Natural Language Processing (NLP) to generate customer insights in color consultation platform for MEIA
region

Context

    • Company operates in four segments:
      Decorative Paints, Marine Coatings,
      Protective Coatings, and Powder
      Coatings. Each segment offers its own
      solutions, but they all share our vision
      of using paints and coatings to protect
      property.
    • Company use WhatsApp Bot for Business
      for Middle East India and Africa in
      different languages like English, Arabic,
      Hindi etc.
    • Basic reporting is being currently used
      which shows basic KPI information
      related of marketing and demographics
      like total conversations, key points etc.

Challenge(s)

    • The existing reporting is unable to
      provide detailed analysis of the
      customer conversations like interests
      of customer, which paint colour they
      are more interested in, which part of
      house is commonly enquired and
      about the customer sentiments during
      the conversation.

Solution

    • To meet the key business requirement, an
      AI based NLP (Natural Language
      Processing) solution is required.
    • This AI based solution will read the
      conversation and provide a text summary
      of understand the meaning of the
      conversation.
    • From the text summary, key phrases can
      be extracted to know main pointers of the
      conversations
    • From the conversation, sentiment can be
      analyzed to get a comparative idea of
      customer satisfaction.
    • Also, as conversations are happening in
      multiple languages hence a language
      detection and conversion is also required
      for better analysis.
    • For showing the end-result a reporting
      layer is used
“ Your dealer management application has transformed day to day operations, ensuring seamless collaboration, efficient order placement & tracking. ”
– CIO

Engagement coverage and highlights

    • Azure cloud-based solution.
    • Incremental data load to load only newly arrived
      data to increase and optimize performance.
    • Calculating response time of the conversations and
      identifying conversations which did not initiate or
      in which either the customer or consultant did not
      get back.
    • Mapping numeric responses in conversation like
      1,2 to their appropriate categories of language
      English/Arabic or paint area type Interior/Exterior etc.
    • Removing bot conversations and compressing the
      conversation in a list based on Conversation ID.
    • Perform NLP by detecting the language, translating
      Arabic text, and summarizing it. Perform sentiment
      analysis, extract key phrases, and translate the
      summary, sentiment, and key phrases into Arabic.
    • Exception handling and workflow notification on
      Outlook were created to keep a note of the data
      push.
    • Triggers are created at each part of processing for
      smooth data transfers.

Timeline

investment
Solution discovery & Envisioning

Four Weeks

integration
Implementation

40 Weeks

management
User adoption

Two Weeks

tablet
Maintenance & updates, support

Dedicated Support team

30+

KPIs tracked

20+

Countries covered

700+

Customer inquired colors identified

85%

Model Accuracy

60,000+

Conversations Analyzed of English & Arabic languages

Featured technologies

Screenshot 2024-09-30 102731