Reading Time: 9 minutes

In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns.

But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic.

This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die,” explores how businesses can translate raw data into actionable insights that drive real results.

Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.

Ankur Kothari Q&A Interview

1. What types of customer engagement data are most valuable for making strategic business decisions?

Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns.

Second would be demographic information: age, location, income, and other relevant personal characteristics.

Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews.

Fourth would be the customer journey data.

We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data.

2. How do you distinguish between data that is actionable versus data that is just noise?

First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance.

Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in.

You also want to make sure that there is consistency across sources.

Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory.

Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy.

By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions.

3. How can customer engagement data be used to identify and prioritize new business opportunities?

First, it helps us to uncover unmet needs.

By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points.

Second would be identifying emerging needs.

Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly.

Third would be segmentation analysis.

Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies.

Last is to build competitive differentiation.

Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions.

4. Can you share an example of where data insights directly influenced a critical decision?

I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings.

We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms.

That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs.

That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial.

5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time?

When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences.

We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments.

Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content.

With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns.

6. How are you doing the 1:1 personalization?

We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater to the right offer.

So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer.

That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience.

We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers.

7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service?

Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved.

The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments.

Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention.

So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization.

8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights?

I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights.

Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement.

Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant.

As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively.

So there’s a lack of understanding of marketing and sales as domains.

It’s a huge effort and can take a lot of investment.

Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing.

9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data?

If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge.

Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side.

Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important.

10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before?

First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do.

And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations.

The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it.

Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one.

11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations?

We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI.

We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals.

We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization.

12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data?

I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points.

Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us.

We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels.

Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms.

Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps.

13. How do you ensure data quality and consistency across multiple channels to make these informed decisions?

We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies.

While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing.

We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats.

On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically.

14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years?

The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices.

Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities.

We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases.

As the world is collecting more data, privacy concerns and regulations come into play.

I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies.

And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture.

So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.

This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die.

Download the PDF or request a physical copy of the book here.

Reading Time: 8 minutes

In the B2C marketing landscape, understanding and leveraging customer engagement data is a necessity for survival.

In Chapter 6 of “The Customer Engagement Book: Adapt or Die,” we delve deep into how businesses can harness this data to not only stay afloat but thrive in a data-driven world. To bring this chapter to life, we sat down with Vish Ramkissoon, a leading expert from Publicis Hawkeye.

In our insightful Q&A session, Vish unpacks the critical importance of customer engagement data in making strategic business decisions. He clarifies the distinction between actionable data and mere noise, emphasizing the need for a meticulous approach to data tracking.

Vish’s perspectives on the future of customer engagement data and its role in shaping business strategies in the years to come. This conversation is a must-read for anyone looking to truly understand and adapt to the data-centric future of business.

Vish Ramkissoon Q&A Interview

1. What types of customer engagement data are most valuable for making strategic business decisions?

All customer engagement data is important — any time they interact with one of our assets, whether that be an app or web, or if it’s an interaction such as a click-through or open for direct communication, whether that’s SMS, push in-app, email, and even on paid channels with ads.

All of that information is used to categorize the user and model likes, dislikes and preferences.

We can then use that to customize our next communication with that particular individual or enhance their experience based on previous interactions.

2. How do you distinguish between data that is actionable versus data that is just noise?

We have created a methodology called highly engaged visits (HEV) or high-value tasks, which is another way that we look at interactions on our clients’ digital assets.

Tracking an attribute or an event on an app or on the web for the sake of tracking it makes no sense. If we want to use data smartly, we need to do the due diligence ahead of time to track actionable attributes and events..

The way you configure the tracking will determine what you’re able to do with that data later on and can impact your ability to have ability to be able to have meaningful communication or the next desired best action with a particular customer.

3. How can customer engagement data be used to identify and prioritize new business opportunities?

In retail, that would be like identifying potential cross-category shoppers. So you think of Big Box Retailers, for example. There are many product categories such as electronics, clothing, and groceries.

Understanding how a customer engages with their brand and where they’re shopping can illuminate opportunities to get them to shop cross-category.

The benefit of doing that is obvious in terms of customer lifetime value.

Cross-category shoppers tend to stay longer with the brand, churn risk is vastly reduced, and their customer lifetime value spikes.

4. Can you share an example of where data insights directly influenced a critical decision?

One of the most surprising and impactful insights I’ve uncovered came while working with a pick-up truck manufacturer. At the time, the client was primarily focused on reaching a working-class audience — that was the assumed buyer persona.

But once we enriched their first-party data with a third-party data set and ran the analysis, something unexpected jumped out.

We found a statistically significant group of very affluent buyers (people with high net worth who were purchasing these trucks), not for utility, but for what they symbolized. It completely shifted our understanding of the customer base.

These weren’t just tools for work; for some, they were lifestyle statements.

That insight forced us to rethink our customer engagement strategy: who we were targeting, how we were speaking to them, and what kind of creative we were putting in front of them.

We created tailored content that spoke directly to that audience by highlighting features, experiences, and brand cues that resonated with their values and lifestyle.

What’s powerful is that the client still uses that strategy today.

It’s a great example of how data, especially when it reveals something you weren’t looking for, can fundamentally reshape your approach and drive real, lasting results.

5. How do you use engagement data to shape your marketing strategy in real time or your clients?

From a technology and data strategy perspective, I tend to avoid the term “real-time” in marketing communications, opting instead for “near real-time.”

The distinction is important: being too fast can sometimes backfire. Take abandoned cart emails as an example: sending a follow-up immediately after a cart is abandoned can feel intrusive or even irrelevant.

Instead, we take a test-and-learn approach, experimenting with different delays and message cadences to identify what actually drives engagement and conversion.

This allows us to refine our timing models iteratively and improve performance over time.

Where real-time data does provide clear value is in-session personalization.

For example, on a retail website, when a customer adds an item to their cart, we can trigger an event instantly to update product recommendations. This enhances the browsing experience by surfacing complementary or commonly bundled products, driving higher engagement and average order value.

We also leverage real-time behavioral signals to drive contextual offers.

If a customer has shown interest in a category, say, shoes, but hasn’t added anything to their cart, we can surface a targeted promotion before checkout.

These kinds of real-time capabilities, when implemented thoughtfully, enable us to create more relevant, frictionless customer journeys while maximizing both experience and business outcomes.

6. How can marketers ensure their use of customer engagement data aligns with broader business objectives?

Our objective-setting process is grounded in data.

We don’t move forward without clear evidence to support the decisions behind each goal.

Whether we’re aligning with a C-suite directive, driving toward an OKR, or meeting customer engagement metrics tied to shareholder expectations, we treat data as the foundation, not an afterthought.

In my view, data solves all arguments and should steer prioritization. If we can’t validate a business objective with measurable inputs, it doesn’t belong in the roadmap.

Our job is to ensure every initiative is defensible, trackable, and optimized for impact.

7. What challenges do marketers face when trying to translate customer engagement data into these actionable business insights?

One of our biggest challenges is data fragmentation.

Critical customer data is still trapped in functional silos, product teams manage product usage data, transactional data sits in the CDP, paid media teams rely on data in ad tech platforms, and direct-to-consumer marketers operate on yet another system.

Without a unified profile, it’s impossible to achieve a true 360-degree view of the customer. Democratizing data across the organization is not just a technology problem; it’s a structural and operational one. Solving it is foundational to everything else we want to accomplish as marketers.

The second major challenge is personalization at scale. We serve tens of millions of customers, each with their own preferences, behaviors, and needs. Designing bespoke communications for each one isn’t just impractical, it’s impossible without a fundamentally different approach.

To address this, we’re shifting how we work.

