In this representative example, a marketing team at a Singapore-based consumer brand launched a new product. Details reflect real deployment patterns; specifics have been generalised. They had a campaign landing page, a paid social budget, and two people managing all customer communication. In the first 72 hours after launch, they received 412 inbound enquiries. This is the story of how they handled it — and what it cost them before they had LudiChat in place to do the same thing.
What a product launch inbound surge actually looks like
Most discussions of "lead volume" treat it as an abstract metric. 412 enquiries in 72 hours is not abstract. It is a WhatsApp notification going off every ten minutes at 2am. It is a marketing manager checking their phone between every meeting. It is a team choosing, consciously or not, which messages to answer and which to let slide because there is no physically possible way to respond to all of them in time.
The painful reality of a launch surge is that it peaks exactly when the team is most depleted. Day one of a campaign launch involves the most senior people doing the most things simultaneously — briefing the team, monitoring ad performance, responding to media, managing logistics. That is the day 60% of the most time-sensitive inbound messages arrive. The window in which a prospect is most likely to convert is the window when the team is least available to convert them.
"We launched at 9am. By noon we had 140 messages. By the time anyone could actually sit down and reply, half of those people had already moved on."
The previous launch — without AI
The same brand had run a comparable product launch six months earlier. The inbound volume was smaller — around 280 messages over three days — but the team was the same size. Their analysis afterwards showed that they had responded to 61 messages in the first 24 hours. The other 219 received a reply somewhere between 24 and 96 hours later.
Of the 61 who received a same-day reply, 34% became customers within 14 days. Of the 219 who waited, 9% became customers in the same window. The team estimated conservatively that the response lag cost them 45 to 60 sales on a product with a $180 average order value. That is $8,100 to $10,800 in revenue — from a single launch, from a single operational gap.
The March launch — with LudiChat
LudiChat had been live for six weeks before the March launch. The team had uploaded their product FAQ, their pricing and bundle guide, their shipping policy, and their returns process. The AI was trained on all of it.
When the campaign went live, LudiChat began answering immediately. Questions about pricing were answered from the pricing guide. Questions about shipping timelines were answered from the shipping policy. Questions about bundles and discounts were answered from the bundle guide. The team received notifications only for the 6% of conversations where the AI did not have a clear answer — complex B2B enquiries, complaints about a previous order, requests that required human judgement.
The team handled 25 escalations over 72 hours. They did not handle the other 387. Those 387 conversations were resolved — the prospect received an accurate answer and either converted or self-selected out — without any human involvement.
What the dashboard showed three weeks later
Three weeks after the launch, the team pulled the conversion data. Of the 412 enquiries, 31% had converted to a purchase by that point. That conversion rate was more than three times higher than the previous launch's equivalent figure.
The team's view was that this was not primarily because LudiChat was a better salesperson than they were. It was because LudiChat was present at the moment of highest intent — the moment when someone had just seen the campaign, was curious, and had a question. That moment passed in minutes. A reply that arrived hours later was a reply to a different version of that person's interest.
What this means for campaign planning
The operational lesson from this case is that launch readiness now includes AI readiness. A campaign that drives inbound at a volume your team cannot physically handle is not a successful campaign — it is a successful ad spend that fails at the conversion point.
The practical setup for a launch is: train LudiChat on your product documentation at least two weeks before go-live, test the AI against the questions your team expects most, configure escalation rules for the questions that genuinely need a human, and let the AI handle the surge while your team focuses on the complex exceptions and the follow-up.
If you have a product launch or campaign coming up and want to see how LudiChat handles the inbound surge, we can show you a realistic demo using your own product documentation as the knowledge base.