Turning a website chatbot into a qualified-lead engine.

They didn't need a chatbot. They had one - and it generated noise.

The institution already had a website chatbot - a menu-driven Einstein Bot. That was the problem. Prospects had to navigate fixed options instead of simply asking what they wanted to know, so many dropped off before reaching an answer. The leads that did come through were often low quality - incomplete or unqualified - leaving the admissions team to triage rather than advise.

And with answers spread across a large knowledge base and a separate programme catalogue, the bot was slow to respond, compounding the drop-off. They didn't need a chatbot; they had one. They needed it to actually generate pipeline.

A bot that can't answer plainly quietly caps growth.

For an institution recruiting at this scale, the website is the top of the funnel - the first and often only touch a prospect has before deciding where to enquire. Every drop-off is a prospective student who went to ask a competitor instead; every unqualified lead is advisor time spent triaging rather than converting the ones who are ready. A bot that can't answer plainly isn't a neutral cost - it quietly caps growth at the widest part of the funnel.

We measured first, then rebuilt the flow around what the data showed.

The gains didn't come from a bigger model - they came from measuring first and rebuilding the flow around what the data showed.

• Measured before we optimised. We turned on agent analytics and traced every response end to end, and found that ~80% of prospect queries could be answered from the knowledge base alone - the insight that reshaped the whole flow.
• Made the knowledge base the source of truth. Instead of querying knowledge and website in sequence every time, the agent checks knowledge first and only falls back to a live web crawl for programme and fee data - cutting redundant calls and sharpening response time.
• Added friction where it pays. A short pre-chat qualification form filters out spam and half-formed enquiries up front, so the leads that reach admissions are real and ready to advise.
• Put the numbers in the business's hands. Every metric is reconciled against agreed definitions and published as native Salesforce reports, so stakeholders track the agent's impact themselves rather than relying on a slide.

Three things that decide whether this works.

• Instrument before you optimise. The 80%-answerable finding reshaped the whole design. Without analytics on first, we'd have tuned the wrong things.
• Let prospects ask in their own words. A conversational agent self-qualifies where a menu just filters - the same traffic yields more, and better, leads.
• Friction in the right place raises quality. A short qualification step up front cut junk without shrinking the funnel - volume more than doubled and conversion held.

They own the knowledge and the scoreboard; we built the agent on top.

The institution owned what only it could: the knowledge base content across 1,000+ articles, the live programme and fee data, and the definitions behind every metric that mattered. That last part is where most "impact" claims fall apart - so we reconciled every number against definitions the team agreed to, and published them as native Salesforce reports the institution runs itself. They own the knowledge and they own the scoreboard; we built the agent that sits on top.