29 Sep

Posted at 11:05h in Innovation, Technology
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Most ads are invisible in the wild. People see thousands of ads a day and forget almost all of them, which is why a large share of ad spend has little real impact. In this environment, guessing is expensive. Testing is how you protect your budget and improve creative quality. This guide explains Insightrix ad testing solutions, why they work, and when to use them: OnTopic (tailored omnibus survey checks), AdCheck (quantitative pre-testing with quick turnaround), and Neurolytrix (predictive AI ad testing). Why test creative before launch? Independent meta-analyses have found that creative quality is a top driver of in-market results, as influential as all other media levers combined in some analyses.2 Insightrix echoes this, highlighting creativity as the #1 driver of ROI. In short, improving creativity is the highest leverage move a marketer can make. Corner 1: OnTopic — Tailored, Local Quantitative Checks (Omnibus Survey) What it is OnTopic lets you add a set of questions to a shared, ongoing survey (“omnibus”). It’s a fast and cost-efficient way to validate creative direction with local audiences—ideal when you need a read on headlines, visuals, CTAs, or value propositions without commissioning a comprehensive study. Why it’s effective (beyond “just a survey”) Decision speed: Early decisions (keep/kill/iterate) have outsized budget impact. Omnibus feedback preserves momentum while preventing costly dead ends. In-market, human-based feedback: Because respondents live where you market, results reflect regional language, norms, and purchase contexts that national studies can blur. Frictionless iteration: Quick cycles encourage test → tweak → retest, which compounds creative gains over a few rounds. When to use it When you’re choosing between concepts or copy and need a fast signal. When you want to confirm a product's fit for market before investing in production. When budget is a concern, but you need human-based feedback to make data-driven decisions about your creative. What you’ll leave with Data from a pre-determined set of questions presented in a set of Excel detailed tables. Results include crosstabulations with key demographic variables and a brief written summary of results. Corner 2: AdCheck — Quantitative Monadic Pre-Testing (with Norms) What it is AdCheck is a pre-test methodology developed by Insightrix to facilitate swift turnaround times (3–5 working days). Respondents evaluate ads on key diagnostics related to engagement, relevance, enjoyability, differentiation, and call-to-action effectiveness. Leveraging a monadic approach, respondents evaluate one advertisement at a time (not multiple side by side). This design eliminates order effects and cross-contamination, resulting in cleaner reads on your creative. AdCheck can be used for pretesting TV, digital, print, outdoor, radio, and pre-roll. Why it’s effective (beyond “just a survey”) Decision speed: Early decisions (keep/kill/iterate) have outsized budget impact. Omnibus feedback preserves momentum while preventing costly dead ends. In-market, human-based feedback: Because respondents live where you market, results reflect regional language, norms, and purchase contexts that national studies can blur. Frictionless iteration: Quick cycles encourage test → tweak → retest, which compounds creative gains over a few rounds. When to use it You’re choosing between concepts or copy and need a fast signal. You want to confirm fit-to-market before investing in production. Budget is a concern, but you need human-based feedback to make data-driven decisions about your creative. What you’ll leave with Data from a pre-determined set of questions presented in a set of Excel detailed tables. Results include crosstabulations with key demographic variables and a brief written summary of results. Corner 3: Neurolytrix — Predictive AI Ad Testing (Neuroscience-Backed) What it is Neurolytrix is predictive AI built on the world’s largest combined neuroscience and eye-tracking database, comprising over 200,000 human brains and more than 100 billion datapoints, to forecast how people will perceive, feel about, understand, and remember your ad.1 It returns: Attention heatmaps (what will be looked at) AOI analysis for logos, headlines, pack shots, CTAs Frame-by-frame video curves (start/end attention, peaks, dips) Behavioural scores with explanations of how each is calculated. Impact scores enable you to determine when an ad is ready for production. Reports are typically ready within one business day, and the tool supports all major formats—from billboards and print to social feeds and TV.1 Neurolytrix follows this paradigm: learn from massive human neuroscience/eye-tracking datasets, then apply those learnings to new creative projects, predicting likely attention, emotion, focus, and memory before spending on production or media. Why predictive AI works (the science in brief) Attention predicts outcomes. Large meta-analyses show strong correlations between measured attention and brand outcomes (awareness, consideration, action intent). 