There is a moment that many banking customers know intimately. You log in, and the screen says –

“Good morning, Amit. Your account balance is ₹2,14,330.”

For a fleeting instant, it almost feels personal. Then the next banner offers you a home loan, the same one that has been there since January. The moment evaporates.

Banks and insurers have invested billions in personalization. So why does it still feel like a mail-merge?

This is the central paradox of personalization in Banking, Financial Services, and Insurance (BFSI) – an industry that holds more intimate data about human behavior than almost any other on earth, yet routinely delivers experiences that feel as generic as a mass-market flyer.
The problem is not a lack of investment. According to McKinsey, financial institutions have poured hundreds of billions into digital transformation over the past decade. Personalization platforms, CRM overhauls, and data lakes have proliferated.

Yet a 2023 Accenture survey found that 67% of banking customers feel the personalization they receive is “superficial” or “irrelevant.” The gap between ambition and execution has never been wider.

The reason is both structural and philosophical. Most BFSI personalization today is not really personalization at all. It is sophisticated templating, and understanding the difference is the first step toward closing the gap.

The Illusion of Personalization. When ‘Hi, Amit’ Is as Far as It Goes

Walk into any major bank’s digital product today, and you will find the hallmarks of first-generation personalization – your name on the dashboard, your most-used services surfaced to the top, perhaps a birthday message in November. These are not trivial achievements. They represent years of backend integration and UX work. But they share a fundamental limitation – they are driven by static identity, not dynamic behavior.

This is what we might call “Nominal Personalization”. The customer is recognized, but not understood. The system knows who you are; it does not know what you are doing, what you are about to need, or what would genuinely help you right now.

Compare that to what Contextual Personalization looks like in practice. A bank’s spending analysis feature should not just show you a pie chart of last month’s expenses. It notices that you have booked three international flights in 90 days and proactively surfaces its travel rewards card with a personalized ROI calculation based on your actual spend. Netflix’s recommendation engine – the gold standard most financial brands cite in internal decks but rarely emulate does not just remember what you watched. It models what you are likely to want next, based on the time of day, inferred recent mood shifts from genre switching, and what similar users chose.

The distance between “Hi, Amit” and “Your travel spending suggests you could save ₹18,000 a year with this card” is not merely cosmetic. It is the distance between recognition and relevance, and it is where the vast majority of BFSI brands are stuck.

“Personalization without intelligence is just templating and no amount of first-name tokens changes that.”

Why Most Personalization Is Rule-Based, Static, and Segment-Driven

To understand why BFSI personalization feels hollow, you need to understand how most of it is actually built. Beneath the glossy interfaces, the majority of personalization engines in financial services run on one of three architectures, and all three share the same fundamental flaw.

Rule-Based Engines

The industry workhorse. A compliance team and a marketing team sit down and define conditions – “If  a customer has a savings account and balance > ₹1 lakh and age > 35, show fixed deposit banner.” These rules are legible, auditable, and easy to explain to regulators. They are also brittle. Rules cannot adapt to a context they were not written for. They cannot learn. And as any data scientist who has inherited a legacy rules engine will tell you, they metastasize over time into thickets of contradictory logic that nobody fully understands.

Segment-Driven Targeting

The next evolutionary step beyond pure rules is segmentation. Customers are grouped by age, income band, product holding, or RFM score, and each segment receives tailored messaging. This is better than one-size-fits-all, but it reintroduces the generic experience through the back door. A segment is, by definition, a generalization. “Urban millennial with home loan” is not a person. Treating 400,000 people as if they are the same individual because they share three demographic attributes is not personalization; it is mass customization with extra steps.

Static Profiles

Perhaps the most pernicious limitation. Most CRM systems capture a snapshot of who a customer was at onboarding, updated sporadically when they call the helpline or take a new product. They do not capture the customer’s financial journey in motion—a sudden spike in medical payments, a pattern of late-night small-value transactions that may signal financial stress, or the six consecutive Saturdays spent browsing home loan calculators without converting. These behavioral signals are the raw material of genuine personalization, and they are going largely uncollected or unacted upon.

The Disconnect Between Data and Action

Here is the most striking irony in financial services – banks know more about their customers’ real financial behavior than almost any other institution on earth. Every transaction is a data point. Every ATM withdrawal at 2 a.m. tells a story. Every lapsed SIP is a signal of something. It could be a change in income, a loss of confidence, or a life event.

Yet this data sits largely inert in transaction ledgers, used primarily for fraud detection and regulatory reporting. The behavioral exhaust of daily financial life is not being converted into intelligence that drives the customer experience.

Why? Three structural reasons stand in the way.

Data silos

Retail banking, credit cards, insurance, and wealth management typically run on separate core systems, with separate data warehouses. Even within a single financial group, getting a unified view of one customer’s behavior can require crossing four different technology stacks.

Regulatory caution

Compliance teams are wary of being perceived as using intimate financial data for sales purposes. The result is an overcorrection. Data that could be used to genuinely help customers (flagging potential fraud earlier, proactively offering overdraft protection before a payment bounces) sits unused because no one wants to be the person who signed off on it.

Organizational misalignment

Personalization requires the marketing team, the data science team, the product team, and the technology team to operate in close coordination, with a shared definition of what a good outcome looks like. In most large financial institutions, these teams have separate P&Ls, separate quarterly targets, and separate ideas about what the customer journey should feel like.

Superficial vs. Contextual Personalisation. A Framework

The table below captures the core distinction between where most BFSI brands are today and where the leaders are heading.

The Insurance Sector’s Particular Blind Spot

If banks are behind on personalization, insurers are in a different time zone. The insurance sector has historically had one of the most transactional, low-engagement customer relationships in financial services. Most policyholders interact with their insurer twice: once when they buy the policy, and once when they file a claim. Everything in between is silence.

This is a missed opportunity of staggering proportions. Insurers possess, or could possess, a remarkable breadth of behavioral data by driving patterns (telematics), health metrics (wearables), home usage data (smart home devices), and travel behavior (card transactions). Progressive Insurance’s Snapshot telematics program demonstrated years ago that real-time behavioral data could be used to price risk more accurately and reward good behavior with lower premiums – a form of personalization that is genuinely valuable to the customer.

