AI Upsell & Renewal Advisor

What does this AI Agent workflow deliver?

• Proactive identification of upsell opportunities and at-risk renewals by analyzing customer payment and account data.
• AI-generated, personalized outreach content (emails or Slack messages) for each customer, highlighting relevant product benefits or upgrade offers based on their usage patterns and history.
• Automatic logging of recommended actions and notes into the CRM, ensuring sales teams have up-to-date context on each account.
• Instant Slack alerts to account managers with suggested next steps for high-priority accounts, including a draft message they can review or send to the client.

Outcome:

• Increased revenue through timely upsells and improved renewal rates, thanks to early identification of customer needs or churn signals.
• Time saved for sales reps, as the agent handles data analysis and first-draft communications, letting reps focus on relationship-building and closing deals.
• Consistent follow-ups with customers (no account falls through the cracks) because the AI agent monitors accounts 24/7 and never forgets a task.
• Better customer experience due to well-timed, tailored offers rather than generic sales pitches, making clients feel understood and valued.

Why does it matter?

Pain Point (Sales Teams & Account Managers)	AI Agent Solution (Upsell & Renewal Advisor)
Hard to monitor all customer accounts for upsell or renewal timing.	The agent continuously scans Stripe/CRM data for upcoming renewals, contract anniversaries, or usage spikes that indicate upsell potential – ensuring no opportunity is missed.
Crafting personalized messages for each client is time‑intensive.	The agent uses AI to generate tailored outreach content (email drafts, call scripts) that reference each client’s context (usage, industry, past interactions) pulled from CRM, saving reps hours of research and writing.
Risk of churn not noticed until too late.	Alerts the team via Slack when it detects churn signals (e.g. reduced usage, delayed payments) and even suggests remedial offers or talking points to re-engage the customer.
Inconsistent follow-up processes across team members.	Provides a standardized workflow: every account is monitored and recommended actions are logged in the CRM. Managers can easily track follow-up status and outcomes, ensuring quality control.

Step-by-step setup:

1. Connect data sources: Integrate Stripe (your billing platform) and your CRM in Unitron AI, and connect Slack for real-time notifications.
2. Define monitoring criteria: Configure the agent to watch for key events such as upcoming subscription renewals, trial endings, payment failures, or usage thresholds (if usage data is available in the CRM or Stripe).
3. Schedule regular checks: Set the agent to run on a schedule (e.g. daily) to analyze account data. It will pull customer records from the CRM and transaction info from Stripe to stay updated on each account’s status.
4. AI analysis and insights: The agent uses AI to identify patterns or triggers. For example, it flags a customer nearing their plan limits (potential upsell) or one with declining activity (potential churn). It summarizes each finding in plain language for easy understanding.
5. Draft personalized outreach: For each opportunity, the AI agent generates a tailored message or email. For an upsell, it might highlight how upgrading will benefit the client (using their specific usage data and relevant new features). For a renewal, it might recap the value the client got over the term and introduce an incentive to renew.
6. Notify and log: The agent sends a Slack alert to the responsible rep or a #sales channel, including the AI-drafted message and key details (customer name, current plan, recommendation). At the same time, it logs these notes under the account in the CRM (e.g. creating a task or note with the upsell/renewal suggestion).
7. Human review and action: The sales representative reviews the AI’s suggestion. With minimal tweaking, they can send the email or call the client armed with the AI-provided talking points. As reps act on these suggestions, they can provide feedback (for example, tagging if the recommendation was useful or not), allowing the agent to learn and refine its criteria over time for even more accurate future suggestions.