Category: Customer Data

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 del Cliente (CDP) like FLYDE can enable you to implement MMM in your business. 

One of the most common questions we get from clients at FLYDE is:

Can we identify anonymous visitors using fingerprinting, and do we need consent for that?

It is an important question. As customer data strategies become more sophisticated, marketing teams are looking for ways to understand user behavior earlier in the journey. In many marketing platforms, anonymous website visits are siloed from the rest of the customer journey. Let’s imagine an anonymous user clicks a campaign, visits a few pages, and then returns to your site several times. The portion of the customer journey before the user identifies themself is lost. That’s where navigation fingerprinting comes in.

FROM THIRD-PARTY COOKIES TO FINGERPRINTING

For years, marketers relied on third-party cookies to track users across websites. These cookies powered everything from ad targeting to personalization and attribution. But browser updates and privacy regulations have changed the rules.

• Safari and Firefox began blocking third-party cookies by default
• Google Chrome is in the process of phasing them out
• Privacy regulations have tightened consent requirements
• Users are more aware and selective about how their data is tracked

As third-party cookies disappear, navigation fingerprinting has gained traction as an alternative. But it is not a free pass. Like cookies, fingerprinting is also subject to privacy regulation when used for marketing purposes.

WHAT IS NAVIGATION FINGERPRINTING?

Navigation fingerprinting, also known as browser fingerprinting or device fingerprinting, is a technique used to identify a device based on technical characteristics, without placing a cookie.

When someone visits a site, their browser reveals a combination of traits such as:

• Browser version and operating system
• Language, timezone, and screen resolution
• Installed plugins or font
• Device inputs (touch versus keyboard)

When combined, these signals form a kind of digital fingerprint. With the right setup, this fingerprint can be used to recognize a returning visitor, even if they are browsing anonymously.

WHY MARKETERS WANT TO USE IT

In theory, fingerprinting allows brands to:

• Track anonymous visitors across sessions
• Trigger personalized experiences earlier in the journey
• Detect fraud or suspicious activity
• Match anonymous behavior to user profiles once identification occurs

It is a powerful tool. For example, advertising platforms like Meta or Google only give aggregated insights for anonymous users. But with FLYDE’s browser tracking, you can tie an ad campaign to an individual user if they arrive on your site through a tagged UTM and later identify themselves (ie. by leaving an email) and give consent. This lets you link the user’s anonymous behavior to known user data, giving you a complete view of their journey, from top-of-funnel browsing to conversion.

HOW FLYDE SUPPORTS PERFORMANCE AND PRIVACY

At FLYDE, we believe privacy and performance can go hand in hand. We support fingerprinting and advanced browser tracking, but always within a responsible, user-centric framework.

Privacy is guaranteed on two levels:

1. Cookie consent required: If you install the FLYDE tracking script through Google Tag Manager, tracking only activates once the user accepts your site’s cookie policy.
2. Legal basis for identification: A person is only identified once they register or submit their email. At that point, they’ve accepted your legal terms for data processing.

This allows you to activate valuable data without compromising compliance.

FROM NAVIGATION TO INSIGHT

Once the user has consented, you can activate that data in meaningful ways:

• Segment audiences based on on-site behavior (e.g. pages viewed, time spent)
• Enrich campaigns with cross-channel tracking (e.g. email clicks, ad visits)
• Score leads in real-time using the Lead2Customer algorithm, which assigns a probability of conversion from 0 to 10 based on navigation and engagement patterns

You can trigger personalized flows, build predictive segments, and prioritize follow-up efforts with the confidence of having a full vision of their customer journey.

HOW FLYDE CAN HELP

Fingerprinting can help you better understand customer behavior, even at the earliest stage of the journey. FLYDE helps you make the most of every interaction, whether you’re tracking anonymous users across sessions, launching predictive models, or building smarter segments.

Contact us to schedule a meeting to discuss how a Customer Data Platform del Cliente (CDP) like FLYDE can unlock the full customer journey, starting with anonuymous web browsing. 

