The client is a popular beverage company that manages and grows a diverse portfolio of consumer-focused brands across multiple markets and distribution networks. They work closely with distributors, retailers, and on-premises & off-premises business partners to strengthen market presence and meet evolving consumer preferences. The brand is known for its customer-first approach, entrepreneurial mindset, and emphasis on building premium beverage experiences.A case study on how UBTI developed an AI-powered conversational analytics platform that unified distributor, retailer, pricing, and market intelligence data into a single real-time business experience.
Client
The client is a popular beverage company that manages and grows a diverse portfolio of consumer-focused brands across multiple markets and distribution networks. They work closely with distributors, retailers, and on-premises & off-premises business partners to strengthen market presence and meet evolving consumer preferences. The brand is known for its customer-first approach, entrepreneurial mindset, and emphasis on building premium beverage experiences.
Challenges Faced by the Client
The client operates through a three-tier distribution model involving suppliers, distributors, and retailers, through which products ultimately reach consumers. Since they manage several brands and locations, teams regularly rely on sales, pricing, and market data to track performance and growth. Although this data was already available inside Microsoft Fabric, leadership teams still had issues in accessing and analyzing it independently:
- Leadership lacks direct access to operational data: Whenever management teams needed sales updates, distributor performance details, or market insights, they had to depend on analytics or technical teams to retrieve the data.
- Existing reports could not answer new questions: Dashboards and Excel reports only showed fixed information. If users wanted deeper analysis, such as competitor comparisons, state-wise performance, or retailer trends, additional manual analysis was required every time.
- Important business data was spread across systems: Depletion data, pricing structures, market intelligence, and outlet details were available in separate datasets. As a result, teams could not easily view all related business insights in one place.
- Different teams worked with different numbers: Since teams accessed and interpreted data separately, important business KPIs such as revenue, market share, margins, and distribution performance were not always calculated consistently across departments.
Solutions
To address the client’s operational and analytical challenges, our Microsoft-certified experts at UBTI developed an AI-powered conversational analytics chatbot that enables business users to talk with data using simple natural-language questions with these features:
- Conversational AI analytics platform: We developed a custom conversational chatbot using React, Python, Django, and Azure OpenAI. Users can ask questions just like they would ask a business analyst. The system understands the request, identifies required data, and provides a response in formats such as tables, summaries, or text explanations, based on the prompt.
- Multiple datasets connected: To support meaningful business analysis, the AI chatbot is integrated with important commercial datasets already available inside Microsoft Fabric. This includes depletion data, pricing information, market intelligence, and outlet-level details. As a result, the platform could combine related information from multiple sources into a single response, eliminating the need for users to search across different systems.
- Live querying for business users: The solution automatically understands user questions and pulls live data directly from the source systems. This allowed business users to check distributor performance, pricing trends, retailer activity, and market insights without writing technical queries or depending on analytics teams.
- Smart term recognition capabilities: Since users may enter short forms, incomplete names, or spelling mistakes while asking questions, the chatbot is designed to recognize brand names, distributor names, retailer names, and market terms more accurately before pulling the data. This improved the quality and relevance of responses.
- Enterprise-grade governance: The AI chatbot is integrated with Azure AD to provide controlled login access across teams. Along with this, features such as conversation tracking, export support, and governed data access were implemented to ensure the platform could securely support multiple commercial teams.
Solutions Benefits
With the implementation of our conversational AI analytics chatbot, the client significantly improved how commercial teams accessed and analyzed business information across the firm:
- Leadership teams reviewed business performance faster: Senior management teams could quickly check distributor sales, pricing performance, market share, and retailer trends, helping them make faster commercial decisions during planning and review discussions.
- Sales teams identified expansion opportunities more easily: By combining outlet data, competitor presence, and distribution insights in one platform, sales teams could identify high-potential markets and regions with low distributor coverage more effectively.
- Business users analyzed data without technical dependency: Since the platform understood natural-language questions, teams across sales, finance, etc. could directly access and analyze business data without writing SQL queries or requesting manual reports.
- Field teams prepared meetings with live insights: Before meeting distributors or retailers, field teams could instantly retrieve updated sales, market, and retailer information from the chatbot, helping them prepare discussions using current business data.
Conclusion
The client was facing growing challenges in accessing and analyzing critical commercial data across its distributor, retailer, pricing, and market intelligence operations. As business teams continued to depend on technical support for everyday analysis, decision-making cycles slowed, and operational visibility remained limited across departments. To address this, we developed a conversational AI analytics platform that enabled users to interact with live business data via simple natural-language questions while combining multiple commercial datasets into a single, connected analytical experience. Now, leadership teams and business users gain faster access to meaningful insights, helping them make more informed decisions across operations. UB Technology Innovations, Inc. continues to help retail and consumer-focused organizations convert complex sales, pricing, and market data into real-time conversational AI for business intelligence.
