Digital Saathi (Farmer App)

Target audience
Team
Target area
Project duration

Need

Business need
User need

Design Process

Discover and Define

User research

Here we have understood the pain points, behaviors, needs, motivations, and preferences of users.

  • Methods: Interviews, surveys, observations
  • Interviewed: 30 Farmers
  • Progressive: 22 Farmers
  • Apps used: Facebook, Whatsapp, Youtube and other agri apps
Competitor research

I conducted thorough competitive and market research, analyzing major competitor’s products to understand their functionality and processing methods. This informed our app’s feature prioritization based on user needs.

Key takeaways

As per our research, we have created “how-might-we” questions that helped us think of ways we could solve user’s problems.

How might we...

Ideate: Problem meet solution

After research, brainstorming and idea generation, we have come up with important features, that would solve user’s current needs and problems and prioritize the features by applying Muscow method.

MuSCoW method

We used MoSCoW method to group each of our feature ideas from most essential to least essential, based on our data and the problems we found that our users were experiencing.

Agri e-commerce market (Agri Input market)
Users get access quality crop products at a reasonable rate.

Crop sell market (Agri output market)
Get better value realization of their crop.

Forum
Post issues/problems, connect with other farmers.

Farm advisory
Agri experts provide advice on crop practices.

My farms
Farmers can add their farm details.

 Crop doctor (AI/ML model)

Identify diseases and generate information about the crops.

News
Know about daily agri news.

Market price
Stay informed about market prices.

Weather card
Accurate weather in real-time

Prototype and Test

“If a picture is worth 1000 words, a prototype is worth 1000 meetings.”
— Tom & David Kelley

  • Test Methodology – Guerrilla test, Problem discovery, A/B testing
  • Participants – 20 people
Version -1​
Forum_old
Services_old
Usability test findings
Final version

Incorporated user feedback into the second sample and organized the services into categories. We adjusted the layout slightly to accommodate additional services and offer banners, ensuring users are not overwhelmed with cognitive load.

Small Android-1
Small Android
Usability test findings

Our Star Feature

Crop doctor (AI/ML tool to recognize diseases of crops)
Happy flow

Crop selection

Take photo/select from gallery

ML model analyze disease

Solution screen

Version -1​
Usability test findings
Version -2
Low fi prototype

Test findings

The Impact

User engagement increases:

  • Session duration increase in New Homepage: 11%
  • Adoption rate increases after implementation of
    onboarding screen and help videos: 20%
  • Bounce rate decreases: 40%

Playstore data:

  • App installation: 50K+
  • Ratings: 4 stars
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