Category: Marketing

The Customer Data Platform (CDP) use cases with the highest return in ecommerce are not necessarily the most sophisticated ones but rather the ones that act on the variables that most affect the bottom line: customer acquisition cost, margin per sale, customer retention, and long-term customer value.

The return on investment (ROI) of a CDP depends on which use cases are activated and with what precision. Unified data only generates return when it translates into more precise decisions about who to target, when, with what message, and also who not to target.

For more use cases with full activation detail and ROI calculation, we recommend the ebook CDP: How to Turn Data into Business by Enrique Miralda, available in Spanish as a free download. 

1. AUDIENCE SUPPRESSION: HOW TO USE A CDP TO STOP WASTING AD SPEND 

A significant portion of advertising spend in ecommerce is wasted on impacting users who should not see the particular ad. For example, customers who bought in the last 48 hours should not still see ads for the same product. Users with an open complaint should not be targeted in an upselling campaign. Repeat buyers with a high probability of returning organically should not consume our acquisition budget. Without unified data across channels, these overlaps are unavoidable.

A CDP makes it possible to build and update in real time the segments that should be excluded from each campaign and sync them automatically with paid media platforms. The team no longer needs to manage lists manually or depend on periodic exports. Exclusion happens continuously and automatically.

To illustrate: an electronics retailer with both an online channel and physical stores found that 19% of its active Meta campaign audience had made a purchase in-store within the last 30 days. That segment was being excluded manually every two weeks, leaving an unnecessary window of exposure. By automating the exclusion with the CDP, that overlap disappears continuously. The budget stops being spent on reaching customers who have already converted, without any intervention needed from the team.

2.CHURN REDUCTION: HOW TO USE A CDP TO ACT ON AT-RISK CUSTOMERS BEFORE IT’S TOO LATE

Most companies detect churn after it has already happened: the customer cancelled their subscription, didn’t renew, or simply stopped buying. By that point, intervention is more expensive and less effective.

A CDP makes it possible to identify risk signals before the customer makes that decision: declining visit frequency, a drop in average order value, email opens without conversion, customer service tickets left unresolved. With those signals consolidated in a single profile, it becomes possible to trigger early interventions, segmented by risk level, with the right channel and message for each customer.

A cosmetics brand defined a risk segment based on three combined signals: more than 60 days without account access, a last order with a registered complaint, and no interaction with the last four emails. With the CDP, that audience was identified and triggered a specific retention sequence: a initial email acknowledging the previous complaint, a second contact with personalized content based on the type of products purchased, and lastly a phone call from the loyalty team for customers with more than 18 months of history. The interventions arrive while the customer can still be recovered, not after they have already decided to leave.

3. PERSONALIZED ONBOARDING: HOW TO USE A CDP TO AVOID “EARLY DEATH”

A customer signs up, makes a discounted first purchase and disappears without making a second purchase or generating real value for your company. In his ebook, CDP: How to Turn Data into Business, Enrique Miralda calls this “early death”: when a customer leaves before becoming profitable. It happens largely because onboarding treats everyone the same, regardless of how they arrived, what they bought, or which channel they used.

With a CDP, the onboarding process adapts to each customer’s real profile: acquisition channel, categories explored before the first purchase, behaviour in the first days of activity. Client communications can be adapted to respond to that data, instead of activating a generic sequence designed for an average customer who, in practice, represents very few people.

A fashion brand with both an online channel and a physical store found that 38% of its new customers had made their first purchase in-store but had never activated their digital account. That segment was receiving the same welcome sequence as online buyers: a series of emails with calls to action to discover new arrivals on the website. These emails were largely irrelevant to the segment that had not yet had any digital experience with the brand. With the CDP, this segment was identified and targeted with a progressive digital activation sequence offering exclusive benefits for linking their account to the loyalty card and a first online offer based on the categories purchased in-store. A customer who activates their digital account in the first few months has a very different long-term purchase profile from one who never does. The right onboarding does not just improve the initial customer experience; it can determine whether that customer ever becomes profitable.

4. REACTIVATING DORMANT CUSTOMERS: HOW TO USE A CDP TO REACTIVATE THE MOST UNDERUTILIZED CUSTOMER SEGMENT

In any customer base there is a segment with a much lower reactivation cost than acquisition, but which tends to receive less attention: customers who bought, had a positive experience, and simply stopped showing up. There is no clear reason for the drop-off, just a gradual loss of relevance or purchasing habit.

A CDP makes it possible to identify that segment precisely, distinguish it from customers with low reactivation potential, and personalize the message based on each person’s history. The goal is not to reach all inactive customers with the same offer, but to understand which reason to return is most likely to work for each profile.

A gourmet food ecommerce brand segmented its inactive customer base into three groups based on purchase history: customers oriented towards seasonal products, customers with high historical frequency but a low average order value, and customers with few purchases but a high average order value. Each group received a different reactivation campaign in both content and offer. The first group received communications tied to seasonal new arrivals, with no discount; the second, a volume promotion; the third, early access to a product launch. When the message responds to each customer’s real history rather than a generic offer, reactivation stops depending on the discount and starts depending on relevance.

5. OPTIMIZATION OF PROFIT MARGIN: HOW TO USE A CDP TO OFFER THE RIGHT DISCOUNTS

A discount given to a customer who was going to buy anyway is not a promotion. It is an unnecessary erosion of your profit margin. And it happens constantly because without behavioral data there is no way to know who needs the incentive and who does not.

With a CDP, the discounts you offer can be calibrated. For example, you can offer a non-monetary benefit for those who already have high purchase intent and a discount only for those who genuinely need it to decide. Your conversion rate may stay the same, but the margin increases.