Yet most insurers still send the same annual renewal notice to a 28-year-old who runs marathons and a 58-year-old with three chronic conditions. The policy is the unit of analysis, not the person. Until insurers shift from product-centric to life-stage-centric engagement models, they will continue to be seen as vendors rather than partners.

The Road from Templating to Intelligence: What It Actually Takes

Genuine contextual personalization in BFSI is not a product you can buy from a vendor and deploy in a quarter. It is an organizational capability that must be built, and it requires changes at four levels.

Data Infrastructure

Real-time personalization requires real-time data. That means moving beyond nightly batch processing to streaming architectures – Apache Kafka, real-time feature stores, and event-driven decisioning pipelines. It means resolving the identity graph across channels so that a customer’s in-branch conversation, mobile app behavior, and contact center call are understood as part of a single coherent journey. This is expensive and time-consuming, which is why many banks are currently in the middle of multi-year cloud migration programs as a prerequisite.

Model Architecture

Rule engines need to be supplemented—not replaced—by machine learning models that can identify non-obvious behavioral patterns. Next Best Action (NBA) engines, pioneered at scale by firms like Pega and Salesforce, can process hundreds of contextual signals in real time to determine the most relevant intervention for each customer at each moment. The keyword is “supplement”. In a regulated industry, models need to be explainable, and human-readable rules remain essential for compliance.

Organizational Culture

The data science team can build the most sophisticated NBA engine in the world, but if the product team is still thinking in terms of “the home loan campaign” and “the credit card campaign,” the output will still be segment-driven. True personalization requires the organization to shift its mental model from campaigns, broadcast events aimed at groups, to journeys that are continuous, adaptive interactions with individuals.

Customer Trust and Consent

Perhaps the most underappreciated dimension. The more personal the personalization, the more important it is that customers understand how their data is being used and feel in control of it. Personalization that feels surveillance-like erodes the relationship it is meant to strengthen. GDPR and India’s DPDP Act are not obstacles to personalization; they are forcing functions toward a more transparent, consent-based approach that customers will ultimately trust more.

“The banks that will win the next decade are not those with the most data. They are those that can transform behavioral signals into genuinely helpful moments at speed, at scale, with consent.”

What Good Looks Like. Three Emerging Benchmarks

The good news is that the benchmarks for intelligent personalization in BFSI are becoming clearer. Three institutions stand out not for having solved the problem, but for having genuinely moved the needle.

DBS Bank (Singapore) has been widely recognized as one of the world’s most innovative banks, and its personalization strategy is a large part of why. DBS’s AI-driven insights engine, deployed across its mobile app in Singapore, India, and Indonesia, analyzes transaction patterns to generate proactive financial nudges. A customer whose grocery spend has increased 30% over three months might receive a suggestion to review their monthly budget. A customer who has been making EMI payments on time for 18 months might receive a pre-approved personal loan offer before they think to ask for one. DBS reported in its 2023 annual report that AI-personalized interactions drove a 20% higher product acceptance rate compared to generic outreach.

Bajaj Finserv (India) has made significant strides in using its Experia app to move from product-push to life-stage-aware engagement. Customers who have just taken a home loan are not immediately cross-sold a credit card. Instead, the system identifies that they are likely in a post-purchase consolidation phase and surfaces relevant content: home insurance comparisons, utility payment automation, and property tax reminders. The insight is simple but profound: the best time to sell the next product is not always now.

Conclusion. Intelligence Is Not Optional

Personalization has become one of the most overused words in financial services marketing. It appears in every strategy deck, every vendor pitch, every digital transformation roadmap. And yet, for most customers, the lived experience of financial services personalization remains generic at best and patronizing at worst.

The reason is not that the data does not exist. It does. The reason is not that the technology is unavailable. It is. The reason is that most BFSI organizations have confused the scaffolding of personalization such as CRM platforms, customer data platforms, marketing automation tools with personalization itself.

Real personalization is not about using someone’s first name. It is not about remembering their birthday. It is not about showing them products that their demographic segment statistically tends to buy. Real personalization is about understanding the specific, individual context of a specific human being at a specific moment and responding in a way that is genuinely useful, timely, and respectful of their autonomy.

That requires intelligence: computational intelligence to process signals at scale, organizational intelligence to act on them quickly, and emotional intelligence to know when to intervene and when to stay silent.

The institutions that crack this, and a small number are genuinely beginning to, will not just win market share. They will redefine what it means to have a relationship with a financial institution. They will transform a category that has been defined by transactions into one defined by trust. Personalization without intelligence is just templating. And in a world where customers have never had more choices, templating is no longer enough.

Sources: 

McKinsey & Company – Next in Personalization 2021, 2023 Update. | Accenture – Banking Consumer Study 2023. | Salesforce — State of the Connected Customer, 5th Edition 2023. | DBS Bank – Annual Report 2023. I  Forrester Research – The State of Digital Banking 2023. | Bajaj Finserv – Experia Product Documentation 2023. | HDFC Bank – echnology and Digital Transformation Report 2022–2023.

The post The Real Reason BFSI Personalization Still Feels Generic. appeared first on Fixbracket.

The question arrived quietly, midway through a quarterly business review at one of India’s largest retail conglomerates. The Chief Marketing Officer paused, looked around the room, and asked:

‘Who, exactly, is our most valuable customer?’

What followed was not an answer. It was a slow, revealing unraveling. The CRM team cited loyalty-tier rankings. E-commerce pulled up purchase frequency from the marketplace dashboard. Digital marketing referenced ROAS-weighted cohorts. Finance had its own lifetime value model. Each number was real. None agreed.

The meeting had stumbled onto the central paradox of the modern Indian enterprise – an organisation drowning in data, yet starved of insight.

