Rajesh Jain of Netcore aims to bring ad waste down to near zero for clients by 2027

Every day, consumers receive between 20 and 40 marketing messages. Most are ignored. The ones that do get opened rarely convert. And the customers who engage today often disappear by next quarter. This cycle of attention, disengagement, and reacquisition has created ad waste, according to Rajesh Jain, Founder and Group CEO of Netcore Cloud. He notes the ad waste problem drains an estimated $500 to $700 billion annually from global digital advertising budgets.

According to Jain's analysis, 90% of marketing budgets go toward acquiring new customers, while only 10% is allocated to retention. What’s astounding is that 70% of acquisition spending is actually spent reacquiring past customers who were once loyal but drifted away due to irrelevant communication. Four out of five customers who click on marketing messages in one quarter stop engaging by the next.

Most brands plateau at 8 to 10% CRM contribution to total revenue. Marketing has become a cost centre rather than a profit engine, with CMOs struggling to demonstrate clear ROI. The promise of delivering the right message to the right person at the right time remains largely unfulfilled despite decades of investment in marketing technology.

"Success, to me, is simple. It's about eliminating ad waste," Jain says. "Today, nearly 70% of marketing budgets are spent on inefficiencies, reacquiring the same customers multiple times instead of retaining them. That's pure waste."

Netcore Cloud's new partnership with Google Cloud aims to reset this equation through agentic marketing, a shift from automated execution to autonomous decision-making powered by AI agents.

How AI agents solve marketing's impossible problem

Traditional marketing automation has helped brands execute campaigns faster, but it hasn't solved the core problem of relevance at scale. Marketers typically create eight to ten broad segments, like "90-day inactive users," "high-value customers," because creating personalised content for more granular audiences isn't humanly scalable. The result is generic, broadcast-style messaging that fails to engage.

"Agentic marketing is about bringing autonomy into operations," Jain explains. "Automation has existed for a long time, automating journeys, processes, and repetitive tasks. But autonomous marketing, where agents come in, goes a step further. It's about decision-making. These agents won't just execute; they'll make decisions."

The partnership combines Netcore's martech data, tracking how customers interact with websites and apps, what they read, ignore, or engage with, with Google Cloud's infrastructure and AI capabilities. Google provides tools like BigQuery for data consolidation, Vertex AI for model deployment, and foundational models for text and image generation. Netcore contributes behavioural segmentation, affinity scores, propensity indicators, and churn prediction models built from years of customer engagement data.

Jain describes customers through a framework he calls BRTN: Best (loyal and engaged), Rest (lapsing or disengaging), Test (first-time or trial buyers), and Next (new leads to be acquired). The invisible problem is that brands focus heavily on Best and Next while ignoring Rest, the customers who are drifting away. “Brands lose customers due to irrelevant communication,” Jain explains. These customers move from best to rest, then to test, and finally become lost. Brands then spend again on ad tech to reacquire them, creating a cycle of loss and reacquisition that represents the invisible marketing problem.

Together, the platforms aim to enable a shift from ten broad segments to potentially ten thousand micro-segments, with AI agents automatically generating tailored content for each. This addresses what Jain describes as marketing's "impossible problem": achieving both scale and relevance simultaneously.

The system works through four integrated layers. The data layer combines information from websites, apps, CRMs, and external platforms like Salesforce and Google Analytics. The decisioning layer combines multiple AI models, both large language models and smaller specialised ones, to determine optimal content, timing, and channel selection for each individual. The agent layer includes specialised AI agents like a Shopping Assistant, Merchandising Agent, and Insights Agent, orchestrated by a Co-Marketer Agent that executes end-to-end campaigns. Finally, a learning layer captures attribution and analytics data, feeding performance insights back into the system for continuous optimisation.

Using Crocs as an example for demonstration, Netcore showcased how marketers can activate data-led segments, for instance, reactivating dormant customers or targeting those browsing specific products like "buy-one-get-one" deals. Each segment received distinct creatives, urgency-driven messages for near-term buyers, re-engagement nudges for inactive ones, and tailored visuals for long-term prospects. Moreover, each customer can be engaged via their preferred channel, whether app push, WhatsApp, email, or others, and even at their preferred time, such as weekends or late nights. Once approved, campaigns can be executed automatically, achieving real-time scale and personalisation.

In beta pilots, this approach has shown significant results, according to the platform. Performance lifts of 25 to 40% have been recorded, measured through CRM contribution to total revenue, the metric Netcore and its clients prioritise most. In fast-cycle categories like e-commerce and quick-service brands, the focus is on direct revenue uplift. In long-cycle categories like insurance or automotive, the emphasis shifts to lead nurturing and keeping prospects engaged until conversion.

