Shweta Sharma, CEO of Hakuhodo Data Labs India and Chief Business Officer at AdGlobal360, is a trailblazer in digital commerce with over 15 years of experience in, shaping brand performance across India and global marketsShe leads with a future-forward mindset, building platforms like eGenie, 1Commerce, a unified solution that integrates media automation, product information management (PIM), digital shelf tracking, and business monitoring, including eBuX, a real-time digital shelf intelligence platform.
At the intersection of retail media, AI, and consumer behavior, Shweta champions simplicity in a complex digital landscape. In this conversation, she unpacks how brands can scale smarter, act faster, and lead with clarity, while reflecting on the role of curiosity and continuous learning in crafting resilient, modern leadership.
In today’s fragmented retail landscape, where influence doesn’t always lead directly to purchase, what signals should brands be watching more closely, especially in high-consideration categories?
In high-consideration categories, brands need to move beyond just transactional metrics. They need to look for compound signals like repeated product searches, cross-SKU comparisons, price tracking behavior, review depth, wishlist additions, and competitor SKU switching. On quick commerce platforms, even browsing behavior across pack sizes or delivery time checks reveal evolving intent. Equally, digital shelf metrics like, availability, pricing stability, share of search, and review sentiment, directly correlate with purchase readiness. The journey may span multiple sessions and platforms, but these live signals allow brands to proactively optimize media, stock, content, and offers to nurture that intent towards conversion.
There’s a lot of buzz around next-gen KPIs, but many marketers are still focused on CTRs and conversions. How would you explain the shift to someone grounded in those legacy metrics?
Traditionally, KPIs like CTR and conversions worked well in relatively linear journeys. But formats like quick commerce have compressed purchase behavior into minutes, where speed, availability, pricing, and visibility dynamically drive outcomes. Metrics like share of search, stock status, price competitiveness, attach rates, and engagement velocity have become far more relevant than just CTR.
In addition, as consumers increasingly rely on AI-powered discovery tools to guide purchase decisions, whether through conversational search or shopping assistants, AI discoverability is emerging as a new performance layer. This means optimizing structured product data, content quality, reviews, and ratings to ensure that AI models surface your brand accurately and favorably during pre-purchase research.
The shift is no longer just about measuring clicks, but about ensuring your brand is “found” across human and machine-led discovery pathways, and optimizing in real time based on live signals.
Consumers today often bounce across multiple touchpoints in minutes. How should brands be rethinking the way they interpret and respond to these fast-moving intent signals?
Speed and context are now critical. Brands need to adopt signal aggregation frameworks that unify intent data across media, marketplaces, search, and social in near real-time. Rather than seeing each touchpoint in isolation, brands must build a dynamic intent map, constantly recalibrating based on evolving consumer behavior. AI-powered models can cluster these signals into actionable cohorts, allowing brands to serve contextually relevant messaging aligned with where the consumer is in their journey, whether it’s awareness, evaluation, or purchase. Ultimately, the goal is to orchestrate adaptive media and content decisions that match the consumer’s pace, rather than waiting for traditional campaign reporting cycles to catch up.
New commerce formats like quick commerce, social commerce, and live shopping are changing the game. How are they challenging the relevance of traditional KPIs?
These new formats compress the purchase funnel dramatically- awareness, engagement, and conversion happen simultaneously, often within minutes. Traditional KPIs like last-click attribution or static ROAS don’t capture this fluidity. Instead, time-to-decision, engagement velocity, and shoppable content conversion rates become more relevant. Quick commerce, for instance, is driven by availability, visibility, and immediacy of response rather than pure reach. Social commerce thrives on community signals, influencer authenticity, and real-time social proof. The industry needs to evolve towards experience-based KPIs that factor in platform-native behaviors, conversion windows, and cross-channel synergies, rather than relying solely on linear attribution models.
When brands start focusing on micro-moments and real-time signals, how crucial does creative agility become? And do you see AI playing a meaningful role in that agility, if so, how?
Creative agility becomes absolutely crucial when brands start optimizing for micro-moments, those intent-rich windows when consumers are actively searching or buying. This is especially true in quick commerce, where decision-making is instant and shelf space is highly competitive.
Take a platform like Blinkit, and a high-velocity category like staples, say, Basmati rice or cooking oil. A consumer searching at 6:30 PM is often looking to buy immediately. Now if the brand and creative doesn’t reflect brand RTB, freshness, price drops, or urgency cues, and it’s a lost moment.
Traditional creative cycles can’t keep up. That’s where creative agility becomes a differentiator.
And this is exactly where AI plays a meaningful role.
AI enables agility across three critical areas:
So yes, in the age of micro-moments, AI-powered creative agility isn’t just a good-to-have but it’s essential for relevance, performance, and consumer trust.
Can you share an example where shifting the focus from attribution to real-time signal interpretation led to a tangible improvement in campaign performance? What did that change look like in practice?
Absolutely. We worked with a personal care brand that sells heavily on quick commerce platforms. Instead of waiting for standard reports to tell us which ads or channels drove conversions, we started looking at real-time marketplace signals with things like stock availability, pricing changes, share of search, and even competitor moves happening live on the platform.
For example, if we saw that one SKU was out of stock or that a competitor suddenly dropped prices, we could immediately adjust bids, shift budgets to in-stock SKUs, or update creatives accordingly, all within hours, not days.
By responding to these live signals rather than waiting for post-campaign data, we were able to increase conversions by nearly 20% in just a few weeks. It showed us how powerful real-time optimization can be, especially on fast-moving platforms like quick commerce.
With festive demand picking up as early as Q3, brands often face a spike in both traffic and noise. During these high-intensity periods, should KPIs still revolve around conversions or is it time to prioritise speed, responsiveness, and in-the-moment learning?
During high-intensity festive periods, while conversions remain a core objective, brands that focus solely on bottom-funnel metrics risk missing out on larger opportunities. The ability to sense demand surges, inventory risks, price shifts, and competitive moves in real-time becomes equally crucial. KPIs like media responsiveness, creative refresh frequency, inventory velocity, and share of visibility across search and digital shelf should sit alongside traditional conversion metrics. In these volatile windows, speed of reaction often dictates the size of opportunity captured. Brands that adopt adaptive optimization frameworks driven by live signals typically outperform those anchored to fixed campaign structures during peak seasons.