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.

You’ve assigned points to job titles, tracked email opens, and called the hot leads who ghosted. Welcome to the world of traditional lead scoring.

For years, marketers have relied on scoring models that evaluate leads based on demographics and surface-level actions like website visits or email clicks. But these models often fail to capture true buyer intent. They are based on assumptions as opposed to behavior, and they often overlook high intent leads with atypical characteristics.

THE PROBLEM WITH TRADITIONAL LEAD SCORING 

Despite being a foundational tool in marketing, traditional lead scoring has major drawbacks:

• Inaccuracy – Based on incomplete or outdated data.
  
  
• Subjectivity – Scoring criteria are often inconsistent or biased.
  
  
• Lack of Scalability – Difficult to maintain effectively as lead volume grows.
  
  
• Blind Spots – Ignores pre-identification behavior (e.g. anonymous browsing).
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These models can overlook high-intent leads who don’t fit your ideal buyer persona. Let’s imagine, your sales team typically targets CEOs or other high-level decision makers. With traditional lead scoring methods, you could easily overlook a junior employee who is doing research for his/her boss, who is the CEO. 

AI-powered lead scoring, however, goes beyond assumptions, delivering real-time insights that help you prioritize the right leads, faster.

Traditional lead scoring sees behaviors. AI understands their intent.

MEET LEAD2CUSTOMER: FLYDE’S AI MODEL THAT UNDERSTANDS THE WHOLE JOURNEY

FLYDE’s platform replaces the outdated model with something smarter: Lead2Customer, our AI-powered predictive model that evaluates leads based on real behavior, not assumptions.

Unlike traditional methods that rely heavily on demographic filters, Lead2Customer looks at a rich set of behavioral signals across the entire funnel, such as:

• Website navigation patterns (even before users identify themselves)
  
  
• Newsletter signups
  
  
• Email marketing open and click-through rates
  
  
• Webinar attendance
  
  
• Social media engagement

HOW IT WORKS

The Lead2Customer algorithm uses machine learning to calculate a dynamic conversion probability, expressed as a percentage. This means every lead in your CRM isn’t just labeled “hot” or “cold”—they’re scored in real time based on how likely they are to convert.

Unlike traditional systems in which leads are scored periodically, AI systems can adjust scores in real time as new data becomes available. This means that your sales and marketing teams can act even when a lead’s behavior suddenly changes. Imagine for example, that a lead suddenly shows new interest by attending a webinar, downloading a white paper, and visiting your pricing page all within an hour. AI doesn’t have to wait for your weekly scoring batch; it can immediately flag the lead and your sales team can reach out.

What’s more? It learns and improves over time. As your AI system observes how leads convert (or fail to), it learns to identify better indicators, continuously optimizing the scoring model to match your evolving data. This ongoing learning process is one of the most valuable aspects of AI-powered lead scoring, as it ensures that your system is always evolving to reflect changes in customer behavior, industry trends, and marketing strategies.

HOW AI-POWERED LEAD SCORING IS CHANGING THE GAME

AI-powered lead scoring methods, like Lead2Customer, enable your sales and marketing teams to work more efficiently and effectively:

• Behavior-based scoring – Uncover high-potential leads who don’t match your typical buyer persona.
  
  
• Full-funnel visibility – Capture both anonymous and identified user behavior.
  
  
• Real-time adaptability – Prioritize leads based on the latest interactions.
  
  
• Increased conversion rates – Focus on the leads that matter most, when it matters most.
  
  
• Smarter use of resources – Don’t waste time on dead-end prospects.
  
  
• Faster response times – Engage leads at peak interest.
  
  
• More personalization – Tailor content and timing to the moment.