An omnichannel fashion brand used its CDP to identify a segment of customers with high-intent behavior: three or more visits to the same category within five days, products added to the cart, and repeated visits to the size guide page. That segment was automatically excluded from the 15% coupon offer planned for that Friday and instead received an email showing the products they had viewed and their availability in their size, with no financial incentive. A high-intent customer does not need the discount to decide and giving it to them anyway is absorbing a cost does not increase conversions. It is one of the cases where the ROI of a CDP materializes most directly, not by doing more, but by optimizing profit margin without affecting conversions.

MORE USE CASES WITH REAL IMPACT

Enrique Miralda documents high-impact use cases in his ebook, with full detail on how to activate each one, what to measure, and how to calculate the real impact.

Acquiring a new customer costs between five and twenty-five times more than retaining an existing one. And yet, most retail companies continue to direct the majority of their marketing budget toward acquisition.

And yet, most retail companies continue to direct the majority of their marketing budget toward acquisition, even though 65% of their revenue comes from just 8% of their most loyal customers.

The explanation seems obvious. Without acquisition, there is no business.

But framing client strategy as a choice between acquisition and retention is an oversimplification. The real problem is that many companies do not have a precise understanding of when a customer becomes profitable. And without that answer, any debate about budget allocation is, at best, an educated guess.

Striking the right balance between acquisition and retention depends on three factors that vary significantly across businesses: product margin, purchase type and the point in the customer cycle at which real value is generated.

For a premium furniture or appliance retailer, the cost of acquisition may be recovered on the first order. The customer arrives with the decision practically made, clear purchase intent and a high ticket value. In that case, the strategic priority is not necessarily to accelerate a second purchase, but to convert a good experience into a recommendation.

For a fashion brand investing in paid social and offering welcome discounts, the first purchase is likely not to be profitable. Profitability typically arrives with the second purchase, when the customer buys without an incentive.

For a jewelry brand or luxury label, the return can take years to materialize, sometimes not until the third or fourth transaction. Acquisition costs are high and purchase frequency very low. The key to profitability lies in the long-term client relationship.

Without understanding that profitability threshold, it is impossible to decide how much to invest in acquisition, how much in retention, and which levers to activate at each stage of the customer cycle. Yet many companies still cannot answer that question clearly.

WHY FINDING THE RIGHT BALANCE IS SO DIFFICULT

Measurement and attribution present significant challenges.

Customers typically interact with a brand across multiple platforms before making their first purchase. A social media ad, a recommendation from a friend, an organic website visit or an email can all be part of the same decision-making process.

The complexity increases further after the first purchase.

Imagine a customer who is satisfied with their order, joins the loyalty program, receives an email three weeks later, and returns to buy. What brought them back? The product? The loyalty rewards program? The email? A combination of all three?

The effects accumulate over time and rarely appear cleanly in any dashboard. Understanding them requires a longer view of the customer cycle and a different attribution logic.

This is why many marketing decisions are still made based on partial or short-term metrics. And when metrics are incomplete, investment tends to concentrate on what is most visible and easiest to measure: acquisition.

THE REAL COST OF IGNORING RETENTION

The well-known finding that acquiring a new customer costs between five and twenty-five times more than retaining an existing one appears consistently throughout the marketing literature and has been widely cited by Harvard Business Review.

A similarly striking finding comes from relationship marketing research. It is estimated that a 5% increase in customer retention can increase profits by between 25% and 95%, according to research by Frederick Reichheld of Bainand Company.

The potential impact is too significant to treat retention as a secondary priority.

But activating that lever correctly requires more than increasing the volume of CRM campaigns. It requires understanding precisely who your customers are, what value they generate, and where they are in the cycle.

And that is where many organizations run into another obstacle.

HAVING DATA IS NOT THE SAME AS HAVING CUSTOMER INTELLIGENCE

Today’s retail companies accumulate large amounts of information: purchase histories, web behavior, loyalty data, customer service interactions and email opens.

But having data does not mean truly knowing the customer.

In many organizations, information is fragmented across multiple systems. Purchase history lives in Shopify. Loyalty promotions in another platform. Browsing behavior in Google Analytics. Support interactions in Zendesk. Email campaigns in Klaviyo.

Each system knows something about the customer, but none of them actually knows the customer.

Without a layer that unifies those signals, it is impossible to have a clear picture of who that customer is, what interests them, and where they are in the cycle.

That is why the first step toward a solid client strategy is usually not segmentation or artificial intelligence. It is data architecture.

A Customer Data Platform (CDP) makes it possible to unify those sources into a single, actionable customer profile. It is not just a marketing tool. It is the foundation that allows any acquisition or retention strategy to operate with real business logic.

HOW METRICS ARE CONVERTED INTO BUSINESS DECISIONS

When customer data is centralized, segmentation can begin to answer questions that are relevant to the business. Which customers generate the most value? Who is at risk of churning? And who has the greatest growth potential?

Predictive models can identify early churn signals, such as drops in purchase frequency, absence of interaction or a declining average order value.

What distinguishes companies that grow sustainably is their ability to convert operational metrics into business decisions. Many teams still measure email open rates, click-through rates and volume of communications sent. These are useful metrics, but they rarely answer the questions that actually matter.

How many at-risk customers were recovered?

How many moved from a first to a second purchase as a result of a specific initiative?

How much did the lifetime value of customers acquired last quarter increase?

When analysis focuses on the complete customer cycle, the debate between acquisition and retention begins to lose its relevance. Both become levers within a single strategy.

ROADMAP FOR BUILDING A CUSTOMER STRATEGY

1. Identify when your customer becomes profitable. Do not assume it is the same for every business. Knowing the threshold precisely is the prerequisite for everything else.

A premium furniture or appliance retailer may recover acquisition costs from the first order. A fashion brand running paid social with a welcome discount rarely sees profitability on the first transaction: the threshold comes with the second purchase, the one made without an incentive. A jewelry or luxury brand may not see a return until the third or fourth transaction, sometimes years later. When acquisition costs are structurally high and purchase frequency very low, the bet is not transactional but rather relational. 

