Mokobara – Case Study

Mokobara – New age D2C luggage brand  Ticketing Software: Freshchat and Whatsapp The Challenges: Mokobara had a pure volume problem with WISMO queries. They received more than 500 ‘where is my order?’ queries per day via Whatsapp; and their agents were spending valuable time to answer these  Mokobara has a very wide range of products…


Mokobara – New age D2C luggage brand 

Ticketing Software: Freshchat and Whatsapp

The Challenges:

  • WISMO (Where Is My Order) Volume:

Mokobara had a pure volume problem with WISMO queries. They received more than 500 ‘where is my order?’ queries per day via Whatsapp; and their agents were spending valuable time to answer these 

  • Product Queries 

Mokobara has a very wide range of products – from travel luggage to backpacks to hand bags. And their customer base is very young. This meant they wanted product recommendations and queries responded to fast, to make a purchase decision 

  • Policy Related Queries

They also receive a sizeable number of queries around policies like returns, damaged items, warranty and offers

The Solution: The Macha AI Self Service Widget

WISMO Solution

A simple order tracking system that gives separate delivery statuses for products within a single order along with an AI explanation for why some products have been delivered and some are still under processing

Product Queries Solution

The most practical solution: editing the existing user email by appending a distinct identifier, such as adding a “1” to it (“X1@companyname.com“). This approach ensures that previous ticket records remain intact while freeing up the email for integration into Zendesk’s workflow.

Policy Queries Solution:

Our AI chatbot easily answers questions around policies and programs. All they had to do was feed the AI with all their policy and FAQ data. The fact that adding data is as simple as ‘copy+paste’ enables the team to update this on an ongoing basis.

Success Metrics

  1. Number of WISMO Requests: 10000+
  2. Number of resolved AI chats around product and policies: 20000+ 
  3. Hours saved by Support Team: 1000+