Data Analysis Methods for Creative Teams

Data after launch? Too late. The best data shapes the work while it’s being made. I hear it all the time, “data doesn’t explain why.” Of course it doesn’t. Most teams collect it after decisions are already made. The real shift is timing. Data should evolve your team’s learning through the process, not chase performance after the fact. Here’s how we make data-informed design actually work. 1️⃣ Start with intent Don’t open a tool yet. Figure out: → User Needs: What problems are users trying to solve? → Business Goals: What outcomes will this impact? Purpose before process keeps teams from chasing numbers that don’t matter. When you know the intent, the right technique becomes obvious. 2️⃣ Choose your stack Every kind of learning fits one of three modes: → Exploratory: Uncover new needs and opportunities → Evaluative: Test how well something works → Comparative: Decide between options We use these modes to measure progress. Our open-source Helio Glare framework pairs Research and Design Stacks for real-world measurement across websites, apps, products, and campaigns. Know which mode you’re in before you measure anything. 3️⃣ Identify the approach A weak question collects noise. A strong one reveals a blind spot. The best questions define a gap in understanding, point to observable behavior, and can be measured. Once you know that gap, your approach, exploring, evaluating, or comparing, becomes clear. 4️⃣ Apply the techniques Each approach has matching methods and metrics: → Exploratory: open surveys, journeys (usefulness, satisfaction) → Evaluative: usability tests, first-click tests (completion, comprehension)  → Comparative: a/b, multivariate concept testing (desirability, confidence) Techniques create evidence. Metrics turn that evidence into signals. Choose a tool to collect your data based on your goal. 5️⃣ Ready your data Data builds trust when it’s transparent and helps your team tell the story behind decisions. You will need to share findings: → Project level: Inside your design tools or dashboards → Cross-team: Summaries in shared workspaces → Leadership: Rollups that link findings to KPIs Always reference sources, methods, and metrics so others can trust the results. In Helio Glare, we help teams build data into their workflows, measure a single UX metric, and apply those learnings across projects, like this example from the Salesforce event registration page. (https://lnkd.in/gUbZiqUs) When feedback becomes visible, repeatable, and trusted, you can turn it into Design Signals, patterns of evidence that guide decisions and connect user behavior to business outcomes. Data stops being numbers. It becomes direction. 👉 We’re building a community of product and design leaders through Helio Glare. If you care about how design creates real value, join us: https://lnkd.in/ggHXcVQZ

Most brands analyze creative tests by looking at ROAS and CPA. That's like judging a restaurant by the bill instead of the food. ↳ Here's how to actually find winning patterns: Looking at performance metrics alone tells you IF something works. But it doesn't tell you WHY it works or how to replicate it. The Framework That Actually Works: 𝟭. 𝗦𝗽𝗹𝗶𝘁 𝗬𝗼𝘂𝗿 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗜𝗻𝘁𝗼 𝗧𝘄𝗼 𝗕𝘂𝗰𝗸𝗲𝘁𝘀 Primary metrics = Performance (tells you IF it works) - Spend, Purchases, CPA Secondary metrics = Storytelling (tells you WHY it works) - Scroll Stop Rate (hook strength) - Hold Rate (narrative engagement) - Outbound CTR (offer appeal) Why this matters: Performance metrics help you scale winners. Behavioral metrics help you create more winners. 𝟮. 𝗨𝘀𝗲 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝘁𝗼 𝗙𝗶𝘅 𝗨𝗻𝗱𝗲𝗿𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗲𝗿𝘀 Don't change offers randomly. Let the data guide you: Low Scroll Stop Rate = Weak hook → Test bold claims, fast motion, pattern breaks Poor Hold Rate = Boring narrative → Improve pacing, cut slow parts Low Outbound CTR = Weak CTA/offer → Test different positioning Why this works: You're fixing the actual problem, not guessing at solutions. 𝟯. 𝗙𝗶𝗻𝗱 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗶𝗻 𝗬𝗼𝘂𝗿 𝗪𝗶𝗻𝗻𝗲𝗿𝘀 Stop looking at winning ads in isolation. Find common threads: Do they use specific hook styles? Similar pacing structures? Particular testimonial formats? Build a Creative Optimization Library documenting what works. Why this matters: Patterns create predictable processes. Processes eliminate guesswork. 𝟰. 𝗧𝗲𝘀𝘁 𝗪𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 Most brands test random variations. Instead: If Scroll Stop Rate is bad → Test new hooks If Hold Rate is weak → Adjust storytelling If CTR is low → Optimize offer positioning Why this works: Every test has a clear objective and higher success probability. What You Can Expect: Fewer failed creative tests → Faster winner identification → Predictable creative production process → Higher overall ROAS from better optimization The Psychology: → Behavior data reveals true audience preferences. → Patterns show what actually drives action. → Purpose-driven testing eliminates waste. Next Steps: Week 1: Set up behavioral metric tracking Week 2: Analyze your last 10 winners for patterns Week 3: Build your Creative Optimization Library Week 4: Implement purpose-driven testing Be honest... Are you iterating creatives based on data, or gut instinct?

