AI Agent Success: $75,000 Campaign for 2026

When you have agents making proactive calls, you’ve got to measure what actually works. It’s especially true now with all the AI agent customer service platforms popping up. Focusing on the right metrics, ones that reflect real value, not just how many calls you made, is what separates a great outreach program from one that’s just making noise. So what does a win look like when your team initiates the contact?

We just wrapped a full-scale agent-initiated customer service campaign for a B2B SaaS company that sells project management software. Their goal was simple: stop the bleeding from at-risk small to medium-sized business (SMB) clients and get people to use the underutilized advanced features they were already paying for. The campaign ran for three months, from July 1, 2026, to September 30, 2026, on a $75,000 budget.

Our whole strategy was built on predictive analytics, using data from their platform usage logs to identify clients showing early churn warnings like declining login frequency, less module usage, or more support tickets about basic functions. This was informed, targeted intervention, not a cold calling campaign. We also created a segment of clients based on their subscription tier and the advanced features available to them that they weren’t touching. Our bet was that a personalized, proactive conversation could pull these clients back in and show them the full value of their subscription.

Creative Approach and Messaging

The creative was all about empathy and problem-solving. We armed our agents with detailed customer profiles that included historical usage data, past support chats, and their current subscription details. The messaging was framed to first understand a client’s current headaches and then offer specific, tailored solutions, often by demoing an underused feature that would solve their exact problem. For the churn prevention calls, the script was about reinforcing value and solving problems, while the feature adoption script focused on benefits directly tied to their business operations. We wrote two distinct call scripts and built in enough flexibility for agents to adapt to the real-time flow of the conversation.

Agents got extensive training on active listening and handling objections, not just reading the scripts. This was a big deal. We wanted actual conversations. We also brought in a new dynamic scripting tool from ExampleCRM Tool, which fed agents real-time prompts and knowledge base articles based on keywords it picked up during the call. This was a massive confidence booster and let them answer tough questions on the spot. The tool became invaluable for keeping our messaging consistent while still letting agents personalize their calls.

Targeting and Segmentation

Our targeting was surgical. For churn mitigation, we zeroed in on roughly 1,200 SMB clients whose churn risk score was over 70% (on a 0-100 scale), based on the client’s own internal machine learning model. For feature adoption, we found 1,800 clients with access to advanced reporting or integration features who hadn’t touched them in the last six months. This level of detail in segmentation let us make our agent training and messaging incredibly specific, avoiding the generic outreach that never works. Most clients were in the Atlanta metropolitan area, which gave us some localized business context to work with, even though the software works anywhere.

Campaign Performance: What Worked and What Didn’t

The results were mixed, but the insights were worth it. Our overall contact rate, actually getting the target decision-maker on the phone, was 38%, which is solid for proactive outreach. We actually beat our initial 30% projection. The conversion rates, however, were a different story for each segment.

Churn Mitigation Segment

• Impressions (Calls Attempted): 5,500
• Successful Engagements (Meaningful Conversations): 950
• Cost Per Lead (CPL – if we consider a successful engagement a “lead”): $78.95
• Churn Reduction Rate: Of the 950 clients we had good conversations with, 180 of them (18.9%) showed better engagement afterward (more logins, more feature use) and didn’t churn in the next three months. This prevented an estimated $120,000 in churn revenue based on average client lifetime value.
• Return on Ad Spend (ROAS – calculated against prevented churn revenue): 1.6x

Personalization driven by data was the clear winner here. Agents could bring up specific usage patterns right away, which made the call feel like a genuine check-in. The dynamic scripting tool was especially good for handling technical questions about why a feature wasn’t being used, turning a moment of frustration into a productive one. Agents reported that clients really appreciated the proactive approach and felt like the company actually cared if they succeeded. This segment’s performance showed the power of getting ahead of problems.

Feature Adoption Segment

• Impressions (Calls Attempted): 8,200
• Successful Engagements (Meaningful Conversations): 1,100
• Cost Per Lead (CPL): $68.18
• Feature Adoption Rate: Out of 1,100 engaged clients, only 88 (a disappointing 8%) started using the targeted advanced features during the campaign.
• Return on Ad Spend (ROAS): 0.3x (based on projected uplift from feature adoption, which was lower than expected).

This part of the campaign was tougher. We got more people on the phone, but the adoption rate was low. We learned that even though clients were happy to talk, they didn’t feel an immediate need for the advanced features we were pushing. The creative approach, while informative, didn’t create a strong enough reason to act *now*. Simply showing them the value wasn’t enough. We needed to connect that feature to an urgent pain point or a clear, immediate ROI for their specific business.

Optimization Steps Taken

After the first month, we looked at the data and made some big changes. We ran weekly feedback sessions with the agent team to get qualitative insights you just can’t see in the numbers. One thing they kept hearing was that clients in the feature adoption segment felt the advanced features just weren’t relevant to how they worked, even though our data said they should be. It was a targeting problem.

First, we sharpened our targeting for the feature adoption group. We stopped looking at just who *had access* to a feature. Instead, we started cross-referencing that with other data, like which clients had recently hired more people or taken on bigger projects. The logic was that these growing companies would have an immediate need for better collaboration tools or resource tracking. This tweak made our target list smaller but way more qualified.

Second, we reworked the messaging on the feature adoption calls. We switched from a generic “here’s a cool feature” pitch to a “here’s how this feature solves a specific problem you’re probably facing” approach. For example, instead of just explaining what Gantt charts are, agents would ask about project timeline issues and then introduce Gantt charts as the perfect tool for visualizing and managing complex dependencies. This meant more scenario-based training for the agents, but it worked.

Third, we added a follow-up email sequence for everyone. It wasn’t just a call summary. It was a personalized email with links to short video tutorials for a feature they agreed to try, or case studies for the at-risk clients. We also included a direct link to book a deeper dive with a product specialist. This blended approach gave the initial call more staying power.

These changes paid off in the next two months. The feature adoption rate for our refined segment jumped to 15% in September, and our churn reduction rate climbed to over 20%. Across the whole campaign, the Cost Per Successful Engagement (CPSE) averaged out to $12.50, proving we were getting more efficient.

The agent feedback loop was probably our single biggest lesson. Our agents on the front lines had invaluable insights into customer reactions that our data models were missing. For instance, agents kept hearing that clients felt overwhelmed by the sheer number of features, which suggested a need for better onboarding or a simpler UI for the advanced stuff. This feedback didn’t just help us tweak the campaign. It gave the product development team some very valuable, real-world input.

This campaign proved that while AI agent customer service tools are great for efficiency and data insights, you can’t replace the human element for complex, relationship-building talks. Proactive engagement has to balance smart data with real empathy, especially when you’re dealing with sensitive stuff like churn risk or trying to change user behavior. Spotting the problem isn’t enough. You have to deliver a compelling, personalized solution. I’m convinced the future of these campaigns is an even tighter loop between AI-driven insights and human agent execution which will let us create truly predictive and hyper-personalized customer journeys.