Open rate: 42%. Click rate: 3.8%. And then what? For most CRM teams, the answer stops there. Engagement metrics tell you whether someone noticed your message. They don’t tell you whether that person bought something, renewed a subscription, or took any action that actually matters to your business.
That’s the gap Conversion Goal was built to close. Alaïs Pluyaud, Senior Product Manager at Batch, explains how it works and what it changes for marketing teams day to day.
Alaïs, what was missing for CRM teams before Conversion Goal?
One simple thing: the ability to connect a CRM activation to a real business outcome. We kept hearing the same frustration: “My open rate looks fine but did this campaign actually drive purchases?”
Opens and clicks are engagement signals. They tell you someone interacted with your message. They don’t tell you whether that person bought something, renewed a subscription, or took any action that actually matters.
And business impact means something different for every vertical. A retailer tracks purchase volume over a short attribution window. A media company follows subscription renewals over a much longer one. A services brand measures form submissions or booked appointments. Conversion Goal is built to adapt to all of these.
Before Conversion Goal, measuring this meant involving a data or engineering team, cross-referencing exports, and waiting. Most CRM teams didn’t have that autonomy. That’s the gap we built this for.
How does the setup actually work?
Three inputs, and you’re done.
First, your conversion event the user action you want to track. Something like completed_purchase if you’re following purchases.
Then, the attribution window the timeframe in which the action must occur after message receipt to count as a conversion. One day, three days, a week you set it based on your purchase cycle.
Finally, optional business tracking: activate it to see the total revenue generated by every basket that counted as a conversion. Not just how many people converted how much those conversions were worth.
One thing worth explaining: Conversion Goal uses last-click attribution. The last interaction with your Batch message gets credit for the conversion. If someone clicks your email and buys within 48 hours, that campaign is credited.
Everything surfaces in your Batch analytics. No exports, no data warehouse queries.
What are the main use cases?
Three come up constantly.
Experimentation. When you run an A/B test or a holdout group, you can measure the revenue increment between variants. The difference between “this variant got more clicks” and “this variant drove 12% more revenue” is the difference between optimizing for proxies and optimizing for outcomes.
Purchase analysis. On promo code or sale campaigns, you get a direct read on revenue impact. One retail client measured a 3.2% conversion rate and €14,000 in revenue on a recent promo campaign visible directly in their Batch dashboard.
Channel profitability. SMS and email both cost money. Conversion Goal lets you calculate RPM revenue per thousand messages sent and decide: is this channel profitable for this audience?
It became Batch’s fastest-adopted feature ever. Were you expecting that?
Honestly, no not at that speed. Within weeks of launch, almost every client had started using it. That’s unusual for analytics features, which typically have a longer ramp-up.
I think it reflects how real the need was. CRM teams had been waiting for this autonomy for years. The moment they could set a conversion goal in three clicks and see the results in their dashboard, the value was immediately obvious.
There’s also an important context: Batch is often the reference data source for these clients. Their profiles, their events, their engagement history it’s all already there. Being able to work on performance directly against that reliable data, without routing it through a third-party tool, was a pressing ask.
Does this change Batch’s positioning in any meaningful way?
Yes, and deliberately so. We’re taking another step toward a genuinely all-in-one, performance-oriented platform. Conversion Goal lets teams systematize a ROI-driven approach directly in the platform.
What also interests me longer-term is what this opens up on the agentic side. Today, our AI agents can understand the performance of communications. Tomorrow, they’ll be able to self-adjust to maximize it. Conversion Goal lays the foundation for that loop.
Does the current regulatory landscape change anything?
It adds another reason to act, yes. The CNIL published its final recommendation on email tracking pixels in April 2026 open tracking now requires explicit consent, and Apple Mail Privacy Protection had already been degrading that signal for years.
But the underlying need was there long before. Even with a perfectly reliable open rate, it wouldn’t tell you whether your campaign generated revenue. That’s the real question and that’s the one Conversion Goal answers.
Bottom line?
Three things.
Autonomy: measure conversions without waiting on your data team, on the most reliable data source the one already in Batch.
Agile experimentation: improve campaigns continuously with metrics tied to your actual business.
ROI clarity: see which campaigns generate real value and decide what to stop, what to scale.
That’s the real promise of Conversion Goal. Not one more metric. A continuous improvement loop, built directly into the platform and the foundation for our agents to optimize it on your behalf tomorrow.
Mickael Bentz
Head of Product Management @ Batch
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