The core issue today is that most marketing messages are irrelevant, leading to disengagement. Jain points to untapped opportunities where brands could react to real-time contexts, like showing personalised local offers when a customer lands in a new city, but few are executing at this level.

Martech can move towards a variable pricing model

The inability to identify the “invisible middle” has led to an increase in marketing spends. When analysing public data from companies like Nykaa, Mamaearth, and FirstCry, marketing spends in FY24 to FY25 grew faster than revenue, an indicator of inefficiency driven by reacquisition costs, as per Jain. Many D2C brands have already reached most of their potential customer base within a decade. What they are doing now is simply reacquiring existing customers, which adds no new value.

To address this invisible middle, Netcore has introduced NeoMails, which it calls "living emails" that evolve in real time. Unlike static promotional messages, NeoMails can include mini-games, quizzes, personalised product recommendations, or even embedded ads from non-competing brands. Users can interact, make purchases, or provide zero-party data without leaving the email. Offers update dynamically when reopened, and users earn "atomic rewards," micro-incentives that accumulate across multiple brands.

The strategy draws on research showing that attention precedes transaction. As marketing scholar Byron Sharp wrote in ‘How Brands Grow,’ people don't have brand loyalty; they have category loyalty. A brand's job is to ensure mental availability and stay top of mind. Email continues to perform strongly in this regard, offering up to 63 times higher conversion rates for engaged users compared to other channels.

Netcore has also introduced a new business model called Progency (Product + Agents + Agency), which shifts from traditional input-based pricing to outcome-based compensation. Instead of charging fixed platform fees plus per-user or per-message costs, the platform now offers variable pricing tied to actual revenue outcomes.

Under this Alpha pricing model, it aligns its targets with client marketing teams. If both sides exceed expectations, it shares in the "alpha," the upside achieved beyond projected targets. For e-commerce clients, success is measured through CRM contribution to total revenue, repeat purchase rates, and conversion rates across the customer journey. For banking and financial services, metrics shift to engagement, lead generation, and lead conversion depending on business models and tech stacks.

Jain shares, “Instead of measuring campaigns or clicks, we now focus on revenue contribution, repeat purchase rates, and customer retention. Our goal is to help brands increase repeat purchases to 30-40% and beyond. This represents a dramatic shift in how we operate.”

CMOs need to become Chief Profit Officers

Agentic marketing could go beyond technology to reshape marketing organisations and the broader agency ecosystem. Jain sees this as an opportunity for marketing leaders while simultaneously disrupting traditional agency models.

"For the first time, CMOs can truly be in the race to become CEOs," Jain says. "Traditionally, very few marketers make it to the top, but this transformation opens up that opportunity."

To succeed in this new environment, Jain believes CMOs must evolve into what he calls Chief Profit Officers, requiring three key skillsets. 

• First, a deep understanding of AI's potential, particularly in enhancing customer relationships. 

• Second, financial literacy around profit and loss, recognising that every customer's profitability contributes to the overall company P&L. 

• Third, an entrepreneurial mindset to identify impossible problems that AI can now solve.

This evolution coincides with disruption in the traditional agency model. Global agencies have experienced declining stock performance and strategic pivots in recent years. When platforms like Google and Meta start offering creative tools and automation at minimal cost, agencies need to redefine their value proposition.

Jain observes that agencies possess deep domain expertise, customer behaviour understanding, and creative insight, capabilities that remain valuable. However, they risk becoming redundant if they don’t reinvent themselves.

"Agencies today risk being reduced to intermediaries unless they own or integrate with technology platforms. To stay relevant, they'll need to upskill, reskill, and reimagine their functions," Jain notes. "Naturally, such disruption will lead to workforce shifts and even layoffs. We've seen similar moves from global companies like Amazon and UPS recently."

Given that the workforce is seeing a significant impact, the path forward requires agencies and brands to focus on what Jain calls “pre-skilling and retraining”, teaching people not just how to use AI tools, but how to leverage them effectively to create new value.

Early adopters, much like those who embraced the internet early, will gain significant competitive advantages. 

"If by 2027 we can bring that ad waste down to near zero, enabling brands to invest that money instead in innovation, product development, and customer experience, that would be true success," Jain concludes.

Jain envisions moving marketing budgets from acquisition-driven spending toward retention strategies. In the future, agentic marketing will offer not just efficiency but also effectiveness. Jain believes agentic AI could offer brands a path to profitability, but would require both marketing executives and agencies alike to transform themselves quickly enough to lead it.