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This project was an idea from the Sales Manager of one of our existing customers. Previously, he had difficulties on how to keep track which products are sold most, how to keep track the conversation between the salesperson and customer, and how to keep track customer's sentiment of bought products. He also had a big concern on how to reduce paper usage of weekly (and even daily) printed out sales reports.

The idea is to utilize Natural Language Processing (NLP) and Sentiment Analysis to extract conversation directly from messenger service, extract products, brands, types, or models information, analyze the conversation's sentiment based on weighed words scoring and of course eliminate the need to print sales report as everything is done in the web application. We thus submit this solution for SME SEEDx Development Challenge 2020.

Read more about the challenge here.


BeOne Analyzer Demo Video:



Submission Details

Solution Name:

BeOne Analyzer

Solution Description:

The solution helps the Sales Manager to identify some critical information on his department. By using the web-based application, he can extract information from the conversation between his team and customer such as how well the sales of each product, brand, type, or model, analysis of each conversation's sentiment (very negative, negative, neutral, positive, or very positive). Furthermore, for each negative sentiment, he can create a follow up by creating a service call document on SAP Business One.

Solution Use Case:

The solution is targeted at trading and distribution industries, or the sales department in general. The solution consists of:

  1. A messenger's conversation extractor to fetch conversations from messenger and convert them into a spreadsheet file.

  2. A web-based application to register words scoring, products, brands, types, and models.

  3. A web-based application as the dashboard to show charts of products, brands, types, or models, and the sentiments of each conversation.

  4. Follow-up module for creating service call document on SAP Business One.

Persona Identified:

Pain Points:

  1. Unable to track customers' sentiments toward products, brands, types, or models from the conversation between the sales team and customers.

  2. Unable to track which product is being asked or talked often in conversations.

  3. High paper usage as the sales team needs to deliver printed sales reports to the sales manager weekly (and if needed, daily).

Solution Details:

BeOne Analyzer consists of some core modules:

  1. Messenger conversation extractor and spreadsheet converter. Some information extracted: conversation id, subject, date, sender, and receiver. The spreadsheet will then be imported into the web-based analyzer.

  2. A web application to import the spreadsheet file, analyze, and show the data on a single page dashboard. The dashboard will show a table of conversations data, charts for all conversations' sentiments, and charts showing each product's proportions of being talked in conversations.

  3. Menu on the web application to create service call document on SAP Business One for each negative sentiment.

Screenshot of the web application

Screenshot of the web application dashboard


Solution Technology:

UX Technology Used: HTML5, CSS3

Platform Technology Used: BeOne Cloud Server

Latest Technology Used:

  1. Natural Language Processing (NLP) for information extraction

  2. Sentiment Analysis using Word Scoring Algorithm

Framework Used:

  1. Node JS


  1. MySQL



Go-to-Market Strategy

Industry Focus: Trading and Distribution

Marketing Strategy:

  1. Social Media Marketing

  2. Monthly Workshop

  3. Beone Channel Partners

Road Map:

Future development will include:

  1. Automatic fetching and processing so spreadsheet files will no longer be needed.

  2. More messenger platforms integration as currently the solution is connected to only 1 messenger.

  3. Mobile application as an alternative to the web-based analyzer application.

Contact Information:

Partner: PT. Beone Optima Solusi

Country: Indonesia

Team Name: BeOne Intelligent Enterprise Team

Team Members: Ronnie Eko Prasetyo, Oki Wijaya, Yoseph Sunarli, Reza Stefano, Brian Billardo


The Challenge is a big chance for us, the first participating partner from Indonesia, to begin researching, investing, and exploring the fields of Intelligent Enterprise and Industry 4.0. We are really excited to keep submitting new solutions as we believe a lot of wild ideas about future technology and innovation can be delivered into real things starting right now.

For more information about the development challenge, you may refer to the SME SEEDx Development Challenge 2020.
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