Our AI-generated summary
Our AI-generated summary
Challenge
A multinational consumer goods company needed a scalable way to turn fragmented market and company data into living, testable personas that product and marketing teams could interact with. The goal was to synthesize representative personas for the UK and Italian markets to understand consumption patterns, preferences and to test new product or marketing ideas in a human-like conversational format.
Solution overview
LTPlabs delivered a proof-of-concept that combined respondent-level clustering, LLM enrichment and a conversational web application to produce 100+ market-specific personas with rich profiles covering demography, psychography, motivations, decision criteria ,communication style and brand/consumption behavior.
Our approach integrated rigorous respondent-level data engineering with pragmatic LLM engineering and secure deployment (LiteLLM + Streamlit for PoC), allowing the client to move rapidly from raw market signals to an operational, interactive persona library. The conversational tool included document RAG, web search and multimedia inputs to explore marketing ideas and surface actionable insights.
Methodology & technical approach
- Data integration
We combined structured respondent-level sources (tracker and retail data) with open and proprietary sources (Census, Eurobarometer, Eurostat, OECD, WorldBank, World Values Survey, Euromonitor and qualitative research) to build a robust feature set for clustering and persona definition. - Clustering & persona construction
Persona creation started from clustering algorithms (K-means, hierarchical clustering and DBSCAN) applied to respondent-level data to identify recurring usage and demographic patterns. Cluster centroids were then used to generate representative persona skeletons (average/mode values) for each market, and a custom name generation module ensured realistic name variability. - LLMenrichment & persona framing
Large language models were used to enrich each persona with inferred features from unstructured sources, photorealistic/avatar images, third-person biographical stories and market context (product category knowledge, pricing, laws and trends). Each LLM output carried structured response tags (attributes) and reasoning tags (sources/explanations) to improve auditability and trust. - Conversational interface & toolkit
We built a web application (PoC) that allows users to select a persona and interact with it using a conversational LLM. The conversational layer included a toolkit with document RAG for company documents, web search, and support for multimedia (image recognition) to explore marketing ideas. - Multimodal & multilingual capabilities
The PoC included speech capabilities (speech-to-text and text-to-speech) in multiple languages and image recognition for rapid idea exploration, enabling richer, more natural interactions with personas across markets.
Outcomes & impact
- Fast, human-like market exploration: Product and marketing teams could have conversational, contextual exchanges with representative consumers to test hypotheses before committing to costly research or launches.
- Comprehensive market coverage: The persona library synthesized multiple data sources into robust, believable profiles that captured both macro trends and individual-level behaviors (brand funnels, usage frequency, category adoption).
- Actionable insight: The tool made it straightforward to assign product brands to personas using aggregate market data and to surface likely consumer reactions to product or marketing concepts.
Our AI-generated summary
Our AI-generated summary