One strategy involves building a library of pre-approved modular content, offers, copy, imagery, and templates that can be dynamically assembled to create highly relevant, personalized customer experiences. This allows us to maintain control over brand and compliance while scaling 1:1 communications efficiently.

We’re also actively experimenting with generative AI.

By training models on our brand voice, tone, and eligibility rules, and putting the right guardrails in place, we can safely generate personalized content that meets our standards; legal, creative, and strategic.

Done right, this allows us to combine precision and scale in ways that were simply not possible before.

8. What technologies or tools have you found most effective for gathering and analyzing customer engagement data?

To effectively understand customer engagement, we rely on a stack of technologies that work in concert. First, we use behavioral tracking platforms that capture event and attribute data across both web and app environments.

These tools give us deep visibility into session behavior, how customers navigate, what they engage with, and where friction points exist.

We also place a strong emphasis on our direct-to-consumer communication stack—covering in-app messaging, push notifications, SMS, and email.

Every touchpoint in that system generates interaction data, which becomes critical for understanding how customers respond to different types of messaging and timing strategies.

To unify all of this, we lean heavily on our composable customer data platform (CDP). The CDP is where everything comes together, behavioral signals, transactional data, engagement metrics.

It gives us that single, holistic view of the customer, which is foundational for personalized experiences at scale.

And finally, analytics. We approach this from two angles: data visualization and performance measurement.

Visualization helps us uncover patterns and insights quickly, while more traditional analytics allows us to evaluate effectiveness, whether in near real-time or through post-campaign analysis.

Together, this ecosystem allows us to move from reactive to predictive, and ultimately, to proactive engagement strategies.

9. How do you ensure data quality and consistency across multiple channels to make informed decisions?

Data quality starts upstream at the point of collection.

One of the most common mistakes organizations make is moving too quickly to define what data they want without spending enough time thinking about how that data is collected and structured. If we don’t get that part right, everything downstream analytics, personalization, reporting suffers.

Standardization is key.

We invest heavily in upfront schema design and data normalization, because consistent input leads to scalable and reliable analysis.

A simple example I often give is a date field. If a date is captured as a string instead of a proper date format, you lose the ability to apply even basic logic like filtering by time windows. Multiply that across dozens of fields and channels, and it becomes a serious liability.

We also build validation logic directly into our data entry and ingestion points.

Take email, for example. It’s surprising how many systems still allow invalid email formats into their databases. A basic syntax check ensuring the presence of an “@” symbol, a domain, and the absence of illegal characters.

So we can avoid collecting a bunch of garbage and then realize, “Oh well, only 90% or 80% of the email addresses we have are contactable.”

Ultimately, data quality isn’t a one-time fix. It requires a disciplined approach, embedded into both our technology stack and our operational culture. We think about data integrity from day one—because without it, we’re flying blind.

10. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years?

Over the next five years, customer engagement data will move from being a supportive asset to a strategic cornerstone of business decision-making. The capabilities of technology platforms, especially in data processing and AI are evolving rapidly.

What’s lagging isn’t the tech, it’s organizational readiness, particularly with how teams, especially in marketing are structured and how they operate.

To truly leverage engagement data, we need to shift from a campaign-centric mindset to a decision-centric one.

Instead of asking, “What campaign should we send next?” The better question is, “What decision should we make for this customer, at this moment?” That’s the shift from broadcasting to orchestrating meaningful, contextual, one-to-one interactions.

But that level of personalization requires scale, and scale demands automation.

This is where AI becomes essential. We’re going to rely more heavily on AI to generate content dynamically within brand parameters, compliance rules, and contextual relevance so we can meet customers where they are, with messaging that actually matters.

What ties it all together is data.

Engagement data is what fuels both the decisioning logic and the AI models behind it. And as data becomes richer and more immediate, our ability to turn insight into action will define a competitive advantage.

The businesses that embrace that evolution not just in technology, but in mindset, will lead.

This interview Q&A was hosted with Vish Ramkissoon, Chief Technology and Data Officer at Publicis Groupe/Hawkeye, for Chapter 6 of The Customer Engagement Book: Adapt or Die.

Download the PDF or request a physical copy of the book here.

Reading Time: 7 minutes

The way brands look at data has changed over the past few years. While in the last decade, brands may have focused on data collection (the more data, the merrier), over the passage of time, marketers are dawning upon the realization that unless data is actionable, it’s not really usable.

Marketers have struggled for years to draw actionable insight from fragmented customer data that exists in silos across countless tools. 

Rather than adding yet another tool to their tech stack to mitigate this silo problem, marketers are re-evaluating their entire tech stack. As a result, forward-thinking brands realize that to unlock truly personalized, real-time customer experiences requires tapping into the single source of truth: their data warehouse.

Let’s explore why this shift is happening and how a CDEP like MoEngage is the perfect partner in this new paradigm.

From Passive to Active: The Evolution of The Modern Data Warehouses

Data warehouses aren’t new.

Traditionally, many businesses relied on on-premise warehouses, which were powerful but often rigid, expensive to scale, and sometimes siloed. 

The game-changer has been the advent of cloud data warehouses (such as Snowflake, Google BigQuery, Databricks, Amazon Redshift, and Azure Synapse).

Cloud data warehouses offer consumer brands the following advantages:

• Scalability: Easily handle vast and growing datasets without massive upfront infrastructure investment.
• Flexibility: Integrate structured and semi-structured data from teams like sales, customer support, finance, marketing, etc.
• Performance: Process complex queries rapidly, enabling faster insights.
• Accessibility: Democratize data access for various teams (with appropriate governance).

Gone are the days when data warehouses were merely passive repositories of data. Today, they often contain the most complete, up-to-date, and trustworthy business view.

Benefits of the Warehouse-First Approach for Campaigns

1. Real-time data: Data warehouses often provide real-time data, enabling marketers to understand the pulse of the customer and make decisions based on timely and accurate information rather than the slightly-dated information that ends up in a CDP (customer data platform).
2. Real-time analytics: A warehouse-first approach helps marketers to understand the current state of the business (as in ‘what is happening right now’. This real-time analytics also helps them spot deviations in key performance indicators (KPIs), detect problems proactively, and make faster decisions.
3. Real-time segmentation: Traditional segmentation relies on slightly stale data (hours or days old). By automatically moving customers into or out of segments based on their latest actions/changes in attributes, marketers can personalize the customer experience in real time, thereby becoming more relevant and timely.
4. Data consistency and quality: Loading data into a warehouse involves data cleansing, validation, standardization, and transformation, which improves the quality of data in the warehouse.
5. Speed, scalability, and cost-effectiveness: Data warehouses offer scalable storage and processing power, allowing businesses to adapt to their evolving needs rapidly. Compared to storing and processing data within multiple tools, data warehouses are a more cost-effective way for businesses to manage massive amounts of diverse data.
6. Faster time-to-value (Potentially): Once set up, activating new data points or segments can be quicker if the data already resides in the warehouse. This also means that campaigns can be launched faster vs. when data needs to be copied into a CDP before being sent to an email platform or an SMS automation tool.
7. Improved data governance: By consolidating all data into a central repository, data warehouses ensure that data governance policies, standards, and procedures are standardized across the board.

CDEP and the Warehouse-First Approach

Forward-thinking consumer brands have realized that customer data management and customer engagement cannot exist in silos. 

For consumer brands that need to respond to the pulse of the customer in real-time, the martech stack needs to be warehouse-native. 

This is where a CDEP (Customer Data and Engagement Platform) fits in like a glove.

What Makes MoEngage the Perfect Warehouse-Native CDEP?

A CDEP helps consumer brands leverage warehouse as the central system without creating new data silos. 

