Introducing: Experiments

Until now, testing a promotion meant either guessing, or stitching together external tools, segment attributes, and spreadsheets. Experiments change that. Here's what makes it useful.

Run any promotion against any other. 10% off versus $10 off on the same welcome offer. A coupon code versus loyalty points for the same reactivation flow. Or any promotion against a control group with no incentive at all.

See the side-by-side that matters. Average session value, revenue, items per session, discounts given, and four other metrics, all in one view.

Trust the result. Every averaged metric comes with a confidence score, so you can tell which differences are real and which are just noise.

Discount strategies tend to be sized higher than they need to be. You give 500 points where 100 would have worked. You give 20% off where 10% would have closed the sale.

Experiments is how you find the floor without giving up conversion.

Test 100 points against 500 points to win back a dormant customer. Test free shipping against 10% off at checkout. The minimum effective incentive is usually smaller than what's running today, and putting the two options side by side is the only way to know by how much.

If you're already running A/B tests today, you know the setup. You have variants in Optimizely or Adobe Target. Segments get passed into Talon.One as profile attributes. Two near-identical campaigns get built to honour the split. A spreadsheet pulls the results back together at the end. It works, but every test costs a week of setup, and every readout costs another week of waiting on the analytics team.

With Experiments, you build one set of rules with two effect sections (Variant A and Variant B), set the traffic split, and read the results in the same place you launched the test. Segments stay where they are, the campaigns stay in sync automatically, and there's nothing to reconcile after the test ends.

If you don't have an experimentation tool, Talon.One handles the split for you. Variant assignment is based on customer profile ID and persists for the duration of the experiment, so the same customer always sees the same variant.

If you already have one, keep using it. Pass the variant assignment from Optimizely, Adobe Target, or wherever it lives into Talon.One with your session updates. Your promotion test stays in sync with whatever else you're testing, and the results still land in Talon.One.

The dashboard shows both variants side by side across seven metrics. Every averaged metric has a confidence score, calculated with Welch's t-test (the same method most experimentation platforms use). At 90% or above, the difference is statistically significant.

When one variant wins cleanly, Talon.One flags the winner for you. When one variant lifts AOV while the other protects margin, it flags the trade-off instead and lets you make the call.

Once you've decided, one click promotes the winner to a standalone campaign in a disabled state, ready for you to review and activate.

Experiments is the foundation of a broader shift in how Talon.One works. Today, the platform is where you execute your promotion strategy. Tomorrow, it's where your strategy is shaped with every experiment feeding a continuous loop of insight back into the next campaign you run.

We're already working on what comes after: multivariate testing, segmented results, audience-based assignment, sequential experiments, and expanded metrics that connect performance to spend and margin. Over time, machine learning will surface patterns across your experiments that aren't obvious from the variant view alone e.g. combinations of customer traits, cart compositions, or contexts where one approach consistently outperforms another.

But none of that works without the data you generate today. Every experiment you run now is what makes the next layer smarter. Experiments is available now to all Talon.One customers. Read the docs for more detail, or try first with an interactive demo.