In a MediaBrief exclusive, Abhijat Shukla, Vice President – Data Science at WebEngage, weighs in on India’s mobile-first, multi-screen and privacy-conscious media ecosystem, where marketers are moving beyond impressions and clicks to prove real business impact. Shukla outlines the shift from scale metrics to attention, the role of AI in decoding cross-screen journeys, and how privacy-first measurement is reshaping marketing effectiveness in India.
With advertisers increasingly prioritizing outcome-based accountability, retail media measurement models are evolving beyond traditional impressions and clicks to focus on tangible business and brand outcomes. Speaking on this evolution, Abhijat Shukla, Vice President – Data Science at WebEngage, says, “As marketers increasingly focus on demonstrating the business outcomes of their advertising investments, the measurement paradigm is shifting from media presence to customer economics. Rather than asking, ‘Did someone see or click?’ the new questions are: ‘Did this change what the customer did, how quickly they did it, and how valuable they became?’”
Shukla adds, “Engagement metrics are now the path itself, not the point—whether the user progresses from browse → consider → buy → repeat across the interaction depth and overall quality of the sessions.
“Retention is measured using metrics like time to next purchase, repeat velocity, category growth, and lifetime value lift—none of which include return visits.
“What this does,” Shukla explains, “is that it enables marketers to distinguish noise from actual behavior changes, shifting the focus from CTRs to incrementality and CLV scores.”
Decoding complex consumer journeys
Speaking about how AI is helping decode increasingly complex consumer journeys across commerce, mobile, social, and digital media channels to deliver more accurate and actionable performance insights, Abhijat Shukla says, “Modern customer journeys are probabilistic graphs, not funnels. One customer may browse on mobile, get nudged on WhatsApp, research on the web, convert in-store, and re-engage in the app. Traditional attribution collapses this into the last click, losing most of the signal.
“AI,” he adds, “approaches every touchpoint as part of a learning system, stitching web, app, store, and CRM data into a single, evolving customer view.
“While propensity models estimate conversion, repeat, and churn, causal and incrementality models identify which message, on which channel, at what moment, actually changed behavior. Instead of asking, ‘Which ad got the click?’ AI finally lets marketers answer, ‘Which cocktail of nudges drove higher spend, faster conversion, and repeat?,” says Shukla.
Navigating privacy-first ecosystems
With privacy-first ecosystems becoming the norm, measurement innovation is evolving to balance transparency, compliance, and actionable business intelligence. Addressing this crucial aspect of measurement, Shukla says, “Privacy doesn’t kill measurement—privacy kills surveillance-based measurement.”
“The ongoing phase,” he explains, “is founded on three legs: First, First-Party Truth – Measurement with owned, consented, and verified data—the ground truth of what happened—is where it all begins.”
Second, Relevance – Rather than monitoring people, algorithms rely on cohorts, behavioral patterns, response curves, and diminishing returns, enabling forecasts without revealing identities.
And finally, AI-Driven Inference – When a case has missing data, the impact is inferred from responses in prior cases with a similar profile, or from look-alike profiles and causal relationships identified by the AI.”
“The future of measurement,” Shukla says, “is not about less data; it is about better, cleaner, economically meaningful data, data built for decisions, not surveillance.”
Leveraging India’s unique digital signals
Outlining how platforms like WebEngage are leveraging India’s digital landscape—characterized by mobile-first audiences, vernacular content consumption, and growing online commerce—to build more contextually relevant and outcome-driven measurement frameworks, Shukla says, “Examining the behavioral data over time allows marketers to understand how the campaigns have affected customer actions.
“Marketers,” he says, “will gain insight into when to engage with the customer based on AI-assisted segmenting and contextual information that will identify the customer’s intention at that point in time, enabling improved measurements of performance versus desired outcomes, such as activation, repeat usage, and long-term value.
“By using this method,” Shukla says, “marketers in India will have measurements of consumers that reflect how the brand is actually interacting with them, allowing for more accurate and substantial insights into the effectiveness of campaigns.
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