A warehouse-native CDEP like MoEngage minimizes data duplication and data movement, leverages the warehouse’s processing power, enhances security, and can lower costs in the long run.

To evaluate why a CDEP (Customer Data and Engagement Platform) like MoEngage shines in a warehouse-first marketing approach, we need to understand these three pillars:

1) Capabilities of a CDEP

Data Ingestion, Unification, and Management Capabilities

Instead of extensive data storage, a warehouse-native CDEP connects directly to your data warehouse. It allows marketers to build segments using rich data in situ within the warehouse environment, leveraging the warehouse’s processing power and data freshness.

• Data Ingestion: A CDEP comes equipped with flexible and robust data management capabilities that can capture a complete view of customer interactions. Sources of data ingestion include event streaming, batch data uploads, platform integrations (via connectors and APIs), and data warehouse integrations (Snowflake, BigQuery, RedShift, etc).
• Data Storage: A CDEP would leverage your existing data warehouse as the primary, persistent storage location for comprehensive customer data.

• Data Unification: CDEPs can unify data from various sources into a 360-degree customer view for each individual, resolving identity across devices and channels. CDEPs do this by ingesting and recognizing identifiers (email, phone, user ID, device ID etc) and applying deterministic matching rules to stitch identifiers and associated data together.
• Data Governance, Privacy & Security: CDEPs have best-in-class data governance, privacy and security features like Data Access Management, Data Encryption, PII Masking and Tokenization, and Data Retention Policies. These measures ensure that data remains protected at all costs.

Integrated Engagement Capabilities

A CDEP seamlessly combines data processing with built-in omnichannel campaign orchestration and delivery across multiple channels. These best-in-class engagement capabilities are further augmented with AI capabilities that can help consumer brands improve the customer experience.

• Omnichannel Messaging Capabilities

With over 11 channels, including email, SMS, push notification, WhatsApp, etc., a CDEP like MoEngage can help your brand build and optimize personalized customer journeys across channels and devices.

• AI Capabilities

Generative and predictive AI capabilities offered by a CDEP can easily learn from the warehouse data and use AI agents to suggest the ideal customer journeys, decisions that help brands optimize campaigns, and generate segments.

Segmentational Capabilities

A CDEP can generate actionable segments in real time based on demographics and attributes, behavioral and transactional data, and propensity.

A CDEP uses fresh and comprehensive data in the warehouse, allowing marketers to build customer segments based on demographics, attributes, behavior, transactions, or propensity scores.

Analytical Capabilities

After unifying data from different sources, a CDEP can help marketers make sense of the data through the power of its analytical capabilities. Be it identifying trends and patterns in customer behavior, segmenting the audience, predicting the likelihood of certain actions in the future, or informing future strategy, CDEP’s robust analytical capabilities can help marketers with actionable insights in real time.

• Reporting and Dashboards: Built-in data visualization capabilities can help marketing, growth, and product teams across consumer brands track key business metrics like campaign engagement rates, conversion rates, churn rates, DAUs, and MAUs.
• Behavioral Analytics: Features like funnel, RFM, and cohort analysis can help consumer brands analyze behavioral trends of customers.

2) Scalability, Reliability, and Elasticity

Reliability offered by a CDEP means that the platform is consistently available without any downtimes or disruptions. A reliable CDEP ensures high availability, fast response times, and stable performance for brands like yours.

A reliable CDEP is built on elastic infrastructure, which means that the platform’s capabilities will be available to you even during periods of extensive and high usage by other customers.

3) Customer Support 

If you’re planning to migrate to a warehouse-native CDEP, you might have some concerns about the amount of effort, developer bandwidth, friction, and migration costs that you could incur while making the switch.

MoUpgrade program by MoEngage accelerates migration timelines and delivers faster time-to-value (TTV) in weeks instead of months or years. 

The program allows for a smooth migration to a warehouse-native CDEP without any disruptions to ongoing campaigns, data collection cadences, or your brand’s existing data pipelines.

Conclusion:

This shift towards warehouse-first marketing marks a pivotal change, moving beyond fragmented data collection to leveraging the data warehouse as the single source of truth. A warehouse-native CDEP like MoEngage emerges as the ideal solution.

By integrating data management, engagement, and analytics directly with the warehouse, CDEPs empower brands to deliver truly contextual, real-time experiences, driving better results and faster time-to-value in 2025 and beyond.

Talk to our team to understand how a CDEP can help your brand better harness the power of your data warehouse.

Reading Time: 4 minutes

Building strong customer relationships through meaningful engagement is paramount for success in today’s rapidly evolving digital landscape. This is especially true in India’s competitive financial services and insurance sectors. Leading financial and insurance brands increasingly recognize the need to move beyond traditional marketing approaches to deliver personalized and seamless experiences across all customer touchpoints.
However, many still face significant challenges that hinder their ability to achieve true customer-centricity. This article explores the common hurdles some of the biggest players face in the Indian financial and insurance markets and how a modern customer engagement platform like MoEngage can provide effective solutions.

Key Challenges Hindering Effective Customer Engagement for Financial and Insurance Brands

Several recurring pain points impede the efforts of major financial and insurance institutions in India to optimize their marketing strategies and enhance customer engagement:

1. Siloed Systems and Fragmented Data

Many organizations struggle with disjointed marketing systems that operate in isolation. This lack of integration leads to data silos, making it difficult to gain a holistic view of the customer and deliver consistent messaging across channels. For instance, one of the leading mutual fund houses relied on separate systems for email campaigns without a unified performance marketing setup, hindering effective segmentation. Similarly, major insurance brands often manage web and app engagement through multiple tools, preventing the creation of centralized customer profiles and leading to fragmented customer experiences.

2. Manual and Time-Consuming Processes

Reliance on manual workflows for campaign management and customer interactions consumes valuable marketing bandwidth and reduces overall productivity. The absence of robust automation tools makes it challenging to personalize customer journeys and streamline routine tasks like email campaigns and notifications. This can slow down campaign execution and time-to-market for new products and offers.

3. Limitations in Personalization and Segmentation

Without integrated solutions and advanced analytics, brands face difficulties in segmenting their audience effectively and delivering personalized messaging. Basic segmentation capabilities prevent granular targeting based on customer behavior and preferences, resulting in generic communications that lack relevance and impact.

4. Heavy IT Dependency

Managing customer communications often requires substantial involvement from IT departments, slowing down campaign execution and reducing marketing team agility. The absence of user-friendly, low-code, or no-code solutions restricts marketers’ ability to create and launch campaigns independently.

5. Challenges in Mobile App Engagement

Despite having a mobile presence, some of the most prominent insurance players struggle with low mobile app adoption and engagement. Generic and intrusive push notifications can lead to customer churn and hinder retention efforts.

Discover how your insurance brand can revive revenue by transforming renewals:

6. Cost and Complexity of Integration

Concerns about the cost of implementing new automation tools and the complexity of integrating them with existing infrastructure are significant considerations for many organizations. The desire for solutions that are easy to implement and offer a strong return on investment is paramount.

7. Security and Compliance

Handling Personally Identifiable Information (PII) necessitates robust security measures and compliance with industry standards. Brands need assurance that their marketing automation platform can safeguard customer data effectively.

The Solution: A Unified Customer Engagement Platform for Financial and Insurance Brands

To overcome these multifaceted challenges and unlock the potential of customer engagement, leading financial and insurance brands are increasingly turning to comprehensive marketing automation platforms like MoEngage. Such platforms offer a centralized hub for managing customer interactions across various channels, leveraging data and AI to deliver personalized experiences at scale.

