GrabTim LangeProblem & solution
Designing products and services based on Jobs to be Done
Summary
The Jobs to be Done (JTBD) framework helps organizations understand consumer goals by defining a job as the progress a consumer seeks to achieve in a specific context. To prioritize new features, GrabFood applied this framework and force diagrams to investigate consumer motivations and friction points. Interviews revealed that working parents ordering family meals experienced anxiety from navigating excessive individual choices during checkout. To address this obstacle, Grab developed an algorithm within the GrabMerchant application that identified complementary food items and automatically suggested ready-made bundles. Following the release, thousands of merchants adopted one-tap bundle creation, streamlining ordering for families while supporting restaurant growth.
Context
A team at GrabFood needed to prioritize features and products to build, but lacked a fundamental understanding of why consumers were using GrabFood or other food delivery services.
Approach / What changed
Grab applied the Jobs to be Done (JTBD) framework and force diagrams to conduct customer interviews, identifying checkout anxiety among working parents, and subsequently built an algorithm in the GrabMerchant app that automatically matches complementary items to suggest instant bundles to merchants.
Takeaways
A single product can fulfill multiple Jobs to be Done depending on context, such as milkshakes serving as filling morning commute snacks or afternoon treats for children.
GrabFood used force diagrams to analyze consumer decisions across four dimensions: current jobs, prior solutions, push/pull triggers, and inertia or anxieties.
GrabMerchant integrated an algorithm that identifies complementary menu items and suggests one-tap bundles, which thousands of restaurants adopted to reduce customer checkout friction.
Source and freshness
- Source
- engineering.grab.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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