SMARTER LEAD SCORING STARTS WITH SMARTER DATA

To power AI-driven scoring, you need unified, real-time customer data. That’s where FLYDE’s Customer Data Platform (CDP) comes into play. FLYDE pulls data from every touchpoint—website interactions, email engagement, social activity, and many more—creating a centralized customer profile. This unified data layer allows AI to update lead scores dynamically across all platforms, ensuring that your marketing and sales teams are always working with the most accurate and up-to-date insights.

With FLYDE powering your lead scoring process, your team can make faster, smarter decisions, prioritize the highest-value opportunities, and ensure that every lead counts.

Contact us to schedule a demo to find out how FLYDE can help you unlock the full potential of AI to boost the success of your marketing and sales teams.

In a perfect world, a customer clicks on an ad, falls in love with your product, and converts on the spot. You know exactly which campaign worked, which channel gets credit, and where to increase your ad spend. Easy.

But we don’t live in a perfect world. The customer journey isn’t single-channel or linear. We live in the age of omnichannel marketing. The reality is that a single purchase might be influenced by a Google search, a TikTok video, a webinar, a promotional email, or a conversation with your sales team.

Attribution—figuring out which touchpoints actually matter in the buyer’s journey—is no longer simple. It’s a messy, multi-source puzzle. And without solving it, you risk spending your budget in the wrong places.

So, let’s dive in and examine what attribution really means in omnichannel marketing campaigns and what challenges we face as marketers to assign credit where credit is due.  

WHAT IS ATTRIBUTION?

At its core, attribution is about assigning credit to each step that helps take a customer from “just looking” to “just bought.”

In single-channel or linear journeys, this used to be easy. But today, marketers rely on a mix of digital and offline channels working together, which means that the process of attribution has had to evolve.

Let’s look at a few common attribution models:

• First-touch: Gives all credit to the first interaction. If we want to focus on awareness metrics, this is a great approach, but it offers little insight in terms of conversions.  

• Last-touch: Credits the final click before a conversion. Many platforms use this as the default model, but it represents an oversimplification of the customer journey.

• Linear: Spreads credit evenly across all touchpoints. Here, the whole journey is taken into account, but not very strategically.

• Time-decay: Gives more credit to recent touchpoints. This model is well-suited to long nurture cycles.

• U-shaped (position-based): Emphasizes the first and last touchpoints, with less credit to the middle. Here, there is an emphasis on the awareness and decision stages of the funnel, but the model is apt to under-credit important engagement actions.

• Data-driven: Uses machine learning to assign weights based on actual conversion data. This model is ideal—but requires strong data hygiene and scale.

Each model has its own advantages and its own bias. In complex, omnichannel campaigns with many different touchpoints, it becomes increasingly important to move beyond simplistic models and embrace AI-powered attribution, which can analyze massive, messy datasets and zero in on what is driving conversions.

WHY DOES ATTRIBUTION GET COMPLICATED IN OMNICHANNEL CAMPAIGNS?

In the world of omnichannel marketing, the customer journey rarely follows a predictable path. The customer journey nowadays is non-linear, fragmented, and often, a portion of the journey is undertaken while the user is still anonymous.

Here’s why attribution is so tricky today:

• Device-hopping behavior: Your lead might see an Instagram ad on a mobile, Google your product on a laptop, and sign up for your newsletter from a desktop at work. The right tracking set-up is essential for connecting the dots.
  
  
• Walled gardens: Platforms like Meta, Google, and Amazon often don’t share data with each other—or with you! In these cases, each platform may allow advertising and data analysis within its own ecosystem using proprietary attribution and tracking methods, while limiting access to raw data for export to other platforms.
  
  
• Offline influences: Sales calls, print materials, events, or word-of-mouth are all powerful but hard to track.
  
  
• Privacy regulations: With the deprecation of third-party cookies and tighter data regulations, user-level tracking is more limited, making granular attribution even more challenging.

The result? A lot of guesswork and misallocated spending.

HOW TO IMPLEMENT ATTRIBUTION STRATEGIES FOR OMNICHANNEL MARKETING CAMPAIGNS 

The key to approaching attribution for omnichannel marketing is to stop aiming for perfect attribution—and start aiming for actionable insight.

