Ways To Use Behavioral Insights For Market Segmentation

If you’re segmenting based on engagement, you’re already behind. Everyone does 30/60/90 day engagement windows. It’s not advanced. It’s basic hygiene. Here’s the real segmentation play most marketers miss: Segment by intent signals, not just opens/clicks. Examples: • Viewed shipping/returns policy? ➝ Hit with reassurance focused CTA • Time on product page > 30 seconds? ➝ Trigger a cart based reminder • Opened 5+ product emails but never clicked? ➝ Try plain text emails with a customer story • AOV based segments - low priced vs high priced ➝ show them the right products • FAQ viewers ➝ Give them more trust • Recent abandon carts/checkouts ➝ Leverage their interests • Time since they opted in for a coupon ➝ Remind them about it • Time since last purchase ➝ Show them complimentary products The list goes on and on... THEN add your engagement for best deliverability Engagement ≠ intent. Intent = actual buying behavior. Stop treating every click the same. Treat the reason behind the click differently.

Ryan Serhant trusted me to market his real estate sales course. Two months later, we did $500K in sales in just two weeks. Here’s the 4-step process I used: When Ryan hired me to lead the marketing for his new real estate sales course, Sell It Like Serhant, I was thrilled, but nervous: • I was 20 years old. • I recently left my job at VaynerMedia to start an agency. • I knew this could be a HUGE case study if I delivered. So, I swung for the fences. Feeling inspired, I decided to try creating a marketing strategy using psychographic data. Unlike demographic data, which focuses on factors like age, sex, and location, psychographic data focuses on interests, lifestyles, and behaviors, providing deeper insights into motivations, preferences, and decision-making processes. So, I reached out to world-famous market researcher Howard Moskowitz, and I asked him to help me… 1) Conduct Market Research We launched a study in New York with 100+ participants to assess how different descriptions of Ryan resonated with different demographics. Here’s an example: Younger audiences (18-24) found a “former hand model turned real estate agent” more engaging than “a 35-year-old self-made millionaire,” which slightly older audiences (25-44) favored. Our assumptions: → Younger audiences found it compelling that an unconventional start could lead to success. → Those closer to Ryan’s age admired his achievements within their own timeframe. Takeaway: Relatability drives engagement. 2) Segmentation Using the data from our research, we began segmenting Ryan’s audience: • Young people with an interest in real estate • Entry-level agents • Experienced agents • Agents with kids • Agents in key markets … and the list goes on. In total, we created 20+ different groups (including overlap). 3) Develop an Ad Strategy Next, we scripted video ads for each segment. Each script incorporated learnings from our research, positioning Ryan in a way that we knew was most likely to resonate with the specified audience. This got our “foot in the door.” From there, we spoke to their pain points: Young people → feeling lost Entry-level agents → starting Experienced agents → scaling Agents with kids → time management NYC agents → competitive market Using these pain points, we positioned Sell It Like Serhant as the solution. 4) Launch! Ironically, this was the easiest part. I set up the campaigns in Facebook Ads, uploaded our assets, and hit “publish.” As the saying goes: “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.” – Abraham Lincoln We had already sharpened the axe, so when the ads went live, we saw crazy results: → Sold $500K+ of the course (over 1,000 purchases!) → Best-selling course on Thinkific in 2019 → 2nd best-selling course on Thinkific of all-time … in just two weeks! Know your audience. Speak their language. Solve their problems. Whether it's ads or organic content, that’s how you drive results.

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. 

My Favorite Analyses: the Recency-Frequency matrix. This simple yet powerful framework goes beyond traditional segmentation to provide actionable insights into customer behavior. By focusing on how recently and how often customers engage with your brand, you can tailor your strategies to maximize lifetime value. Why it works: - Recency: Customers who have purchased recently are more likely to purchase again. It's a strong indicator of engagement and future behavior. - Frequency: Customers who purchase more often demonstrate loyalty and satisfaction, leading to a higher customer value. Recency and Frequency are the most important indicators of customer value, exhibiting more correlation to CLV than Monetary Value which is the third component in traditional RFM analyses. The Recency-Frequency matrix helps you categorize your customers into segments based on behaviors instead of factors like demographics or psychographics that imply actions. The analysis reveals distinct customer segments that require unique marketing strategies, including your Champions, the customers who Need Attention, and those who have Already Churned. Implementing the Matrix: Depending on the size of your customer dataset, the Recency-Frequency matrix can be built in a spreadsheet or a more hefty tool like SQL or R. - Excel/Google Sheets: Use `MAXIFS`, `COUNT`, `PERCENTRANK`, and a pivot table to build the Recency-Frequency matrix, but watch out for row limits. - SQL: Leverage functions like `DATEDIFF` and `COUNT` to calculate metrics, and segment with `NTILE`. - R: The `RFM` package handles large datasets with ease, offering advanced segmentation and visualization. This approach isn’t just theory — it’s a data-backed method for ensuring your marketing dollars are spent where they’ll make the most impact. DM me if you'd like to learn more, including the marketing strategies that I most commonly recommend for each Recency-Frequency matrix customer segment. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #MyFavoriteAnalyses #ROI #MROI