2. Revisit the segmentation logic based on the business model.

For high-frequency businesses such as online supermarket or pharmacy, the priority is usually detecting early churn signals: small drops in frequency are warning signs that predictive models can identify before the customer is lost. For low-frequency businesses such as furniture or electronics, the key is to identify customers with the greatest potential for recommendation or to upsell at the right moment. If segmentation does not make this distinction, it is not serving the business.

3. Design initiatives for the critical moment in each business’s customer cycle.

For most ecommerce businesses, that moment is the transition between the first and second purchase, the most decisive and most neglected stretch. For high-frequency businesses, it is early churn prevention, where predictive intelligence adds the most value by acting on risk signals weeks in advance. For high-ticket businesses, it is the post-sale experience and the long-term relationship. The question is not “what campaign do we launch?” but “what does this segment need, in this type of business, at this specific moment?”

4. Define who owns the client strategy, with real authority over budget and KPIs.

In many organizations the customer belongs to several teams but to none in particular. Marketing, CRM and ecommerce each work on parts of the cycle, but no one has a complete view of the customer or direct responsibility for their profitability. In a business where the customer becomes profitable on the second purchase, someone has to be accountable for ensuring that second purchase happens.

5. Incorporate advocacy as a growth engine, especially in businesses where paid media shows diminishing returns.

A satisfied customer who recommends the brand can generate new customers at near-zero acquisition cost and with a retention rate higher than any paid channel. For high-ticket, low-frequency businesses, advocacy is not an add-on: it is the most efficient path to growth. For high-frequency businesses, it is the lever that closes the loop between retention and acquisition. In both cases, it is the most systematically overlooked element in marketing plans.

FLYDE TALKS 5

These questions will be at the center of FLYDE Talks 5, on 24 March at 6pm CET via LinkedIn Live, where FLYDE CEO and Founder Paco Herranz and digital strategy expert Enrique Miralda will discuss how to build a true client strategy in retail.

You can register for the event here.  Kindly note that the session will be held in Spanish.

Marketing attribution measures the contribution of individual channels and touchpoints to conversions. It provides insight into how each interaction influences the customer journey and is critical for the optimization of budget allocation and campaign performance. Accurate attribution requires integrated datasets, including CRM records, website analytics, advertising platforms, and customer engagement data. Without clean and comprehensive data, even advanced models will produce misleading conclusions.

For technically minded marketers, understanding the mechanics and limitations of different attribution models is essential for selecting and implementing an attribution strategy. Let’s look at common models and their practical applications.

COMMON ATTRIBUTION MODELS

Last-click attribution

This model assigns all conversion credit to the final touchpoint. It is simple to implement and useful for evaluating channels that directly close conversions. However, it neglects the influence of earlier interactions, which may have been crucial in acquiring and nurturing the customer such as social or display campaigns. Last-click attribution is often biased toward retargeting campaigns.

Limitation: Overvalues closing channels such as brand search or affiliates.
Problematic in: Ecommerce with significant upper-funnel investment (social, influencers).

Example: Imagine you work for an ecommerce business, and you want to run a retargeting campaign for users with abandoned carts. You impact your target audience through an organic social post, an email marketing campaign, or a series of display ads. Last-click attribution will measure which of these touchpoints directly closed the sale.

First-click attribution

First-click attribution allocates all credit to the initial interaction. This highlights the role of awareness campaigns at the top of the funnel. While valuable for assessing early engagement, it can overvalue the first touchpoint and fail to recognize the cumulative effect of multiple interactions.

Limitation: Ignores remarketing or nurturing efforts.
Problematic in: B2B, long cycles, or high-involvement products.

Example: Suppose you run a SaaS company launching a new product. A potential customer first discovers your brand through a LinkedIn post, later sees a display ad, and finally clicks a retargeting email to sign up for a trial. First-click attribution will assign all credit to the LinkedIn post, which is useful if you are looking to discover which channel is driving the most awareness at the top of the funnel.

Linear attribution

Linear attribution distributes credit evenly across all touchpoints. Each interaction receives an equal fraction of the conversion value. This model is appropriate when all touchpoints are expected to contribute similarly, but it does not differentiate based on influence or timing. Linear models are limited in handling complex journeys where certain touchpoints have disproportionate impact.

Limitation: Assumes all touchpoints carry the same weight.
Problematic in: Industries where one touchpoint clearly dominates.

Example: Imagine a fashion retailer running a multi-channel campaign including Instagram ads, an email newsletter, and a Google search ad. A customer interacts with all three touchpoints before purchasing. Linear attribution will assign equal credit to the Instagram ad, the newsletter, and the search ad, which is helpful when you want to understand how all touchpoints collectively contributed to the conversion.

Time decay attribution

Time decay attribution applies exponential weighting to touchpoints based on their proximity to conversion. More recent interactions receive higher credit. The decay function can be calibrated to match conversion windows. This approach accounts for recency effects but may undervalue early engagement in long-cycle campaigns. Also, campaigns with irregular conversion timelines may require recalibration to avoid skewed insights.

Limitation: Undervalues early demand-generation efforts.
Problematic in: Impulse-purchase consumer goods.

Example: Consider a B2B company with a long sales cycle. A lead first downloads an e-book via organic search, later engages with a webinar, and finally clicks a demo request email a month later. Time decay attribution will give the most credit to the demo request email while still recognizing the earlier touchpoints. This approach is useful when you want to emphasize touchpoints closer to conversion.

Position-based attribution

Position-based models assign fixed weights to first and last interactions while distributing the remainder across middle touchpoints. Common configurations include a 40-20-40 split. This model seeks to balance recognition of awareness and conversion touchpoints but may underestimate the impact of middle-channel interactions. In multi-channel campaigns with longer sales cycles, this model may not reflect true influence without adjustments.

Limitation: Ignores key mid-funnel touchpoints.
Problematic in: Services with many intermediate steps in the funnel.

