Duration: 45 days
Role: Product Manager for iDiva, conducted behavioral analysis via heat maps, analytics and visual recordings, metrics were captured during user research and observations were recorded over a period of 3 weeks.
Challenge:
Sales from the beauty box were stagnant and not growing since its inception. Even with all the creative & performance marketing push, the funnel percentage would not increase. Following, I started to analyse trends in the purchase patterns and figure few bottlenecks - completely via session recordings, heat maps and mobile-friendliness of the checkout page.
The next phase was to speak to the previous purchasers and conduct interviews with customer support team in order to understand user queries. Also, along with my UX team, we sat on calls with customer support representatives, in order to connect directly with the users who were either trying to purchase or had queries regrading the product.
Furthermore, we curated lists of quick fixes on the mobile platform - which I believed would decrease blockers, increase trust and build retention with the brand.
Approach:
-Behavioral analyses via Heat maps, raw data and analytics tools.
-Analyzing user patterns, and drop-offs we came up with UX based solutions to fix conversion percentages.
Beauty-box , was created as the perfect go-to beauty essentials box for female millennials, with the primary objective of closing inventory in a timeline of a month since the box was limited. Since, iDiva was new to commerce and was offering a new product to its user base - who were only habitual to consume content via videos, this was a huge step in terms of the organisation's new ventures and its ability to gain trust factor with shopping.
On similar lines, capturing data was a slow and meticulous task since everybody in the system was new to commerce and shopping metrics- learning curves were involved. Likewise, we conducted user interviews of the current user base- in order to form personas in that it would help us in tackling the current conversion problems.
A few of the problems associated post-launch are highlighted below
Low add to cart to checkout ratio
Drop off's during checkout and shipping
The growing cost of user acquisitions/user, due to high bounce rate on the cart page
Multiple fixes were done on the cart page fixing the current information gap. Since this was the first product launched by iDiva site users were showing hesitance to complete the purchase.
Moreover, in order to enable trust and increase brand validation we fixed the current state of the cart page, solutions to which are shown below in the figure.
Problem & Solution 1
Apart from the fixes shown, we enabled the below changes to improve user interactions on the cart page.
- Added tap to add coupon interaction, decreasing user effort, hence faster selection for related discounts.
- Since the brand was offering money back guarantee for all its new users- this was not reflected in the checkout process leading to a decrease in conversion percentage, this was added to increase organisation recall and trust.
During analysis of user session recordings, it was noticed that majority of drop off's were happening on the checkout/login page, as users were reluctant to login via Facebook/google (due to privacy issues associated with a new shopping brand) and likewise didn't get clarity on guest checkout CTA.
Few changes were made to the page in order to declutter the login interface via the following - Differentiation between existing and new users - (tabular signup and login)
Few changes are mentioned below, please see figure for in-depth solutions
- Place order as "guest" was highlighted during the login process, increasing the focus on guest checkout call to action.
- Position of the sign-up CTA was changed in order to increase sign ups for the brand, as this would help increase trust factor with the brand - since most users are hesitant to share Facebook / google user permissions or profile data with a new brand.
After analysing all the marketing campaigns and relevant landing pages leading to the checkout page, we noticed that once the user was convinced on the product, he or she would spend most time on the payment page. Moreover, the heat map analysis via hotjar helped us decode multiple factors which were effecting drop-offs, such as - we noticed significant clicks / taps on the payment page - helping us understand errors with the module and how we could help expedite the payment process.
Below are the solutions made to the payment screen, highlighted in the figure.
- Product name and details were made consistent throughout the checkout process for order validation.
- One page - checkout service via shipping > Payment gateway selection > Review & Place order, this increased the overall conversion since users were able to finish checkout in a single screen as opposed to a checkout timeline which had multiple screens.
- Since this was a new shopping brand, we added multiple security certificates in order to build trust of a secured transaction.
