Behavioral Targeting Analysis

Most brands think they’re speaking to their target audience, when they’re actually speaking to a fictional character they made up five years ago. 🙊 And here’s the kicker: that character is still probably shaping your messaging, content, creative, campaigns, and media budget. It’s a common mistake across industries. One fictional “ideal customer” ends up shaping everything. Meanwhile, the real audience has moved on, diversified, or changed entirely. But the playbook stays the same. Take the outdoor industry as an example. I've seen this a few times, but let’s use a bike brand to illustrate. This brand built everything around one rider: the 25-year-old male shredder. He’s up before sunrise, hits the trail hard, and posts his Strava stats before lunch. Hardcore. Gear-obsessed. Lives and breathes the sport. But when you actually look at the data, he’s just one piece of the puzzle. The target audience that's actually tuning in and watching your content, following your athletes, reading your newsletters, visiting your website, showing up at events, exploring product pages, and making decisions today (doing things that actually justify our marketing efforts)? It's not just that 25-year-old muse that you're so proud of. It's also: → Casual e-mountain bikers → Retired dads rediscovering the outdoors → Millennial women who want community and connection These are real, valuable, buying audiences. But if your messaging, creative, and media mix is built for one “ideal customer", you’re likely spending money talking to the wrong crowd. Audience strategy isn’t about guessing who you think they are. It’s about using real behavioral data to understand who’s actually spending time with your brand today and who’s showing signs they might tomorrow. That means throwing out outdated personas and replacing them with something far more dynamic: Start building behavioral segments based on real signals like what people are watching, who they’re following, what they’re clicking, and where they’re spending time. I talked about this on the Backcountry Marketing Podcast with Cole Heilborn. We got into things like: 🤔 The disconnect between who brands think they’re reaching and who’s actually showing up 🧑🧑🧒🧒 Why static personas no longer reflect real audience behavior 🔎 How to identify and prioritize high-value audience segments using actual data If you work in outdoor, CPG, or just care about staying relevant, it might be worth a listen if you can tolerate my voice for an hour 😂

6 months ago I sat in a meeting with a sales team frustrated by their pipeline. "We’re targeting the right companies in the right industries. We’re going after the right titles. But our conversions are low and slow," their VP said. They had done everything by the book, classic ICP scoring, firmographic targeting, ideal personas. But their pipeline wasn't growing as quickly as they had hoped. Out of curiosity, we ran their entire prospect list through a different kind of analysis, one that prioritized accounts not just by surface-level firmographics, but by actual buying dynamics. Instead of just looking at industry or company size, we analyzed which companies were already using competing technologies and then looked at when those technologies were first detected on their websites or in their job posts. We estimated the likely contract timelines, knowing that those technologies typically lock customers into multi-year agreements. By calculating the time since those technologies were adopted, we could pinpoint when those companies were likely approaching renewal and when they’d actually be open to switching. This shifted everything. Accounts they had previously marked as “lower priority” suddenly became top targets, not because of a guess, but because we knew they were likely nearing a decision point. In competitive markets, timing isn’t just important, it’s everything. Once they adjusted their outreach based on this renewal-driven prioritization, their pipeline started moving again. It wasn’t about targeting more accounts. It was about targeting the right ones, at the right moment.