“We had seventeen dashboards and still could not answer a basic question about our customer with confidence. That was the moment we understood something was fundamentally broken.” – Chief Data Officer, leading Indian FMCG company

This gap between data availability and data usability is precisely where Customer Data Platforms (CDPs) have found their moment. And across sectors from banking to beauty to quick commerce, Indian enterprises are moving fast to close it.

The Fragmentation Problem and Why India Has It Worst

The modern Indian consumer is a study in digital omnipresence. She might discover a product on Instagram Reels, research it on a marketplace, message the brand on WhatsApp, earn loyalty points via a superapp, and still close the transaction at a physical store. Each interaction leaves a data signature. In the vast majority of Indian enterprises, each of those signatures lives in a different silo.

This problem is not unique to India, but India’s version of it is particularly acute, shaped by three structural forces that compound the challenge.

A Market Too Heterogeneous to Generalise

India is not one consumer market. It is a federation of several dozen or more, differentiated by language, culture, purchasing power, and digital maturity. A campaign that converts in Coimbatore may confuse consumers in Chandigarh. A price architecture built for metros collapses in Tier 3 towns. Without data unified at the individual level, not aggregated by segment or region, personalisation remains, at best, a sophisticated guess.

According to McKinsey’s 2023 India consumer research, brands that achieve genuine micro-segment personalisation report revenue lifts of 10-15% compared to peers relying on broad demographic targeting. In India’s fiercely competitive consumer landscape, that gap is existential.

The Omnichannel Explosion

India added over 250 million new internet users between 2019 and 2023, pushing total online reach past 900 million. This expansion was not gradual. It was compressed, turbocharged by cheap data, affordable smartphones, and a pandemic that digitised behaviours overnight.

As a result, brands must now maintain coherent relationships across channels they did not anticipate, at a pace their legacy infrastructure was never designed to support. Customers are omnichannel by default. Most enterprise data systems are still mono-channel by design.

The Privacy Inflection

India’s Digital Personal Data Protection (DPDP) Act, enacted in August 2023, marks a structural turning point. While enforcement timelines continue to be clarified, the direction is unambiguous – third-party data is becoming legally and commercially riskier to acquire, and customer consent is becoming a non-negotiable foundation for any data strategy.

In this environment, first-party data collected directly, transparently, and consensually is no longer merely a compliance advantage. It is an emerging competitive moat.

What the DPDP Act Means for Marketers

Under India’s Digital Personal Data Protection Act, 2023, organisations must obtain explicit, granular consent before collecting and processing personal data. Consent must be specific to the purpose and easily withdrawable. Non-compliance carries penalties of up to ₹250 crore per violation. For marketers, this makes the CDP – as a system of record for consent and first-party data – not a strategic option, but a regulatory necessity.

What a CDP Actually Does and What It Does Not

There is considerable noise in the market about what a Customer Data Platform is, much of it vendor-generated. The definition that matters strategically is straightforward:

A CDP ingests data from every touchpoint where a customer interacts with a business and resolves it into a single, persistent, updatable profile that is accessible to every function – marketing, product, sales, and service.

It is not a CRM, which manages current relationships. It is not a data warehouse, which stores historical transactions. And it is not a marketing automation tool, which executes campaigns. It is the connective tissue between all of these. It is the system that ensures every function is working from the same understanding of who the customer is.

“A CDP is to customer intelligence what electricity is to appliances – a prerequisite, not a product. The value lies entirely in what you build on top of it.”

This distinction matters because many implementations falter precisely here. Organisations invest in the platform but underinvest in the capability to use it. The technology arrives; the transformation does not. A 2023 Nasscom survey of Indian enterprises found that 61% of organisations that had deployed a CDP reported being ‘in early stages’ of extracting strategic value from it, citing talent gaps and misaligned incentives as the primary barriers.

From Theory to Traction: Four Indian Enterprises Rewriting the Playbook

Across banking, retail, beauty, and consumer goods, a cohort of Indian companies has moved beyond proof-of-concept into what practitioners are calling the ‘operational phase’ of CDP maturity, where unified data is beginning to reshape strategy, not just campaign performance.

The Organisational Fault Lines CDPs Expose

Executives who have navigated a CDP implementation tend to share a consistent observation – the technology was, by some distance, the easier part. The harder work was organisational, and it exposed tensions that had long existed beneath the surface.

Data Silos Are Power Silos

In most large Indian enterprises, data silos are not technical failures. They are political ones. When the CRM is owned by sales, behavioural analytics by digital marketing, and the transactional database by finance, each team has both an incentive and an implicit mandate to protect its data territory. Integration requires not just APIs, but agreements – about ownership, accountability, and what happens when unified data tells a story that someone would prefer remained untold.

The most successful CDP implementations in India have almost universally been championed at the C-suite level. Without executive sponsorship that cuts across departmental boundaries, the platform becomes just another integration project: technically complete, strategically inert.

The Talent Gap Is Real 

India produces engineering talent at an extraordinary scale. What it still lacks, at the intersection of commerce and data, is the business-technical hybrid: professionals who can sit between a marketing team and a data engineering team, translating commercial questions into analytical frameworks and data outputs back into strategic decisions.

A 2023 Deloitte India study found that 68% of enterprises identify ‘insufficient internal analytics talent’ as a primary barrier to extracting value from data investments ahead of budget constraints and technology limitations. This gap does not close through hiring alone. It requires deliberate investment in capability building: training marketing and product teams to think in data, and training data teams to think in business outcomes.

The Patience Problem

Unlike performance marketing tools that deliver measurable results within a campaign cycle, a CDP’s value is compounding and lagging. The first quarter of unified data looks, on most dashboards, not dramatically different from the last quarter of fragmented data. The difference emerges over time as models improve, as teams build interpretive confidence, and as the understanding of the customer deepens from a snapshot into a moving picture.

In organisations where marketing leaders are measured on quarterly numbers, this creates a structural tension that has derailed many otherwise sound implementations. The business case for CDPs must therefore be made not just analytically but narratively, framing the investment as infrastructure, with the patient honesty that infrastructure demands.

