Shatabdi Biswas

A product designer who

crafts intentional experiences

2020-2022

Upsell

Product Design

Interaction Design

Designing Recommendations

Strategies and solutions to evolve from "selling" to be a valuable recommendation

2020-2022

Upsell

Interaction Design

Designing Recommendations

Strategies and solutions to evolve from "selling" to be a valuable recommendation

*Optimised for the mobile, full case study on Desktop version

Impact and Outcomes

Order Share

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Avg item value

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Avg item value

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Avg item value

GMV

My Role

Product Designer- End to end product thinking and strategic design

Team

1 Researcher, 1 Product manager, 2 mobile developers, 1 web developer, 2 backend engineers

Timeline

2020-2022 | Continious Design

Market

ios, Android, Web | 25 countries across the world dominantly in Asia, 5+ Delivery Hero brands

Context

Delivery Hero connects customers with local restaurants and shops they love. It is present in around 65 countries across four continents through 11 brands, I worked in FoodPanda providing the recommendations for different brands. The team had launched cross-selling for Foodpanda in June 2019 with a simple algorithm. As we grew into a global service, challenges with different market needs and the need to stay relevant with recommendations became more evident.

Goal

Cross-selling is recommending additional, complementary products to a customer who is already making a purchase. Strategies and solutions for cross-selling to solve problems varying in different markets, brands, and business verticals. to Cross-Selling evolves from "selling" to be a valuable assistant to our users, recommending items during their ordering journey, and improving business metrics.

The Problem

Cross-sell interaction was only 8% with a conversion rate of 2.8%

Before I joined the team, the team had launched the MVP on checkout, the easiest implementation considering that cross-sell recommendation is always tied to the items in the cart where the intention of buying is the strongest.

Over 90% of users overlooked it. Average basket value increased by just 1% in the quarter. Usability research uncovered problems the data did not tell us — attention to price, items, and details on the cart.

The Reveal

One size doesn't fit all

One size doesn't fit all

Nordics had the thought process that discounted/cheaper restaurants had lesser quality; Asians are more pro-deals and discounts. One strategy across all countries was treating different users the same — despite clear cultural differences in decision-making.

Cross-selling was too late in the funnel.

Users had already made up their decision by going through the menu extensively, back and forth. Cross-selling in the. cart was over looked

Logic and contents

If the first two items were irrelevant in the swimlane, users did not scroll further. Only 10% of users swipe past the first cross-sell item

Recommending dishes at the right journey in the right moment with the right product

The Challenge

We used the same strategy across all countries, but data showed different behaviours: TW users added 30% drinks, 24% big mains, 22% small mains; Hungary added 45% main dishes.

Serving 20+ countries with different cultures, different buying behaviours, and languages, "how might we make cross-sell relevant to each behaviour?"

Strategy 1:

We realized that the challenge in our team was multiple folds, we did not have a data team to connect specific items to users and intentions, our screen estate was limited and any change in the interface that required larger estate would need to go through a rigorous round of reviews and experiments. I took a creative leap to challenge the space that we had and explored with filters that would bring in popular item from non-main dish categories.

Quick filters:

Filters allow users to choose items from the top two categories that are most relevant for the country. Providing filters in the cross-sell swimlane would help users with more choice from the desired category helping them to complete a meal in the cart rather than having to go back to the menu.

73% higher conversions than variant B

Variation A

Variation B

Impact of Strategy 1

A/B test

We released the A/B test experiment in Philippines, Taiwan, Hongkong

Metrics:
Our success metrics were aligned with our company OKR i.e increase order share and conversion rate with a higher basket value from cross-selling recommendations

Result

Increased Average order value by ~2.5%,
Items per order by ~1.5% Gross Food Value by ~3% Discoverability of items from different categories by 27% Deals conversion increased by 13%
Add to cart by 6.8 % Item Modifier by 39.72% Quick adds decreased by 9.9% We significantly reduced the back and forth on menu

Strategy 2:
Persuasion during food decision-making

On average, a user takes 6 minutes at our Menu. Within these 6 minutes, they add the first item in 4 minutes. In the other 2 minutes, our hypothesis was that the user spends browsing, giving cross-selling the opportunity to recommend more products.

