AT A GLANCE
- Industry
- Dairy ingredients · B2B
- Starting point
- Product discovery and sample requests
- Expansion
- Customer engagement and sales enablement
- Reach
- 6 channels and portals
01 / ENGAGE
Product Knowledge.
Ready for Buyers.
A product assistant helped buyers explore ingredients, clarify their requirements, and request samples using the enterprise’s product and application knowledge.
Questions about ingredients, applications, and end uses revealed demand across bakery, beverage, and nutrition—three growth segments for the commercial team to investigate.
Customer engagement expanded across six channels and portals, with a reported 87% customer satisfaction score.
02 / UNDERSTAND DEMAND
From Buyer Questions
to Market Evidence.
Sample requests became more useful when connected to research, distributor activity, customer history, and product knowledge.
AI-assisted analysis helped teams identify patterns in buyer needs, assess confidence, and choose actions to evaluate.
Market & Business Knowledge
Research reports, market and category trends, distributor sell-through, CRM opportunity categories, and product/application data.
Customer & Field Signals
Chat transcripts, sample requests, website journeys and clicks, sales/CRM notes, and distributor feedback.
Teams used these insights to prioritize demand, target campaigns, and route opportunities. The pipeline included 15+ structured sources normalized weekly and 10K+ monthly behavior signals captured.
03 / EXPAND
Extend What Works.
Teams expanded into sales activity, adapting content, distributor rules, and materials to each customer while sharing the same knowledge foundation.
| Phase | Focus | Reported result |
|---|---|---|
| Initial launch | Product inquiries and sample requests | +75% inbound leads |
| Commercial intelligence | Research + behavior across three growth segments | 25+ evidence sources |
| Commercial expansion | Targeted engagement and sales actions | +50% qualified leads |
The lead metrics belong to different rollout phases: initial product engagement and subsequent commercial expansion.
04 / CLOSE THE LOOP
Learn from
Each Outcome.
Conversations revealed application needs and buying intent. Teams used those signals to guide actions, then added confirmed outcomes to the record for the next decision.
ILLUSTRATIVE EXAMPLE / HIGH-PROTEIN BAKERY
A Buyer Explores an Application
A bakery manufacturer reviews content for a high-protein product.
The Conversation Reveals the Need
The buyer asks whether an ingredient fits its formulation.
Knowledge Guides the Next Step
AI suggests an application, product family, and relevant sample path.
A Concrete Outcome Returns to the Record
A sample request or technical meeting becomes new evidence for follow-up.
This episode illustrates the learning model in the case material. It is not a separate customer testimonial or a reported order.
Teams learn which applications attract interest and which actions move buyers forward, building a reusable record of what works.
05 / APPLY IT TO YOUR BUSINESS
From Interest
to a Meeting.
Use the same approach with Zennie, your AI assistant, to prepare materials, coordinate follow-up, and respond to the buyer’s next step.
ILLUSTRATIVE WORKFLOW / FROM INTEREST TO MEETING
Set the Goal
Define the audience and desired outcome. Shape the task with enterprise knowledge, conversations, and campaign history.
Understand the Account
Use People enrichment and company research to connect earlier interactions with current business priorities.
Prepare Relevant Outreach
Prepare an email or landing page from approved knowledge and the buyer’s needs. Save it in Assets for follow-up.
Assign the Follow-Up
Let Zennie send outreach and track responses. Or have a teammate follow up and CC the AI SDR agent for next-step suggestions.
Arrange a Meeting
Arrange a customer meeting with Zennie. Keep the commitment in Calendar and the customer context ready for the team.
Learn from the Response
Record the action, response, and outcome. Use that experience to inform the next campaign.
This platform example is not an additional deployment or the source of the reported +50% result.
Meet ZennieTHE BIGGER PICTURE
Build on
What Buyers Reveal.
Product expertise becomes more useful when teams can connect it to buyer needs and the actions that move them forward.
About these results +
Company-reported results from different phases of one deployment. Inbound and qualified leads are separate measures. The 10K+ captured and 15K+ analyzed monthly behavior signals cover different source scopes. Measurement periods, cohort definitions, and attribution methods are not specified.