5 Eye-tracking & recall are linked. Peer-reviewed studies and recent articles demonstrate that the duration and focus of attention are related to memory and brand recall, validating the attention/memory predictors used by AI systems. 6 Model strength comes from human ground truth. Best-in-class AI testing (e.g., Neurolytrix) is trained on hundreds of thousands of human-rated ads and validated to predict in-market performance within minutes, reliably illustrating how survey-based truth can powerfully train survey-free prediction for speed and scale.6 What the metrics tell you → Indicate whether attention concentrates on one or two elements that matter (e.g., product + CTA). Diffuse attention = diluted message. Estimate mental effort; high demand can overwhelm viewers and suppress recall (useful for simplifying layouts or pacing). Understand that engagement approximates a positive emotional pull, derived from fast-response paradigms that correlate with felt emotions. Learn how memorable post-exposure recall is - critical in fast-scroll environments where only what “sticks” can drive later behaviour. What this unlocks (beyond “a score”) Edit guidance: concrete design and copy edits, video pacing tips, and benchmarking vs. category norms to close performance gaps. Scale without prohibitive cost: test many versions, formats, and placements; reserve surveys for the few finalists that need deeper human interpretation. Evidence of impact: case studies show 18% increases in engagement and 20–73% improvements in CTR after AI-guided optimization. Connect the cornerstones of ad testing (without waste) A simple playbook: Start broad with OnTopic Use omnibus pulses to pressure-test messaging, headlines, and visuals with local audiences. Kill the weak, keep 2–3 promising routes. Model and refine with Neurolytrix Run predictive AI on the survivors. Utilize heatmaps, attention curves, and cognitive demand to refine the hierarchy, simplify, and enhance clarity. (Reports are typically ready in a day, so iteration is fast.) Validate with AdCheck Put the refined creative into a monadic, normed test to quantify memorability, engagement, brand association, and motivation across media. Use industry norms to confirm it’s strong enough to scale (portal turnaround 3–5 days). Launch, learn, loop Compare early brand-lift/behavioural results to the predicted attention and memory patterns. Feed what you learn into the next cycle. Why the sequence works: OnTopic prevents over-investing in a weak idea. Neurolytrix pinpoints how to make a good idea work harder. AdCheck verifies that the final cut meets category benchmarks and moves brand & action KPIs. Choosing the right tool for the job Insightrix Blends Context and Realism Where it Matters Most All testing has trade-offs. Surveys risk context loss; AI risks model bias if trained on unrepresentative data; live tests risk time and budget. The way forward is method blending, using quick pulses for direction, predictive AI for design precision, and surveys for human-validated certainty. That combination mirrors what large, validated programs now show: attention, memory, and clarity are reliable leading indicators of brand outcomes. The Trifecta of Effective Ad Intelligence Ad clutter is a genuine concern, and budgets are finite. The surest path to impact is systematic testing: fast when speed matters, deep when risk is high, and predictive when scale is needed. With OnTopic, AdCheck, and Neurolytrix, you can cover all three. If you’re planning a new campaign, start by deciding what decision you need to make next: directional, developmental, or go/no-go, and pick the corner that gets you there with the least waste. Ready to make your ads work harder? Don’t leave creative effectiveness to chance. Discover which Insightrix ad testing solution fits your next campaign - connect with our team today and start testing smarter. 1Insightrix – Neurolytrix Ad Intelligence Platform 2Insightrix Research AdCheck – Advertising Testing for Business 3Neurolytrix Explaining Metrics Whitepaper 4Nielsen/NCS Five Keys to Advertising Effectiveness; creative’s outsized contribution. 5Monadic testing reliability and order effects. 6Attention ↔ brand outcomes meta-analyses. 7Eye-tracking, attention and recall links.  ...