“The CDP did not change our results in Q1. It changed how we make decisions. By Q3, that started showing up in the numbers.” – VP Marketing, Indian D2C unicorn, Series C

Why First-Party Data Is Now a Race

If the strategic case for CDPs was compelling before 2022, the emergence of generative AI and large language models has made it urgent. AI-driven personalisation, whether in content generation, next-best-offer modelling, churn prediction, or dynamic pricing, is only as good as the data it is trained on. Organisations with unified, consented, high-quality first-party data can build AI systems that genuinely reflect their customers. Organisations working from noisy, fragmented datasets are building AI on structured guesswork.

India’s AI-in-enterprise market is projected to grow from $6.1 billion in 2023 to over $28 billion by 2028, according to Nasscom – a near five-fold expansion in five years. The organisations positioned to capture that value disproportionately will be those who built the data foundation first.

The Generative AI Dependency

Every generative AI application in marketing – personalised content at scale, conversational commerce, predictive customer service is critically dependent on the quality and completeness of underlying customer data. A CDP does not make AI possible; poor data makes it counterproductive. India’s AI investment wave and its CDP adoption curve are not parallel trends. They are the same trend, viewed from different angles.

India’s regulatory environment reinforces this dynamic. As the DPDP Act constrains the use of third-party data, organisations that have built robust first-party data assets and the platforms to activate them will find themselves with a structural advantage that is difficult and slow for competitors to replicate.

What Indian Business Leaders Should Do Now

The strategic path forward is neither uniform nor simple. But several principles have emerged consistently from the organisations executing most effectively.

Start With the Question, Not the Platform

The most common failure mode in CDP adoption is beginning with a technology selection process. The right starting point is a set of business questions that cannot currently be answered, and working backwards to identify what data infrastructure would enable them. Technology is a means. Starting with it as an end produces platforms that are well-integrated but strategically purposeless.

Treat Consent Architecture as a Competitive Asset

In the post-DPDP world, how an organisation manages customer data consent, storage, and usage is not a legal question alone. It is a trust question. Brands that build demonstrably ethical data practices are constructing a relationship asset that compounds over time. Those who treat consent as a compliance checkbox will find the regulatory floor rises faster than their systems can adapt.

Build for India’s Edge, Not India’s Average

The temptation in any large-scale data initiative is to optimise for the median customer. In a market as diverse as India, the median customer is a statistical abstraction who describes almost no one. The real commercial value lies at the edges in the micro-segments, the regional variations, the context-specific behaviours that drive actual purchase decisions. Invest in the capability to act on granularity.

Measure Differently Or Measure the Wrong Things

If CDPs are evaluated against the same metrics as campaign tools – immediate ROAS, short-term revenue uplift, next-quarter conversion rates- they will almost always disappoint. Build a measurement framework that captures leading indicators of long-term customer value – profile completeness and enrichment rates, segment migration velocity, prediction model accuracy over time, and cross-channel engagement depth. These metrics are slower but truer.

The Shift That Cannot Be Deferred

India’s most sophisticated enterprises are no longer asking whether to invest in customer data infrastructure. They are asking how quickly they can do it and whether enough of their existing data estate can be salvaged into something coherent enough to build on.

The urgency is justified. As AI reshapes the economics of personalisation, and as privacy regulation raises the cost of third-party data dependency, the gap between organisations that control their customer understanding and those that do not will widen.

But the deepest shift is not technological. It is conceptual.

For decades, Indian marketing has been structured around campaigns planned in sprints, measured in cycles, and optimised for the immediate response. CDPs enable a different model entirely – one where the customer relationship is continuous, the data is cumulative, and the engagement is genuinely contextual rather than situationally generic.

That is the shift from campaigns to customer systems, from data collection to data intelligence, from the average to the individual. The companies that make it will not simply market better. They will compete differently.

For Indian enterprises navigating scale, diversity, and compounding competitive pressure simultaneously, that shift is no longer a strategic option. It is the terrain on which the next decade of growth will be won or lost.

SOURCES & REFERENCES

1. Gartner (2024). Magic Quadrant for Customer Data Platforms. Gartner Research.
2. RedSeer Strategy Consultants (2023). India Digital Consumer Report 2023. RedSeer.
3. IAMAI & Kantar (2023). Internet in India 2023. Internet and Mobile Association of India.
4. Salesforce (2023). State of the Connected Customer — India Edition. Salesforce Research.
5. McKinsey & Company (2023). The Next Frontier of Customer Engagement: AI-Enabled Personalization at Scale. McKinsey Digital.
6. Nasscom (2023). AI Adoption in Indian Enterprises: Barriers and Opportunities. Nasscom Research.
7. Tata Consultancy Services (2023). The Data-Driven Enterprise: Insights from 500 Indian CXOs. TCS Thought Leadership.
8. Titan Company Ltd. (2023). Annual Report 2022-23. BSE India.
9. HDFC Bank (2023). Annual Report 2022-23 — Technology & Digital Initiatives. HDFC Bank Investor Relations.
10. Reliance Retail Ventures Ltd. (2023). Annual Report 2022-23. Reliance Industries.
11. Nykaa (FSN E-Commerce Ventures Ltd.) (2023). Investor Day Presentation. Nykaa IR.
12. Zomato Ltd. (2023). Annual Report 2022-23. Zomato Investor Relations.
13. Ministry of Electronics & IT, Government of India (2023). Digital Personal Data Protection Act, 2023.
14. Boston Consulting Group (2023). From Mass to Micro: Personalization Imperatives in Indian Consumer Markets. BCG Henderson Institute.
15. Deloitte India (2023). The Analytics Talent Gap: Bridging the Distance Between Data and Decision. Deloitte Insights.
16. IDC India (2023). India Customer Data Platform Market Forecast, 2023-2027. IDC.

Note: Case study figures represent outcomes reported in public company filings, investor presentations, and industry research. Specific internal metrics are attributional to source documents cited. This article has been prepared for informational and strategic discussion purposes.

The post Why Indian Companies Are Betting Big on CDPs. appeared first on Fixbracket.

It’s 9:30 PM in Mumbai.

