All insights · Published 2026-11-07
InsightsHow we use AI to improve lead quality, not just lead count
In short
A high lead count can hide poor quality. AI helps in three places: before the ad, when a lead arrives, and after the sale.
1. Before the ad
Analyse the messages and questions you already receive to learn what a serious buyer looks like, then build the message and audience around that.
2. At intake
Classify the enquiry automatically and ask two qualifying questions on WhatsApp (budget and timeline) before handing it to sales.
3. After the sale
Send quality signals back to the platforms through a Conversion API, so the algorithm learns from real customers rather than from every form submission.
A real example, with its limits
In the Code It campaign (September 2026), total spend across Snapchat, Meta, Google and TikTok was SAR 44,435 for 285 leads, or SAR 155.91 per lead. Leads flow into MiniCRM and SAVOXX through automated workflows, and the lead form was hardened with phone-number OTP verification, which deliberately reduces lead volume.
SAR 155.91 is total spend divided by leads. It includes an awareness campaign run by management decision, so it is a blended figure, not the cost of a sales-qualified lead or of a customer. Closing the sale is the sales team's responsibility.
A caution
Automation does not fix bad data. If the targeting is wrong, a model will classify the wrong leads with more precision.
Frequently asked questions
- Does stricter lead filtering reduce results?
- It reduces lead volume on purpose. Compare lead counts only with periods that used the same form and filter, and judge quality by what happens after the lead arrives.