MoEngage provides a powerful solution by:

1. Enabling Seamless Integration

MoEngage’s ability to integrate seamlessly with existing CRM and other systems eliminates data silos and creates a unified view of the customer. This allows for segmentation and personalization based on accurate, real-time information.

2. Orchestrating Omnichannel Engagement

MoEngage facilitates consistent and personalized communication across multiple channels, including email, push notifications, in-app messaging, SMS, WhatsApp, and website. This ensures a cohesive customer journey and enhances engagement.

3. Offering Advanced Segmentation and Behavioral Analytics

The platform’s sophisticated segmentation capabilities allow brands to engage customers based on their behavior, preferences, and real-time actions. AI-powered analytics provide valuable insights into customer journeys, enabling continuous optimization of marketing efforts.

4. Providing Intuitive Campaign Management Tools

MoEngage’s drag-and-drop campaign builder empowers marketing teams to create and launch campaigns quickly and independently, reducing reliance on IT.

5. Delivering Personalized Website Experiences

Features like behavioral retargeting and dynamic content delivery can significantly enhance website engagement, reduce bounce rates, and improve conversion rates for returning visitors.

6. Improving Mobile Engagement

MoEngage enables the delivery of contextual, behavior-triggered push notifications and personalized in-app messaging, boosting app adoption and retention.

7. Ensuring Robust Security and Compliance

MoEngage strongly emphasizes data security and complies with industry standards for handling PII, providing the necessary safeguards for customer information.

Key Benefits of Embracing a Modern Martech Platform

By adopting a comprehensive platform for data unification, management, and customer engagement, India’s leading financial and insurance brands can realize significant benefits:

• Enhanced Customer Engagement and Retention: Personalized and timely communication across preferred channels leads to higher engagement rates and stronger customer loyalty.
• Increased Conversion Rates and ROI: Smarter segmentation and targeted messaging drive better lead-to-conversion ratios and improve overall marketing ROI.
• Faster Campaign Execution and Reduced Time-to-Market: Low-code environments and streamlined workflows enable marketing teams to launch campaigns more quickly and efficiently.
• Reduced Operational Complexity and IT Dependency: Centralizing communication management under a single platform simplifies workflows and reduces the need for extensive IT involvement.
• Improved Customer Insights and Data-Driven Decision Making: Robust analytics and reporting capabilities provide real-time data to track campaign performance and optimize marketing strategies continuously.
• Scalability and Future-Proofing: Modern platforms are designed to scale with the business’s growing needs, offering advanced features as required.

A growing need marks the Indian financial services and insurance landscape for customer-centricity. Leading brands can transform their marketing strategies by addressing the challenges of fragmented systems, manual processes, and limited personalization capabilities with a unified customer engagement platform. Embracing automation, personalization, and omnichannel orchestration will streamline operations and enhance efficiency, foster stronger customer relationships, drive business growth, and secure a competitive edge in the digital era.

The journey towards a more engaging and data-driven future for these industries is well underway, with modern martech platforms playing a pivotal role in unlocking their full customer engagement potential.

Join the transformation today and unlock the full potential of your customer engagement as India’s leading financial and insurance brands with an in-depth product demo of MoEngage!

Reading Time: 5 minutes

Are you tired of seeing multiple customer profiles associated with the same attributes?

Are you unable to map your customers’ physical and digital touchpoints?

Are you unable to keep up with changing consumer behavior?

Are you on the verge of just losing it?

Well, you probably need an Identity Resolution platform!

In this article, I will explain everything you need to know about Identity Resolution, its incredible benefits, and why you probably need an Identity Resolution platform right now!

Let’s start by understanding what identity resolution means and why it matters to a business of your scale.

What is Identity Resolution?

User Identity Resolution is the process of connecting fragmented pieces of a customer’s data across multiple touchpoints.

Businesses like yours can gain valuable insights into customer behavior, preferences, and engagement by linking different interactions your customers take on various

• devices (smartphones, tablets, desktops, laptops, smart watches, TVs, digital kiosks, or more),
• channels (emails, social media, text messages, push notifications, or more) and
• platforms (mobile app, website, TV app, or more)

What Are The Different Types of Identity Resolution?

There are broadly 2 types of Identity Resolution – probabilistic and deterministic.

Probabilistic Identity Resolution

This approach uses algorithms to match customer records by using customer attributes across different databases. The Probabilistic Identity Resolution approach is based on the likelihood or probability that two records belong to the same customer. Probabilistic Identity Resolution links different customer accounts based on the similarity of attributes such as name, address, purchase history, or more.

For example, if your customer makes a purchase on your retail website using an email address different from the one they used to create an account, you can still link the purchases together based on similar shipping addresses, payment methods, and purchase history.

Another example would be a customer opening a credit card account at your bank’s physical branch using a different residential address than the one associated with their checking account. When your customer requests a mortgage, you can use Probabilistic Identity Resolution to link their different accounts based on phone numbers, social security numbers, and similar financial behavior.

Deterministic Identity Resolution

This approach involves using a single unique identifier (or a ‘key’) to map out customer records across different databases. The unique identifier for Deterministic Identity Resolution can be email addresses, phone numbers, or account IDs specific to your platform.

For example, if your customer purchases sneakers on your online retail website or mobile app after creating an account using their email address and uses the same email address when purchasing a cleaning kit for sneakers on the physical branch of your retail outlet, you can link the two purchases to the same customer thanks to Deterministic Identity Resolution by considering their email address as the unique identifier.

Let’s take another example. If your customer opens both a checking account and a savings account at your bank’s physical branch with the same phone number, you can use their phone number as a unique identifier. When this same customer applies for a loan either through your website, mobile app, or another physical branch with the same phone number, you can easily map their behavior together through Deterministic Identity Resolution.

Why is Identity Resolution Important?

User Identity Resolution is essential to create a unified view of each individual customer. This holistic understanding allows you to deliver personalized and seamless experiences, improving customer engagement, satisfaction, and loyalty. Identity Resolution also helps you build effective audience segments and get an accurate count of your actual customer base.

Let’s take a look at how Identity Resolution can make a difference for shopping brands

Imagine Alex, a frequent shopper who lands on your online fashion retailer website on her laptop thanks to your Google Search Ad campaign. She drops off.

After a few days, she sees your ad on Instagram in the morning and remembers your brand name. She installs your mobile app via this Instagram ad, browses your collection, and adds a few items to her wishlist.

In the evening, on her drive back home from work, Alex stops by your retail outlet and purchases the items on her wishlist.

Without Identity Resolution, you will struggle to recognize Alex as the same customer across these different interactions.

But thanks to Identity Resolution, you can connect Alex’s online and offline activities and map her journey to a purchase.

You can use this information to provide a unified experience and personalized recommendations tailored to her preferences – discount coupons to increase the foot traffic of your retail outlet, free shipping to her home address to encourage mobile app engagement, and more. You are now Alex’s trusted brand to keep up with the latest fashion trends and buy gifts for special events.

Let’s see why Identity Resolution is critical for banking and financial services

Your bank serves a diverse customer base, including individuals, families, and small businesses, doesn’t it?

Let’s say your customer, Luke, visits your physical branch in Brooklyn to open a new savings account. Luke provides his contact information and discusses his financial goals with your bank’s customer service representative.

During the weekend, Luke visits your bank’s website to explore mortgage options for his new home.

He browses the different loan offers you host on your website and uses your online calculator to predict his monthly payments. Luke then asks your customer service website bot for mortgage rates and information about the application process.

Without Identity Resolution’s capabilities, you will be unable to map Luke’s customer journey—his in-branch visit, website interaction, and customer service inquiries. This lack of understanding will prevent you from gaining valuable insights into his financial needs, preferences, and interests.

