In short
An international group of 3 schools built a scoring model with Skolbot, tailored to its admissions criteria.
Each conversation is read along 2 axes: intent and capacity.
In the CRM, 100% of leads arrive with the reason for their score, a summary, open questions and the next step.
The team knows who to call back and how to open the conversation.
A score that prepares the conversation
Skolbot and the admissions team of an international group of 3 schools built a lead score on 2 axes: intent and capacity. The team wanted to know who to call back first, with the information needed to open a relevant conversation. The criteria were defined together, one by one.
A highly motivated applicant has chosen her program and started her application, but her English is at B1. A generic scoring model would rate her cold; here, she is warm and her CRM record recommends English preparation. The advisor opens the call with that offer and raises the right topic in the first minute.
A priority always needs an explanation
A bare category is not enough for admissions to choose who to call and what to offer.
Criteria that are too generic
Motivation does not tell the whole story. The team needed to separate how far the applicant had progressed from their capacity to join the program.
Barriers to understand
Admissions identified weak English and funding as the deciding barriers. These topics have to be clear before the callback.
A call with no context
A hot, warm or cold rating is not enough without an explanation. The advisor needs the open questions and a next step.
Build the criteria with admissions
Skolbot worked with the team to link each rating to a precise reading of the student’s plans.
- 01
Define each criterion together.
The admissions team shared its own qualification criteria. Skolbot took them one by one to build a scoring model tailored to the group.
- 02
Read the plans and the barriers.
Intent shows in the chosen program, the target start term and how far the application has progressed. Capacity takes English level and funding into account.
- 03
Explain the rating in the CRM.
Each lead arrives rated hot, warm or cold, with the reason for the score. The advisor also finds the summary, the open questions and the next step.
- 04
Wait, then refine with the team.
Interrupted conversations are flagged and stay open for 7 days, so the score is based on a complete conversation. The director of admissions reviews the scores with Skolbot. The criteria evolve case by case, based on the team’s feedback.
| Score | Reason | Next step |
|---|---|---|
| Hot | Program chosen, C1 English, funding planned | Call today |
| Warm | Interested, B1 English to confirm | Offer an English test |
| Cold | Plans two years out, funding not raised | Follow up next fall |
Every callback starts with the right topic
axes: intent and capacity
The 2 axes give admissions a reading of each student’s plans and how feasible they are. Every lead arrives in the CRM with the reason for its rating. The team can choose who to call back first and prepare a suitable offer.
The advisor sees the questions still open before picking up the phone. An identified barrier becomes a topic to address during the call. Reviews with the director of admissions then refine the criteria based on real situations.
“Before Skolbot, our advisors called prospects back without always knowing what they needed or how interested they were. Now every CRM record tells us which applicants to prioritize, what they are looking for and what questions they have. Our teams know who to call, when, and with what arguments. Every conversation is more relevant and more effective.”
Frequently asked questions
How do you build a lead scoring model for school admissions?
Build it with the admissions team, criterion by criterion: that is how Skolbot built it for an international group of 3 schools. The model uses the team’s own criteria. The director of admissions then reviews the scores with Skolbot and adjusts the criteria based on real cases.
What criteria should you use to qualify a prospective student?
An international group of 3 schools scores prospects with Skolbot on 2 axes: intent and capacity. Intent covers the chosen program, the target start term and how far the application has progressed. Capacity covers English level and funding, which admissions identified as the deciding barriers.
How do you prioritize prospect callbacks in an admissions CRM?
Each lead gets a hot, warm or cold rating with an explanation in the CRM. The advisor finds the conversation summary, the open questions and the next step. This information guides the order of callbacks and what each call covers.
Should an applicant with weak English be rated cold?
Not necessarily: at an international group of 3 schools, a motivated applicant with a chosen program and a started application stays warm despite a B1 level. Her CRM record recommends English preparation. The advisor can raise this option from the start of the call.
How do you avoid scoring a lead on an incomplete conversation?
Skolbot flags partial conversations and keeps them open for 7 days, so the score is based on a complete conversation. Admissions avoid a rating built on truncated information. The team’s feedback is also used to review the criteria case by case.