Category: Artificial Intelligence

Success starts behind the scenes. While marketing, sales, and product innovation often steal the spotlight, inventory management can make or break your profitability and customer experience.

Think of your stock as a dynamic, strategic asset. Managed well, it fuels growth. Neglected, it quietly drains your resources and undermines your business.

THE COSTS OF INVENTORY MISMANAGEMENT 

Poorly managed stock has immediate and costly consequences:

• Stockouts: A customer is ready to buy, but the item isn’t in stock. That’s not just a lost sale; it’s a missed opportunity and a negative customer experience. According to a study carried out by the IHL Consulting Group, an estimated $1.2 trillion in global retail sales was lost in 2023 due to stockout.
  
  
• Excess inventory: You’re tying up capital, crowding your warehouse, and risking spoilage, theft, or obsolescence. Overstock quietly erodes your bottom line. The IHL Group estimated that excess inventory cost retailers about $562 billion in 2023 due to markdowns and obsolescence.
  
  
• Inefficient operations: Poor stock management leads to delays, fulfillment mistakes, and disrupts workflows. The consequences in terms of employee productivity and job satisfaction can also be far-reaching.
  
  

THE BENEFITS OF SMART STOCK MANAGEMENT

According to the Institute for Business Forecasting, a 15% increase in inventory forecasting accuracy translates into a 3% increase in earnings before interest and tax (EBIT). Great inventory management is strategic. When done right, it delivers:

• Increased customer satisfaction: Reliable stock availability builds trust and keeps customers coming back.
  
  
• Reduced costs: You’ll be able to avoid surcharges for rush shipping, unnecessary storage fees, and waste from obsolete stock.
  
  
• Better cash flow: Freeing up capital from excess inventory gives you more flexibility to invest in growing your business.
  
  
• Efficient operations: With clear processes and real-time data, your team can move faster and make fewer mistakes.
  
  
• Smarter decisions: Accurate inventory data allows you to make smarter, data-driven business decisions. It helps guide pricing, purchasing, and marketing based on real demand.
  
  

THE ROLE OF TECHNOLOGY

Fortunately, artificial intelligence (AI) and machine learning (ML) are transforming inventory management by enabling more accurate demand forecasting. Demand forecasting is the practice of using historical data, market trends, and advanced analytics to predict future customer demand for a product or service. It empowers businesses to make smarter decisions across inventory, production, staffing, and budgeting—ultimately reducing waste, avoiding stockouts, and improving operational efficiency.

AI/ML-powered demand forecasting delivers key advantages for inventory management, including:

• Real-time visibility: Instantly see what’s in stock, where it is, and what needs replenishing.
  
  
• Automation: Streamline purchasing, receiving, and fulfillment processes to boost efficiency and reduce errors.
  
  
• Advanced analytics: Detect trends, optimize inventory levels, and identify bottlenecks or slow-moving stock.
  
  
• System integration: Centralize data from sales, finance, and e-commerce platforms. A Customer Data Platform (CDP) like FLYDE can help unify and enrich this data for smarter forecasting.

INVENTORY MANAGEMENT HEALTH CHECKLIST

How effective is your inventory management system? If you answer “no” to several of these questions, it might be time to rethink your approach.

Data & Visibility

• Can you see inventory levels in real time across all channels and warehouses?
• Do you have a centralized view of customer demand trends?
• Is your inventory data integrated with sales, marketing, and finance systems?

Forecasting & Planning

• Are your forecasts based on historical data and real customer behavior?
• Are your forecasting models updated regularly?
• Can you confidently anticipate when you will encounter a spike or a lull in demand?

Efficiency & Operations

• Is your restocking process automated (or is it manually triggered)?
• Are fulfillment mistakes (e.g., wrong items, delayed shipments) a rare exception?
• Do you know your inventory turnover rate?

Financial Impact

• Are you confident that your inventory is not tying up more capital than necessary?
• Are you able to avoid paying extra fees for expedited shipping or unnecessary storage?
• Does your team have fast access to accurate data to make stock decisions?

HOW FLYDE CAN HELP

To accurately forecast demand and optimize inventory, a Customer Data Platform (CDP) like FLYDE is 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 and enriches it with socio-demographic and interaction data.  With FLYDE’s ML algorithms, you’ll be able to analyze the behavior of your customers, observe in real time how their movements affect the demand for your products, and anticipate future demand.

Contact us to schedule a demo to find out how FLYDE approaches demand forecasting in our easy-to-use Customer Data Platform.