By implementing Identity Resolution, you can link these interactions to create a holistic view of Luke’s banking profile, send him personalized financial advice, and recommend tailored solutions that align with his needs. You can now position yourself as a trusted financial partner in Luke’s journey towards achieving his financial objectives!

How Can Identity Resolution Platforms Help You?

By connecting the dots between customer data points, creating a unified view of individual customers, and mapping their journey, you can unlock higher engagement, loyalty, and revenue – all thanks to Identity Resolution platforms!

User Identity Resolution platforms can help you better understand your customers by providing a unified view of their interactions across multiple devices, channels, and platforms. Here are a few examples:

Identity Resolution for Shopping brands (E-commerce, Retail, D2C)

Let’s say a visitor browses your brand’s website and creates an account with their email address and phone number. They browse your collection but decide to visit your physical retail store to make a purchase. While completing the transaction at your offline store, the person submits their phone number at your billing counter. Using the phone number attribute, you can now link the visitor’s browsing activity on the website to their purchase at your physical store.

Identity Resolution for Media & Entertainment brands (Video streaming, Audio streaming, Digital publications)

Most Media & Entertainment platforms like yours allow customers to create an account using multiple methods: email login, mobile OTP, and social media logins like Facebook or Google. Each login method generates different customer profiles for the same individual. With an Identity Resolution platform like MoEngage, you can consolidate these multiple records into a single unified profile, allowing you to learn their content choices and preferences to recommend relevant content and drive more repeat subscriptions.

Conclusion

With an advanced cross-channel and cross-device identity management system, you can break down data silos, streamline communication, and improve customer experiences across physical and digital touchpoints.

With MoEngage’s Identity Resolution capabilities, you can say goodbye to fragmented data and disconnected customer interactions. Use MoEngage to empower your teams to forge meaningful connections with customers and drive growth.

If you’d like a personalized demo of Identity Resolution and other capabilities of MoEngage’s Data Suite, you can reach out to our product experts here.

Reading Time: 7 minutes

Every company has trillions of terabytes of raw data. But the problem is that the data is present in silos, making it harder for marketing and product teams to derive incisive insights and see the entire picture.

Why seamless offline-online integration is crucial for B2C brands?

Today’s consumer expects a seamless and integrated experience across brand channels—from the retail outlet to a kiosk to an email campaign or a social media post. Your consumers won’t accept anything that’s even slightly inconsistent.

Fragmented consumer experiences, especially across offline and online channels, can result in customer dissatisfaction, negative reviews, lower customer lifetime value (CLV), and customer churn.

Fragmentation in consumer experiences can irk current or potential customers. If one of your channels is not up to par with others, that can significantly impact your brand’s customer satisfaction (CSAT) scores. The effect can cascade down on customer spending, loyalty, and revenue.

This is especially true for legacy or traditional brands. More often than not, the average legacy brand is still catching up on adopting newer digital channels while making sense of billions of zero-party and first-party data. At the same time, they have trillions of data points stored offline (on-prem databases, older hardware and software, etc.).

Is there a way for legacy brands to seamlessly integrate offline and online data to build a complete customer profile?

The answer is YES, but most B2C brands often encounter numerous difficulties when achieving seamless offline-online integration.

What are the challenges faced by B2C brands in offline-online integration?

Some of the most common yet persistent challenges faced by B2C brands include:

1. Data silos

Offline data (in-store purchases, call center interactions, loyalty CRMs) and online data (website, app, social media, digital ads) are often stored in separate systems, adding complexities to offline-online integration.

2. Identity resolution

Some potential areas of friction in identity resolution include the following: 

• Many legacy CDPs (Customer Data Platforms) rely on pre-defined identity graphs, which might not meet your brand’s specific needs.
• Identity resolution across offline and online touchpoints may lack real-time syncing, leading to mismatched profiles.
• Many data management solutions (like Salesforce Data Cloud or Adobe Experience Cloud) don’t natively integrate, resulting in internal data silos.

3. Real-time data processing

Offline data may take hours or days to update, while online data flows in real-time. This delay in syncing data can cost brands in terms of opportunities for customer engagement.

4. Data accuracy and quality

Offline data is often manually entered into the POS systems, which can cause errors, duplications, and inconsistencies. Online data, while it flows in real time, runs the risk of misattributions and misinterpretations. The effects of poor data accuracy and quality can cascade down to campaign performance and customer perception.

5. Gaps in customer experience

The issues in integrating offline data in real-time can result in a fragmented customer experience. Example: A customer receiving online recommendations for an item returned by the customer in the store can irk the customer.

Do you need a CDP for offline-online integration?

The answer is a yes and a no. 

Legacy CDPs have existed since 2013. CDPs were designed to collect, unify, and manage customer data from multiple sources, including offline and online channels, to create a single customer view and enable more personalized marketing efforts.

However, in reality, legacy CDPs have created more internal silos than fulfill the promise of unifying customer profiles from various sources. Additionally, most CDPs have a complex implementation process and high maintenance costs, which only adds fuel to the additional data fragmentation problems they pose.

Many B2C brands now store data in warehouses and expect a CDEP that can build 360-degree customer profiles, segment audiences, and personalize communications, all using data directly from the warehouse.

How Does MoEngage Help Brands with Seamless Offline-Online Integration?

MoEngage’s best-in-class data management capabilities help B2C brands effectively collect and orchestrate data.

Advanced Identity Resolution 

Identity resolution links and matches individual attributes from offline and online sources to create a unified identity representation. By combining various data elements, it establishes connections and creates a comprehensive view of the customers. 

Identity resolution enables marketers to accurately segment and personalize the customers based on data collected across various online and offline resources and avoid duplications and errors.

Seamless Integration with Data Warehouses and Cloud Storage

MoEngage allows seamless integration with data warehouses such as Snowflake, Amazon RedShift, and Google BigQuery. MoEngage also enables seamless integration with cloud storage like SFTP, Amazon S3, Google Cloud Storage, and Microsoft Azure Blob.

These integrations can help:

1. Reduce dependencies on tech teams
2. Accelerate data processing
3. Reduce integration timelines

Leveraging Seamless Offline-Online(O2O) Integration Across Industries

Brands across industries can leverage offline-online integration capabilities to solve critical business use cases:

1) Retail/ E-commerce-

•  In-Store Behavior Integration: Retail brands can track offline interactions (e.g., loyalty card usage in-store purchases) and combine them with digital behavior (app browsing, online orders) to create a 360-degree customer profile. (For example, if a customer purchases gluten-free pasta in-store at a grocery supermarket, brands can trigger a personalized email or a push notification with gluten-free recipes.)
• Real-time Inventory Sync: Retail brands can automatically pause campaigns for out-of-stock items (Eg, Stopping ads for sold-out seasonal products like strawberries or Christmas hams.)
• Dynamic Product Replacement: Retail brands can suggest alternatives if a product in a customer’s cart is unavailable (Eg, If Honey Nut Cheerios is out-of-stock, the store can suggest similar in-stock options)
• Click-and-Collect (BOPIS—Buy Online, Pick Up In-Store): Customers can order online and pick up their items from the nearest store. Benefits: Reduced delivery costs and increase in foot traffic
• Loyalty Points Integration: If a customer earns points in a retail outlet, with seamless offline-online integration, brands can automatically update their online profiles in real time, thereby enabling personalized offers based on their loyalty tier.

2) Financial Services- 

• Pre-Approved Loans: Banks can send pre-approved loan offers to customers with good credit scores. Customers can then finalize the application at a branch.
• Call Center Data Sync: Customers’ interactions with the call center can be synced with their online profile, allowing financial service brands to offer tailored support and offers based on past interactions.
• Digital Banking with Offline Support: Customers can utilize self-service to manage most transactions but visit branches for complex queries. With seamless online-offline integration, advisors can access full online activity history and help customers troubleshoot quickly.
• Location-based Personalization: Banks can leverage geo-fencing to notify mobile app customers of personalized branch offers when they visit.
• Hybrid Wealth Management: Financial service brands can engage/ educate customers online. Customers can then choose to conduct the high-value transactions in person.
  