Here’s how to get started:

1. Unify your tracking setup:
   • Implement clean, consistent UTM parameters
   • Your CRM and ad platforms must be connected. A Customer Data Platform (CDP) like FLYDE can bring it all together (more on that later)
     
     
2. Invest in smarter analytics:
   • Develop funnel-based dashboards tied to your KPIs
   • Implement machine learning models if your data volume allows
     
     
3. Set realistic expectations:
   • Attribution will never be 100% accurate
   • Focus on directional insight that can inform your strategic decisions
   • Align attribution analysis to business outcomes (not just clicks)

Instead of chasing perfection, chase progress. Map the journeys, unify the data, and use a tool like FLYDE to reveal insight. The goal isn’t to give perfect credit; it’s to make smarter, more confident decisions.

FLYDE’S VISION ON SMARTER ATTRIBUTION 

To address these omnichannel challenges and the need for a unified view, a Customer Data Platform (CDP) like FLYDE becomes essential for consolidating data from various sources.

FLYDE centralizes data from touchpoints across paid media, CRM, social, email, web navigation, and offline events. Whether you’re working with dozens of fragmented sources or just trying to get a full view of the customer journey, FLYDE brings your data together to offer clarity and insight.

Here’s a real-world example:

Imagine you run a lead-gen campaign using a CPC paid search campaign in Google, Meta ads, a product webinar, and follow-up email flows. With FLYDE:

• All touchpoints are stitched together—even across platforms.
• You can see how many leads saw an ad and attended the webinar.
• You can compare performance across acquisition and nurture phases.
• Attribution is based on your journey logic, not just Google’s last-click default.

This kind of transparency doesn’t just look good in reports—it drives better decision-making. When you know what’s working, you can double down. When something’s underperforming, you can pivot fast. Ultimately, effective attribution leads to optimized advertising spend, a deeper understanding of customer behavior, and improved ROI.

Contact us for a demo and we can show you how FLYDE approaches omnichannel attribution in our easy-to-use Customer Data Platform.

In the world of customer analytics, RFM analysis has long been a favorite for segmenting customers based on their Recency, Frequency, and Monetary behaviors. While RFM provides a solid foundation, many businesses are looking for more advanced segmentation techniques to capture the full picture of customer behavior. One such method is Customer Lifetime Value (CLV) modeling, which estimates the total revenue a customer is likely to generate over their entire relationship with your brand.

In this post, we’ll explore how CLV modeling works, its benefits, and how it complements—or even surpasses—traditional RFM analysis.

WHAT IS CUSTOMER LIFETIME VALUE (CLV)? 

Customer Lifetime Value (CLV) is a prediction of the net profit attributed to the entire relationship with a customer. CLV is forward-looking. It allows marketers to estimate not only who your best customers are today, but also who will be most valuable in the future.

Key Components of CLV:

• Purchase Frequency: How often a customer is expected to buy.
• Average Order Value: The typical value of each transaction.
• Customer Lifespan: The estimated duration of the relationship.
• Profit Margin: The profitability of each sale.

By incorporating these elements, CLV modeling provides a dynamic and comprehensive view of customer value.

WHY MOVE BEYOND RFM?

RFM analysis is great for quick segmentation, but it has its limitations:

• Historical Focus: RFM is inherently backward-looking. It categorizes customers based on past behavior without necessarily predicting future potential.
• Lack of Predictive Power: While RFM can identify segments, it doesn’t forecast future revenue or profit, which is essential for long-term planning.
• Simplistic Assumptions: RFM treats all transactions equally, ignoring nuances like evolving market conditions.

CLV modeling, on the other hand, addresses these gaps by providing actionable insights into future customer value.

HOW TO IMPLEMENT CLV MODELING FOR ADVANCED SEGMENTATION

1. Data Collection and Integration: Start by gathering comprehensive customer data—transaction histories, behavioral data, and engagement metrics. A Customer Data Platform (CDP) like FLYDE can integrate data from multiple sources, ensuring you have a unified view of customer interactions.
   