Last yr, I went to Joshua Tree and saw a 70-year-old grandma driving a Harley-Davidson. Why does this matter to DTC?   Most DTC brands blindly focus on the demographics and lifestyle profiles of their customers.   (Grandmas, young, male, household income.)   . . . When what is more predictive is their behavior.   "Who are our customers?" Think actions: ➝ Acquired through Google. ➝ Visited our site 3 times before purchasing. ➝ Haven’t been back in 4 days. The more you focus on behavioral segments first, the easier it will be to grow your business. Three reasons why behavioral profiling gives you an edge:   1️⃣ More predictive. Who is more likely to buy from you in the future: The person who last visited your website yesterday or the person who last visited two years ago?   Recency matters.   Who is more likely to buy from you in the future, the customer who bought from you once before or the customer who bought from you ten times before?   Frequency matters. This is why at PostPilot, we build most retention campaigns on a Recency Frequency (RF) basis.   2️⃣ More helpful in selling to your existing customers. Two guys: Steve (household income of 20K) and Joe (household income of 200K).    Poor Steve’s bought from you before. Rich Joe hasn’t.   In Steve’s case, he bought a jump rope from you before. You want to sell more stuff to your customers. Based on what you’ve seen from your customer base, people who buy jump ropes ultimately buy kettlebells.   So your next offer to Steve is a kettlebell. And maybe a warm-up band.    Like many of your customers before, Steve buys the kettlebell as the natural second purchase.   And Joe still hasn’t made a purchase yet.   The behavioral record will help us increase our CLV from Steve, where demographic information won’t do that. 3️⃣ Behavioral segmentation is WAY more actionable. It doesn’t help me to know that the typical customers on my website might read Time magazine or live in New Jersey or are an average age of 51.   But if I know... ➝ Products they’ve purchased before ➝ Last time they opened an email ➝ How they were acquired . . . And all kinds of behavioral factors, I can act.   I can set up rules in tools like Klaviyo and PostPilot, and I can market to them differently and sell to them differently. It’s much more actionable. And automate-able.   BTW. . .    I’m not arguing that demographic segmentation is useless.    Certainly, it’s helpful.    (Really, the Holy Grail is when you can combine behavioral with demographic segmentation.)   But RF(M) behavior should be your first and consistent focus.    And direct mail can help there. We build all the following campaign types around RF: ➝ Winbacks/VIP winbacks ➝ Second-purchase campaigns ➝ Cross-sells & upsells ➝ Subscriber reactivation ➝ Replenishment reminders   Set yourself up and drive repurchases from your own Harley Grannies. 

Are you playing your guessing game with #ICP – Why does AI not help you? Many businesses struggle to identify which prospects will truly drive sustainable growth, even when their systems contain untapped data. Organizations often default to subjective criteria or outdated assumptions when defining their ICP. Marketing teams invest in broad campaigns, sales chase mismatched leads, and leadership wonders why growth stalls. The root issue is a misalignment between perceived value and actual customer behavior. ❇️ Last year, a SaaS company approached me with a common challenge: stagnant revenue despite a “high-quality” pipeline. Their team insisted their platform served “any mid-sized company.” ... By analyzing their customer data, however, we discovered that 72% of their long-term clients operated in regulatory-heavy industries, a detail absent from their ICP. ... They’d overlooked a critical pattern because it contradicted internal biases. ☑️ We implemented an AI-driven approach to ICP refinement, focusing on three areas: ✴️ Behavioral Analysis: Tracking feature usage and onboarding success rates to identify adoption drivers. ✴️ Firmographic Gaps: Contrasting closed-won accounts with lost deals to surface hidden firmographic trends. ✴️ Churn Signals: Pinpointing shared characteristics among clients who downgraded or exited. Within weeks, patterns emerged that reshaped their targeting strategy. Within six months, their sales cycle shortened by 22%. ✅ Historical customer data often reveals unmet needs your team hasn’t articulated. ✅ AI excels at detecting nonlinear relationships (e.g., a combination of industry, tech stack, and decision-making hierarchy) that humans might dismiss as noise. ✅ An ICP is not static; it requires continuous validation against real-world outcomes. Your highest-value customers don’t always fit the persona your team envisions. They fit the profile your data validates. If your current ICP feels more like a hypothesis than an actionable strategy, let’s discuss how AI can improve your targeting precision Roarr Catalyst Group, we specialize in transforming fragmented data into a clear, dynamic ICP, helping organizations allocate resources to accounts most likely to convert and scale. Message me with “ICP Analysis” to explore how we can identify your hidden high-value segments. (Approach grounded in methodology, not trends.) #SaaS #Sales #b2bsales #b2b #Marketing #innovation #technology #Future #AIagent #GTM #AI P.S. The gap between your current pipeline and untapped revenue often lies in the data you already own. Are you leveraging it fully? Cut the BS. Real Execution. Real GTM. Real Revenue Growth. ✂️