Example: A travel agency runs campaigns across Facebook ads, Google search, and through email marketing. A customer first clicks a Facebook ad, then sees a Google search ad, and finally completes a booking through an email. Position-based attribution might assign 40% credit to the Facebook ad, 20% to the Google ad, and 40% to the email, which balances recognition of the initial and final interactions while acknowledging the middle step.

Algorithmic attribution (Data-driven)

Algorithmic attribution leverages historical data and machine learning to assign conversion credit dynamically. Unlike linear models that assign equal credit, algorithmic models weight interactions based on observed influence on conversions. Models consider correlation and causation between touchpoints, the order of interactions, and channel-specific performance. Algorithmic attribution requires large, clean datasets and robust analytical infrastructure but provides the most granular and accurate insights.

Limitation: Requires large volumes of clean, traceable data.
Problematic in: SMBs, businesses with little data history, or with offline channels.

Example: An e-commerce platform uses multiple channels including Instagram, paid search, display, and email. A customer interacts with several of these before converting. Algorithmic attribution analyzes historical data to determine the actual influence of each touchpoint and might assign higher credit to Instagram and display, with less to email, based on observed contribution patterns. This is useful when you have enough data to understand nuanced interactions and want the most precise insight into channel performance.

HOW DO I CHOOSE THE ATTRIBUTION RIGHT MODEL FOR MY BUSINESS?

The choice of attribution model depends on business objectives, the complexity of the customer journey, channel mix, and available data infrastructure. Awareness-driven campaigns may benefit from first-click or position-based models, while performance-driven initiatives can leverage last-click or algorithmic models. Evaluating model outputs against historical performance helps identify biases and refine the attribution framework.

To make any of these models truly reliable, businesses must first centralize their data. Bringing CRM, ad platforms, web analytics, engagement data, etc. into a single platform produces the data set that serves as the foundation for advanced modeling. Incomplete or fragmented datasets, inconsistent UTM tagging, and discrepancies between CRM, analytics, and advertising platforms compromise attribution models. Failure to track activity across devices and browsers can also result in misallocated credit and inaccurate performance insights.

Without that unified dataset, even the most sophisticated attribution methods can only produce fragmented and biased insights. A Customer Data Platform (CDP) like FLYDE is designed to centralize data sources and enable implementation of advanced data analytics. Contact us at FLYDE to schedule a demo and we can show you how to prepare your data and implement advanced attribution modelling.

In this episode of FLYDE Talks, Luis Serrano, Head of Growth at Real Madrid, sits down with Paco Herranz, Founder and CEO of FLYDE, to explore how the concept of Growth Marketing has evolved in an environment shaped by artificial intelligence, extreme personalization, and data privacy—and how it can be applied to the unique context of football.

With this new episode of FLYDE Talks, we continue to bring together leading voices from across the marketing world to discuss, clearly and without jargon, the ideas that are transforming the industry today.

WHAT DO WE REALLY MEAN BY GROWTH?

Paco opens the conversation with a question every growth professional has asked themselves: What exactly do we mean by “growth”?

For Luis, the term has expanded significantly. What once referred to scaling digital channels now means understanding growth from a holistic perspective: digital and physical channels, data, user experience, and brand value.

“We’re no longer just talking about digital channels,” he says. “We’re talking about everything.”

Growth is no longer about funnel optimization alone; it’s about connecting every touchpoint between the user and the brand under one unified objective.

REAL MADRID’S ‘NORTH STAR’: THE SATISFIED MADRIDISTA

Paco and Luis agree that successful growth depends on having a clear metric that guides the overall strategy: the famous North Star Metric.

At Real Madrid, that North Star is the satisfied Madridista: a fan who trusts the club, shares their digital identity, and enjoys a full, consistent experience across online and offline channels. The satisfied Madridista is the “guiding star” behind every growth initiative at the club.

To measure that satisfaction, the team tracks KPIs that range from fan acquisition and retention, including engagement metrics, NPS (Net Promoter Score), and churn. The challenge lies in turning every interaction into a source of value, for both the fan and the brand.

‘ONE FAN, ONE EXPERIENCE’: UNIFYING DATA FOR ONE-ON-ONE PERSONALIZATION

From FLYDE’s perspective, growth can only scale if data is unified. It starts with data collection—first, second, and third party—and continues with data unification to create a single customer profile, the key to enable precise segmentation, activation, and measurement.

The unified customer profile is the foundation of any growth strategy. It allows teams to move from analysis to action: building micro-audiences, orchestrating omnichannel campaigns, and, most importantly, measuring attribution accurately. The real challenge isn’t gathering more data, but rather knowing where each impact truly comes from.

Real Madrid applies this philosophy with a simple vision: One fan, one experience.
From email to app, store to stadium, every interaction is tracked and optimized to deliver the best possible experience within the club’s ecosystem.

The ultimate goal is true micro-segmentation, evolving from “many-to-many” to “one-to-one,” offering each fan exactly what they need. As Luis puts it simply: “If I have a cat, why are you offering me dog food?”

Read more about the importance of data unification.

‘SEO IS NOT DEAD, AND GEO IS SEO.”

“SEO isn’t dead—and GEO is SEO.”

Through experiments with LLMs and metasearch engines, Luis found that generative AIs don’t search websites directly—they search search engines. In other words, for an AI to index your content, you still need to rank well on traditional search engines first.

So optimizing for visibility in AI results still means doing SEO: paying attention to microformats, structured data, and quality content. New tools, like Adobe’s LLM Optimizer, can even estimate how readable and indexable your content is for AI.

The takeaway is clear: the future of organic traffic will be hybrid and those who master SEO today will remain visible in the age of AI. At least based on what we know today.

MACHINE LEARNING AND GENERATIVE AI: THE NEW MARKETING DUO

Luis asks Paco how FLYDE integrates AI, and Paco explains that for him, AI isn’t a trend but a natural evolution of data-driven marketing.

