My best growth experiment: Nitesh Sharoff, growth marketing & analytics consultant

Nitesh Sharoff, growth marketing & analytics consultant, shares his best growth experiment, and tips for succeeding using the right data

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The rise and rise of data analytics has brought with it superior digital experiences, personalisation and a golden era of hyper-growth companies, driven to success by a constantly-experimental mindset. We’re speaking to digital experts about the most effective growth experiments they’ve conducted and their tricks for improving customer experience, boosting ROI and increasing revenue, to help teams develop a more experimental approach to business growth.

Let’s kick off our new series with Nitesh Sharoff, growth marketing and analytics consultant, and founder of Growth Runner, who works with companies to find the ‘right data’ for supercharging growth.

“The best experiments focus on understanding human behaviour, and creating experiments based on that,” Nitesh begins. He tells us about Dan Ariely, a behavioural economist and the inspiration behind many of Nitesh’s most successful ‘growth hacks’.



“Ariely describes an aspect of human behavioural bias using the example of travel marketing,” he explains. “If users have the choice between an all-inclusive trip to Rome or to Paris, the results are 50/50, since both cities are equally appealing. But when you add a third, completely useless option, the results become skewed. For example, if you added a third option of a trip Rome that’s all-inclusive, but you have to pay for all your own coffee, no one picks that option. Yet something really interesting does happen: Around 70% of users end up picking the Rome, all-inclusive option. Something in their psychology makes them choose this option, when before it had been evenly distributed.”

READ MORE: Metric Matters: Nitesh Sharoff, growth marketing & analytics consultant

This experiment really stood out to Nitesh. So he decided to try it out with one of his clients

“I was working with an electronic gadgets company with huge amounts of traffic. They ran deals all over the web, bringing in hundreds of thousands users every day,” Nitesh says. “But, because of the campaigns, people landing on the site were extremely targeted and just wanted that specific product. With bargain buyers, it’s very hard for companies to get them to spend more.

“I decided to run a similar experiment to Ariely’s on the checkout to see if we could get them to pay a bit more. So, we played around with the shipping options. For this company, customers had to pay $1.99 for economy shipping, or they could choose priority for $3.99, which guaranteed delivery in 1-2 days. I introduced a third option, which was the same priority delivery, for the same price, but had a 2-4 day delivery. This option (which again, made no sense to pick) lead to a huge increase in the number of users choosing the original priority delivery option. And this tweak – which took five minutes to set up – made the company $11,000 a day.

“Playing around with people’s psychological reasoning can have a huge impact on the decisions they make when they land on your site,” Nitesh goes on. “ is a great example of a company that uses behavioural biases to their benefit really well. For example, even when a hotel is booked, they leave it visible on the screen for users to see what they’ve missed out on. They trigger alerts letting users know only two rooms are left, which affects their psychology and pushes them to book. Groupon built an entire business model around behavioural biases in this way too.

“Businesses really need to understand their data, but they also need to understand these behavioural biases,” Nitesh concludes. “You see the best performance when the person in charge of UX has a growth mindset and is effectively using the right data. That’s because they’re a designer who’s thinking like a marketer, so they’re focused on the user experience, as well as the emotions that the user goes through.”

Read Nitesh’s full interview on how to effectively use data analytics, find the ‘right data’ and encourage a growth mindset.

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