Established by Ordivo Group in 2018, OrderOnline has quickly become a driving force behind Indonesia’s thriving social commerce market. OrderOnline offers an end-to-end solution for organizations and individuals selling on social platforms like Facebook Marketplace, typically through social ads, landing pages, and storefronts.
OrderOnline built its social commerce platform on the MongoDB Community Edition, and later migrated to MongoDB Atlas in 2022.
The platform provides everything from managing orders to handling logistics for companies and individuals selling on social platforms. It addresses common social commerce pain points, such as complex logistics, failed deliveries, and unmanageable order processing due to scale.
Speaking at MongoDB.local Jakarta 2024, Wafda Mufti, Vice President of Technology for Ordivo Group, explained how his slogan—“Simple Input, High Accuracy”—drove OrderOnline to become one of Indonesia’s leading social commerce companies.
“We have sellers using storefronts, landing pages, and checkout forms. Thanks to MongoDB’s flexibility, we can manage these unpredictable business processes. We even store our front-end structure in MongoDB,” said Mufti. “Thanks to MongoDB, we can ensure we have the highest quality of data.”
Mufti also shared how the company is using MongoDB Atlas Search and MongoDB Atlas Vector Search to power innovative search and AI use cases.
Scaling social commerce with MongoDB Atlas
Five years after its launch, OrderOnline had grown to 40,000 users and was handling 1.5 million transactions each month. This fast growth led to challenges, particularly around managing data at scale and ensuring high success rates for sellers.
Most of OrderOnline’s users drive orders by using a wide range of sources. These include social ads, landing pages, and storefronts. Many of OrderOnline’s orders are handled via WhatsApp through Click to WhatsApp Ads (CTWA).
Initially, managing orders via platforms like WhatsApp was feasible. However, as social commerce became more popular, the volume of orders increased, which quickly became overwhelming. Furthermore, for large sellers who do not handle their own products, OrderOnline had to manage order packing and shipping, as well as managing returns.
“We were overwhelmed with orders, but we wanted to manage our SLAs,” said Mufti. “We wanted to ensure products were well-delivered.”
MongoDB Atlas’s flexibility has enabled OrderOnline to manage unpredictable business processes, and to efficiently handle various complex tasks associated with order management and logistics. Because MongoDB Atlas is designed for fast iteration, it enables OrderOnline to swiftly adapt its platform in response to changing business needs and user demands.
MongoDB Atlas also supports high scalability. This empowers OrderOnline to manage a growing user base and increasing transaction volumes without compromising performance.
Additionally, MongoDB Atlas’s reliability under high transactional loads ensures that OrderOnline can maintain quick response times—a core part of their SLA. This is critical for maintaining the agility needed in the dynamic world of social commerce.
“We have a monitoring system that triggers alarms if response times fall below one second,” noted Mufti.
Another critical SLA that OnlineOrder tracks is the delivery success rate. Previously, deliveries were only successful 94% of the time. Using MongoDB Atlas, OrderOnline built OExpress, a service that sellers can use to customize the number of delivery attempts based on specific service agreements. An upper limit cap of up to five delivery attempts is also mandated. OExpress closely tracks delivery attempts data. This ensures packages are delivered and minimizes returns and damages.
“Thanks to MongoDB, we have achieved a success rate of 98.4%,” said Mufti. “We can manage multiple attempts to deliver to the customers, so sellers don’t have to worry about dealing with delivery issues anymore when using a marketplace.”
Beyond deliveries, OrderOnline identified seamless search and customer support integrations as key operations that MongoDB could enhance.
AI and search: conversion rates jump by 56%
As OrderOnline’s business grew, scalability created specific challenges with CTWAs. Particularly, OrderOnline’s platform struggled to manage and make sense of the growing volume of inconsistent data types it was receiving, such as location, postal codes, and product details—accurate input of data is vital to ensuring orders are being processed and delivered.
“People want [to be able to input] freeform text. They want things to be simple and easy, and not be restricted by rigid formats,” said Mufti. “But we still have to ensure data accuracy.”
One of the standout features that helped OrderOnline improve search accuracy and management is MongoDB Atlas Search.
Fuzzy search in MongoDB Atlas Search can handle typo errors when searching for districts. For example, if a user types “Surabaya,” Atlas Search will still fetch results for “Surabaya”. Furthermore, synonyms in MongoDB Atlas Search can handle shortened names for provinces and districts in Indonesia. For example, “Jabar” for Jawa Barat or “Jateng” for Jawa Tengah. Acronyms are also handled.
“Because there’s AI in the background, there’s no need to manually input zip codes for example. Our engine can search for it,” said Mufti. “Someone clicks, then places an order, fills out the form, and it goes straight into our order management system, which supports fuzzy search.”
As OrderOnline grew, it also needed to increase customer support with 24/7 availability and fast response times.
MongoDB Atlas Vector Search supported the development of a seamless and user-friendly interface with the creation of an AI Chatbot. This chatbot provides sellers with ease in managing customer interactions, checking stock availability, and calculating shipping costs.
“If the ad contains a WhatsApp link, it will be directly managed by the chatbot. The chatbot even checks shipping costs, compares prices, and shows how much it would cost if you purchased five items,” explained Mufti. “The AI handles requests for photos, checks stock availability, and much more. And once a deal is closed, it goes directly into our order management system.”
Before the creation of the AI chatbot with MongoDB Atlas Vector Search, the WhatsApp conversion rate was 50%. Out of 100 interactions, 50 would successfully close the deal. With the implementation of AI, this rate has increased to 78%.
Building on these successes, OrderOnline is now looking at further business and geographic expansion supported by MongoDB’s global reach, with the aim to help more sellers throughout Indonesia make the best out of social commerce.
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