A young founder launches a sneaker brand. Within minutes, her ads are live, not negotiated, not manually placed, but algorithmically auctioned across apps, OTT platforms, and mobile screens.

By midnight, she knows exactly which audience in Lucknow clicked, which user in Kochi converted, and which creative worked best in Hindi vs Tamil.

This is not the future of advertising in India.
This is now.

India’s AdTech ecosystem has quietly transformed from a fragmented, media-buying engine into one of the most sophisticated, data-driven marketing infrastructures in the world. With the market touching nearly $ 200 million in 2025 and continuing to grow rapidly, AdTech is no longer a support system – it is the backbone of fast-forward marketing.

And yet, beneath this scale lies a deeper shift – from volume to value, from reach to relevance, and from experimentation to execution.

Here are the top AdTech trends in India that no marketer, founder, or investor can afford to ignore.

1. The Programmatic Takeover

A few years ago, media buying in India was about relationships and negotiations. Today, it’s about algorithms.

Programmatic advertising now accounts for over 40% of digital media buying in India, fundamentally changing how campaigns are executed.

What’s driving this?

This transformation is being powered by a combination of technologies – real-time bidding that enables instant, data-driven media buying, large-scale audience segmentation that allows brands to target highly specific consumer cohorts, and AI-led optimisation that continuously refines campaigns for better performance and efficiency. Together, these forces are shifting advertising from broad outreach to precise, intelligent engagement.

Case in point:

A leading Indian fintech brand shifted 70% of its media spend to programmatic platforms. Instead of targeting “urban millennials,” it began targeting “users who checked loan eligibility twice in 48 hours.”

Conversion rates improved by over 2x – not because of higher spend, but better precision.

What it means:

Programmatic is no longer optional. It is becoming the operating system of digital advertising in India, with the market projected to reach $17.6 billion by 2033.

2. AI is Infrastructure

For years, AI sat in presentations. In 2025, it moved into production.

From generating creatives to fine-tuning bids, AI is now deeply woven into every layer of the AdTech stack – powering predictive targeting, enabling dynamic creative optimisation (DCO), and automating budget allocation to drive smarter, more efficient campaign outcomes.

The shift:

Earlier: “Let’s test 5 creatives.”
Now: “Let AI generate and optimise 500 variations in real time.”

India’s AdTech growth is heavily fuelled by AI and machine learning, enabling hyper-personalised campaigns and real-time optimisation.

Case study:

An Indian D2C beauty brand used AI-led creative testing across regional languages. Instead of one campaign, it ran 120+ micro-variants tailored to geography and language.

Result:

• 35% higher engagement
• 22% lower cost per acquisition (CPA)

3. The Rise of Connected TV (CTV)

The Indian living room has changed.

Smart TVs, OTT platforms, and cheap data have turned video into the most powerful advertising medium in the country.

Why this matters:
India may be mobile-first, but it is rapidly becoming a video-led market. As consumer behaviour shifts, brands are moving toward richer, immersive storytelling that combines sight, sound, and motion. This evolution is reflected in industry trends, where video and CTV are emerging as some of the fastest-growing segments in both the Indian and global AdTech landscape.

Case study:

A leading FMCG brand traditionally reliant on TV shifted part of its budget to OTT platforms.

Instead of broad TV reach:

• It targeted households watching cooking content
• Served region-specific ads during prime OTT hours

Result:

• Better recall
• Measurable attribution – which TV never offered

The implication:

CTV is blurring the lines between digital and traditional media – bringing performance marketing into the living room.

4. Privacy-First Advertising

For years, AdTech thrived on tracking users across the internet. That era is ending.

With global privacy regulations tightening and platforms restricting third-party cookies, India is entering a privacy-first AdTech era.

What’s taking shape:
The ecosystem is moving toward first-party data strategies, the adoption of data clean rooms, and a renewed focus on contextual targeting. At its core, the shift reflects a broader transition to consent-led frameworks and privacy-first approaches to audience targeting.

Case study:

An Indian e-commerce platform built its own first-party data ecosystem:

• Loyalty programs
• App-based engagement
• Email + WhatsApp integrations

Instead of relying on third-party cookies, it built direct relationships with users.

Outcome:

• Higher data accuracy
• Better personalization
• Reduced dependency on external platforms

5. Retail Media Networks – Where Ads Meet Commerce

If there’s one trend redefining ROI in AdTech – it’s retail media.

E-commerce platforms are no longer just selling products. They are selling advertising inventory.

Why this is gaining rapid momentum:
Ads are now being delivered much closer to the point of purchase intent, platforms are leveraging rich first-party data, and attribution has become far more transparent and measurable.

Case study:

An Indian electronics brand advertising on a major marketplace:

• Sponsored listings during search
• Display ads on product pages
• Retargeting within the platform

Result:

• Immediate visibility
• Direct sales attribution
• Measurable ROI

The macro shift:

AdTech is merging with commerce.
The funnel is collapsing – moving from awareness to purchase in a single platform.

6. Tier 2 – 4 India: The Next Billion Users

The next wave of digital growth in India is not in metros—it’s in Bharat.

Regional language content and consumption are exploding, especially across:

• Short video platforms
• OTT apps
• News and entertainment ecosystems

What this implies for AdTech:
It calls for multilingual targeting capabilities, localised creative optimisation, and messaging that is deeply rooted in regional and cultural contexts. Programmatic demand is increasingly driven by regional content consumption across Tier 2 – 4 cities.

Case study:

A fintech app launched campaigns in 6 Indian languages instead of English-only.

Outcome:

• 3x increase in installs from non-metro cities
• Lower acquisition cost due to less competition

The insight:

India isn’t a single, uniform market- it’s a mosaic of diverse micro-markets. AdTech is now empowering brands to deliver personalised experiences at scale across this complexity.

7. Measurement, Attribution & the Death of Vanity Metrics

In 2025, something fundamental changed.

The industry realized:
More impressions ≠ More value

As ad spend grew, confidence in metrics such as clicks and impressions declined.