Solution 1

While on the menu on average, 30% of Foodpanda session users interact with at least one item modifier. The buying intent of our users is strong as they choose multiple toppings and in the mental model of choice reduction. We introduce cross-sell to leverage these two behaviors of choosing and choice reduction by making it possible to select Drinks, dessert ( the browsing time for categories are extra 2 min out of six) in one user flow.

Solution 2

1.) Xsell loads after IM- height change.gif

Solution 2

Items that do not have topping opens a half bottom sheet that shows product details. To keep the cross-selling experience consistent we introduced recommendations with only one closest matching item.

We excluded the category of the main items from the recommendation. Eg: If a user opens Coca-Cola, the recommendation shows only desserts and side dishes.

Impact of Strategy 2

A/B test

We tested each variation against its control individually in the first round of tests, and a variation against each other in different markets to gather insights about which user journey brings the most delight and value to our users.

Cycle of experimentation:

We went beyond co-occurrence and similar users' data to test with the right number of items to be shown, ranking of relevant items, how many times should we show a bottom sheet in a session, how users react to items with images and without images, discounts that may influence decisions. These experiments helped us to gather insights about user preferences in different countries.

Result

We increased the cross-sell order share, conversion and add to the cart on an avg by 40%* In a particular test it increased the cross-sell order rate by 100+%

We launched the full rollout in SG along with tests running in 10+countries in one quarter.

Beyond Food

We were first to introduced cross selling on Order Tracking Page combing the problem users missed adding small items with the opportunity of launch groceries marts in East Asian countries. We saw an uplift of 10% in new orders from the Order Tracking Page. Cross selling was subsequently added to other grocery flows as the team gained more data and behavioural insights

Backoffice

A tool to build customized market-driven strategies.

Challenge

The back office provided the Product Specialists with the ability to alter cross-selling recommendations according to insights, deals, and user behavior. They can run experiments to gather insights on which kind of tailored recommendations work best for end-users.

Goal

The goal of the tool was to make our internal users (Product Specialists) independently run, override X-sell recommendation algorithms according to market and user needs. The tool also enabled users to track data and learnings of each experiment. This saved time for our developers and made our internal users experiment and iterate on algorithms faster.

Learnings

Designing recommendations is basically designing invisible UI

A lot of decisions and ideations that I took as a part of the team were UX and data-related. These decisions impacted the algorithm, pricing strategy, user context, etc. A lot of times when designing for recommendations the best design skill is to ask a lot of questions and apply product thinking to impact how things work, how the experience is measured, and which is the best solution to induce maximum impact.

Customer value is not a constant, but a moving target.

Assuming that every market behaves the same to cross-selling is not be the best strategy. Every market has not only behavourial differences but cultural as well.

Continually test and learn

Constantly testing new approaches on multiple platforms, and at various stages of the consumer decision journey helped us to understand which strategies work the best for which markets and drive maximum user and business value rather than assuming the best.

Designing Recommendations

Designing Recommendations

Impact and Outcomes

My Role

Team

Timeline

Market

Context

Goal

The Problem

The Reveal

One size doesn't fit all

One size doesn't fit all

Cross-selling was too late in the funnel.

Logic and contents

Recommending dishes at the right journey in the right moment with the right product

Recommending dishes at the right journey in the right moment with the right product

The Challenge

Strategy 1:

Quick filters:

Impact of Strategy 1

A/B test

Result

Strategy 2:Persuasion during food decision-making

Solution 1

Solution 2

Solution 2

Impact of Strategy 2

A/B test

Cycle of experimentation:

Result

Beyond Food

Backoffice

Challenge

Goal

Learnings

Designing recommendations is basically designing invisible UI

Customer value is not a constant, but a moving target.

Continually test and learn

Read Next

Read Next

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Driving a 7.8% GMV increase through zero-to-one Group Rides experience, unlocking a completely new way to grow ride volume.

Strategy 2:
Persuasion during food decision-making