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.

FLYDE is an easy-to-use Customer Data Platform designed to meet the needs of businesses looking to improve their customers’ experience, increase retention, ROI, ROAS, and Customer Engagement.

In this blog, we will discuss the new functionality of the platform: Customer Profiling guided by predictive AI models.

What is customer profiling?

Customer profiling is a tool that allows businesses to better understand their customers and tailor their offerings to meet their specific needs. With FLYDE’s customer profiling, businesses can select a product from their catalog and see the main characteristics of the customers who purchase it. Predictive AI models evaluate all available data your company has on your customers such as their behavior, purchase patterns, product preferences, browsing habits, and more.

How does FLYDE’s customer profiling work?

Suppose a company has a product in its catalog and wants to better understand its precise customers to adapt its marketing strategy. With FLYDE, the company can select that product and use predictive AI models to identify the main characteristics of the customers who purchase it. For example, the models may reveal that customers who buy that product earn more than €40,000, log into the ecommerce page twice a week, and often make purchases in physical stores.

How can customer profiling improve your marketing strategy?

Predictive AI-guided customer profiling can significantly improve your marketing strategy by allowing you to better understand your customers and tailor your offerings to their specific needs. By using this new functionality of FLYDE, you can personalize your marketing messages to meet the specific needs and desires of your customers.

Additionally, customer profiling also allows you to improve the segmentation of your marketing campaigns. By better understanding your customers and grouping them into segments based on their characteristics, you can personalize your messages and offers for each specific segment. This will allow you to reach your customers with more relevant messages, increasing the likelihood of conversion to sales.

Identifying new potential customers with Customer Profiling

In addition to helping you better understand your current customers, FLYDE’s customer profiling also allows you to identify new potential customers. The platform’s AI uses the purchase behavior data of your current customers to find patterns and similarities between them.

These patterns help you identify new business opportunities by finding potential customers who have a similar profile to your current customers. By understanding your customers’ preferences and behaviors, you can use that information to reach a wider audience that is more likely to buy your products or services.

With FLYDE’s customer profiling, you can discover new market segments that may not have been on your radar and adjust your marketing strategy to reach them more effectively. This can result in a higher return on investment and increased customer loyalty.

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)!

In recent years we have seen an unprecedented advance in Artificial Intelligence. ChatGPT, Wall-e, and other models that simulate human behavior and learning. But it has not yet been possible for an AI to develop Emotional Intelligence, the question here is whether it is possible for it to achieve it.

It is complicated to know if a friend or a family member needs a hug, humans use our Emotional Intelligence to read the needs of that person. Could we then know if a person we don’t know directly needs a hug or not?

Emotional Intelligence: how does it work?

In order to move from prejudice to judgment, human beings need information or data that bring us closer to knowledge about a specific issue. Thus, Emotional Intelligence, like any other type of Intelligence, needs to be fed with knowledge. This knowledge is composed of the new information that we have obtained about the concrete issue and the validation of the previous knowledge of similar situations that we have lived.

When a friend tells us about a situation he/she is living, we compare this information with data from the past, we see if it can be something problematic or not for that person and we act accordingly, with a hug or a reprimand, everything will depend on the judgment we make with the information we have.

Emotional Intelligence vs Artificial Intelligence

The obvious question that follows is: Will AI ever be able to do all this? Well, the answer is just as obvious: Who knows?

What we can be sure of is the current capabilities of AI. Artificial Intelligence today can approach and even surpass humans in part of the process mentioned above.

Continuing with the explanation of what Emotional Intelligence is, the part of generating and processing the information prior to making the judgment, can currently be carried out with AI. Even a good AI model, with enough data from a person (basic, attitudinal and behavioral), could perform this data processing better than a human being.

At present, AI still needs the human to make that final judgment with the processed data. AI could tell you that a person is bad and how bad they are, but it is a human who has to decide whether to give them a hug or not, and above all it is the one who has to give that hug.

The need to understand your customers

All of this brings us to a fundamental point for companies in terms of technological evolution. Artificial Intelligence at the current point where it is allows companies to know if your customer wants a hug or not, and how much they want it or if you are going to lose that customer by giving it to them.

Knowing the level of Customer Engagement of a customer, based on the processing of all the data that we companies have at our fingertips, we will be able to locate the point at which a customer is with our brand, and not only that. As we have previously commented, AI can do things in a similar or even better way than humans and, therefore, we could currently predict what our customer is going to do once we embrace him.