 

22 Sep

Posted at 13:27h in Innovation
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Let me be honest, AI in market research feels a bit like when we first got dial-up internet in the '90s. We knew it was going to change everything, but we also spent way too much time debating whether it was safe to buy things online. Spoiler alert: it was, and now we order groceries from our phones while wearing pyjamas. After watching our industry grapple with AI for the past few years, I have learned that most of our anxiety stems from not understanding the basics. So, here is what I have observed, without the corporate fluff. The "Is It Safe?" Question (Spoiler: It Depends) Remember when your parents told you not to get in cars with strangers, then Uber became a thing? AI security follows similar logic. The question is not whether AI is safe; it is about knowing which version you are using and what you are sharing. Here is the reality: free ChatGPT is like shouting your ideas across a crowded mall. Great for brainstorming your next campaign theme, terrible for discussing proprietary client data. Paid, enterprise versions are more like having a private conversation in a soundproof booth, still not perfect, but significantly more secure. The rule that should be adopted (and honestly, I wish more people would embrace it): if it is not yours, do not share it. Client data belongs to clients. Your company's strategic insights belong to your company. Pretty straightforward, yet with the ease of AI, like ordering fast food or going through the drive-through, then shopping at the grocery store and making your own meal…the trend of convenience is a real thing. The 'Test Kitchen' Approach One of the best pieces of advice I have heard is to treat AI like a test kitchen. Experiment with innovative ideas in open environments, refine them in secure spaces, then validate by going back to the open world with sanitized versions. It's similar to how we used to burn CDs; you would evaluate the mix, perfect it, and then share the final cut (while keeping the original safe). This prevents the echo chamber effect, where your internal AI becomes as isolated as that friend who only watches Netflix recommendations and wonders why everything feels the same. Beyond the Administrative Stuff Yes, AI can transcribe meetings and set agendas. That is like using a smartphone to make calls…technically correct but missing the point entirely. The real value comes from treating AI as a colleague who never gets tired, does not take things personally, and always has time to brainstorm. But here is the catch: not all AI colleagues are created equal. Using the wrong AI for the wrong task is like asking your artsy friend to fix your computer, you might get creative solutions, but probably not the ones you need. I have started thinking about it this way: Excel manages the counting, AI oversees the "what if" scenarios. Different tools, different strengths. The Power of the Prompt (And Why Your Experience Matters More Than Ever) Here is something that took me a while to realize: the quality of AI output is not really about the AI; it is about the person asking the questions. Your years of experience, industry knowledge, and understanding of context are what make AI useful. Think of it like this: AI is incredibly good at providing answers, but it has no idea whether those answers make sense in your specific situation. That is where your expertise becomes invaluable. You need to know enough about your industry, your clients, and your data to craft questions that will generate meaningful responses. More importantly, you need the experience to recognize when AI is giving you solid insights versus when it is essentially playing truth or dare with your business decisions. Truth responses are grounded in logic, backed by recognizable patterns, and align with industry knowledge you can verify. Dare responses sound impressive, but push you toward conclusions that could be risky if you do not validate them first. The difference is not always noticeable, which is why your professional judgment becomes more critical, not less. This evolution of our work is fascinating. We can now move faster than ever before, generating multiple analysis scenarios and exploring ideas at speeds that would have taken weeks in the past. But this speed comes with a responsibility to think more critically about what we are getting. The human experience is not being replaced; it is becoming more sophisticated. We are evolving from information gatherers to information validators and strategic interpreters. Understanding What We're Actually Working With Current AI is essentially a very sophisticated autocomplete. It is like that friend who finishes your sentences but sometimes gets it completely wrong because they are guessing based on what usually comes next, not what you meant to say. The newer reasoning models are different; they walk through their thinking process, kind of like showing their work on a math test. These are the ones worth paying attention to because they are starting to think more like humans and less like very clever parrots. The Transparency Thing (It is Not Going Away) Clients are getting smarter about AI, which means we need to get better at explaining how we use it. This is not about legal coverage; it is about maintaining trust in an industry built on credibility. I have started including a simple "How We Work" section in proposals that explains our AI usage the same way we would define any other methodology. Turns out, most clients appreciate the honesty and want to understand how these tools enhance our work. And here is something I did not expect: sustainability concerns are becoming real. Running AI for everything is like leaving all your electronics plugged in; it adds up. If you are not going to use those meeting transcripts, maybe skip the AI note-taker. The Speed of Change (Buckle Up) Remember how quickly we went from Blockbuster to Netflix to "Netflix and chill" becoming a cultural phenomenon? AI development is moving faster than that. New capabilities appear monthly, not yearly. This creates a dilemma: move too fast and risk security issues, move too slow and risk irrelevance. The market researchers I respect most are taking a "learn while doing" approach, implementing thoughtfully but consistently, rather than waiting for the perfect solution. What Actually Works After watching various approaches succeed and fail, here is what I have noticed works: Start with tedious tasks. Let AI manage the administrative work while you figure out its quirks. Once you trust it with scheduling, you will feel more comfortable asking it to analyze data patterns. Treat it like training a new team member. Be patient, provide context, and do not expect perfection immediately. The AI assistants that work best are the ones that have been "taught" a company's style and preferences over time. Keep humans in the loop. AI can suggest, analyze, and generate ideas, but humans still need to interpret, strategize, and maintain client relationships. We are not being replaced, we are being augmented. The Bottom Line AI in market research is not about replacing human insight—it is about amplifying it. The researchers who will thrive are those who learn to work with these tools thoughtfully, not those who either embrace them blindly or avoid them entirely. We are still in the preliminary stages of figuring this out, and that is okay. The key is staying curious, being transparent with clients, and remembering that our value has always been in connecting data to human understanding. AI gives us better tools to make those connections. The future belongs to researchers who can leverage AI's capabilities while maintaining the critical thinking, ethical standards, and genuine human connection that define outstanding market research. And honestly? That sounds like an exciting future to me. From the desk of Shonna Caldwell. ...