Your AI companion for Herobot Chatbot Marketing.

Find the questions your chatbot is not answering well.

A chatbot transcript can contain the clearest possible signal from prospects and customers: the language they use when they cannot get what they need. Strawberry can help review visible Herobot conversation history and distinguish a true unresolved question from a visitor who simply left the chat. It can group similar wording, such as repeated requests for pricing detail, implementation guidance, product compatibility, account access help, or policy clarification.

Ask it to create a structured issue list with the question theme, representative conversation examples, current chatbot response, likely gap, and suggested destination for the fix. That destination may be a new answer, a revised decision-tree branch, a better clarification prompt, or a knowledge-base article that the chatbot can reference.

The result is more useful than a raw transcript export. Your team gets a prioritized backlog based on recurring user intent, rather than relying on whoever happened to notice a problematic exchange first. Review the findings with marketing, support, and subject-matter owners before deciding which content or flow updates belong in Herobot.

Diagnose human handoffs as flow signals, not just support volume.

A fallback to a human can be the right experience when a visitor needs a specialist.

It can also reveal an unclear chatbot route, missing qualification question, failed intent match, or answer that did not resolve the request. Strawberry can examine the visible sequence around handoffs in Herobot: what the visitor asked, which flow or response appeared, whether the visitor repeated themselves, and how the escalation occurred.

Use that analysis to separate healthy escalations from avoidable ones. For example, a sales conversation may need a human once the buyer asks for a custom proposal. By contrast, repeated handoffs after questions about standard plan limits may point to missing or hard-to-find answer content. A companion can summarize patterns by conversation topic, flow step, language used by visitors, and apparent handoff reason.

That gives operators a practical way to decide whether to improve a chatbot answer, add a routing branch, or clarify an expectation. It also helps them preserve the existing human route when it is the best outcome. It also makes handoff review more collaborative: marketing can assess acquisition intent, support can assess service burden, and content owners can validate the proposed resolution.

Strengthen answer quality across your chatbot and knowledge base.

Chatbot quality depends on more than whether an answer exists.

The answer needs to be accurate, current, specific enough to help, and aligned with the next step a visitor should take. Strawberry can help compare the answers visible in Herobot with the knowledge-base material your team uses as a source of truth. It can call out answers that are inconsistent with an article, omit key conditions, use stale product language, or make a broad claim where the source includes important exceptions.

Ask for a quality review that includes the answer text, supporting article or policy, concern found, confidence level, and a proposed revision. For visitor-facing copy, request drafts in the tone appropriate to your brand: concise for common questions, explanatory for complex setup issues, and direct when a human should take over.

This approach also helps reveal knowledge-base gaps. If Herobot repeatedly encounters a question without a trustworthy source article, the right fix may be new documentation rather than a one-off chatbot reply. Build a review queue that connects recurring conversation demand to content work, so the knowledge base improves where real visitors are already asking for help.

Run a weekly chatbot-quality review that turns evidence into improvements.

Set a concrete cadence: every Monday, review the previous seven days of Herobot conversations with the same repeatable process. First, collect the most frequent unresolved question themes and the conversation examples that support them. Next, inspect fallback-to-human cases to identify whether each handoff was appropriate, premature, or caused by a missing answer or broken flow. Then compare the highest-volume answer topics with their knowledge-base support and note any content that needs verification.

Strawberry can help organize these findings into a weekly review brief with counts, representative phrasing, affected flows, likely root causes, and recommended owners. The purpose is not to change every chatbot response immediately. It is to create an evidence-based decision point for the people responsible for messaging, content, support operations, and chatbot configuration.

End each review by selecting a small number of approved changes: perhaps one new knowledge-base article, two revised answers, and one flow adjustment. Track the issue source and expected outcome, then check the following week whether unresolved questions or avoidable handoffs declined. This cadence keeps the chatbot responsive to changing visitor needs without making every individual conversation a separate project.

Frequently asked questions