What’s taking their place:
Marketers are shifting toward incrementality testing, Multi-Touch Attribution (MTA), attention-based metrics, and a sharper focus on ROI-driven measurement.

Case study:

A gaming company in India moved away from install-based attribution to Lifetime Value (LTV) tracking.

Instead of optimising for installs, it optimised for:

• In-app purchases
• Retention
• Engagement

Result:

• Higher profitability
• Lower churn

Finally

 The rise of full-stack AdTech ecosystems marks a fundamental shift in how brands operate; moving away from heavy reliance on walled gardens toward owning and integrating their data, media, and analytics capabilities.

This transition is driven by the need for greater transparency, cost efficiency, and control over customer data. In a maturing and increasingly competitive landscape shaped by both global giants and strong homegrown players, brands are building in-house capabilities, from CDPs to programmatic teams and analytics dashboards.

The result is faster optimisation, reduced dependency on external partners, and sharper performance outcomes. At its core, this signals a larger transformation: brands are no longer just advertisers – they are becoming media companies in their own right.

Let’s go back to that founder in Mumbai.

She didn’t just launch a campaign.

She plugged into an ecosystem that learns in real time, seamlessly adapts across languages, optimises performance across platforms, and tracks every outcome with precision. That is the power of AdTech in India today.

India’s new advertising reality – the difference between noise and impact is no longer budget. It is intelligence.

The post Inside India’s AdTech Awakening: Trends Changing Everything. appeared first on Fixbracket.

In this article, we’ll cover what deepfakes are, how they work, and what are the dangers we can expect from deepfakes.

Let’s dive right into it!

What Are Deepfakes or Deepfake AI?

Deepfakes or Deepfake AI is a type of artificial intelligence that crafts convincing fake images, audio, and videos by merging the power of “deep learning” with the concept of generating deceptive content.

This technology can seamlessly substitute one individual for another in pre-existing content or fabricate entirely new material, portraying people engaging in actions or uttering words they never actually did.

The primary concern with deepfakes stems from their capacity to disseminate misinformation that appears authentic.

How Are Deepfakes Created?

Deepfakes are created using a sophisticated process that involves intricate algorithms and neural networks.

One common technique utilizes a type of neural network called an autoencoder. An autoencoder consists of an encoder and a decoder.

The encoder takes an input, such as an image or video frame, and compresses it into a condensed representation called a latent space. This latent space contains essential features of the input but in a more compact form. The decoder then reconstructs the original input from this condensed representation. In the context of deepfakes, the autoencoder is trained on a vast dataset of real images, allowing it to learn the distinctive features of human faces.

Another key approach involves the use of Generative Adversarial Networks (GANs). A GAN consists of a generator and a discriminator

The generator generates synthetic content, such as a fake image or video frame, while the discriminator evaluates whether the content is real or generated. The generator continually refines its output to fool the discriminator, and the discriminator becomes adept at distinguishing real from fake.

These techniques are combined.

Role of GAN in Deepfake AI

The generator, often based on a GAN, produces realistic content by learning from the patterns and features identified by the autoencoder. The discriminator helps refine the generated content, making it more convincing.

This intricate interplay between autoencoders and GANs allows deepfake algorithms to create highly convincing fake media, replacing faces in videos or generating entirely new content that can be challenging to differentiate from reality. The continuous refinement of these algorithms contributes to the growing sophistication of deepfake technology.

How Do Deepfakes Work?

As touched upon earlier, Deepfakes replicate facial and expressive data in a manner not easily discernible to the naked eye. On the other hand, Generative Adversarial Networks (GANs) generate data through deep learning, employing both generators and discriminators.

Presently, there are applications like DeepFaceLab that autonomously learn from provided inputs, meticulously capturing facial expressions layer by layer to manipulate data effectively.

In essence, it functions as a video editing tool, seamlessly integrating with the original video to replace faces as desired.

Although achieving higher accuracy will require additional time, the technology is already gaining substantial traction in the film and entertainment industries. Moreover, marketers are exploring its potential for creating diverse promotional content without the need for hiring models or actors.

Additionally, there are smaller-scale project applications utilizing Deepfake technology for amusing face-swapping and the creation of humorous videos.

Some Common Use Cases of Deepfake AI

The versatility of deepfakes extends across various domains:

Entertainment: Deepfakes add a touch of amusement in satire and parody, offering viewers a dose of humor even when they recognize the content’s fictional nature.

Caller Services & Customer Support: In the realm of caller response services, deepfakes step in to provide tailored and personalized responses, especially in scenarios involving call forwarding and receptionist duties. They are also leveraged in customer phone support, incorporating fake voices for routine tasks like checking account balances and addressing complaints.

Fraud and Blackmailing: The darker side sees deepfakes being misused to impersonate individuals, aiming to illicitly acquire sensitive personal information such as bank accounts and credit card details. Additionally, these deceptive techniques find application in activities like blackmail, reputation damage, and cyberbullying.

Misinformation and Political Manipulation: The realm of deepfake videos extends to the manipulation of public opinion, where politicians or trusted figures are featured in fabricated scenarios. This unsettling trend has the potential to significantly impact political discourse, injecting an element of misinformation and influencing the perspectives of the public.

How Deepfake AI Poses Cybersecurity Threats

As we march ahead, technology is advancing rapidly and becoming increasingly accessible worldwide. Consider smartphones as an example – we’ve seen the evolution from keypads to touchscreens, and from passcodes to face unlock.

It’s intriguing how a seemingly innocuous video, when manipulated or misused, can sow chaos. The internet hosts videos that blur the line between reality and fiction. Despite the difficulty in discerning their authenticity, subtle details like facial structure, expressions, and iris features enhance their realism.

This underscores the looming threat of deepfakes to cybersecurity.

To counteract such risks, various governments have enacted laws targeting this technology, aiming to prevent its malicious use.

In 2019, the World Intellectual Property Organization (WIPO) took a stand by submitting a draft that specifically addresses the challenges posed by deepfakes and proposes measures to address them.

How Dangerous Are Deepfakes?