The marketing curse 😂 The fix: Dara Denney's 5 step framework that brings data and creatives team together, battle tested with over $100M of ad spend. Dara's take: Creative freedom is a myth. “You need to attack the sources of ambiguity within the creative process. This is the secret to building high performing creative teams" 1. Remove ambiguity with SOPs "The most ambiguous parts of the creating process have the biggest impact on performance" Think of all the ambiguity that exists in your creative production workflow: Research: Who is conducting competitor research? Where is the team documenting customer reviews, and how are you using the performance data you’ve collected? Roadmap: Is everyone clear about the goals and tasks in your creative production pipeline? Or does every new request feel chaotic? Performance: Does your designer know why the last ad bombed? Is data on performance understood or locked in some spreadsheet? To remove ambiguity, Dara suggests formalizing the creative project lifecycle stages research, execution, review, client submission, and launch—for streamlined creation. She calls these stages Standard Operating Procedures (SOPs). 2. Hire a dedicated Creative Strategist Creative strategists remove ambiguity from the creative process by doing the hard work of understanding customer psychology, the competitor landscape, deep s of performance data, and uncovering the strategic problems that ads need to solve. Without a creative strategist, your growth and creative teams become disconnected. For in house teams, this leads to internal politics, mistrust between teams, and low output. 3. Make data accessible AND exciting Not sure which metrics to narrow down on? Focus on your primary KPIs, such as spend, purchases, and cost per lead. These metrics will give you a good understanding of your campaign's performance. Additionally, look at storytelling KPIs, like drop off rates, average video watch time, hook and hold rates, and CTRs. Use a visual analytics platforms to make the data accessible and interesting for your creatives (that's what Motion (Creative Analytics) does btw) 4. Roll out a sprint structure Here's a simple structure you can start with: - Monthly roadmaps, metric checkpoints, bi-weekly retros - Keep the process on track with daily stand-ups Regularly analyze ad formats and metrics as a team during your live sessions and set up a Slack channel for sharing high and low performing ads where you can chat async on what you're seeing 5. Build a data driven creative culture You need to embed Creative Strategy into your org culture. Start all brainstorms with a data download. Ex: share CI research, customer insights, past performance but make sure you start from data or bring it into how you operate. To keep momentum up, create a "win" Slack channel to celebrate learnings and top performing ads and conduct monthly retros to keep the team aligned and engaged with data.

Lunar Solar’s approach to performance creative testing Our approach is guided by a data-driven feedback loop that feeds rapid creative testing.   Between our creative studio and performance creative teams, we can craft a broad range of asset types to fuel our creative testing and build better media programs.   Here’s how I like to think about creative strategy, which any brand or media/creative team can use to improve their ad performance.   1️⃣ We start with “strategic testing” to find breakthrough assets that outperform the status quo → Hypothesis-driven: We use brand and macro trends to identify new strategies, overarching campaigns, and new creative directions.   → Theming & asset type: We look for asset types or themes to work within, such as testimonials, founder stories, etc.   → Brand & product: Based on survey data, reviews, and social listening tools, we test value props to identify strong problem <> solution messaging. → Offer/audience fit Persona mapping to test new assets against the appropriate audiences and find product <> audience fit on each platform. This is all a fancy way of saying we take data-driven swings at the bat. It’s more formal than throwing things against the wall and seeing what sticks, but really, we’re looking to find some breakthrough assets that stick.   2️⃣ Iterative testing Once we have strong foundational concepts, we drive incremental improvement to grind out better KPIs and prevent longer term creative fatigue.   For example, we’ll take a look at:   → Structural: These are changes to the offer, i.e., discount, bundle.   → Format: Changing the creative design, backgrounds, colors, elements, etc.    → Messaging: Adjustments to the educational and selling copy, i.e., different words and phrases, etc.   → Sequencing: Changing the order of visual elements in the campaign.   —- We measure with reporting platforms (performance) and customer surveying (for context and recall).   I think we were the first if not one of the first, agencies on Motion, which we leverage alongside KnoCommerce.   That data powers a rapid iteration process powered by some of the new generative AI tooling, which we’re quite bullish on.   And that’s how we keep the creative testing flywheel going…