   
2. Define the CLV Model: Start by selecting a CLV model that aligns with your business goals and data maturity. The most common approaches include:
   
   
   • Historical CLV: Based on past purchase behavior, this model helps estimate future value using existing transaction data.
   • Predictive CLV: Uses statistical or machine learning techniques to forecast future customer value based on historical trends, behavioral signals, and engagement patterns.

   At FLYDE, we use a hybrid approach—combining both historical and predictive modeling to get the best of both worlds. Historical CLV powers real-time calculations, giving you an up-to-date view of current customer value. Predictive CLV goes further, projecting customer value over 6, 12, 18, and 24 months to support long-term planning and proactive engagement strategies.

3. Segment Based on CLV: Once you have calculated the CLV for each customer, you can segment your audience into groups such as:
   
   
   • High CLV Customers: Your most valuable customers deserve personalized engagement and loyalty programs.
   • Mid-Tier Customers: Those with moderate potential who could be nurtured to increase their value.
   • Low CLV or At-Risk Customers: Customers who might require re-engagement strategies or cost-effective campaigns to improve retention.
     
     
4. Tailor Marketing Strategies: With your segments defined, develop targeted strategies for each group. For instance: 
   
   
   • High CLV: Offer exclusive deals, early access to new products, or premium support.
   • Mid-Tier: Encourage upsells and cross-sells through personalized recommendations
   • Low CLV: Implement re-engagement campaigns or educational content to drive increased interaction.
     
     
5. Measure and Refine: Use performance metrics such as conversion rates, retention rates, and overall revenue growth to continuously evaluate your CLV segments. Regularly update your model with fresh data to keep your segmentation relevant.

THE BENEFITS OF CLV-BASED SEGMENTATION

• Resource Optimization: By focusing on high-value customers, you can allocate your marketing budget more effectively.
• Enhanced Personalization: Tailored messaging based on predicted future value fosters stronger customer relationships.
• Improved Forecasting: CLV modeling provides a forward-looking view that helps in strategic planning and setting realistic growth targets.
• Customer-Centric Strategies: Understanding customer potential allows you to design loyalty programs and re-engagement strategies that resonate with each segment.

While RFM analysis offers a quick snapshot of customer behavior, advanced segmentation through Customer Lifetime Value modeling provides insights that drive long-term success. By predicting future customer value and tailoring your marketing strategies accordingly, you can maximize ROI, enhance customer satisfaction, and build sustainable growth.

WHY FLYDE?

Embracing advanced segmentation with CLV modeling can transform your customer engagement and drive sustainable growth. FLYDE’s CDP automates data collection and integration from various touchpoints, providing a comprehensive view of customer interactions necessary for accurate CLV calculations.

Do you want your company to move on to the next level? A CDP is the key tool that will allow you to maximize the potential of your data and grow your business. Having control over all your data is now very simple.

Moreover, if you do not have IT or Data Scientist teams, this tool will allow you to outsource this function. And if you have them but want to reduce their workload and give more autonomy to your marketing and business teams when it comes to working with data, implementing an easy-to-use CDP would be the best option for your company. It will allow any single member of your company to use it, as this softwares are prepared for them.

Third-Party Cookies are slowly agonizing, the main browsers have already killed them. But, what would this mean for companies? Are we facing a new Marketing Era? 

All your doubts will be solved in this article. Also we will see the possible alternatives or if there are not feasible alternatives for Third-Party Cookies and companies will have to completely change their consumer-based strategies. 

Privacy 

Third-Party Cookies have been used as an incredibly efficient tool, allowing companies to track their customers to have a complete vision of them. This tool generates value to companies in terms of customer knowledge and segmentation possibilities. However, it was incredibly invasive for many users, who saw it as an attack on their privacy.

