B2B technology and SaaS·India·90 days·Client name withheld
18,742 conversations.1,146 potential B2B leads.90 days.
A B2B technology company used an AI website chatbot to engage visitors, identify the companies behind them, capture and validate business information, and route people toward the right next step. These are the counts it reported over 90 days.
- Conversations handledEvery conversation
- 18,742
- Potential B2B leads identified6.1% of conversations
- 1,146
- Quotations shared1.3% of conversations
- 241
Traffic was arriving. Prospects were not.
The client was receiving a steady flow of website visitors, but a large percentage of potential prospects were leaving the website without engaging with the sales team.
- Anonymous, general visitorsCompany identification
- Unknown business intentRequirement and intent signals
- Repetitive enquiriesAutomated initial assistance
- After hours visitors24/7 engagement
- Limited sales contextStructured information before handoff
The client implemented an AI powered B2B chatbot to automate the initial conversation, identify prospects, and route high intent users to the appropriate next step.
What the chatbot learned before sales got involved.
These are the attributes the chatbot was able to capture while a conversation was happening. Together they are what allowed the sales team to receive more context before engaging with a prospect.
01 Identity
4 of 11 captured
- Visitor name
- Company name
- Business email
- Email validity
02 Business context
8 of 11 captured
- Company profile
- Requirement
- Product or service interest
- Estimated requirement
03 Intent and action
11 of 11 captured
- Purchase intent
- Need for sales assistance
- Request for quotation
What happened to 18,742 conversations.
The chatbot did not simply answer questions. It progressively collected and analyzed information during conversations to determine whether a visitor could represent a potential business opportunity. Each stage below is a reported count.
- 0118,742Users handled by AI chatbot66.6% continued6,256 did not continue
- 0212,486Users identified by company name71.8% continued3,522 did not continue
- 038,964Email IDs collected87.2% continued1,143 did not continue
- 047,821Email IDs successfully validatedQualification filter14.7% continued6,675 did not continue
- 051,146Potential B2B leads identified37.3% continued718 did not continue
- 06428Users transferred to live support66.8% continued142 did not continue
- 07286Quotations requested84.3% continued45 did not continue
- 08241Quotations shared
The other 17,596 conversations were handled by the chatbot too. Identifying the potential prospects first is what kept sales representatives out of them.
Method. All counts on this page are the client reported totals for a single 90 day implementation period. The client is not named. Every percentage shown is calculated from those counts, and nothing is projected beyond that window.
Sales handoff
428
Users transferred to live support
2.3% of all 18,742 conversations
Human attention when it was more appropriate.
The AI chatbot handled the initial conversations and transferred users to live support when human assistance was more appropriate. 428 of 18,742 conversations reached that point.
This helped the sales team focus their time on the conversations that required human involvement, and it meant they received more context before engaging with a prospect.
Common transfer triggers
- Detailed product discussions
- Pricing negotiations
- Complex technical questions
- Enterprise requirements
- Custom requirements
- High purchase intent
- Direct request to speak with sales
Quotation generation
286 requested a quotation. 241 received one.
84.3%
of quotation requests resulted in a quotation being shared
286 quotation requests
241quotations shared
45not shared
The chatbot helped capture the required information before the quotation process, reducing unnecessary back and forth between prospects and the sales team.
The remaining 45 requests did not result in a quotation being shared. The reported figures do not say why, and this page does not fill that in.
The website became an active qualification channel.
The implementation changed the role of the website from being primarily an information channel into an active lead identification and qualification channel.
12,486
Users identified by company name
Anonymous traffic
Traffic became identifiable
Company and contact information could be captured during the conversation itself, so visitors who would otherwise have left without contacting the company could still be identified as potential buyers.
1,146
Potential B2B leads identified
Sales qualified manually
Qualification moved before sales
Potential B2B prospects were identified before a sales representative was involved, conversations carried more structure, and visitors were engaged even when the sales team was unavailable.
428
Users transferred to live support
Every enquiry reached a person
Human attention became selective
High intent users were transferred to live support when required, and the sales team received additional context before engaging, so human time went to the conversations that needed it.
The takeaway
AI did not replace the sales team.
It helped the sales team focus on the conversations that mattered.
The biggest impact of the AI chatbot was not simply the number of conversations it handled. It was its ability to turn anonymous website visitors into identifiable, qualified and actionable B2B opportunities.
In 90 days the chatbot handled 18,742 users, identified 1,146 potential B2B leads, validated 7,821 email IDs, identified 12,486 companies, transferred 428 users to live support, and helped facilitate 241 quotations.
1.3%
of conversations became a shared quotation. The rest of them are on this page too, which is the only reason to believe that figure.
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