The Pricing Strategy That Led to a 15% Increase in Conversion Rate for High-Ticket Filtration Products

Learn From Success Story

Experimenting with prices and pricing strategy is a bold decision for any business.

But when transparency is at stake, and research points in the direction of price inclusion, the only way forward is through careful execution of a robust hypothesis based on solid data.

Digital performance agency, iProspect managed to instill the confidence go the pricing experimentation route for a client with high-ticket items sold through a network of franchisees.

And also achieved a significant 15% uptick in conversion rates on the site.

This case study details how seasoned CRO experts crafted the hypothesis and their approach to the successful A/B test.

In 2018, iProspect won more than 200 awards including 15 leadership recognition awards and 25 Agency of the Year titles, and was named Digiday’s Agency of The Year. iProspect is named a Leader in The Forrester Wave™: Search Marketing Agencies, Q4 2017, #1 Global Digital Performance Agency by RECMA, Industry Agency of Choice at The International Performance Marketing Awards, and took home 4 Effie awards across the globe in 2018.

iProspect is part of the Dentsu Aegis Network, a wholly owned subsidiary of Dentsu Inc.

Go to www.iprospect.com or follow them on Twitter @iProspect.

Hypothesis:

As with any great hypothesis, the process started with research and data mining.

Since the main goal of the testing drive was to increase lead submissions through query forms – a mid-commitment action – iProspect scrutinized the obvious friction points on the site keeping potential leads from sharing their contact details.

The most glaring omission was pricing information.

However, the items being sold came at a premium and were distributed through a network of franchisees and so it was difficult to settle on the inclusion of concrete price points that all stakeholders could agree upon.

iProspect leveraged research and industry best practices to formulate a pre-hypothesis that even the mention of a “starting at” price, with a monthly spend indicator could add much needed transparency to the offer during a person’s decision-making journey.

Their specialists then conducted several user studies on the website to bolster this pre-hypothesis with qualitative data.

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…. Participants always commented on the lack of transparency in pricing. One participant in the study said that they would find it much easier if, transparency-wise, all prices were listed directly on the site. They felt that they were being put at a significant disadvantage when the pricing wasn’t readily available.

Karen Kysar, Director, UX/CRO
iProspect

The Hypothesis Formulated:

Site visitors who see pricing on the website will convert significantly better (on form submissions) than prospects who see no pricing information.

Methodology:

Test Traffic

22,000

Test Duration:

4 weeks

Confidence Score:

95.26%

Treatment

The rendering of the A/B/C test was fairly simple.

The actual challenge lay in providing pricing transparency, while maintaining the freedom of the various franchisees to set their own prices and provide their own financing options.

iProspect came up with the effective solution of having the stakeholders agree on a range of prices, and then picking a “low” and a “high” starting price from that spectrum for the two variants that would challenge the control.

The variants and the original are shown below.

The A/B/C test was served using Convert Experiences. The goal was set to “lead submissions”.

The Control saw an estimated 8,500 visitors, who got no pricing information.

The variant with the “high” starting price got 5,000 visitors and the variant with the “low” starting price received 8,500 people. The test was run until it reached 95.26% statistical significance, and it was live for an entire month.

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The reason there is a lower number of visitors for Variation 2 (high starting price) is because it was switched off once a clear winner was indicated in Variation 1, or the variation with the “low” starting price. This was a calculated move and since the conclusion of the test in January, the conversion rate on the winner has continued to improve.

Kristin Warwick, Lead, UX/CRO
iProspect

iProspect used Convert Experiences to continue serving the winning variant to all site visitors, until the design was deployed on the site.

Result:

In short, the variation with pricing won over the control experience. This is what iProspect had anticipated all along.

There was a 15% increase in the conversion rate of lead submissions through query forms for the variant with the low monthly price point.

The most notable findings from the test were:

  • The variant with the “high” starting price performed worse than the original for some categories of items. But overall both variants outperformed the control.
  • There was a higher lift in the conversion rate for visitors in the age group of 25 to 34-years-old.
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The client has traditionally stayed away from showing pricing because their products do come at a premium, but when we’re able to help a user understand what impact a purchase will have on their monthly budget by showing monthly payment pricing, we’re able to instil confidence and remove the fear of the product being out of their reach financially. Transparency is always the best approach.

Karen Kysar, Director, UX/CRO

The Big Takeaways:

  • Customers always appreciate transparency. Customers rest easy when pricing information is not gated. Something as simple as a price point indicator can be the difference between an interested call and a visitor who bounces.
  • A/B tests still win. Convert can vouch for this. An analysis of our own customer base shows that A/B tests till date are the most widely used experience type on the Convert Experiences platform. Don’t knock its simplicity. With a strong hypothesis an A/B test is capable of showing definitive and impressive results.
  • Granular data is your gold mine. A test keeps giving and gives beyond the winner. As iProspect found in this instance, the analysis of the test data revealed interesting patterns (like the “high” starting price variant performing worse than the control for some item categories), which can be leveraged to further refine pricing strategies, or indeed set personalizations.
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