Not just that, the fact that, as we mentioned, this type of cookies are going to disappear from the main browsers (if they have not already done so), presents us a scenario in which the future of digital marketing seems to be directly focused on the use of the data that the customer provides you with directly: the First-Party Data.

Differences between using 3rd Party Cookies and 1st Party Data 

Third-Party Cookies are used to recap information about users. They have an analytical purpose, as it registers from socio-demographical data to attitudinal data, allowing companies to obtain profiles and segment the market based on habits or interests from each particular user. This is why this has been an incredibly useful tool for companies until now, when their use has become very limited and will soon be obsolete.

It is at this points where we should look in to the future, into the use of First-Party Data. First-Party Data are data obtained by your company directly from your client. Data shared voluntarily by your client and that would help your company know properly your customer’s insights. By using this tool you would be generating a much more in depth relationship with customers, boosting the engagement.

Currently, due to advances in technology, companies have the possibility of collecting an enormous amount of First-party Data in a relatively simple way. The problem for companies start when this data is not used properly, due to problems in the storage and in the way it is sorted. We must bear in mind that is useless to recap all of this data if we are not be able to analyze it properly with the tools available in order to understand our customers and offer them great customer experiences. 

New possibilities that a CDP could give to your company 

Unlock your customer’s value. With a Customer Data Platform everything is possible. A CDP is a tool that would allow your company to store and structure every single piece of data that your company has available. Also, a CDP would join in the data that your company has stored in different platforms (social media, CRM, etc.), and the sociodemographic data available in the market that you wish (Average rent per district, Nº of babies born per district, etc.). Once stored and structured this data, a CDP would allow you to analyze it gaining insights from your customers. 

There are many types of CDP providing clients with different functionalities, allowing your company to benefit in numerous ways of them. If your main goal is to substitute Third-Party cookies, the best option would be for sure the use of an Inteligent CDP, as it would allow you to analyze your data in depth, and with the support of Artificial Intelligence to benefit from those details that could escape human’s eye.  

Why FLYDE

Do you want your company to move on to the next level? A CDP is the key tool that will allow you to maximize the potential of your data and grow your business. Having control over all your data is now very simple.

Moreover, if you do not have IT or Data Scientist teams, this tool will allow you to outsource this function. And if you have them but want to reduce their workload and give more autonomy to your marketing and business teams when it comes to working with data, implementing an easy-to-use CDP would be the best option for your company. It will allow any single member of your company to use it, as this softwares are prepared for them.

Try it for free with our free trial (with auto-generated fake data)!

As the world becomes increasingly competitive, companies must focus on customer satisfaction and loyalty to succeed. While customer acquisition is essential for growth, customer retention is even more critical. Retaining existing customers is essential for the long-term health of any business, as loyal customers tend to spend more, refer more, and cost less to retain.

In this blog, we will discuss marketing retention vs. acquisition and why current trends show that retention is much more important. We will also explore how a Smart Customer Data Platform (CDP) can help companies boost retention.

The importance of Customer Retention

The cost of acquiring new customers is significantly higher than retaining existing ones. According to a study by Harvard Business Review, increasing customer retention by just 5% can increase profits by 25-95%. This is because loyal customers tend to spend more, refer more, and cost less to retain.

Additionally, loyal customers provide valuable feedback and can help businesses improve their products and services. Therefore, it is essential for companies to focus on retaining existing customers.

Customer Retention vs. Customer Acquisition

While customer acquisition is necessary for growth, it should not come at the expense of customer retention. Many companies make the mistake of focusing too much on acquiring new customers and neglecting existing ones. However, research shows that companies that focus on customer retention tend to be more successful in the long run. Customer retention should be a top priority for any company that wants to grow and thrive.

The benefits of focusing on retention over acquisition

Focusing on customer retention over acquisition can provide a number of benefits to companies, including increased customer lifetime value (CLV), improved customer loyalty, and reduced marketing costs. By retaining existing customers, companies can increase CLV by encouraging repeat purchases and reducing churn. Additionally, loyal customers tend to refer others to the company, which can help reduce marketing costs associated with customer acquisition. By prioritizing retention, companies can create a positive cycle of growth that leads to long-term success.

