Proposal Driller...Proposal Driller Motivation behind Proposal Driller Motivation - Futurice wanted...
Transcript of Proposal Driller...Proposal Driller Motivation behind Proposal Driller Motivation - Futurice wanted...
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05.10.2020 Rachhek Shrestha
Proposal DrillerSearch engine for project proposal documents
BERLIN • HELSINKI • LONDON • MUNICH • OSLO • STOCKHOLM • STUTTGART • TAMPERE
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1. Motivation behind Proposal Driller
2. Screenshots
3. An example of a proposal document
4. Proposal Driller’s architecture diagram
5. Tools used to build and evaluate Proposal Driller
6. Challenges
INDEX
BERLIN • HELSINKI • LONDON • MUNICH • OSLO • STOCKHOLM • STUTTGART • TAMPERE
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Proposal Driller
Motivation behind Proposal Driller
Motivation
- Futurice wanted their salespeople to become more customer-centric by utilizing their organization’s data with the help of machine learning and data mining.
What does it mean to be customer-centric?
- Understanding the clients by putting them at the core of what you do.- Pursuing customer happiness and making customer’s success as your own success.
Why do salespeople want to be customer-centric?
- For making a good impression- For building trust- For gaining and growing the business - For being a strategic long term partner
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Proposal Driller
Motivation behind Proposal Driller
How do people in sales try to understand their client?
- They search as much information as they can related to their client.- They want to search for past projects of the customer, similar customer challenges, technology, problems, solutions,
colleagues, etc- For example, project proposal documents are an important source of such knowledge.
What are the pain points for searching proposal documents?
- Project proposal documents are stored in a shared Google Drive and are scattered.- Searching for proposal documents in Google Drive has been a paint point for sales people.- Google Drive search is not user friendly, is time consuming, chaotic and frustrating experience.
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Proposal Driller
Screenshots
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Proposal Driller
Screenshots
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Proposal Driller
Screenshots
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Proposal Driller
Proposal document example
Sections of a typical proposal:1. Title page2. Table of contents3. Company introduction4. Problem definition5. Solution definition6. Team members7. Project plan and pricing8. Reference projects from the past9. Ending slide
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Proposal Driller
Architecture
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Proposal Driller
Tools used
Document acquisition
- Collected 1300 documents (PDF + Google Slides) from the Google Drive using the Drive API and TypeScript.
- Only the file names that contain the word ‘proposal’ or ‘offer’ in the filename.
- The fields collected were as follows:- File name- File type- Created time- Total number of pages- Text separated by pages- Last modifying user
- The documents were stored in Elasticsearch
Google Drive API TypeScript
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Proposal Driller
Tools used
Document enrichment through information extraction
- Problem & solution definition, customer name (using simple heuristic)- Emails (using regex pattern matching)- Employee names and job titles (using Spacy library’s Named Entity Recognition
trained on employee names/job titles from company database)- Document summary and keywords (using Gensim’s summarizer function)- Topics (using Topic modeling in R made by a colleague)- Top 5 Similar documents list (using Gensim’s Doc2Vec library)- Vector embedding of document (using Python implementation of Tensorflow’s
Universal Sentence Encoder)- In addition, data sources from various company’s database was also merged
using Python.
Python Spacy: library for NLP Gensim: library for topic modeling and NLP
Tensorflow: used for converting documents to embedded vectors
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Proposal Driller
Tools used
Search engine platform
- Elasticsearch is an open source search engine- Provides lot of features out of the box- Inverted index for storing documents- Indexing API for storing documents into the inverted index- Search API for searching the documents that are stored in the inverted
index- Query Domain Specific language for building complicated queries to
match and rank the documents- Feature to search by keyword and search by embedding vectors
Elasticsearch hosted in Amazon Web Services Elasticsearch query examples
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Proposal Driller
Tools used
Backend
- Used as an intermediary between the frontend application and the elasticsearch API
- Handled user interaction logging- Handled query processing- Used Tensorflow JS to convert the queries to embedded vector form
Javascript version of Tensorflow that runs on the browser/web
Node js for backend server
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Proposal Driller
Tools used
Search engine user interface
- Made using Elasticsearch search- ui. It is a React JS framework for creating search user interfaces easily.
- Provides lot of rich features like filters, pagination, highlighting etc. out of the box
- Works great with Elasticsearch search platform- Easily customizable- Front end features for Proposal Driller
- Home page showing recent searches, opened documents etc.- Search box with query autocomplete- Filters- Sorting- Pagination- ‘Did you mean’ queries- Search results pane with expandable icons- Relevance judgment collector- User interaction logging- Query version selector
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Proposal Driller
Tools used
Evaluation of search results
- Evaluation of usefulness of functionality- Done by collecting user ratings through a questionnaire
- Evaluation of extracted information- Done manually for a small subset of documents
- Evaluation of relevance of search results- Done by gathering relevance judgments for a documents over a
representative set of search queries- Used Precision @ K and Discounted Cumulative Gain (DCG)- Elasticsearch has evaluation APIs that make it easier to calculate
these scores
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Proposal Driller
Challenges
- Non technical challenges- Talking with salespeople and understanding the core of their problem.- Understanding the meaning of relevancy that fits everyone
- Technical challenges- Real world data is messy
- Not having a fixed format of proposal documents- Irrelevant information inside the proposals
- Not having a pre-defined evaluation dataset- Made it hard to evaluate the application
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Thank you!Kiitos!Danke!Tack!
BERLIN • HELSINKI • LONDON • MUNICH • OSLO • STOCKHOLM • STUTTGART • TAMPERE
- Rachhek Shrestha- Data Scientist
https://www.linkedin.com/in/rachhekshrestha/