  Read more about the importance of offline and online data integration for financial service institutions to leverage AI to the fullest.

3) Healthcare

• Post-Consultation Follow-ups: After a consultation, hospitals/ healthcare providers can trigger a post-consultation email or push notification asking the patient to choose their follow-up slot.
• Pharmacy behavior ingestion: Pharmacies/ drug stores can send product recommendations online based on in-store purchases/ pick-ups. 
• Online Prescription Refills and Pickup: Pharmacies and drug stores can send customers reminders about medication refills and let the customer pick them up from the pharmacy.

4) Quick-Service Restaurants (QSR)

• Geofencing for Real-Time Offers: Restaurants can send customers location-based promotions to app customers in the vicinity.
• Loyalty Programs and Personalized Offers: Customer loyalty points earned at physical locations and online will be updated in real-time, enabling QSR brands to personalize offers and recommendations based on their loyalty tier.
• Restaurant behavior ingestion:  QSR brands can integrate offline purchase details with digital behavior (app browsing, cart abandonment, past orders) to create a 360-degree customer profile.

Conclusion

Offline-online integration (O2O) is a necessity for brands to stay competitive and ensure customers stay loyal. Consumers expect seamless, personalized experiences across every touchpoint, and fragmented data silos only lead to missed opportunities, lower engagement, and customer churn. While traditional CDPs promised a unified customer view, many have fallen short. The future lies in integrated customer data and engagement platforms that break down silos and power real-time personalization.

Leading consumer brands like Sephora, McAfee, Flipkart, Samsung, Nestle, Poshmark, Citibank, 7-Eleven, and many more are already leveraging seamless O2O integration to drive revenue and retention.

Want to know more about how you can drive O2O integration at your enterprise? Talk to our sales team to see how we can help you unlock the full potential of your customer data.

Reading Time: 6 minutes

MoEngage processes over 250 billion events every month, reaching speeds of up to 500 million events per hour at the rate of 250K per second.

If you were to travel at this speed (250K miles per second), you would reach the moon before you could say one Mississippi!

Brands worldwide use MoEngage to send more than 3 billion messages in a day, including 2.5 billion push notifications, over 250 million emails, and 20 million WhatsApp messages.

Here are some graphics to help you visualize the scale at which MoEngage operates:

What does the reliability of a SaaS platform mean?

‘Reliability’ for a SaaS platform in the customer engagement space like MoEngage means the platform is consistently available without any downtime or disruptions. A reliable customer engagement platform ensures high availability, fast response times, and stable performance for brands like yours.

A reliable customer engagement platform is built on an elastic infrastructure. This means the platform’s capabilities will be available to you even during periods of extensive and high usage by the rest of its customers.

Why is reliability important?

Let’s say you decide to run a flash sale in your retail outlet in Queens, New York. After all, sales are great for building customer engagement, generating a burst of revenue, and capturing shopping patterns, aren’t they?

You want to make as much noise as possible about this sale. You want to send out emails, push notifications, and text messages. You also want to show ads on TikTok, Instagram, or Facebook and show banners of this sale to all visitors coming to your website or mobile app from New York.

You plan to send out comms about this sale only to specific shoppers who have visited your store in Queens in the past year. You also decide to promote this sale to customers who haven’t shopped online or in your stores for over six months but made their last purchase from New York – frequent buyers don’t need a sale to drive a purchase, do they?

So, while you run ads to bring visitors from New York to your website, only the audience with infrequent interactions with your brand will see details about the sale.

Setting this up needs a lot of data and a reliable customer engagement platform (CEP). Your CEP needs a fail-proof data architecture to fetch this data from multiple sources, convert it into a format it understands, interpret it, and help you build customer cohorts. The platform also needs to be able to filter the audience in real time based on either location or past behavior.

A single failure can lead to a domino effect on your flash sale, directly impacting your revenue and deviating from your projections. Some common failures look like this:

1. Unable to send messages, emails, or push notifications due to the size of the audience
2. Missing audience interactions such as email opens or clicks on social media ads
3. Poor or incorrect campaign optimization due to missed engagement
4. Incorrect or incomplete data ingested from your websites or multiple individual sources
5. Wrong insights, analytics, or reporting

Why do these failures occur?

While there can be several reasons for the failure of large-scale data and campaign operations, one of the most common is the lack of your customer engagement platform’s capabilities to deal with unpredictable patterns.

You’re not the only brand that may decide to run a flash sale. Maybe a large bank needs to process millions of data points at the start of the new fiscal year. Perhaps that video streaming platform has a spike in traffic because of the FIFA World Cup tournament.

But you shouldn’t suffer because your customer engagement platform is unreliable. Your revenue should not be impacted because your customer engagement platform cannot deal with unpredictable spikes in traffic or data operations.

How did MoEngage solve this challenge?

Our fundamental objective and guiding principle has been to achieve true elasticity across all of our systems.

Building an infrastructure that navigates the failures mentioned above requires 3 critical steps:

1. Data ingestion needs to happen in real-time at a scale of billions
2. The ingested data must be processed and prepared for use in campaigns
3. Comms over billions of emails, push notifications, text messages, in-app messages, website banners, and more need to be successfully sent

While the team had successfully solved the BIG challenge of ingesting data in real-time, the next step was to ensure that our customers could efficiently utilize this ingested data to set up real-time campaigns.

This meant ensuring every component in our infrastructure (processing, memory, storage, and others) could adapt dynamically to changing demand–achieving true elasticity!

But attaining elasticity is easier said than done, especially after our ingestion speeds increased by 10x!

The engineering team had to guarantee that MoEngage could automatically scale resources up or down in real-time based on workload demands while ensuring consistent performance and SLA guarantees. The team also figured they needed to implement an automated monitoring and trigger-based scaling system that adjusted resources without human intervention.

This mammoth task did not deter our superstars.

Our engineers rolled up their sleeves and came up with a unique solution to efficiently process customer interactions (like clicks, searches, or purchases) from multiple sources while handling unpredictable traffic patterns and maintaining consistent processing times.

Btw, your tech and engineering buddies will love this, so make sure you show them what we built!

The solution built by our engineering team works through a sequence of components:

1. Events are first captured via APIs and stored in a ‘message store.’
2. A monitoring component continuously polls this store to analyze traffic patterns and predict resource needs.
3. When processing capacity needs to be increased (for example, when ingesting many data points or sending out millions of comms at once, like in your flash sale), a ‘clone orchestrator’ dynamically creates additional processing instances to handle the workload.

Neat, isn’t it?

The system’s ability to scale resources up and down based on real-time demand and historical traffic patterns makes it innovative and unique. Our engineering team used asynchronous, non-blocking methods to write data efficiently and then distribute events across resources to prevent a single application from monopolizing system resources.

How does MoEngage’s elastic infrastructure help you?

Our innovative solution predicts traffic patterns and dynamically scales our resources to maintain service level agreements (SLAs) even during unexpected traffic spikes.

So, a video streaming platform needing extra resources from MoEngage to help it drive more viewers to the FIFA World Cup tournament will not impact your plan of launching a flash sale!

No incomplete data ingestion attempts.
No missing customer interactions.
No incorrect analytics.
No failed comms.

Oh, and I forgot to mention – ALL of this happens while keeping your data safe and secure, thanks to our patented technology that tokenizes PII information!