How a Smart CDP can help boost retention

A Smart CDP can provide a comprehensive view of a company’s customers, allowing them to personalize marketing messages, improve customer service, and identify opportunities to upsell and cross-sell.

For example, let’s say a retailer collects data on a customer’s purchase history, browsing behavior, and preferences. With a Smart CDP, the retailer can use this data to send targeted marketing messages based on the customer’s interests and behavior. They can also use the data to recommend complementary products, provide personalized promotions, and improve customer service by addressing the customer’s specific needs and concerns. By using a Smart CDP, the retailer can create a more personalized and engaging experience for their customers, which can lead to increased loyalty and retention.

WHY FLYDE?

Do you want your company to move on to the next level? A CDP is the key tool that will allow you to maximize the potential of your data and grow your business. Having control over all your data is now very simple.

Moreover, if you do not have IT or Data Scientist teams, this tool will allow you to outsource this function. And if you have them but want to reduce their workload and give more autonomy to your marketing and business teams when it comes to working with data, implementing an easy-to-use CDP would be the best option for your company. It will allow any single member of your company to use it, as this softwares are prepared for them.

According to Harvard Business Review, 73% of consumers use multiple channels throughout their purchase process.

In today’s digital age, customers expect seamless, personalized experiences across all channels. To meet these expectations, companies need to implement omnichannel marketing strategies that integrate all of their marketing channels. However, this can be challenging without the right tools and technology.

In this blog, we’ll explore the benefits of omnichannel marketing for companies and how a customer data platform (CDP) can help make it easier and more effective.

How to Create Omnichannel Customer 360 Profiles

To create omnichannel customer 360 profiles, companies need to aggregate and unify customer data from all sources. This includes online and offline interactions, such as purchases, website visits, social media engagement, and in-store interactions. A CDP can help with this by ingesting data from various sources, cleaning and deduplicating it, and stitching it together into a single, comprehensive customer profile.

Once the data is integrated into the CDP, the company can start building out the 360-degree view of each customer. This involves mapping out each customer’s interactions across different channels, tracking their preferences and behavior, and segmenting them based on attributes such as demographics, purchase history, and engagement.

How to Obtain Insights from Customer 360 Profiles

Once a retailer has created omnichannel customer 360 profiles, they can start leveraging the insights to improve their customer experience. A CDP, as well as ingesting and structuring data, can be used to obtain insights, with the different tools that conform this type of platforms.

One way to obtain insights from customer 360 profiles is to use real-time personalization. By leveraging the data in the CDP, retailers can create personalized experiences for each customer based on their preferences and behavior. This can include personalized product recommendations, targeted promotions, and customized messaging.

Applying Artificial Intelligence with a Smart CDP

AI can be used to analyze large volumes of customer data and uncover insights that might not be apparent from manual analysis. For example, AI-powered predictive analytics can be used to anticipate customer needs and preferences, as well as identify opportunities for cross-selling and upselling.

Another way to apply AI to customer 360 profiles is through machine learning. By training machine learning models on customer data, retailers can automate tasks such as product recommendations and content personalization, as well as improve the accuracy of predictive analytics.

Summary

Creating omnichannel customer 360 profiles with a CDP is a critical step for retailers looking to provide personalized, seamless experiences across all channels. By leveraging AI-powered analytics and personalization, retailers can gain valuable insights from these profiles and use them to improve the customer experience. So, if you’re a retailer looking to enhance your customer engagement, consider implementing a CDP and start creating omnichannel customer 360 profiles today.

WHY FLYDE?

Do you want your company to move on to the next level? A CDP is the key tool that will allow you to maximize the potential of your data and grow your business. Having control over all your data is now very simple.

