Case Study: How Can You Optimise a Price Calculator to Persuade Users to Buy Expensive Products?

This nutrition company had an online calculator that told people how much of a particular nutrient they are likely to need in a day and what it would cost them to buy that from the company.

Google Analytics data showed that when users interacted with the calculator, the conversion rates were higher. It wasn’t clear what the correlation was: whether users who wanted to buy were interacting with the calculator or that users who engaged with it were encouraged to buy.

However, during user research, we saw that users who saw the calculator thought there was something wrong with it and did not interact with it. This was because the calculator looked more like a generic text advert and the design was not aligned with the brand’s style.

Hypothesis and Psychological Technique Applied

We hypothesised that by redesigning the calculator to align with the brand’s style and highlighting why users should use the calculator, more people will interact with it, which in turn will drive conversions and sales.

Experiment

For the Variation, we changed the design of the calculator altogether, making sure the new one aligned with the brand style.

We added in the brand logo, CTAs in the brand colour, and a headline and copy above the calculator to explain what it was and how it helps.

We also made the calculator input fields easier and displayed the cost of the nutrient prominently instead of just the quantity required.

Optimising price calculator

Results

From the observed data, we were able to see that the Variation showed a high probability of being better than the Control on all devices.

Learnings

The experiment showed that optimising a useful tool on the site adds value to the customer buying experience and results in higher conversions and sales.

This was a risky experiment because the products on the site are slightly expensive. Highlighting the cost of the product alongside telling them the quantity of products they’d need meant that they could potentially drop off instead of completing the transaction.

Indeed, we had anticipated price-sensitive customers to drop off. But this also meant that the customers who did proceed to checkout were unlikely to drop off in between because they’re already aware of the price and the quantity they need to buy.

In fact, the first iteration of this experiment did not perform well. But with a few minor tweaks, this second iteration increased conversions and sales significantly, and was rolled out on all devices.

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