Understanding Customer Behavior For Ecommerce Retargeting

Remarketing is often the misunderstood middle child of performance marketing. Let’s break a couple of myths🔨 🎯 One size fits all fits probably no one:  I’ve seen many companies burn money on campaigns that don’t recognize that every section of their audience has their own motivations. Why, if I had a penny for every time I visited a site with no intent to purchase their product at all, only to spot a “Schedule a Demo Today” ad by them on whichever site I visit, I’d probably be the richest guy in SaaS! I read somewhere that 84% of users either ignore or are put off by retargeting ads! Shows how important it is to get it right. Start doing these things: - Segment visitors by page depth (1 page vs 3+ pages) - Track time-on-site thresholds (>2 min = higher intent) - Create separate campaigns for pricing page visitors vs. blog readers Tailor your content based on your audience’s behavior and stage in the buyer journey (URL path visitors, action completers, cart abandoners) 🎯 Retargeting works like a mosquito coil:  Retargeting is not plug and play, and it typically doesn’t stop with one level. Retarget for all customer stages. Not only demo and trial signups. This insulates your prospects from leaving the funnel midway. We’ve had cases where we spent thousands of dollars on a retargeting campaign only to make zero sales. But here’s what happened afterward ⭐ : When we triggered another retargeting campaign for the warmer folks from the previous campaign, giving them BOFU content, we made sales. A lot of it! What’s to learn here? You’re unlikely to be bet on with just the first touch point. You have to build that awareness consistently. Create a 3-tier remarketing structure: > Tier 1 (Cold): Educational content, industry reports > Tier 2 (Warm): Case studies, comparison guides > Tier 3 (Hot): Free trials, demos, limited-time offers Build custom audiences for each segment, assign specific content types to each, and implement frequency caps based on ‘bucket temperature’. Also, the focus should also be on increasing the credibility of your company rather than only pushing them towards the CTA. Here's one customized Google + LinkedIn campaign strategy we used for a client recently. What are some retargeting tactics that’s worked for you?

It's very possible to drive incrementality targeting existing customers on Meta. In fact, if well executed, it can and should be a part of your evergreen strategy for reactivation and profitability. But we find that most brands target a broad "all customer" list with minimal exlcusions (past 30 day purchaser) and use this campaign to modulate overall channel ROAS (oof). Incrementality is often below 1.0 and this campaign becomes a money pit to paint a rosier picture. So how can you make customer remarketing for you? Meta is a strong starting place because out-of-the-box audience match rates can be as high as 80%. In other words, if you take a list of known users/customers there is a good chance you can reach most of them on Meta. Audience strategy matters a LOT. Here's where I would start: - Lapsed Customers - 12mo+ since purchase (or whatever makes sene for your business) - Unreachable customers - unsubs, undeliverables etc from your email and SMS lists - I would exclude anyone who clearly only came in on a discount/major incentive For measurement: - Its necessary to run a lift study. - Because these are known customers, you can and should do the split design work on your side. This way, you can monitor customer behavior over longer time horizons and be sure you aren't just pulling purchases forward. For creative: - I like to focus on new products, new colorways, and new stories or value props. Give them something interesting to pay attention to and a reason to come back. - I would avoid any sort of promotion, at least in initial testing. You can revisit this as you see incrementality results roll in. I ran a test like this for an ecomm brand and we saw a 40% lift in reactivation, with same order profitability. The scale of the campaign was not huge (3-5% of our program), but it largely became a set it and forget it profit machine for us. This is a fairly easy test you can setup in a few days. Give it a shot. #Reactivation #Measurement #Incrementality

I just found the exact moment customers decide to buy again vs. disappear forever. It's not when you think. After years building retention strategies, I thought I knew the key touchpoints: → Purchase confirmations → Shipping updates → First unboxing experience Standard retention playbook stuff. Then, a brand I work with ran customer data through Triple Whale's Moby AI. It completely flipped my assumptions. Moby analyzed 12 months of customer behavior. What it found shocked me. The decision point isn't in the first 30 days. It's not even in the first purchase experience. It happens at day 47. During the second browse session. Before they even add to cart again. Here's what Moby spotted: → Customers who browse NEW categories (not their original purchase) within 45-50 days = 89% repeat buyer rate → Those who browse the SAME category = Only 23% repeat buyer rate This pattern was invisible to us. We were optimizing the wrong moments. While we focused on post-purchase flows, the real retention lever was curiosity-driven browsing seven weeks later. The results: → Testing retention campaigns based on cross-category browsing → 34% higher repeat purchase rates so far Moby isn't just analyzing data. It's trained on $55B+ in ecommerce behavior patterns. It sees connections humans miss. Shopify invested $25M+ in Triple Whale because this level of insight changes everything. Retention isn't about perfect onboarding anymore. It's about understanding the invisible moments that predict lifetime value. And most of us are optimizing the wrong moments. Comment "MOBY" and I'll share the agent library that's changing how brands are thinking about customer retention. Check it out: https://bit.ly/4nJ3Axs #TWPartner

There are several key ways to boost sales on Amazon. One of the most impactful: Target your customers when they are most likely to buy again. To do this, you need to understand your customer’s repeat purchase behavior. Here’s how we do this for our clients: At Cartograph we built an analytics dashboard to help our clients better understand the repeat purchase behavior of their customers. The data was being produced by the customer and ingested by the brands, but it wasn’t being leveraged to its greatest potential. So Cartograph build tools to analyze, visualize, and act on this data. What we discovered was different from what we expected. We segmented repeat purchases into 4 categories: 1. How many people repurchase within 0-15 days? 2. How many people repurchase within 16-30 days? 3. How many people repurchase within 31-60 days? 4. How many people repurchase within 3 months? For some product categories, we created additional cohorts for as far as 6 months beyond the initial date of purchase. When repurchases were grouped into cohorts and spread across an x-axis, a strange curve emerged in the graph. It was a bimodal distribution: The first peak occurred within two weeks of the initial purchase, then a trough, and then a second peak between month 1 and month 2. When we began to connect other brands to the Repurchasing Dashboard, we saw the exact same results with varying intensity — Two peaks, one trough. After weeks of research, we were able to piece together the cause of this behavior. Take, for example, a tube of toothpaste. A single tube will last about 3 months before it has to be replaced. Therefore, you would expect to see a single slope that grows exponentially as it approaches the 90-day mark. But, there are a shocking number of repeat purchases between days 0 and 15. As it turns out, the most likely (and most common) explanation for this consumer behavior is: - Stocking up with a larger supply of the product - Deciding to order more of that same item (or a slight variant of that item) for yourself or for friends/family. - Clicking “Buy Now” twice in a row (instead of adding several items to a single cart) That’s why, for every single product, we saw two large repurchasing spikes: One at the beginning and one towards the end. That’s what makes this so interesting — From our analysis, many of those curves have bimodal distributions of varying shape and intensity. In fact, we found that the most common repurchasing behavior came 30-45 days after that first transaction. This allows us to help our clients by tailoring our look back windows with DSP to target customers when they are most likely to buy again. As a marketer, the more familiar you are with that curve, the better equipped you’ll be to answer those three vital questions: “What products should I be promoting?” “When should I be promoting them?” “Who should I be promoting them to?”