FLYDE uses Machine Learning for key tasks:

• Measuring KPIs
• Detecting customer value patterns and projections
• Predicting churn
• Recommending products or audiences

On top of that, FLYDE has developed Brain, a generative AI layer across the platform. Brain acts as a data assistant, enabling any user, technical or not, to interact directly with their data ecosystem: building audiences, suggesting actions, analyzing campaigns, or even generating complex queries.

Its mission is to democratize access to data and remove the “blank page fear.”

As Luis jokes: “AI is like a shrimp cocktail—we have so many things to pick from that we don’t know where to start.”

THE CDP: THE NATURAL EVOLUTION OF THE CRM

Both speakers agree that a Customer Data Platform (CDP) like FLYDE is the strategic backbone that ties everything together.

At Real Madrid, the CDP is built around the Madridista Community, integrating data from e-commerce, the app, the Bernabéu tour, RMP Play, social media, and even in-stadium activity.

Thanks to this integration, the club can microsegment and activate data in real time. For instance, if a user is near the stadium, the system can trigger a personalized app notification with an offer or reminder.

The result is a coherent, contextual, and measurable experience—where data powers emotion.

Contact us to learn more about what the FLYDE CDP could do for your business. 

PRIVACY AND REGULATION: GROWTH WITH RESPECT FOR THE USER

The new era of marketing comes with a non-negotiable condition: privacy.

Luis emphasizes that Real Madrid applies a strict transparency policy because trust is part of the fan experience itself.

Meanwhile, FLYDE advocates for ethical data usage. Its technology supports privacy-safe attribution using inferred data (such as average age, income level, or household type) to improve performance without compromising user trust.

The goal isn’t to know more, but to use what we already know better.

TOWARD A MORE HUMAN, MEASURABLE MARKETING

Growth marketing in 2025 operates at the crossroads of AI, CDPs, and customer experience.

But beneath it all lies a single principle: brands grow when they understand that data only matters when it creates satisfaction, trust, and real value.

Paco leaves us with an important conlcusion. Sustainable growth is born from the connection between data and people—and when done right, that connection is the future of marketing.

Growth marketers today need to be able to optimize campaigns across multiple channels, unify fragmented data, manage acquisition costs, and adapt to rapid industry changes. The challenge is not just executing campaigns but building a scalable, data-informed growth engine that drives sustainable results. The world of marketing has never been more complex… nor full of opportunity!

In this blog, we explore five critical challenges marketers face and how leveraging data activation and analytics can turn these obstacles into growth opportunities. By addressing these critical areas, marketing professionals can implement measured, scalable strategies that drive both immediate performance and long-term customer value.

For those who want to explore these challenges in depth, watch the full recording of FLYDE Talks 2 with Luis Serrano, Head of Growth at Real Madrid, where he shares practical insights on building scalable, data-driven growth strategies.

CHALLENGE 1: BALANCE SUSTAINABLE LONG-TERM GROWTH WITH SHORT-TERM GAINS

Marketing teams often face tension between demonstrating immediate results and nurturing long-term value. Sacrificing customer retention and lifetime value in favor of marketing qualified leads (MQL) or short-term revenue can create the illusion of growth while undermining sustainable performance. Advanced analytics can quantify the impact of retention versus acquisition, allowing leaders to defend their strategy with data rather than intuition. By modeling customer lifetime value alongside near-term KPIs, teams can make informed trade-offs that satisfy stakeholders without compromising long-term growth.

CHALLENGE 2: UNIFYING FRAGMENTED DATA

A single, reliable view of the customer is foundational for effective growth marketing, yet most organizations struggle with siloed systems, from CRM and ads platforms to product analytics and web tracking. Fragmentation reduces visibility and often biases measurement toward easily attributable channels.

Centralizing data enables marketers to track the full journey, uncover hidden opportunities, and deploy more precise targeting strategies. Tools that integrate data in real time and provide actionable segmentation allow for campaigns that are both sophisticated and measurable. If you are looking to build a sustainable and scalable growth strategy, unified data is an absolute must.

CHALLENGE 3: CHANNEL SATURATION AND RISING ACQUISITION COSTS

Undoubtedly many growth marketers are feeling the pressure of escalating customer acquisition costs (CAC) and formerly profitable channels becoming less cost ineffective. Growth marketers need frameworks for modeling CAC sensitivity and simulating different channel strategies to stay ahead. By forecasting cost changes and evaluating alternative acquisition levers, teams can anticipate disruptions and allocate budget dynamically, rather than reacting when profitability declines. This analytical rigor separates reactive teams from those driving consistent, scalable growth.

CHALLENGE 4: AI ADOPTION THAT ADDS TRUE VALUE 

Artificial intelligence can accelerate campaign execution and uncover new insights, but its effectiveness depends on thoughtful application. Simply using AI to automate routine tasks does not differentiate a brand. Leading teams integrate AI into predictive modeling, hyper-personalization, and attribution analysis. To maximize impact, efficient algorithms must be used together with human creativity. The key is leveraging AI to provide insight and support smarter decision making rather than merely speed or volume.

CHALLENGE 5: CONTINUOUS INDUSTRY EVOLUTION

Growth marketing operates in a dynamic environment where technological, regulatory, and behavioral shifts can rapidly alter the rules of engagement. Strategies must be adaptive, incorporate scenario planning, and operate within agile measurement frameworks. Teams that continuously stress-test assumptions and adapt to emerging trends are better positioned to respond to disruptions while maintaining momentum.

LEARN MORE FROM AN INDUSTRY LEADER

The challenges of growth marketing are complex, but actionable strategies exist. In FLYDE Talks 2, Luis Serrano, Head of Growth at Real Madrid, to explores these topics in depth. Watch the full recording of the event. 

