[MUSIC PLAYING] In the exploration phase we used quantitative data to see where our customers were going, we used qualitative data to see what they were doing and now we know why. Remember, we're not looking to find a solution to a problem through research in the exploration phase. All we might find is the suggestion or evidence for a solution. The test is where you'll discover whether or not you were right about your findings as being the solution to the problem.
We need to be careful about making assumptions. The worst thing we can do is assume a change in our store will improve conversions when all it ends up doing is lowering them. That's why conversion rate optimization can be a controversial subject. What might work for some might not work for others. With that in mind, we now have enough data to create hypotheses and begin testing. We'll make changes to our copy, change or add components in our funnel, improve the user experience, and better position our offers for conversions.
Eventually, we'll evaluate the test, scale the results, and make more money. Ultimately that's why we're learning this. So first let's make an educated guess using the observations we made in the exploration phase. For my store I observed several interesting things. I noticed my customers on mobile devices had do a lot of scrolling before finding the add to cart button. I also noticed that customers on mobile devices didn't have an easy way to access their cart after adding a product to it.
They had to open up a collapsed menu and do all these crazy things to find the link to their cart. I also noticed by looking at heat maps that some customers tried swiping through product images instead of clicking the arrows to navigate through them. Like I said, I made several other observations which could have had their own test performed based on an educated guess. For the purposes of this lesson I'm going to start with one hypothesis, one implementation of a test, and one evaluation.
I suggest only focusing on one test at a time when it makes sense. We don't want to incorrectly attribute a lift or dropping conversions to one test or another when we're running several tests at once. My hypothesis is that if I were to improve the mobile user experience on the yoga wheel product page specifically by moving the add to cart button to the top of the page to a more visible area, creating a more accessible and noticeable link to the cart and allowing customers to swipe through the product images I'll see an increase in conversions.
Next is the implementation step which is pretty straightforward. With this test there's two ways I can go about it. One I can split test design improvement for mobile and have one version without the changes and one with the changes to see which gets more purchases. Or two I can run the test for a few weeks and compare the conversion percentages to the previous weeks without the design change. Which approach you go with is up to you and whether or not you have a tool such as VWO that can help you split test this.
Either way we have a control version of the yoga wheel product page that I'm able to compare my results to. Once we have a sample size large enough, either that it's fairly comparable to the control or is a definitive split test winner, it's time to evaluate the results. For example my hypothesis from earlier was correct. After implementing these design changes on mobile and making it easier for my customers to navigate to the cart on their mobile device, I saw a lift of 0.8% in conversions compared to the control and a lift of 1.9% in reached checkout.
From here I would look to scale my learnings. What did this test teach me? Where else can I apply these learnings? In this case it's very simple. I would ensure all the product pages on my website had these improvements to the mobile design. I would also look at what other pages within the funnel on mobile such as the checkout page could be improved in terms of design and user experience. The next step would be to repeat the process again.
We need to go back into the exploration phase and begin to make observations that would help us make an educated guess for a test. In the end using the optimization system and having a conversion mindset helped us increase our conversion percentages and ultimately make us more money. Instead of buying more traffic or looking for more sources of traffic, we optimize our existing traffic. Not only did we make more money, we understand our customers a lot better now and we're in a much better position to understand them even more in the future.
Over the next few months I recommend focusing on quick wins. Start with the things already making you money or getting you traffic and see how you could potentially double or triple that without buying more traffic. [MUSIC PLAYING]