Deepfakes, while generally operating within legal boundaries, pose significant risks that span various areas:

1. There’s a potential for blackmail and damage to someone’s reputation, potentially leading to legal challenges for the individuals affected.

2. The dissemination of political disinformation becomes a concern, especially when threat actors from nation-states leverage this technology for malicious purposes.

3. Elections could face interference, manifesting as the creation of fabricated videos featuring candidates, raising concerns about the integrity of the democratic process.

4. Stock markets may be susceptible to manipulation, with counterfeit content created to influence stock prices, posing potential economic risks.

5. Instances of fraud may arise, involving the impersonation of individuals to illicitly access financial accounts and other personally identifiable information (PII). These risks collectively underscore the need for vigilance and regulatory measures in managing the impact of deepfake technology.

The Real Dangers of Deepfake AI

DeepFakes are currently employed across a spectrum of applications, encompassing the amusing, the questionable, and the potentially harmful. Numerous videos crafted through DeepFake technology exist purely for entertainment purposes without posing any harm.

Take, for instance, the amusing scenario of Jon Snow expressing remorse for Game of Thrones. Moreover, DeepFakes have found their way into the film industry, as seen when a youthful version of Harrison Ford was seamlessly integrated into Han Solo’s character in Solo: A Star Wars Story.

However, there’s an escalating trend in the malevolent use of DeepFakes.

Shockingly, around 96% of DeepFakes circulating online fall into the explicit category, featuring manipulated images of celebrities or well-known women overlaid onto the faces of adult film actors. This poses a significant threat, particularly to women who become unintended targets of such deceitful practices.

Looking ahead, the growing menace of DeepFakes extends beyond explicit content. A looming concern is the erosion of trust in visual and auditory information.

Distinguishing between authentic and manipulated photos or videos is becoming increasingly challenging. This erosion of trust carries substantial implications; for instance, legal proceedings may encounter difficulties in verifying the authenticity of evidence.

Furthermore, DeepFakes pose a potential threat to security systems relying on facial or voice recognition. As technology advances, there’s a risk that these systems could be deceived by sophisticated DeepFake manipulations.

The unsettling prospect of not being able to discern whether a person on the other end of a phone call is genuine or merely a voice-and-face emulation created through DeepFake technology adds another layer of concern to the evolving landscape of digital deception.

How to Spot A Deepfake?

Spotting deepfakes has become increasingly challenging due to the advancing sophistication of the technology behind their creation.

Back in 2018, a U.S. research team illustrated that deepfake faces lacked the natural blinking observed in humans, offering a promising method for distinguishing real from fake images and videos.

However, as soon as this insight was shared, deepfake creators promptly addressed the issue, refining their technology to make detection even more formidable. Ironically, efforts to develop tools for identifying deepfakes often unintentionally contribute to the improvement of deepfake technology itself.

For the average person, recognizing a deepfake without the aid of Artificial Intelligence can be an arduous task. Yet, a discerning eye can still catch telltale signs of fakery. Mismatched ears, teeth, and eyes with the facial outline, inconsistent lip-syncing, difficulty in replicating individual strands of hair, and an unnaturally smooth facial appearance are common red flags.

Nevertheless, the relentless progress in technology is making it increasingly challenging to distinguish deepfakes from reality, as they continually evolve to appear more authentic.

In this scenario, the role of spotting deepfakes is gradually shifting towards Artificial Intelligence, with major tech companies heavily investing in the development of technologies capable of identifying these deceptive creations in photos and videos.

Conclusion

The rise of deep fakes is considered a significant societal threat owing to their capacity to generate deceptive content. Various governmental bodies have enacted legislation to tackle these issues.

As technology advances, it becomes increasingly sophisticated, emphasizing the importance of ethical usage.

Prominent figures in leadership express genuine concern about the 21st-century peril posed by AI, particularly deepfake technology. The apprehension stems from its potential for malicious purposes, such as deceit and manipulation. As AI evolves, it is crucial to exercise caution, ensuring its responsible and secure utilization.

Establishing and enforcing rules is imperative to prevent harm caused by its misuse in our global community.

People Also Read:

How ethical is AI and things you need to know.

Can AI Replace Marketing Jobs soon?

Top 10 AI Tools For Business Productivity.

10 Jobs AI Can Replace By 2030.

Reddit betting on AI Keyword Research Tool For SEO.

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Selling products and services isn’t always a breeze, right? An efficient sales process does make it look like one though.

Even for seasoned sales champs, getting those potential leads to seal the deal can be a head-scratcher.

Those business textbooks rave about a seven-step sales process—it’s like a fundamental roadmap. But the real magic happens when you tailor these steps to fit your business and, more importantly, your customers.

So, here’s the deal: a well-defined sales process is like your secret sales weapon. It’s not just a plan; it’s your sales game plan.

In this article, we will cover it all; what a sales process is, how to get the best results from it, and how to build one in seven easy steps.

Why Build A Sales Process?

Ever thought of a sales process as the GPS for your sales team’s journey?

It’s like that trusty map guiding them from a pool of potential leads to a treasure trove of happy customers. Seriously, without this map, all the hustle your marketing team puts into finding leads might vanish into thin air!

This sales process isn’t just a guide for the newbies; it’s like the ultimate playbook for sales success. It’s the key that helps fresh team members get the hang of things and smoothly navigate through different sales stages like seasoned pros.

Oh, and here’s the exciting part—this process is a money magnet. When you nail down that sales process, it’s like handing your team a magical formula for closing deals faster and, you guessed it, boosting the bottom line.

Now that we’ve chatted about why a sales process is a total game-changer, let’s roll up our sleeves and look at the stages it usually cruises through.

A Comprehensive Journey For Building A Sales Process In 7 Easy Steps

1. Prospecting – The Strategic Hunt for New Customers

Consider our software company aiming to target small to medium-sized IT firms. The prospecting phase involves meticulous research and identification of potential leads. Drawing from past successful clients, detailed buyer personas are crafted. These personas delve into demographics, pain points, and preferences, enabling us to craft targeted, personalized communications that resonate with the ideal customer profile.