For many businesses, Black Friday–Cyber Monday (BFCM) is the most intense moment of the year for the business’s data. Traffic surges, transactions peak, and first-time buyers arrive in waves. For many brands, this weekend generates a huge portion of their annual revenue.

But the real objective shouldn’t be just to make the sale. The goal should be to convert those new buyers into long-term customers. Without a way to unify and activate data, brands often miss the opportunity to build loyalty after the sale, leaving a massive amount of valuable customer information and opportunity for growth on the table.

THE DATA CHAOS OF BFCM

During BFCM, data flows in from every direction: websites, mobile apps, paid ads, emails, and ecommerce platforms. The omnichannel nature of data sources presents a significant challenge. Customers appear under different IDs, creating fragmented and duplicated records that are almost impossible to activate for retargeting or loyalty campaigns later on. Instead of starting the new year with a stronger customer base, many brands are stuck cleaning up a data mess.

This is where a Customer Data Platform (CDP) becomes an essential tool. A CDP like FLYDE is built to handle this exact challenge by bringing all your customer data into one unified, intelligent platform.

HOW CAN FLYDE HELP?

Real-Time Identity Resolution

One of the most pressing technical challenges of BFCM data management is identity resolution. With traffic and transactions peaking, businesses need a way to link anonymous browsing sessions to known customer profiles.

A CDP like FLYDE combines first-party data such as emails, phone numbers, and loyalty IDs with anonymous digital signals. By resolving identities in real time, the platform eliminates duplicate records and builds a single, accurate profile of each customer. This ensures that even when activity spikes, businesses maintain a complete and coherent view of their customers’ journeys.

Smarter Segmentation

Not all BFCM buyers are equal. Some are loyal customers taking advantage of promotions. Others are deal hunters who may never return without the right follow-up. Treating both groups the same reduces efficiency and limits retention.

McKinsey research shows that companies excelling at customer personalization generate 40% more revenue from those activities than their peers. Advanced segmentation supported by a CDP enables businesses to separate high-value customers from bargain-driven shoppers. For example, FLYDE allows marketers to distinguish between customers who only purchase discounted items and those who also explore full-price collections. This insight shapes tailored post-purchase communication that increases the chance of long-term retention.

Omnichannel Activation

Clean, segmented data is only valuable when it can be activated across the right channels. Modern CDPs sync enriched profiles with platforms such as Meta Ads, Google Ads, email marketing tools, and SMS systems. This allows marketing teams to stop wasting spend on customers who already converted, deliver personalized journeys in the channels where customers are most active, and build targeted retargeting campaigns that deliver higher returns.

By closing the loop between data collection, unification, and activation, businesses ensure that the customer relationships formed during BFCM extend beyond a one-time transaction.

A PRACTICAL EXAMPLE

A consumer electronics retailer uses FLYDE ahead of BFCM to connect its Shopify store, email marketing platform, and Meta Ads. They create unified profiles for 120,000 customers and segment them by purchase margin, that is, those who buy discounted items versus those who pay full price.

After BFCM, the retailer can use FLYDE to trigger automated post-purchase journeys:

• High-value buyers receive loyalty rewards and exclusive early access to new products.
• One-time bargain hunters receive special re-engagement offers to encourage a second purchase.

Impact:

• Reduced duplicate ad spend.
• Higher retention rate for BFCM buyers compared to the previous year.
• Increased email engagement with tailored post-BFCM messaging.

READY TO GET STARTED?

BFCM is more than just a sales spike; it’s a data spike. Without a unified view of your customers, you’re missing a massive opportunity for long-term growth. With a CDP like FLYDE, brands can transform this sales surge into structured intelligence, ensuring they build a loyal customer base instead of just generating short-term revenue.

Ready to maximize your next BFCM? Contact us at FLYDE to book a demo and see how a our intuitive CDP can revolutionize your BFCM data strategy.

At FLYDE, we talk with marketing teams every day about data, performance, and the customer journey. We often hear the same questions, so we’ve gathered the five most common ones, with clear answers and links to our blogs for more in-depth information.

1. WHAT IS MARKETING MIX MODELING (MMM) AND HOW CAN WE IMPLEMENT IT?

Marketing Mix Modeling (MMM) is a statistical technique that helps you understand which marketing channels are actually driving results. It analyzes variables like advertising, pricing, promotions, and seasonality to measure their impact on sales, conversions, and revenue. It uses historical, aggregated data, so it doesn’t rely on cookies or user-level tracking. That’s why we’re seeing more and more marketing teams turn to MMM.

More information on MMM and how to implement it:

👉 What is Marketing Mix Modeling?

2. CAN WE IDENTIFY ANONYMOUS WEBSITE USERS WITH NAVIGATION FINGERPRINTING AND IS THEIR CONSENT NEEDED?

Browser fingerprinting is a technique that can identify a device based on its technical characteristics (browser, screen resolution, language, etc.) without installing cookies. It allows you to track anonymous users across multiple sessions to better analyze user behavior in the early stages of the customer journey.

More information on fingerprinting and how to ensure user privacy:

👉 Navigation Fingerprinting: Tracking Anonymous Users Without Cookies

3. WHAT IS RFM ANALYSIS AND HOW DOES IT IMPROVE CUSTOMER SEGMENTATION?

RFM analysis is a statistical technique that involves analyzing customer data in terms of Recency (how recently they purchased), Frequency (how often they purchase), and Monetary value (how much they spend) to gain insights into the behavior of different customer groups. These groups can be used to optimize customer segmentation, improve retention, and maximize ROI and Customer Lifetime Value (CLV).

More information on RFM analysis and the role of a CDP:

👉RFM Analysis

4. WHAT IS A CUSTOMER DATA PLATFORM (CDP)?

A CDP unifies data from multiple sources, organizes it into unique profiles, and makes it actionable in real time. A CDP is a key component of modern data-driven marketing strategies.

