I’d like to share some of my ideas on we could potentially think about planning to drive lower bounce rate on site or pages. Bounce rate is measured as a percentage on (single visits per entry visits).
In other words, it measures on those people who entered the page directly, what percentage of them would leave immediately without further exploring the site. Note: This post is not about the technical aspect of tracking.
The question is “what is a good bounce rate that you should be aiming for?”
I have few ideas and my hope is to inspire a thinking to help tackle the work in improving the consumer experience and setting some expectations around bounce rate.
Simply looking at historical bounce rate and continuously aim for better results. So if we know average monthly bounce rate is at 50% for a fiscal year, aim to target 49%. Generally speaking, 1% of 1 million visitors per month is 10,000 visitors per month from bouncers to prospects.
1% improvement in monthly bounce rate could translate to $120K in an annual lift in sales (at a conversion rate of 1% and AOV of $100). That’s a great sales lift, you can now hire one engineer in Silicon Valley.
So methodology #1 is about striving and planning to reduce the average bounce rate over time, by understanding how much lift in prospects/leads/engaged audience you would increase, and set an appropriate benchmark that meets the sales goal.
# of reduced single visits = [(Planned $ lift in sales) / AOV] / Conv Rate
Target Bounce Rate (%) = [(original single visits) – (# of reduced single visits)] / Entry Visits
Derive bounce rate goal by focusing on the outcomes.
Unlike Methodology #1, we can potentially get the target bounce rate by understanding the opportunity lift for the non-sales outcome.
I could almost hear some people say, well we shouldn’t only take the sales and plan it so. Yes, people can arrive and leave as they wish, and we can claim that as a success because some research shows the majority of people are on the site to research products only.
However, if the content or the site is SO awesome, they’ll be leaving you with their email address for newsletters, explore more product pages, read reviews, share the product with friends, buy from the brand and many other great things the site could offer. Segmentation analysis could be done to acquire the value of some non-sales outcome.
So here is a case or sample image after performing similar exercise noted below…
Let’s say a particular page is getting such traffic and relating results at bounce rate of 46%. Say the goal is to double the outcome by 2x, where you happen to know that economic value per traffic is at $5, yielding $76,570 in total value for current converting traffic of 142,723 visits.
So assuming entry traffic, conversion rate, and economic value per traffic remains constant, we’re looking an additional 7,657 visits (or 7,657 less single visits) to achieve that goal.
# of Outcomes x Economic Value per Outcome = Economic Value (or Total Value)
Conv. Rate = # of Outcomes / (Overall Traffic x Bounce Rate)
So… New Bounce Rate = (36,595 – 7,657) / 79,208 = 37%
Another way to approach optimizing bounce rate is to improve the conversion rate.
If you’ve taken conversion rate as # of Outcomes / (Overall Traffic x Bounce Rate)
In this case, the desirable conversion rate is derived while assuming # of Outcomes and Overall Traffic remain constant.
So for example, if you wanted to improve conversion rate from 1.0% to 1.5% when bounce rate is at 46%…
Original Conv. Rate = 1.0% = 10/1000 = 10/(2,174 x 46%)
New Conv. Rate = 1.5% = 10/(2,174 x Z%) <== solve for Z% = 30.7%
New target bounce rate would be 30.7%
Lowering bounce rate is about planning to improve customer “experience”, giving business a higher number of engaged consumers that could potentially lift the value of the outcome by generating more qualified leads or prospects. Hopefully,
I’ve given some ideas for people to better plan expectations for bounce rates. Ideally, we should focus on the outcomes, and plan from there.
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