India’s digital landscape is often hailed as a billion-user opportunity, yet marketers and tech companies are discovering that “one size fits all” personalisation simply does not work here. The country’s sheer diversity across language, region, culture, device and consumption patterns has created a personalisation puzzle that global playbooks struggle to solve. As a result, many well-intentioned personalisation efforts are falling short in a market where context is king and the stakes for getting it right are enormous.
A Billion Users, a Million Preferences
India now hosts one of the world’s largest internet populations. But behind the headline number lies staggering heterogeneity. The country has 22 official languages and over a thousand dialects, reflecting a mosaic of regional identities. Most internet users access content in local languages, and even among urban audiences, regional languages increasingly outperform English. A Punjabi-speaking user in Punjab and a Tamil-speaking user in Tamil Nadu live in separate linguistic universes. A single language interface or message cannot resonate with both.
Language is just one layer. Cultural nuances vary widely by region, community and religion – from food and festivals to family decision-making. Western personalisation strategies often assume cultural sameness, a single dominant language, uniform lifestyle and stable income bands. In India, none of that applies. Something as simple as timing promotional communication around festivals requires dozens of simultaneous calendars.
Income, location and behaviour add another dimension. India’s internet growth is now driven by rural areas and Tier-2 or Tier-3 cities, with users who have different needs and budgets from metro elites. A majority of users are price-sensitive and vernacular-speaking, so an e-commerce site that personalises only around premium products or English content fails to engage the majority. Tier-1 consumers may pay for convenience, smaller-town consumers demand value.
Device access further complicates the equation. India has hundreds of millions of smartphone users, but device quality varies dramatically. A user on a low-cost Android phone with patchy 3G in a village has a different experience from someone on a 5G iPhone in Mumbai. Personalisation engines built for markets with reliable high-speed connectivity can misfire. For instance, recommending HD video content to someone with limited data, or making suggestions based on shared-device behaviour when one family uses the same account. The result is noisy signals, poor predictions and a frustrating experience.
There is also a trust and privacy hurdle. Indian consumers have grown wary of hyper-targeted ads that “follow” them. Relevance must be balanced with respect. Many users are skeptical of tracking and perceive personalised ads as invasive or creepy. In a market where word-of-mouth is powerful, a perception of intrusion can hurt brands fast.
Why Global Playbooks Don’t Fit India
The challenges in India stand in stark contrast to markets like the United States or China. In the US, a relatively homogeneous environment makes scaled personalisation easier – one language, high device ownership, and consistent consumption patterns. In China, a shared language and integrated platforms enable rich unified data.
India contains dozens of sizable linguistic and cultural “internets” under one roof. A recommendation model trained for the US can learn from English-based data at scale. Its Indian counterpart may need separate models for multiple languages and scripts. Early attempts by global players to push Western content struggled until they invested in regional language catalogues, mobile-only price plans, and vernacular experiences.
Global companies also learned that Indian users value different things. Messaging apps took off because they supported local languages and low-data usage. E-commerce players had to add cash-on-delivery, regional language interfaces and voice-based navigation to gain trust and reach new users.
Even in terms of infrastructure, India is different. While broadband speeds and smartphone capabilities are improving, millions still rely on patchy networks or low-storage devices. Users frequently go offline to save data or clear caches, erasing data signals that fuel personalisation. Algorithms trained in Silicon Valley or Beijing can stumble not due to flawed technology but because they don’t understand local constraints.
Data is also extremely fragmented. India does not have a dominant “super app” that captures all user behaviour. Instead, user journeys are scattered across dozens of apps, services, languages and formats. As a result, many personalisation efforts devolve into crude targeting masquerading as personalisation – high frequency, low relevance and low cultural sensitivity.
Personalisation in Practice: Lessons from Indian Brands
Despite the challenges, some Indian companies have succeeded in personalising at scale. The common thread is vernacular and hyper-local focus.
A major e-commerce platform rolled out interfaces in multiple Indian languages, enabling millions of non-English users to shop comfortably. It found that once people switched to the Hindi interface, most stuck with it. The company built internal systems to handle millions of translated terms and even observed unique behaviours by region. Telugu speakers preferred search terms typed in English script, while Kannada and Tamil speakers preferred native scripts.
Fintech and payments players have followed a similar path. A leading digital payments platform expanded its app to support over ten Indian languages and rapidly penetrated small towns and villages. The shift wasn’t just about translation but explaining products in the right tone and linguistic context. This localisation directly correlated to business growth and user trust.
Advertising has demonstrated similar results. Brands running campaigns in Hindi and regional languages saw lower cost per lead, higher click-through rates and higher conversion rates compared to English-only campaigns. Personalisation wasn’t algorithmic – it was linguistic and cultural, and it materially impacted ROI.
Personalisation also appears in product design. Fast-moving consumer goods companies develop different versions of basic products for different regions based on taste preferences. Even packaging and colour palettes are adjusted by city. These decisions represent deeper personalisation than recommending a product via an app – designing different realities for different micro-markets.
Conversely, companies that failed to adapt have stumbled. Streaming services initially targeted English-speaking urban users and saw limited uptake until they commissioned local originals. Apps that remained English-only found their user growth stalling as newer digital citizens turned to Indian alternatives.
Globally, consumers prefer brands that remember who they are and tailor offers accordingly, yet many Indian users still encounter irrelevant recommendations and generic ads. The diversity factor makes the gap wider and more noticeable than elsewhere.
The Road Ahead: Personalisation with an Indian Twist
As India marches toward nearly a billion online users, the pressure is on companies to reinvent personalisation for this uniquely complex market. The consensus is that personalisation at scale can work here, but only when it is hyper-local, deeply contextual and culturally sensitive. The challenge is not just technology. It’s understanding the human texture of India.
In practice, this means investing in multilingual AI, regional data sources and decentralised strategies where local teams shape personalisation. Tech giants are now pouring resources into AI that can understand, translate and interpret India’s diverse languages and code-mixed expressions.
Equally important is maintaining a balance between personalisation and privacy. Consumers don’t reject personalisation; they reject the intrusive, ham-fisted kind. The next wave may involve a shift from surveillance-style prediction to more transparent, participatory and user-controlled experiences.
Ultimately, India may redefine what personalisation at scale means. Instead of one monolithic strategy, it may resemble a patchwork of personalisations – different approaches for different languages, cultures, devices and contexts, stitched together with an overarching lens of inclusivity.
The companies that thrive will be those that see India not as one market of a billion users but a billion micro-markets and use technology to adapt at that scale. It is a daunting challenge, but also one of the richest opportunities in digital business. If personalisation can succeed in India, bridging its linguistic, economic and cultural divides, it may become a blueprint for the rest of the world.
For now, the message from India’s digital frontier is clear: to truly feel personal, personalisation must speak the user’s language – sometimes literally. And in India, that means speaking dozens of languages with countless cultural accents, all at once.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research.