We explain this in more detail and outline the benefits a CDP can bring to your business:

👉What is a Customer Data Platform (CDP)?

5. WHAT IS THE PROCESS FOR IMPLEMENTING A CDP?

FLYDE is an intuitive and simple platform. It can be implemented without the need for a specialized technical team. We invite you to request a demo and we can show you the process in detail.

Schedule a demo:

👉Contact FLYDE

DO YOU HAVE ANOTHER QUESTION ABOUT DATA-DRIVEN MARKETING?

Contact us to let us know, and we’ll address it in a future post. Plus, if you want to see how a CDP can improve your data-driven marketing strategy, request a demo with FLYDE and we can discuss.

Marketing Mix Modeling is a statistical technique that helps marketers understand how different variables such as advertising, pricing, promotions, and seasonality impact business outcomes like sales, conversions, or revenue.

In simpler terms, MMM tells you how much each part of your marketing mix contributes to your results. It is based on historical, aggregated data, without requiring cookies or user-level tracking.

HOW DOES MMM WORK?

MMM analyzes data over time, typically at least two years, to isolate the incremental impact of each factor. It can measure both online and offline efforts such as:

• Paid search, social, and display advertising
• Traditional media like TV, radio, and print
• Promotions and pricing strategies
• Seasonality and external events like weather or competitor activity

By modeling these variables together, MMM provides attribution at the channel level and helps marketers understand the return on the investment (ROI) made in each channel.

WHY IS MMM MAKING A COMEBACK?

Marketing departments are increasingly accountable for justifying every cent they spend and demonstrating clear ROI on their activities. With budgets tightening and the deprecation of third-party cookies looming, many brands are looking back to a powerful, proven solution: Marketing Mix Modeling (MMM).

With the rise of user-level tracking via cookies and clickstream data, MMM took a back seat to multi-touch attribution (MTA). MTA is a marketing measurement model that assigns credit to multiple touchpoints along a customer’s journey to determine which channels and interactions influenced a conversion. Digital tracking, however, is facing significant obstacles due to privacy regulations. As a result MMM is becoming more relevant, because it uses aggregated data as opposed to user-level tracking, and covers both online and offline channels. 

MMM VS. MULTI-TOUCH ATTRIBUTION

Rather than choosing one or the other, many brands are now combining MMM and MTA. MMM provides strategic, high-level planning while MTA supports tactical, in-the-moment optimization.

HOW FLYDE FITS IN: THE ROLE OF A CUSTOMER DATA PLATFORM (CDP)

At FLYDE, we help businesses unify and activate their customer data. This includes making the most of aggregate-level signals, which is where a Customer Data Platform (CDP) plays a crucial role in enhancing MMM.

A CDP is a centralized system that collects and unifies customer data from various sources (online, offline, behavioral, transactional, demographic) into a single, comprehensive customer profile. While MMM focuses on aggregate, historical data for channel-level insights, a CDP complements this by:

• Centralizing all marketing and sales data: A CDP acts as the single source of truth for all your customer-related data, making it easier to gather the diverse datasets needed for robust MMM. This includes data from CRM, ERP, web analytics, advertising platforms, and more.
• Cleaning and enriching datasets for modeling: CDPs are designed to ingest, cleanse, and standardize data from disparate sources. This ensures the data fed into MMM models is accurate, consistent, and complete, leading to more reliable insights. A CDP can also enrich data with additional attributes, improving the depth of your analysis.
• Once MMM provides insights on channel effectiveness and optimal budget allocation, a CDP can act as the bridge to activate these insights. It allows you to push segmentation and targeting recommendations derived from MMM directly to your ad platforms, email marketing tools, and CRM for more effective campaign execution.
• While MMM works with aggregated data, a CDP can provide a richer understanding by linking these aggregate insights with more granular behavioral data. Even without cookies, techniques like navigation fingerprinting (which anonymously tracks user journeys based on browser characteristics and other non-personally identifiable information) can be ingested by a CDP. This allows for a holistic view, where broad MMM findings can be refined and informed by observed customer behaviors, enabling more precise targeting and personalization within privacy boundaries.

This means smarter planning without compromising privacy and better orchestration of omnichannel efforts, from the first anonymous visit to long-term customer retention.

Marketing Mix Modeling aligns with the marketing industry’s most predominant trends: smarter measurement, responsible data use, and data-driven channel strategies.

HOW FLYDE CAN HELP

Want to learn more about how FLYDE supports MMM and helps unlock real omnichannel impact?

Contact us to schedule a meeting to discuss how a Customer Data Platform (CDP) like FLYDE can enable you to implement MMM in your business. 

Longer days. Different routines. Out-of-office replies. Summer changes everything, including your customers’ behavior.

People shop at different hours, dine in new places, travel more (or less), and respond to different channels. The usual patterns of when, where, and how people buy can shift significantly. If your marketing is based on static segments or last year’s assumptions, you risk missing key moments of engagement.

The good news is that with a strong data strategy (and a Customer Data Platform like FLYDE) summer becomes an opportunity. You can fine-tune your segmentation, messaging, and timing to reach customers at the right moment, through the right channel, with offers that align with their seasonal behavior.

PREDICT WHAT’S NEXT WITH BEHAVIOR-BASED MODELS

Customer journeys are rarely linear. In summer, they’re even more unpredictable. FLYDE helps bring clarity to seasonal shifts by:

• Unifying real-time customer behavior across channels like web, email, mobile app, social media, and in-store POS systems.
  
  
• Applying predictive models to anticipate return likelihood, product preferences, or even travel planning windows.
  
  
• Segmenting audiences dynamically based on changes in browsing, booking, or purchase behavior.

Instead of reacting to changes after they happen, FLYDE allows you to anticipate them, so you’re always one step ahead.