If you’re interested in reading more about how this system works, check out this detailed explanation below:

Key takeaways

As a brand that caters to millions of customers, you must ensure smooth, efficient, and successful data operations and campaign management.

A reliable customer engagement platform (CEP) is crucial to ensuring data is ingested from all available sources and is prepped and ready for use in building campaigns. It also ensures comms are delivered to the right audience without snafus. Most importantly, your CEP must not ‘break’ when multiple brands scale up their operations and needs at the same time.

A single failure in any step leads to missed opportunities, lost revenue, and a negative impact on your brand reputation.

MoEngage understands the need for a reliable CEP, and we’ve addressed the challenges via a 3-step framework:

1. Real-time data ingestion at a scale of billions (read more about it here)
2. Processing and preparation of trillions of data points for use in campaigns
3. Successful delivery of millions of customer comms over multiple touchpoints

While this article delves deep into how we address the second step with a unique and innovative approach, watch this space for how we successfully deliver comms on multiple channels in our next article!

Reading Time: 5 minutes

In today’s digital world, customer data isn’t just getting bigger—it’s becoming more complex and layered. Brands capture information beyond fundamental interactions; each data point can hold a treasure trove of insights. Take a purchase, for instance: it’s not only about the items in a customer’s cart; each item comes with unique attributes that tell a richer story about the customer’s preferences and behavior.

To tap into this valuable, nested data, brands are turning to advanced data structures that make it easier to store and utilize complex information across various platforms, such as Customer Data Platforms (CDP) and data warehouses. By leveraging these structures, brands can achieve better organization and visibility of interconnected data, allowing them to activate this information for various purposes.

One of the most effective data structures brands are using is Object Data

Before we explore how brands use this powerful data type, let’s take a moment to understand what Object data is and how it works through a relatable example.

What is Object Data?

Object Data is a flexible data type that organizes information as key-value pairs. It means you can create a set of attributes that describe another attribute. In simpler terms, when you define a custom attribute as an object, you can include additional details that provide a deeper understanding of that object.

Let’s say we have a pet products brand that offers various pet pedigree options. This brand would maintain customer profiles with standard details like name, demographics, and contact information. However, they would also analyze information about each customer’s pets, which could vary widely. Each pet can have unique attributes, such as species, age, breed, and favorite treats.

This pet-related information can be neatly organized under a single attribute called ‘Pets,’ categorized as an Object type. Inside this ‘Pets’ object, you can store multiple attributes—’species,’ ‘age,’ ‘breed,’ and ‘treats’—all in one cohesive structure, as shown in the image below.

Brands increasingly use Object data to store and work with complex information across their systems. They rely on this data to feed into their CEP platforms, like MoEngage, to create meaningful customer segments and deliver personalized experiences. However, significant challenges can arise when the platform doesn’t support object data.

Challenges Brands Face Today

With the growing expectation that CEP tools should support Object data, the lack of this capability creates several obstacles for brands:

• Manual Efforts to Transform Object Data into Simpler Formats

A common challenge is the manual transformation of Object data into more straightforward, simpler formats. Brands must break down complex, interrelated attributes into separate, flat ones to pass this data into their CEP platforms. For example, rather than storing an object like “item,” which may have attributes such as “color,” “size,” and “brand” nested within it, brands have to transform these into standalone attributes like “item-color,” “item-size,” and “item-brand.”

This process requires additional time and effort and leads to a bloated data structure with redundant attributes, complicating data management and making it more challenging to maintain consistency across the tech stack.

• Inability to Get a Consolidated View of Interrelated Data
  Fragmenting Object data into rudimentary attributes results in a disjointed and incomplete view of customer or event data. For instance, when a brand captures data on a customer’s pet, characteristics like “pet type,” “pet age,” and “pet gender” may be broken down into individual points, making it hard to maintain a holistic view of the pet profile.

This disjointed approach affects the accuracy and depth of insights, as brands can’t easily access a unified view of how these attributes relate. Without this consolidated view, understanding customer behavior becomes fragmented, making it challenging to drive meaningful engagement strategies.

• Complex Querying of Fragmented Data with Numerous Attributes
  
  With data spread across multiple attributes, querying becomes significantly more challenging. Brands must create complex queries to activate this data, which slows down the analysis process and raises the risk of errors. Moreover, this fragmented approach restricts the depth of captured and utilized data, making it difficult to handle more advanced use cases or deliver personalized engagement effectively at scale.

MoEngage Support Object Data

MoEngage enables seamless ingestion, storage, and activation of Object data, allowing brands to bypass the challenges previously mentioned and leverage this rich data for impactful engagement.

What This Means for Brands:

• Streamlined Ingestion and Storage: With MoEngage’s support for Object Data, brands can easily integrate this data into the platform. It leads to a smoother data flow, eliminating the need for manual transformations and reducing the chances of errors. Brands can effortlessly collect and store nested data, helping maintain the integrity and richness of the information.
• Unified Customer View: Brands can view nested attributes under Object Data together, providing a consolidated view of customer data. This unified perspective enables clear understanding, facilitates quicker insights, and empowers brands to make informed decisions. With all relevant attributes integrated, brands can quickly identify patterns and trends, allowing them to tailor their engagement strategies effectively.
• Easy Activation of Object Data: MoEngage simplifies the activation of Object Data, enabling brands to leverage it for deeper targeting and more personalized engagement. Brands can run advanced segmentation queries on nested attributes and use these details for highly tailored campaigns that resonate with individual preferences, driving engagement and loyalty.

How brands can activate Object Data across industries

E-commerce:

An E-commerce brand tracks items added to the cart, including their attributes, as ‘Object’ data. They can create a targeted audience of customers who have added apparel items to their carts with a total value exceeding $200 and extend personalized discount offers to them to drive conversion.

BFSI:

A bank captures customer profiles and account details as ‘Object’ data. It may create a targeted audience of customers with active loans over $50,000 and savings balances under $10,000 and offer targeted solutions for better loan management.

Final Thoughts

Support for Object Data is critical for brands eager to seamlessly ingest and activate their rich data. It allows for a smooth flow of information from various tools into MoEngage, enabling brands to gain a unified view of customer data that enhances understanding and insights. Moreover, the easy activation of this data empowers brands to engage their customers more effectively, helping them go above and beyond in delivering delightful experiences.

Feel free to set up a quick demo if you want to get started or learn how to leverage ‘Object’ data on the MoEngage platform.

Reading Time: 4 minutes

It is the month of April.

As the Northern Hemisphere ushers in Spring as nature awakes to new life 🌸, the Southern Hemisphere is bathed in the vibrant colors of Autumn 🍂.

However, this year, the two different hemispheres of the globe were united by one similarity—hundreds of Marketers and Product Managers eagerly tapping their fingers in excitement with a broad grin.

‘Tis the season of MoEngage NEXT, after all!

In the H1 edition of MoEngage NEXT 2024, MoEngage’s CEO, Raviteja Dodda, and CPO, Nalin Goel, unveiled new and powerful capabilities of MoEngage.

Capabilities that help Marketers and Product Managers activate their customer data to make informed decisions.

Capabilities that help brands build delightful memories for their customers at scale.

Here’s a quick summary of what’s new at MoEngage:

1. Supercharge content creation and make your campaigns more impactful with Generative AI!

MoEngage’s Generative AI engine, Merlin AI, just got an upgrade!

Merlin AI can now generate impactful written content for your Push, Email, In-App and On-site Messaging campaigns. This upgrade reduces the time and effort you put in to find the right tone and keywords for your campaigns.

Thanks to Merlin AI, you can now witness higher Click-through Rates and Conversion Rates from your Customer Engagement campaigns!

👉 Read about the new additions to Merlin AI here!