AI, as we can see, is not simply a Chat or Image generator, AI brings a series of infinite possibilities to humans, and companies should not take long to start benefiting from these.

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.

Artificial Intelligence, or AI, is the simulation of human intelligence in machines that are programmed to think and learn like humans. It is a branch of computer science that deals with creating intelligent machines that can think, learn, and adapt like humans.

AI is used in a variety of applications such as image recognition, natural language processing, decision making, and many other fields.

Types of AI that can be useful for marketing teams

Artificial intelligence (AI) is increasingly being used in marketing to help businesses better understand their customers and target their marketing efforts more effectively. Some specific ways that AI is being applied in marketing include:

• Personalization: AI can be used to analyze customer data and create personalized marketing campaigns that are tailored to the individual.
• Chatbots: AI-powered chatbots can be used to engage with customers in real-time, providing them with information and answering their questions.
• Predictive analytics: AI can be used to analyze customer data and make predictions about future buying behavior, which can help businesses make more informed decisions about their marketing strategies.
• Image and video recognition: AI-powered image and video recognition technologies can be used to analyze visual content and extract insights, which can be used to inform marketing strategies.

Data-Driven Marketing

Data-driven marketing is a strategy that uses data and analytics to inform marketing decisions. It involves collecting, analyzing, and interpreting data about customer behavior, demographics, and other factors to gain insights that can be used to improve marketing campaigns and drive business growth.

The goal of data-driven marketing is to use data to understand customer needs and preferences and to develop effective marketing campaigns that will reach and engage the right audience. This approach allows businesses to make data-driven decisions, such as which products to promote, which channels to use, and how to allocate budget.

Customer Data Platform to unify Marketing and Artificial Intelligence

A Customer Data Platform (CDP) is a system that allows businesses to collect, integrate, and manage customer data from a variety of sources in order to gain a single, unified view of the customer. CDPs are used to support data-driven marketing by providing a centralized location for storing and managing customer data, which can then be used to inform marketing decisions. A CDP for it to be useful, needs of Artificial Intelligence models to be run in order to process this huge amounts of data correctly, and to drive predictive models.

Here are a few ways that a CDP can be used for marketing:

1. 1. Customer segmentation: CDPs can be used to segment customers based on their behavior, demographics, and other factors, which can be used to create targeted marketing campaigns.
   2. Personalization: CDPs can be used to create personalized marketing campaigns that are tailored to the individual. This can be done by analyzing customer data and identifying patterns that can be used to create targeted messaging and offers.
   3. Campaign optimization: CDPs can be used to analyze the effectiveness of marketing campaigns and make adjustments as necessary. This can be done by tracking the performance of campaigns and identifying areas where they can be improved.
   4. Data Governance: CDPs help to ensure data governance by providing a central location for storing and managing customer data, and also help to ensure that data is accurate and up-to-date
   5. Cross-channel marketing: CDPs can be used to coordinate marketing efforts across multiple channels, such as email, social media, and web.
   6. Attribution: CDPs can be used to identify which marketing channels are most effective in driving conversions and revenue, which can inform future marketing investments.
   7. Predictive modeling: CDPs can be used to analyze data on customer behavior and make predictions about future buying behavior, which can inform marketing decisions.

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.

Christmas can be the toughest or happiest time of the year for marketeers. The time when sales boost or not showing your marketing’s strengths or weaknesess.

In this blog we are not going to tell you how to become a millionaire nor are we going to explain how to easily become a Christmas hero, our sole purpose is to explain how using data you can drive a proper Christmas marketing strategy.

Data-driven marketing

Base your strategy on data and not intuition. Make sure your KPIs are SMART, can be mapped correctly with the tools you have, and make sure you are not missing key customer data.

At Christmas in particular knowing your customers would be fundamental to drive one-to-one marketing strategies, basic data is fine, but if you have a lot more available use it:

You can track your customer across all your online channels. This will help you to map their Customer Journey. If you already have a tagging tool such as FLYDE’s, then you may be interested to know that you can unify this data with offline data (from physical shops or offline marketing campaigns), and in turn with socio-demographic data, thus obtaining a 360-degree view of your customer.

First-Party Data Strategy

This data is known as first-party data, and you should start using it, firstly, because of the loss of third-party cookies, and secondly, because of the amount of data and possibilities you are missing out on.

Of those data sources we discussed, the spectrum is even greater. Not only is it essential to collect data from all sources, but going one step further in the data you collect can make a fundamental change to your strategy.