Amazon DSP’s Performance+ Just Became A Lot Clearer 🚀 Amazon’s Performance+ strategies use machine learning and behavioral signals to automatically optimize toward your campaign goals — whether that’s awareness, consideration, or conversion. Here’s what it does in a nutshell: 🔶 Behavioral-driven acquisition: Uses real-time shopper intent and browsing behavior to find new customers who resemble your best converters. 🔶 Goal-based optimization: Amazon’s AI adjusts bids and placements dynamically to maximize KPIs like ROAS, DPVR, or conversions. 🔶 Full-funnel adaptability: Works across Prospecting, Remarketing, and Retargeting to drive efficient reach and re-engagement. Now to what's new... 🔥 💡 New DSP Performance+ Insights Amazon quietly rolled out a new Performance+ Insights dashboard — and it’s a major step forward in visibility. It now shows who you’re reaching, how they’re converting, and how fast those conversions happen. 🔶 Audience-level visibility – See which behavioral traits your campaigns are resonating with (e.g., Heat-Free Hair Styling Enthusiasts, Precision Personal Care Enthusiasts) 🔶 Conversion behavior – Track total purchases, impression share, and spend by audience segment 🔶 Time-to-convert analytics – Measure how quickly shoppers purchase after ad exposure (e.g., 57% converting within 24 hours in one of our remarketing tests) 🔶 Optimization potential – Identify high-performing segments, adjust frequency caps, and refine creative to accelerate conversions By pairing Performance+ automation with this new layer of audience insight, advertisers can finally see the “why” behind performance — not just the outcome. These insights help refine targeting strategies, uncover which behavioral traits drive the most value, and guide smarter creative and budget decisions. In short, Performance+ isn’t just optimizing — it’s learning, adapting, and giving advertisers the visibility to do the same. #amazon #amazondsp #amazonads #amazonadvertising #performance+ #btr #btrmedia

$15K/month in linkedin ads. 20 meetings. 2 deals closed. three weeks later: same budget, same meetings. 12 deals closed. the difference? we stopped ignoring the buying signals hiding in plain sight: the founder was proud of his engagement numbers. 400 likes on his last post. 56 comments from prospects. "we're getting meetings," he said. "but we're leaving money on the table." i looked at his linkedin ads manager. CPL had skyrocketed to $180 per lead. all because he was targeting specific companies. "the more you target on linkedin," i explained, "the more linkedin charges you for those clicks." he was paying premium prices for basic demographic targeting. company size. industry. job title. zero intelligence about buying intent. "linkedin's targeting is company demographic level," i said. "they're not looking at website behavior or buying signals." then i showed him what we were missing. his viral post about sales automation got 56 comments. - 3 of those commenters had visited his pricing page that week. - 2 had downloaded his ROI calculator. - 1 had been researching competitors. but his outbound team was treating all 56 commenters the same. - generic follow-up sequences. - missing lead scoring. - zero prioritization. "we're getting meetings with people raising their hands," i said. "but we're not seeing intent." i pulled up our AI targeting data. - same 56 commenters, scored by buying intent. - top 10% showed serious purchase signals. - bottom 50% were just engaging for content. "here's what we're going to do," i said. "layer lead scoring on your linkedin engagement. target outbound to people already showing interest." three weeks later, he called with results. - same $15K ad spend. - 20 meetings booked. - 12 deals closed. "what changed?" he asked. "we focused on behavioral targeting instead of demographics. prioritized prospects already showing buying signals." behavioral targeting delivers better results than demographic targeting. lead scoring identifies the prospects ready to buy. your linkedin engagement is pipeline gold. don't waste it on prospects who aren't ready to buy.