Observed Signal · Oct 19, 2026 · Case Study · Source: AdExchanger · Impact: 3/5 · Sentiment: Positive
How Australian Lender Lendi Measures AI's Hidden Impact
Australian home loan company Lendi Group is investing in AI measurement to understand how chatbots and LLMs impact brand discovery. The company's GM of Growth, Michael McCash, highlights the challenge of measuring AI's influence, as traditional analytics fail to capture how AI platforms surface brands. Lendi uses a combination of in-house multi-touch attribution, Marketing Mix Modeling (MMM) from Mutinex, and the latter's new 'AI Visibility' tool, which mass-prompts LLMs to track brand mentions. They also work with Tracksuit for brand metrics and Askable for surveys. Initial findings show that free-to-air TV was underweighted and efficient, while AI chatbot advertising (ChatGPT, Gemini) has not yet yielded strong ROI. Lendi focuses on measurement and understanding AI's role in customer research, seeing AI chatbots as a new 'first port of call' for consumers.
Represents a practical example of how a major advertiser is adapting to AI-driven discovery, highlighting the importance of AI measurement solutions for marketers.
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Key Takeaways & Evidence Grounding
- Lendi Group works with Mutinex as its new marketing mix modeling (MMM) vendor.
- Lendi is a pilot customer of Mutinex's new AI measurement solution called AI Visibility.
- Lendi spends over $10 million annually on paid media.
- MMM analysis revealed that free-to-air TV was underweighted and efficient.
- Lendi uses Tracksuit for brand metrics and Askable for customer surveys.
Connected Companies & Entities
1 Entity mapped“Lendi's new marketing mix modeling (MMM) vendor and provider of the AI Visibility tool.”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Marketers scramble to measure AI brand visibility
Marketers are increasingly concerned about how AI chatbots and large language models (LLMs) cite or mention their brands in responses, prompting a shift in advertising strategies. The Interactive Advertising Bureau (IAB) is developing a framework to standardize the measurement of AI visibility. Data shows brand citation shares vary significantly across platforms like Microsoft Copilot, ChatGPT, and Gemini, while LLMs show preferences for different source types. Trust in AI search is growing, with 95% of US respondents finding AI answers as trustworthy as search engines. However, measurement is challenging due to the non-deterministic nature of AI models. This has led to new roles like Head of AI Search and the allocation of ad budgets to influence AI recommendations.
Unlocking AI Search: Key Metrics for Brand Success
Peec AI published a practical guide for measuring AI-driven search visibility and its revenue impact. The guide argues that traditional analytics underreport traffic originating from LLMs (ChatGPT, Perplexity, Google AI Overviews) because discovery often produces no click and is later attributed to organic or direct traffic. It recommends KPIs and methods including visibility percentage (share of relevant AI responses mentioning a brand), position/rank within AI responses, brand sentiment in sources LLMs cite, and self-reported attribution to capture conversions from LLMs. Practical measurement techniques include grouping prompts by topic and funnel stage, sampling multiple LLM responses (the guide suggests ~10 samples for quick estimates) and aggregating weekly, plus collecting attribution data at signup or onboarding. The guide highlights limitations of traffic metrics and offers tactical steps to identify and fix sources that shape LLM sentiment.
AI in Marketing: Hype vs. Reality in Measurement
AI, particularly LLMs, has generated hype around marketing measurement but can deliver wrong, unvalidated analyses in media mix modeling (MMM). LLMs are not designed for causal inference, so attribution results from such models can mislead budget decisions despite confident outputs. Vendors marketing AI-powered MMM may obfuscate methodologies and skip essential validation, increasing risk for enterprise marketers. AI can accelerate model estimation and support peripheral tasks—summarizing outputs, clarifying assumptions, and flagging data anomalies—but it cannot replace core causal modeling or guarantee profit. The article urges using an independent validation framework that emphasizes experimentation, forecast reconciliation, and model-quality checks to confirm true causal signals. Alaska Airlines is cited as adopting an open-source MMM solution; with a nine-figure marketing budget, small forecast errors can cause multimillion-dollar misallocations if tooling is unvalidated. Overall, substantial progress comes from demonstrable ROI rather than AI magic, and firms should demand evidence of profit impact over hype.
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