A story beyond ads.

Read time: 5 minutes

We move close to the launch of the first-ever detailed Dream SEO Masterclass where I will cover the fundamentals, updates and modern ways of doing SEO in much depth.

The idea behind this masterclass is to cover the topic from a logical lens and help you to think right while you plan SEO for your website.

We will also cover the impact of AI previews, multimedia results and attribution on search which is the need of the hour.

The curriculum of the session is finalised, you can read through it and join from below:

The signups are also open for a more comprehensive Dream performance marketing masterclass.

This is the most comprehensive masterclass and is conducted in a small group of 10 people only to maintain personalised attention and exclusivity.

Block your seat before it is sold out.

A story beyond ads.

A business without a clear strategy to boost purchase frequency will inevitably struggle with an ongoing cycle of trying to control CAC at the forefront of their marketing efforts, all while losing existing customers from their ecosystem.

Let me simplify this concept with a hypothetical example using a skincare brand—GlowSkin.

Here’s how an effective combination of Data, CDP tools, Communication, and Analytics can transform GlowSkin’s approach:

Here is how GlowSkin can define their Lifetime value cycle by driving each stage of the cycle efficiently:

Data

1. Website Visitors: Shorten the attribution window as much as possible to gauge the performance of acquisition channels like Google and Meta ads more accurately.

   • Example: GlowSkin uses advanced attribution models, like data-driven or algorithmic attribution, to determine the true value of each traffic source (e.g., Instagram ads vs. Google Ads) in converting users who purchase high-margin products like serums.

2. User Actions: Leverage event-based tracking and behavioral analytics to create detailed user personas and predictive models for future actions.

   • Example: GlowSkin implements event-based tracking to monitor actions such as product views, clicks on specific ingredients, or interactions with the blog. This data feeds into machine learning models that predict which users are likely to make a purchase after engaging with educational content.

3. Past Shoppers: Utilize RFM (Recency, Frequency, Monetary) analysis to segment customers based on their shopping behavior and tailor personalized re-engagement strategies.

   • Example: GlowSkin conducts RFM analysis to identify high-value customers who frequently buy premium products and targets them with early access to new product launches and exclusive discounts, boosting customer lifetime value.

4. Anonymous Push Tokens: Integrate with a CDP to cross-reference anonymous push tokens with first-party data, enabling a shift from anonymous to known user profiles as users engage further.

   • Example: GlowSkin syncs anonymous push tokens with data from users who later sign up for an account or subscribe to the newsletter, allowing the brand to progressively enrich user profiles and deliver more personalized push notifications.

5. New Registrations: Implement advanced cohort analysis to track the impact of specific campaigns on new registrations and subsequent user behavior.

   • Example: GlowSkin uses cohort analysis to evaluate how users who registered during a particular Instagram influencer campaign behave differently from those who registered via organic search, helping refine future influencer partnerships.

6. Subscriptions: Deploy AI-driven exit-intent popups with personalized offers to prevent churn at the point of email subscription abandonment.

   • Example: GlowSkin's AI-driven system detects when a user is about to abandon the subscription process and serves a personalized offer, such as a free e-book on skincare routines, leading to higher subscription completion rates.

CDP Tool (Customer Data Platform)

1. Collection of Activities: Integrate omnichannel data collection to create a holistic view of the customer journey, combining online behavior with offline interactions and third-party data.

   • Example: GlowSkin connects online shopping data, in-store purchases from partner retailers, and third-party demographic data to build a comprehensive view of each customer's behavior and preferences, enabling more targeted marketing strategies.

2. Using Analytics/Segmentation: Employ advanced clustering algorithms like k-means or hierarchical clustering to create highly granular customer segments for hyper-personalized marketing.

   • Example: GlowSkin uses k-means clustering to identify micro-segments of customers interested in anti-aging products who also have a high affinity for eco-friendly packaging, allowing for the creation of targeted campaigns that speak directly to these interests.

3. Analyzing User Behavior: Utilize AI-powered heatmaps and predictive path analysis to identify and optimize high-friction points in the customer journey.