HYPERPERSONALIZATION OF MESSAGING AND OFFERS

Seasonal relevance matters. Whether it’s travel planning or heatwave-driven impulse buys, being able to respond quickly makes the difference. With FLYDE, you can activate insights in real time to:

• Deliver personalized promotions based on forecasted demand and individual behavior.
  
  
• Reallocate ad spend dynamically when a campaign underperforms with specific segments.
  
  
• Automate email and advertising campaigns triggered by behavior, not just by a marketing calendar.

This means more relevant interactions, improved engagement rates, and smarter use of your marketing budget.

INDUSTRY IN FOCUS: HOTELS, RESTAURANTS, AND RETAIL

Hotels

A busy hotel could analyze in-stay guest behavior during the peak summer season. By unifying data from their app, restaurant bookings, spa services, etc., they can identify key patterns—such as which guests are most likely to book add-ons like late checkouts, poolside dining, or spa treatments. This allows them to activate targeted in-stay messaging tailored to each guest’s profile, promoting high-margin services at the optimal moment (for example, a post-check-in spa offer or late lunch promo after a pool reservation). As a result, they can expect to see not only an increase in revenue from add-ons but also higher satisfaction scores in post-stay surveys, which in turn can translate into more returning guests.

Restaurants

A high-traffic restaurant might notice that weekday lunch volume drop during the summer months, while evening and weekend group bookings rose. If they identify this pattern early, they can adjust their marketing strategy, and promote group dining offers around local events. 

Retail

A fashion retailer may notice that in summer, browsing behavior shifts from desktop to mobile, particularly in the afternoons. Using the behavioral data available with a CDP, they can adapt campaign timing and creative formats to favor mobile-first formats in order to increase click-through and conversion rates. 

MAKE SEASONAL SHIFTS YOUR COMPETITIVE ADVANTAGE

Many businesses feel the effects of seasonal changes, but few are ready for them. A Customer Data Platform del Cliente (CDP) like FLYDE

helps you move beyond static insights by connecting and activating your customer data in real time. With unified profiles and predictive intelligence, you can identify seasonal patterns early and respond with agility.

Instead of playing catch-up, you’re positioned to anticipate behavior and deliver personalized experiences that reflect real-time needs, whether it’s summer, back-to-school, or the holiday season.

Let’s make your data work harder this summer, and every season after. Contact us for a personalized demo of how to turn customer behavior shifts into strategic opportunities.

MADRID, SPAIN – June, 11, 2025 – FLYDE, a customer data platform offering an easy-to-use solution for businesses to unify fragmented data and drive growth, announces the successful launch of its new thought leadership series, FLYDE Talks. The inaugural event, entitled, “The Challenge of Attribution in Omnichannel Marketing,” drew nearly 200 registrants across Europe and Latin America, signaling strong demand for discussions on practical, data-driven marketing.

Held on June 5, 2025 via LinkedIn Live, the virtual event featured digital marketing expert Andrés Azpilicueta, broadcasting from Mexico City, Mexico alongside Paco Herranz, CEO of FLYDE, based in Madrid, Spain.

Marketers and business leaders tuned in for a candid conversation on the limitations of traditional attribution methods, and how the centralization of customer data can help address the challenges of attribution in omnichannel marketing.

“We are thrilled with the overwhelming response to our first FLYDE Talks event,” said Paco Herranz. “The challenge of attribution in omnichannel marketing is a critical issue for businesses seeking efficiency and growth. Sparking interest among nearly 200 engaged professionals reaffirms the relevance of the series in driving meaningful conversations around data-driven marketing strategies.”

A DEEPER LOOK: INSIGHTS FROM THE LIVE DISCUSSION

The talk explored how marketers can go beyond siloed data and surface-level campaign metrics to fully understand customer journeys and drive profitable decisions. Topics discussed included:

Key Trends in Digital Marketing

• Artificial Intelligence: Its ability to process data and empower human teams is undeniable.
• Zero-Based Budgeting: Requires justifying every euro spent, reinforcing the need for truly omnichannel ROI measurement.

The GA4 Challenge and the End of Cookies

• The shift to Google Analytics 4 and the phasing out of third-party cookies can cause differences of up to 30% in reported sales.
• Defending the budget requires both tactical metrics (by channel) and strategic metrics (a global view).
• FLYDE adds a first-party tracking layer to audit and correct these discrepancies.

Attribution Beyond the Last Click

• Every touchpoint adds value. Limiting to “last click” can lead to misguided decisions.

Strategic Metrics and Segmentation

• Go beyond Cost Per Acquisition (CPA): focus on Customer Lifetime Value and how it varies by type of customer (repeat, occasional, etc.).
• Not all attributes weigh equally: advanced segmentation optimally prioritizes each factor.
• Centralize your data to build the “golden record”: the foundation for reliable attribution and truly personalized marketing.

FLYDE TALKS: A PLATFORM FOR INDUSTRY DIALOGUE

FLYDE Talks is designed to be a recurring series spotlighting challenges (and solutions) facing modern marketing and growth teams. Each event will feature live case studies, expert guests, and real-time walkthroughs across industries like retail, sports, travel and hospitality, and SaaS.

“FLYDE Talks represents a vital initiative for us as a company to create an open space where marketing and data professionals can share knowledge, ask tough questions, and explore innovative strategies together,” said Katie Gortz, Marketing Manager at FLYDE.

FLYDE Talks will resume in September and forthcoming information will be published through the company’s LinkedIn page.

With regard to FLYDE Talks Episode 2, Azpilicueta hinted: “There’s huge potential in showing how a football club, for example, can use data to tailor experiences for different types of fans—casual buyers, season-ticket holders, international shoppers.”

ABOUT FLYDE

FLYDE is a customer data platform built for teams who want full control over how data flows, performs, and drives revenue. By integrating online and offline sources into a single, actionable view, FLYDE helps businesses optimize campaigns, boost retention, and connect with high-value audiences in real time. Contact FLYDE for a demo to learn more.