{"id":1651,"date":"2026-03-24T11:58:44","date_gmt":"2026-03-24T11:58:44","guid":{"rendered":"https:\/\/cms.research.wpp.com\/?post_type=research_feed&p=1651"},"modified":"2026-06-17T12:39:23","modified_gmt":"2026-06-17T12:39:23","slug":"brand-perception-atlas-pod-technical-walkthrough","status":"publish","type":"research_feed","link":"https:\/\/cms.research.wpp.com\/?research_feed=brand-perception-atlas-pod-technical-walkthrough","title":{"rendered":"Brand Perception Atlas Pod: Technical walkthrough"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":19,"featured_media":0,"template":"","meta":{"_acf_changed":false},"tags":[],"content_types":[{"id":51,"name":"Technical Walkthrough","slug":"technical-walkthrough"}],"ppma_author":[{"id":19,"display_name":"Jaclyn Harron","first_name":"Jaclyn","last_name":"Harron","nickname":"jaclyn.harron","user_nicename":"jaclyn-harron","user_email":"jaclyn.harron@satalia.com","biographical_info":"Jaclyn is a Senior Data Scientist and Chartered Statistician, holding a Ph.D. in Applied Statistics. Her work focuses on bridging advanced statistical modelling with real-world applications, translating complex data into meaningful, actionable insight.\r\n\r\nShe has a strong background in both theoretical research and applied data science, specialising in time series forecasting, causal analysis, and machine learning for large-scale, high-dimensional data.","avatar_url":"https:\/\/cms.research.wpp.com\/wp-content\/uploads\/2026\/04\/jaclyn-1.jpg","job_title":null,"is_lead":null,"display_as_researcher":null,"order_priority":null}],"class_list":["post-1651","research_feed","type-research_feed","status-publish","hentry","content_type-technical-walkthrough"],"acf":{"content":"

<\/p>\n

Brand leaders today are navigating without a map. Social media says one thing, surveys say another, and AI-generated content adds yet another layer, leaving strategists with no reliable way to know whether their brand\u2019s identity is consistent or not across channels. The Brand Perception Atlas solves this by bringing together five different sources of brand perception, TikTok, Instagram, Wikipedia, AI-generated summaries, and WPP\u2019s 30-year Brand Asset Valuator\u00ae (BAV) survey, into a single, visual map covering over 200 brands and 4,000+ data points. By placing all of these different signals on one comparable scale, it makes it possible for the first time to see how a brand looks across platforms side by side. Several notable patterns emerge from the analysis, revealing that some brands, e.g. Brand D, an industrial equipment manufacturer, maintain a unified identity everywhere. While others, e.g. Brand G, a global hospitality company, occupy distinct perceptual territories across platforms, that may reflect a deliberate strategy to engage different audiences. The study also identifies “shared equity” pairs, revealing brands in different industries that perform the same emotional job for consumers, despite looking different online.<\/em><\/p>\n

<\/p>\n

<\/p>\n

If you don’t care about the technical details, read our blog post <\/a>instead.<\/strong> The GitHub repo is also coming soon.<\/strong> <\/p>\n

<\/p>\n

<\/p>\n

The Brand Perception Atlas – a technical deep dive<\/h1>\n

<\/p>\n

<\/p>\n

The Brand Perception Atlas<\/em>, is an interactive decision-support tool that helps brand teams understand, compare, and explain brand perception across platforms. It combines embedding-space visualisation (UMAP) with interpretable clusters, and cross-platform consistency scoring. The result is a tool that moves teams from “interesting maps” to recommendations that are better documented and easier to explain, because they link back to specific underlying perception signals and clearly show where sources agree or disagree. These can be used in brand reviews, competitor analysis, and campaign planning.<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

Goal: a newcomer can reproduce the core outcomes in 2\u20133 days. For full setup and running instructions, refer to the GitHub README (GitHub repo coming soon<\/em>) \u2014 this walkthrough provides the conceptual map and domain knowledge needed to understand what the code does and why.<\/em><\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

\ud83e\udded A note on what made this hard.<\/strong> The Brand Perception Atlas<\/em> looks deceptively simple \u2014 embed text, project it, cluster it, display it. In reality, the single hardest problem was getting five fundamentally different perception sources<\/strong> to coexist in a shared space where distances actually mean something. WPP Brand Asset Valuator\u00ae (BAV)<\/a> data comes from structured survey scores transformed through an LLM (Gemini<\/a>) into prose. Social data comes from raw video transcripts and post captions, also transformed through an LLM (Gemini<\/a>). Even though both pass through the same embedding model, the linguistic fingerprint<\/em> of each source dominates the resulting vectors \u2014 the map would split cleanly into “survey-sounding text” vs “social-sounding text” rather than grouping brands by actual perception. Solving this required iterating through prompt normalisation, aggregation rebalancing, and ultimately Procrustes alignment<\/strong> \u2014 a technique borrowed from shape analysis that rotates one embedding subspace onto another using shared anchor brands. Section 6 tells this story in full.<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

1. Architecture overview<\/strong><\/h2>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

\"\"<\/figure>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

Key constraint:<\/strong> All five perception sources must be projected into a shared<\/em> embedding space so that distances are comparable across sensors. The pipeline uses Procrustes alignment<\/strong> to rotate BAV<\/a> vectors into the social subspace via overlapping anchor brands, then UMAP<\/a> projects everything into 2D. The system maps the ideas<\/em> behind the words, not just the text \u2014 “Luxury,” “Premium,” and “Prestigious” land in the same neighbourhood.<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

\ud83d\udca1 Why this architecture isn’t obvious.<\/strong> A naive approach would be: embed everything \u2192 UMAP<\/a> \u2192 cluster \u2192 done. The catch is that embedding models encode how<\/em> something is said as much as what<\/em> is said. A BAV<\/a> narrative generated from “Helpful (45.2), Reliable (38.1)\u2026” reads nothing like a TikTok<\/a> perception report, even when both describe the same brand. Without the Procrustes step between “Ingest” and “UMAP<\/a>,” the map would split by text style<\/em> rather than brand perception<\/em>. The arrows in this diagram look linear, but getting Step 3 (Aggregate) right took more iteration than every other step combined.<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

2. Setup & running<\/strong><\/h2>\n

<\/p>\n

<\/p>\n

Full install and deploy instructions are maintained in the README. Below is a summary for orientation.<\/em><\/p>\n

<\/p>\n

<\/p>\n

Public (GitHub ):<\/strong><\/p>\n

<\/p>\n

<\/p>\n

\n\n\n\n\n\n\n
Mode<\/th>\nDescription<\/th>\nAPI keys required?<\/th>\n<\/tr>\n<\/thead>\n
Default<\/strong><\/td>\nRun locally with provided toy dataset (20 brands, 681 reports) or own dataset in same CSV format<\/td>\nNo<\/td>\n<\/tr>\n
Advanced<\/strong><\/td>\nPlug in own API keys for LLM clustering and embedding models<\/td>\nYes (plug in own API keys)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

<\/p>\n

<\/p>\n

Key dependencies:<\/strong> Python 3.12, lancedb<\/code><\/a>, pandas<\/code><\/a>, numpy<\/code><\/a>, google-cloud-aiplatform<\/code><\/a>, google-genai<\/code><\/a>, tqdm<\/code><\/a>, umap-learn<\/code><\/a>, hdbscan<\/code><\/a>, scikit-learn<\/code><\/a>, streamlit<\/code><\/a><\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

\u26a0\ufe0f Gotchas for newcomers:<\/strong><\/p>\n

<\/p>\n

<\/p>\n