2. Level up your recommendations with the same personalization engine that powers Amazon!

With MoEngage, you can now tap into the world’s most powerful recommendation engine to personalize your website!

We’ve merged our existing Smart Recommendation capabilities with our Website Personalization suite, with the aim of simplifying product discovery for your customers on your website.

You can use MoEngage to deliver tailored product or content recommendations to your website visitors based on their preferences, interactions, occasions, and buying patterns.

👉 Read more about MoEngage’s Website Personalization capabilities here!

3. Run complex experiments and personalization campaigns without breaking customer experience!

The flexibility to experiment, learn, and optimize quickly is a key capability that every marketer wants in their Customer Engagement platform.

Thanks to the latest addition to MoEngage’s Website Personalization suite, this wish has come true!

MoEngage’s Server-side Website Personalization feature allows brands to:

• Test different recommendation models,
• Optimize on-site search algorithms,
• Experiment with dynamic pricing and discounts,
• Launch different types of lead capture forms for maximum conversions,
• Test multiple subscription flows, and
• Personalize in-store digital kiosks

👉 Learn more about MoEngage’s unique Server-side Website Personalization prowess!

4. Activate warehouse data natively without any data syncing hassles!

MoEngage is now a Warehouse-native Customer Engagement platform!

This means that you can directly access and activate the data you have stored in your current data warehouse or cloud storage.

With MoEngage’s Warehouse Audiences, you can query your warehouse data directly, enabling you to create customer segments on the fly without moving or syncing any data.

Thanks to MoEngage, you will now save extensive costs by eliminating the need for reverse ETL tools, reduce engineering dependencies, while improving security and compliance.

👉 Read all about MoEngage’s Warehouse Audiences here!

PS – With the release of MoEngage’s Warehouse Data Analytics in the near future, you will also be able to analyze data and generate actionable insights directly from your warehouse.

5. Unlock deeper customer insights based on characteristics and instantly act on them!

MoEngage’s new launch, User Analysis, enables you to deeply understand your customers based on their characteristics.

By examining preferences, demographics, and other attributes of your customers, you can gather invaluable audience insights and drive data-driven engagement.

With a user-friendly interface, you can use MoEngage’s User Analysis to effortlessly select customer attributes, filter them based on other attributes, and visualize trends for deeper understanding.

👉 Read how MoEngage User Analysis can help you gather deeper insights here!

6. Create In-App Messaging campaigns without dev or design dependencies!

We’re excited to announce a new drag-and-drop In-App Message builder to help you create and edit custom HTML templates without design/development resources!

👉 Read more about MoEngage’s In-App Messaging capabilities here!

7. Verify email domain reputation before sending email campaigns for maximum deliverability!

On the MoEngage dashboard, you can now view the reputations of your email domains while configuring your email campaigns!

This update will help you boost email deliverability rates by letting you pick the domain with the best reputation.

You can also continually access the reputation trends for your domains and implement different strategies to keep improving them.

👉 Read more about this update here!

8. Set channel priorities for transactional messages and save costs!

MoEngage Inform has a new update, Smart Send!

Now prevent delivery failures of transactional messaging and ensure regulatory compliance by setting channel priority based on either the cost of the channel or audience preferences.

With Smart Send, you can create a fallback mechanism by ordering your channels in order of priority. This way, if your service alert or critical alert ever faces a failure, a backup message is sent via the next priority channel.

👉 Read about Smart Send here.

Conclusion and next steps

If you’re an existing MoEngage customer, contact your favorite account manager to get started with these new capabilities. Schedule a demo here if you’re new to MoEngage!

Reading Time: 3 minutes

Editor’s Note: In 2023, we spoke to several B2C marketers about Ramadan marketing campaigns and how different industries try to maximize engagement this time of the year.

Back by popular demand from marketers (and product owners), we’re continuing Ramadan Bytes 2.0  in 2024!

Our guest for this edition is Mohamed Sayed Abd Elsalam, Group Head of CRM Marketing, Loyalty & Customer Insights at Magrabi. In our deep dive, Sayed highlights the importance of Ramadan for marketers and explains how brands with both physical and digital presence can leverage these channels to their benefit.

In this blog, we’ll try to distill his insights into bite-sized, consumable content for all marketers (and product owners) to emulate this Ramadan season!

Brief Introduction About Mohamed Sayed 

Sayed is a customer marketing professional with 14 years of experience in customer retention and acquisition strategies. He has worked in luxury retail companies like MAGRABi, telecommunications MNOs like Orange, and NGOs like The British Council.

How Brands Can Better Plan for Occasions Like Ramadan

As per Sayed, Ramadan is an important season for the Middle Eastern market for a lot of industries. This is when brands invest a lot of money in marketing campaigns to capture as many transactions as possible and have a brand presence in consumers minds. There is usually an uplift in different categories. 

During Ramadan, different shopping and browsing behaviors by customers are observed. This is when marketers should invest in a lot of A/B testing to understand the correct engagement moment. 

Brands Leveraging Physical and Digital Presence Proven to Maximize Conversions During Ramadan

Brands combining physical and digital presence can unlock lots of opportunities in the customer journey, states Sayed.

He further suggests that both channels can complement each other, where customer can start their browsing journey online at the awareness and consideration stage and then make their first purchase at the store. Another good use case for this is the BOPIS model – Buy Online, Pick Up in Store.

Having the combination of both channels can also give more convenience to customers. Ramadan can be a tough month for customers, especially those who fast, so giving the customer the space to shop and to initiate and end his journey at any channel is a much-needed convenience, especially during peak and crowded times around Eid.

Stitching Offline and Online Data Together to Build a Unified Customer View

Sayed emphasizes the importance of stitching data for brands. He says that brands want to gather all the available information about their customers as it’d help them personalize experiences and build a good relationship.

A good example of data that can be used to build a unified customer view or 360 view is the customer’s online browsing behavior and transactional data (offline). A few other insights brands can look at are: 

• Interests on social media 
• Interaction across channels like WhatsApp, SMS, and Emails 
• Other basic data like demographics, birthdate, etc.

These insights combined together can help brands understand their customer better.

At the same time, there are so many ways to gather information from both channels and merge them together. One of the efficient ways of doing it is through an engagement platform like MoEngage.

2 Cents for Marketers Building Ramadan Campaign

Sayed has the following advice for budding CRM Managers and Growth Managers:

• Have a variation of attractive offers across the month, considering that offers have to get more attractive closer to Eid
• Give customers convenient options to complete their transactions on different channels. For example, free delivery offers or store pick-up options 
• Review historical campaigns from the previous Ramadan to understand the right timing through the day to send the campaigns.

Download your exclusive Ramadan Calendar here.

Ready to Win Big This Ramadan?

As consumer behaviors evolve and become more digitally focused, the integration of physical and digital channels will be paramount for brands seeking to maximize conversions during Ramadan. By leveraging both channels effectively, brands can provide a seamless and convenient customer experience, catering to the needs of customers during this busy and important time. 

The importance of data collection and analysis will also continue to grow as brands strive to understand their customers on a deeper level. With the right strategies in place, brands can capitalize on the opportunities presented by Ramadan, improve customer relationships, and drive business growth in the future.

Readers, you can connect with M Abdelsalam for more tips and advice on customer engagement during Ramadan and how to stay on top of customer’s minds.

What to Read Next:

1. How Super Apps Build Their Ramadan Marketing Strategies
2. Driving Engagement During Ramadan with Sustainability
3. Marketing During and Post-Ramadan: Insights From Apparel Group
4. Decoding Hyper-personalization in Retail Marketing with Sharad Harjai
5. Customer Retention Strategies for E-commerce and Retail Brands [Marketer Spotlight]