Making data-driven decisions with confidence.
It’s challenging to make decisions when there is an overload of information and data noise. A/B Testing has proven to be a great method for making more confident and data-driven decisions.
We were continually running experiments, often running several within the Pizza Hut e-comm journey at any one time.
PROJECT
A/B Testing Experiments
CLIENT
Pizza Hut, YUM! Brands
MY ROLE
User Experience Lead Designer.
EXPERIMENTS
One accurate measurement is worth a thousand expert opinions.
What?
A/B Testing is an experiment where two or more variants of a page or screen are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.
Why?
A/B Tests helps take the guesswork away and make well-informed, data-driven decisions.
You and your team will be more confident about the decision that you make and have a stronger argument for or against – when you present to the business.
When?
If you have 2 or more samples of a page or screen that you want to test against each other for a defined set of metrics – A/B Testing is a great solution.
How? The Process.
FRANCE – A/B TEST
Cross-Sell in Basket
PROBLEM / OPPORTUNITY
On the Pizza Hut France site there is currently no opportunity to showcase complimentary products to the customer and encourage them to add additional items to their basket.
HYPOTHESIS
If we display a cross-sell item in the French site basket
We will encourage French customers to add additional product/s to their basket
Resulting in an uplift in basket value and AOV.
OUTCOME
Stat sig positive, overall conversion uplift of XX ppts andXX cents uplift to site AOV.
At implementation, it gives potential overall monthly sales uplift of XX Euro
(Numbers have been obfuscated for confidentiality)
Control Variant
Test Variant
UK – A/B TEST
One-Tap ‘Add To Basket’
PROBLEM / OPPORTUNITY
There is an opportunity to reduce the amount of interaction it takes for a user to add an item to their basket, by bypassing the need to show the Pizza/Sides/Drink/ Dips modal.
HYPOTHESIS
If we decrease the number of steps necessary to add items to the customer’s basket
We will reduce friction and make it more likely for them to complete adding their items
Resulting in a positive uplift in add-to-basket rate and overall conversion.
OUTCOME
Stat sig positive, overall conversion uplift of XX ppts for the Sides page and XX increase to the AOV.
At 100% implementation, it gives a potential overall monthly sales uplift of +£XX
(Numbers have been obfuscated for confidentiality)
Control Variant
Test Variant
UK – A/B TEST
£5 Favourite – “Add Another?” Journey
PROBLEM / OPPORTUNITY
Customers insight has indicated that the current journey for adding multiples of a deal is too time consuming and complex, requiring a lot of back and forth for each deal addition.
HYPOTHESIS
If we make it easier for customers to add multiple £5 Favourite menu items at a time
We will encourage customers to add more £5 favourite items per order
Resulting in an uplift to AOV and site conversion.
OUTCOME
Stat sig positive, overall conversion uplift of XX ppts and XXX cents uplift to site AOV.
(Numbers have been obfuscated for confidentiality)
Control Variant
Test Variant