CompassDTC @CompassDTC
Driving small & midsize CPG brands to DTC success with precision analytics, bold strategies, and unstoppable growth. compassdtc.com Colmar, PA Joined December 2024-
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Pricing strategy should be measured — not guessed. My latest article explores: • price elasticity • promotion strategy • pricing power • customer demand modeling For DTC brands: zurl.co/1mg1i #Pricing #PriceElasticity #MarketingAnalytics
The wineries that win aren’t the ones that react to cancellations – they’re the ones that see them coming. By paying attention to how members behave, how they join, and where they are in their lifecycle, you can identify risk early and take action when it matters most. #wineindustry #wineclub wineindustryadvisor.com/2026/05/07/sto…
@WineIndustry @Analysights Thank you @WineIndustry. Predictive analytics and customer retention modeling can provide wineries with valuable insight into member behavior, retention risk, and long-term customer value. We appreciate the opportunity to support wine industry success.
@UriWeinberger @UriWeinberger thanks for your feedback! Log-log is certainly useful, but there are a lot of other analytical methods that can be used to see whether you’re generating incremental demand.
High sales during promotions don’t automatically mean customers are price sensitive. New post: How DTC brands can estimate true price elasticity using log-log regression, pricing analytics, and customer demand modeling. zurl.co/SukOv #PriceElasticity #DTC #Ecommerce #Analytics
If customers only buy when you discount, your promotions may be reshaping behavior more than growing demand. New post on: • price elasticity • customer conditioning • and measuring promotion dependence in DTC brands zurl.co/70xKY #DTC #CPG #Ecommerce
A promotion spike doesn’t always mean true growth. Sometimes customers are just learning to wait for the next sale. My latest post explores: • price elasticity • promotion dependence • pull-forward demand • and why constant discounting can weaken DTC brands over time zurl.co/GFU9h #DTC #Ecommerce #PricingStrategy #MarketingAnalytics
Customers already tell you what to sell next. It’s in your order data. Most brands just don’t use it. zurl.co/ngyO0 #ecommerce #AOV #DTC
Most “frequently bought together” data goes unused. That’s missed revenue. Use it to: → bundle products → drive upsells → increase AOV Start simple. zurl.co/DippD #ecommerce #DTC #AOV
Most brands chase more traffic. The better move? Increase value per order. Market basket analysis shows you: → what customers already buy together → what to bundle, upsell, or recommend Simple data → real revenue. zurl.co/BGyP4 #ecommerce #DTC #AOV #upselling
Early churn signals: → Smaller orders → Longer gaps → Skipped shipments These aren’t random. They’re warnings. Here’s how to use them: zurl.co/vPbo5 #Ecommerce #Retention #DTC
This is where most brands get churn wrong: They react after customers leave. The best brands predict the drop-off window and intervene before it happens. zurl.co/V8sZe #Churn #Growth #DTC
The real question isn’t: “Will this customer churn?” It’s: “When does churn risk spike?” That’s when you act. zurl.co/Stz4x #Retention #Analytics #DTC
Most churn doesn’t happen suddenly. It builds over time—until it’s too late. Here’s how to predict it before customers leave 👇 zurl.co/ZLmSd #DTC #Churn #Retention
We tested this: Remove behavioral data → model performance collapsed. Lesson: Customer value is driven by behavior, not demographics. More here: 👉 zurl.co/6eqzE #DTC #CustomerData #LTV #MachineLearning
RFM tells you who your best customers are today. Machine learning tells you who they’ll become. Here’s how gradient boosting helps predict customer LTV—and improve retention strategy: 👉 zurl.co/KpOoX #DTC #MarketingAnalytics #LTV #MachineLearning
If you’re optimizing paid media at the national level only… You might be optimizing to a number that doesn’t exist. Geo-level modeling changes the game. zurl.co/nOoiP #MarketingAnalytics #DTC #Growth
National averages can hide poor regional performance. A channel that looks strong overall may be underperforming in key markets. Geo-level modeling helps fix that. zurl.co/edbxO #DataScience #Marketing #ROAS #MMM
Most brands optimize paid media based on national averages. But performance isn’t uniform. Geo-level modeling reveals what works where—so you stop wasting spend in underperforming regions. zurl.co/FohV7 #MarketingAnalytics #PaidMedia #MMM #DTC
Most brands don’t have a measurement problem. They have an allocation problem. Media mix modeling helps solve it. 👇 zurl.co/BvPZ7 #DTC #MarketingAnalytics #MediaMixModeling
Gearoid O Grady @grady_gear15819
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