high Lead Scores .
Case B
Product: Residential Houses in Private
Location: San Luis Potosí
We can see that of the 100% of the people who answered that they did want to visit the development, 60% had a LS of more than 50, 30% had a LS between 25 and 50, and 10% had a LS below 25.
Again, if we focus on high Lead Scores, we are much more likely to make the necessary development visits to meet the sales quota.
Case C
Product: Residential Lots
Location: Querétaro
We have an interesting analysis, since 30% of prospects have scheduled an appointment or are already in communication with the sales team, which is a much higher percentage than the national average . At the same time, we realize that all of them answered that they did want to know the development, and all of them had a Lead Score greater than 50 .
Again, by focusing efforts only on the best leads, the sales team uses their time on those prospects who do continue in the process.
Case D
Taking into account that for this client the status “Current” means that the visit has already been carried out, we obtain that of all the prospects who scheduled, confirmed, or made their visit to the development, 14 had a Lead Score of more than 50 , 8 They were between 25 and 50, and only 2 were under 25.
Once again, by focusing on prospects with the best Lead Score, time is better used, so the number of closings can be increased considerably.
Conclusions
In the table we can see that the industry average for converting leads to sales is 0.95% . If we use the Lead Score tool and only contact those who have a rating of 60 or higher, the closure rate rises to 2.54% . If we call a lead who has a Lead Score of 33 or less (the vast majority of unqualified leads always fall into this range), we have a conversion rate of 0.39% , which is six and a half times lower than if we called to one of 60 or more.
This is the most compelling example of how, by following up only the best prospects, we can dramatically increase the closing rate, making much less effort.
Every time we carry out monitoring with the sales team, we find the same results, the people with the best Lead Score are always much closer to making a purchase, so not having this tool can result in a waste of time, money and effort.
If you want to download a free Excel format to be able to carry out your monitoring in a simple way, click here :
Tracking Template
If you want to know how Perfilan can help you automate your profiling process so you can focus only on prospects who have a high chance of purchasing in your development, request a DEMO with our advisors.
If you want to know how to better profile your prospects with Data Science, download our eBook by clicking on the image below.