Moreover, if you do not have IT or Data Scientist teams, this tool will allow you to outsource this function. And if you have them but want to reduce their workload and give more autonomy to your marketing and business teams when it comes to working with data, implementing an easy-to-use CDP would be the best option for your company. It will allow any single member of your company to use it, as this softwares are prepared for them.

Understanding your customers’ needs and desires is essential to building a successful business. However, it’s not enough to simply ask your customers what they want. To truly understand their needs, you must identify the jobs or tasks they are trying to accomplish. This is where the Jobs-To-Be-Done (JTBD) framework comes in.

In this blog, we’ll see how to improve your JTBD strategy with a Customer Data Platform (CDP). By using a CDP, you can gain a more comprehensive understanding of your customers and their behaviors, which can help you improve your JTBD strategy.

Why Understanding JTBD Is Crucial for Your Business

Jobs-To-Be-Done (JTBD) is a framework that helps businesses understand the needs and desires of their customers by focusing on the jobs or tasks that customers are trying to accomplish. By understanding your customers’ JTBD, you can design products and services that meet their needs and differentiate yourself from competitors.

How to Improve Your JTBD Strategy with a CDP

A Customer Data Platform (CDP) is a data management tool that allows you to collect, unify, and analyze customer data from multiple sources. By using a CDP, you can gain a more comprehensive understanding of your customers and their behaviors, which can help you improve your JTBD strategy.

Here are four ways to improve your JTBD strategy with a CDP:

1. 1. 1. 1. Collect and unify customer data from multiple sources: A CDP allows you to collect and unify customer data from multiple sources, giving you a more comprehensive view of your customers.
         2. Analyze customer data using the JTBD framework: Using the JTBD framework, you can analyze customer data to identify the specific jobs your customers are trying to accomplish.
         3. Personalize marketing and product recommendations: By using the CDP to personalize your marketing and product recommendations, you can better meet your customers’ JTBD and increase engagement and sales.
         4. Optimize inventory to better meet customer needs: By understanding your customers’ JTBD, you can optimize your inventory to better meet their needs, reducing waste and improving your bottom line.

Case Study – How could a retailer improve their JTBD strategy with a CDP?

A retailer uses a CDP to collect and unify customer data from multiple sources, enabling them to gain a comprehensive view of their customers and their behaviors. Using the JTBD framework, the retailer analyzes the data to identify the specific jobs their customers are trying to accomplish when they shop for clothes, such as looking for stylish and fashionable clothes, prioritizing comfort and functionality, or finding clothes for specific occasions.

The retailer uses the CDP to personalize their marketing and product recommendations to better meet their customers’ JTBD. By creating targeted email campaigns and personalized product recommendations, the retailer increases customer engagement and sales, leading to higher customer loyalty and repeat purchases.

Additionally, the retailer is able to optimize their inventory to better meet their customers’ needs, reducing waste and improving their bottom line. In this way, using JTBD and a CDP can help fashion retailers to better understand their customers and improve their business results in a highly competitive industry.

Conclusion and Next Steps

In conclusion, understanding your customers’ JTBD is essential to designing products and services that meet their needs. By using a CDP to collect, unify, and analyze customer data, you can gain a deeper understanding of your customers and improve your JTBD strategy. To get started, consider implementing a CDP and using the four strategies outlined in section two to improve your JTBD strategy. With a more customer-centric approach, you can differentiate yourself from competitors and build stronger, more loyal customer relationships.

WHY FLYDE?

Do you want your company to move on to the next level? A CDP is the key tool that will allow you to maximize the potential of your data and grow your business. Working like the big multinationals in the market, which already have this type of software, and having control over all your data is now very simple.

Moreover, if you do not have IT or Data Scientist teams, this tool will allow you to outsource this function. And if you have them but want to reduce their workload and give more autonomy to your marketing and business teams when it comes to working with data, implementing an easy-to-use CDP would be the best option for your company. It will allow any single member of your company to use it, as this softwares are prepared for them.