For years, Indian marketers talked about martech as if it were a support function, a software layer that sat quietly behind campaigns, CRM emails and app notifications. That description no longer holds. In 2026, the martech stack has moved from the back office to the boardroom because it now sits at the intersection of revenue, retention, data governance and AI productivity.
The pressure is visible in the numbers. Gartner’s 2025 CMO Spend Survey found that average marketing budgets remained flat at 7.7% of company revenue, while paid media alone accounted for 30.6% of marketing budgets. In other words, marketers are being asked to do more with roughly the same top-line budget while media continues to consume the largest share of spend. That is exactly why software that improves targeting, automation, personalisation and measurement is no longer optional.
At the same time, marketers are still not extracting full value from the tools they already own. The Spring 2024 CMO Survey found that companies were using only 56.4% of the martech tools they had purchased. That is one of the most important data points in the industry today because it explains why the market is moving in two directions at once: deeper investment in high-performing platforms, and ruthless consolidation of tools that sit idle.
India’s own market signals point in the same direction. The India MarTech & CommerceTech Report, based on responses from more than 250 marketing leaders, found that over 65% of respondents were spending more than 16% of their budgets on martech. The same report said 63% of respondents were already using martech extensively, while 94% expected martech spending to increase over the next three years. That is not experimentation. That is structural adoption.
This is the context in which India’s largest brands are building their actual stacks. And what they are building is not one platform, but a layered system.
At the base sits customer data. The modern enterprise stack starts by pulling data from apps, websites, CRMs, contact centres, branches, ecommerce systems and messaging channels into a unified profile. Salesforce markets this through Data 360, Adobe through Real-Time CDP, Microsoft through Dynamics 365 Customer Insights, and Indian-origin players such as MoEngage, WebEngage and CleverTap through customer engagement and unified profile products. The language differs by vendor, but the operational goal is the same: create a usable, unified customer view that can drive actions across channels in real time.
Then comes orchestration. This is where the stack becomes visible to the consumer. Push notifications, email, WhatsApp journeys, on-site prompts, cross-sell nudges and retention flows all sit in this execution layer. In India, this layer matters disproportionately because customer engagement is highly mobile, app-heavy and messaging-first. WhatsApp, in particular, has shifted from being just a communication channel to a core engagement rail for brands in banking, retail, media and ecommerce. Meta’s business messaging documentation and large enterprise case studies now make clear that WhatsApp is embedded into customer journeys, not treated as a side tool.
The public case studies reveal how this looks in practice.
IndusInd Bank’s public MoEngage case study is one of the clearest examples of a large Indian enterprise using martech as an operating system, not a campaign utility. According to the case study, the bank reduced campaign execution timelines from 8 to 10 days to less than 10 minutes, integrated 612 offline user attributes into the platform, automated 80% of cross-sells on the INDIE app, and saw 2.6x growth in transactions over six months. The same disclosure states that INDIE crossed 10 million installs within a year and had an active user base of 1.5 million customers. Whatever the caveats of vendor case studies, those figures illustrate the enterprise use case with unusual clarity: the stack is being used to compress time-to-market, unify offline and digital data, and turn engagement into transactions.
Tata Capital’s public implementation story points to a similar pattern. The company said it unified customer data from five distinct internal databases into a 360-degree view and reduced campaign go-live time from days to real-time. That matters because BFSI brands in India do not simply need more messaging capacity. They need compliant, real-time orchestration across websites, apps, SMS, email, WhatsApp, RCS, IVR and contact centres. In sectors where journeys are long, regulated and data-intensive, the value of martech lies in stitching together fragmented touchpoints fast enough to influence behaviour.
Media and telecom show a slightly different version of the same truth. Airtel Xstream’s public case study reports a 30% conversion rate through WhatsApp, 90% delivery rates, a 40x increase in engagement compared with push, and up to 4x increase in engagement compared with in-app messaging. It also says the WhatsApp-led workflow contributed to a 20% increase in app installs. Those numbers are significant because they show that for high-frequency digital businesses, channel choice is not tactical. It is architectural. The stack must be able to identify the user, personalise the message and trigger it on the channel the user actually responds to.
Ecommerce offers yet another proof point. Tata CLiQ Luxury’s public CleverTap case study reports an average 150% uplift in click-through rates and a 159% increase in revenue generated from messaging channels. That is the clearest illustration of why the Indian ecommerce stack is built around retention, cohorts and lifecycle messaging rather than just acquisition. In mature digital categories, martech is increasingly a profit engine, not just a communications layer.
What is changing now is the addition of AI. Reuters reported in 2024 that India’s AI market is expected to reach $17 billion by 2027, with more than 420,000 professionals in AI-related roles and demand for AI talent expected to grow at 15% annually through 2027. That matters for martech because the stack is no longer just about workflow automation. It is becoming the environment in which recommendation engines, next-best-action systems, content generation, predictive scoring and agentic assistance can actually run on first-party data.
But AI also raises the stakes on governance. India’s Digital Personal Data Protection Act requires consent to be free, specific, informed, unconditional and unambiguous, with a clear affirmative action. For large brands, that means the stack cannot merely collect and activate data. It has to prove that the data was collected lawfully, limited to the specified purpose and capable of withdrawal. In practice, the privacy layer is now becoming as important as the campaign layer. The future Indian stack will not just be measured on performance. It will be measured on auditability.
This is why the biggest shift in India’s martech market is not the rise of one vendor over another. It is the move from tool accumulation to system design.
The old model was simple: buy CRM, add email, plug in analytics, maybe experiment with a CDP. The new model is more disciplined. Brands are choosing fewer platforms, demanding tighter integrations, insisting on faster activation and asking harder questions about under-utilisation. The hard truth is that no CMO can justify a bloated stack when only 56.4% of purchased martech is being used and overall marketing budgets remain stuck at 7.7% of revenue.
That is why the Indian enterprise stack in 2026 looks the way it does. Global suites still dominate systems of record and enterprise-wide data environments. India-born engagement platforms remain strong in execution-heavy categories like mobile engagement, retention automation and WhatsApp-led journeys. The combination is not accidental. It reflects a market that wants enterprise control at the core and speed at the edge.
The larger story, however, is even more important. Martech in India is no longer a specialised purchase made by the digital team. It has become the infrastructure through which large brands decide who to target, when to engage, how to personalise, what to measure and increasingly which actions can be automated by AI.
That is not software support.
That is the growth stack.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research.
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