   • Example: GlowSkin implements AI-driven heatmaps to pinpoint where users experience friction, such as confusion over ingredient details on product pages, leading to a redesigned, user-friendly interface that improves conversion rates.

4. Creating a Unified View of Customer: Employ a CDP to unify data across CRM, eCommerce, social media, and customer support systems, enabling dynamic and real-time customer profiles.

   • Example: GlowSkin’s CDP integrates data from various touchpoints, providing a unified and real-time profile of each customer that enables personalized marketing automation, such as sending product replenishment reminders based on past purchase history.

5. Tracking Reports: Implement real-time dashboards with predictive analytics to monitor user behavior trends and forecast future customer actions.

   • Example: GlowSkin's analytics team uses a real-time dashboard that not only tracks user behavior but also uses predictive models to forecast future trends, such as an anticipated increase in demand for specific products during seasonal changes, allowing for proactive inventory management.

Communication

1. Identifying Use Reachability: Utilize cross-channel orchestration platforms to ensure that communications reach users at the optimal time on the most effective channels.

   • Example: GlowSkin deploys a cross-channel orchestration platform that determines whether a customer is more likely to engage with a push notification or an SMS based on historical data, ensuring that communications are timely and effective.

2. Send Time Optimization: Apply machine learning algorithms to optimize send times for individual users based on past behavior and contextual data, like location and device usage (CDP tools are capable of doing this)

   • Example: GlowSkin's email marketing system uses machine learning to determine the best time to send a personalized email to each customer, factoring in time zones, past open rates, and the likelihood of the user being on a mobile device versus a desktop. (A CDP tool is best poised to leverage this).

3. Journey Orchestration: Create dynamic customer journeys using AI-driven predictive models that adapt in real-time based on user behavior.

   • Example: GlowSkin employs AI to dynamically adjust customer journeys—for instance, automatically shifting a user from an educational content flow to a product recommendation flow after detecting increased engagement with specific skincare topics.

4. A/B Testing: Use multi-armed bandit algorithms to optimize A/B testing dynamically, ensuring that the most successful variations are scaled faster.

   • Example: GlowSkin moves beyond traditional A/B testing by using multi-armed bandit algorithms that allocate traffic to the best-performing email subject lines or ad creatives in real-time, reducing the time to determine winners and maximizing impact.

5. Reporting Creation: Automate the generation of performance reports with AI-driven insights, highlighting not just what happened, but why it happened, and what to do next.

   • Example: GlowSkin's reporting tools automatically generate weekly insights that not only detail the performance of retention campaigns but also use AI to suggest next steps, such as tweaking the messaging for specific customer segments based on recent behavior.

Analysis & Predictions

1. Customer Snapshot: Use advanced customer analytics platforms to generate deep insights into customer demographics, psychographics, and purchasing behavior.

   • Example: GlowSkin’s customer snapshot includes not just basic demographic data but also insights into psychographics—like values, lifestyle, and motivations—enabling more effective and resonant marketing strategies.

2. Conversion Funnel: Implement funnel analysis with machine learning to identify subtle patterns and anomalies in user behavior that may indicate bottlenecks or opportunities.

   • Example: GlowSkin uses machine learning to analyze the conversion funnel, identifying nuanced drop-off points that traditional analysis might miss, such as a sudden increase in cart abandonment among a specific user segment, which prompts targeted interventions.

3. Customer Churn: Leverage predictive analytics to calculate customer churn probability and implement retention strategies before the customer decides to leave.

   • Example: GlowSkin uses predictive models to calculate the likelihood of customer churn, allowing the brand to proactively engage with at-risk customers through personalized offers or tailored content that addresses their specific concerns, reducing churn rates significantly.

It takes years for businesses to implement this capability end to end. My attempt is to give you a rounded view.

I understand you may not be fully able to implement this with your current organisational capability and hence you can seek our help.

We have successfully implemented this model for various D2C brands.

That’s it for today folks.

Have a great Sunday ahead.

Cheers,

Apurv