Example: Suppose we identify Tech Innovators, an expanding IT firm seeking streamlined project workflows. Our personalized outreach emphasizes how our tool addresses their need for seamless task allocation and remote team collaboration, standing out from generic approaches.

2. Leads Qualification – Unveiling Ideal Fits

                                                                                             Leads Qualification in Sales Process

Upon receiving responses, the qualification stage unfolds. Extensive research into the specific needs, challenges, and objectives of potential clients is conducted.

This involves scrutinizing their digital presence, understanding their ongoing projects, and aligning these insights with our software’s capabilities. The goal here is to ensure a genuine synergy between what we offer and what the prospect requires.

Example: In assessing Tech Innovators, we discover their emphasis on agile methodologies and cloud-based solutions. Our tool aligns perfectly, addressing their needs for efficient project tracking and team collaboration, confirming a promising fit.

3. Appointment Setting – Crafting Valuable Interactions

The transition to scheduling meetings or product demos marks the engagement phase. Rather than mere sales pitches, these interactions aim to demonstrate the tangible value of our project management tool. Through efficient scheduling tools and engaging communication, the goal is to ensure these engagements are not just booked but result in valuable, productive sessions.

Example: We schedule a demo with Tech Innovators, showcasing how our tool seamlessly integrates with their existing systems and empowers their teams with real-time collaboration, earning their interest and attention.

4. Presentation – Showcasing Unique Value

During presentations or demos, visual aids and case studies become the centerpiece. We showcase real-time project dashboards, task assignment features, and collaborative tools within the software. For Tech Innovators, we highlight success stories where similar IT firms saw a notable surge in efficiency and collaboration after implementing our tool.

Example: Through a live demo, we illustrate how Tech Innovators can save time, streamline their workflows, and enhance team productivity, leaving them visibly impressed and intrigued.

5. Objection Handling – Embracing Challenges as Opportunities

Amidst presentations, objections are inevitable. These objections represent opportunities to address concerns and foster trust. Active listening and empathetic responses are essential.

For instance, when Tech Innovators raised concerns about the learning curve and integration complexities, we showcased our comprehensive onboarding support and user-friendly interface, allaying their fears and building confidence in our solution.

Example: By patiently addressing Tech Innovators’ objections regarding the learning curve, we offered a tailored onboarding plan, showcasing how our solution simplifies integration, easing their apprehensions.

6. Closing – Guiding Towards Commitment

                                                                                           Closing the Sale & Sealing the Deal

Moving towards the decision-making phase, the focus shifts to closing deals. It’s about nurturing prospects toward a positive decision while maintaining respect for their timeline and decision-making process. For Tech Innovators, we emphasized the value proposition—how our tool aligns with their expansion plans, streamlining operations and boosting team productivity.

Example: Offering an exclusive discount for early adoption and highlighting the immediate benefits, we guided Tech Innovators towards a commitment, ensuring a win-win outcome.

7. Post-Sale Engagement for Long-Term Relationships

Post-sale nurturing is crucial for sustained relationships. Continuous support, regular check-ins, and personalized training sessions ensure a seamless transition for Tech Innovators. This ongoing engagement further solidifies their trust and satisfaction with our product, converting them into brand advocates.

Example: By offering Tech Innovators ongoing support, tailored training sessions, and regular follow-ups, we ensure they maximize our tool’s potential, fostering a long-lasting relationship and potential referrals.

Key Takeaways Throughout The Process

The sales process isn’t a one-size-fits-all journey; it’s more like a versatile toolbox that savvy sales professionals tailor to fit their unique products, services, and, most importantly, their customers.

While the classic seven stages provide a solid framework, the magic lies in the ability to customize and finesse these steps to match your customers’ needs. Think of it as mastering the rules to break them in ways that truly resonate with your clientele—transforming from a sales professional into an artist of persuasion.

Here’s the insider scoop on what truly matters in the sales world:

• Understanding Customer Challenges: It’s not merely about having something to sell; it’s about deeply understanding the specific problems your potential buyers grapple with. Dive headfirst into their world, empathize with their pain points, and recognize their unique needs.
• Crafting Tailored Solutions: Once you’ve decoded their challenges, the spotlight turns to tailoring your offerings to tackle these issues head-on. Your product or service should seamlessly integrate as their solution. The real trick? Being able to clearly articulate how your offering directly addresses their needs.
• The Power of Persistence: Following up isn’t just about securing repeat business—it’s about being there consistently throughout the entire journey. Sometimes, the sales process isn’t a neat, linear seven-step progression; it often involves multiple rounds of interactions, presentations, or meetings. Staying connected regularly is the secret sauce, increasing your chances of being present when they’re ready to make that buying decision.
• Sales Mishaps: To fortify the sales process, sidestepping these pitfalls is crucial:
• Sales Process Documentation: Each prospect brings a unique challenge. Documenting every sales approach—what works and what doesn’t—creates a valuable resource. Learning from these experiences helps fine-tune strategies to suit diverse scenarios and prospect preferences.
• Defined Sales Stages: Navigating the sales journey demands a clear roadmap. Defining distinct stages allows better tracking of progress and pinpoints areas requiring refinement. It’s a compass guiding efforts toward successful closure.
• Micro-Goals: While the ultimate goal is vital, breaking it into smaller, achievable milestones is equally essential. These micro-goals act as checkpoints, allowing teams to monitor progress, maintain motivation, and sustain a targeted approach, driving accelerated productivity and success.

By steering away from these common pitfalls—ensuring comprehensive documentation, establishing clear sales stages, and emphasizing micro-goals—sales teams can craft a more adaptable, efficient, and personalized sales process that caters precisely to the diverse needs of prospects.

Conclusion

In the sales journey, the main lessons are to stay adaptable while having a plan, give personalized experiences, and always focus on making clients successful.

Selling isn’t just about transactions; it’s about building lasting relationships and being someone clients can trust. This process isn’t just a straight line; it’s about connecting, understanding, and being a part of clients’ stories.

Successful selling is more than just following steps; it’s about connecting with empathy and making a real difference for the people you work with.

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