By collecting attitudinal and behavioural data, such as the number of times a customer looks at a product before buying it, or their interest in the themes of your offline and online campaigns, or whether they buy for themselves or as a gift (depending on the variability of sizes they are looking for). You will be able to enrich this customer database in order to generate campaigns and strategies that are not based on intuition but on data, thus avoiding failures or recriminations to the marketing team.

Platform to obtain Actionable Insights from first-party data

All this data must come together to bring value to your company, without this unification the data lacks the real value it can achieve. To do this, there is a type of platform that surpasses CRM because it not only allows you to store basic data but also allows you to store large volumes of data or Big Data, and to act on them within the platform itself with Artificial Intelligence processes. This Platform is a Customer Data Platform.

All this will allow your company to have all this structured data, to segment, personalise and predict with countless variables. As we have previously explained, 360º customer profiles open up these new possibilities.

Customer data platform for Christmas

And back to the initial question, how can I save my Christmas campaign with this CDP stuff? You can’t, a CDP doesn’t do magic, nor does Big Data or Artificial Intelligence despite what it seems lately with the boom of this technology.

A CDP or Customer Data Platform is not going to do marketing for you, what it will do is become the fundamental support for your company. Because as we have been seeing for a few years now, the customer must be at the centre of every company, and without a CDP this is not really possible.

So a marketing team that has all the data at its fingertips will be a marketing team that will no longer ask itself why this campaign didn’t work and the previous one did? With data you will get certainty.

You will be able to make predictions about the type of campaign that will work at any given time of the year based on how each campaign has affected your customers. So if I want to launch a campaign at Christmas, I will be able to compare by type of campaign and by date, with those previously carried out, to see the expected performance based on the data; a calculation that increases exponentially its accuracy and simplicity if you have a predictive model like the ones FLYDE has available.

Later, once you have launched the campaign in your CDP, you will be able to track your KPIs and compare them to see where the success or failure has been, where there has been a change with respect to the previous one, what to improve and what to maintain. As we have said throughout the article, a CDP is not going to do marketing for you, but it can help you stop making mistakes and start relying on data.

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.

Acquiring a new customer is 5 to 7 times more expensive than keeping one, we all know this, but why are companies still unable to retain their customers?

The CLV or Customer Lifetime Value, is the value that your customers have for your company, and it is one of the most important calculations for a company.

In this article we will unravel the importance of using a CDP today, in order to optimize your company’s CLV, thus boosting your revenues, and achieving longer lasting relationships with your customers.

What is CLV?

The Customer Lifetime Value is a formula that determines the cost/benefit that a customer can bring to your company. With it, you can determine the appropriate expenditure to invest in marketing/advertising to reach or attract this customer. Without this metric it is impossible to draw logical marketing strategies since you would be constantly carrying out undervalued campaigns or, even worse, overvalued, that is, paying more for obtaining new customers than what they will bring you in the long term.

In order to obtain a concrete but dynamic data from your CLV, it is necessary to obtain a large amount of data and a proper treatment of it. Due to the end of Third-Party Cookies, this data must be obtained directly from your customers: the First-Party Data. Once you start collecting data from your customers, it will be necessary to store, organize and analyze it in an efficient way in order to obtain an accurate CLV. The right tool for this is an Intelligent CDP, which will allow you to do all this and more.

Strategies to improve your CLV with a CDP

How can a CDP help me improve my Customer Lifetime Value? Below we will explain some of the key strategies for a marketing team with an Intelligent CDP at their disposal. With these strategies your company will be able to make a difference with your competitors in terms of CLV.

1. Strengthen your relationship with your customers

Traditional marketing is no longer useful, the market demands strategies focused and oriented to the customer “Customer-Centric“. To really put the customer at the center, and thus strengthen your brand’s relationship with them, it is essential to know them.

Having a real-time picture of your customers, with all the data you can get unified, allows you to know them like never before. In addition to, as it is in real time you will be able to observe the effect of each of your campaigns on each customer, to know which campaigns attract them more or distance them from your brand.

2. Hypersegment your customers

With this 360-degree view of the consumer, you will be able to perform much more concrete and dynamic segmentation than you had ever imagined.

For example: you can generate market segments based on the moment in which customers are in relation to your company, added to other variables such as age, their product preferences, or their estimated average income. This way you will group your customers according to their interest in receiving this month’s newsletter, or in the case of doing more than one newsletter, you will know which one to send to each group of customers.

3. Personalize their experience

Having this 360 picture we will be able to personalize the experiences, something fundamental nowadays. There are too many companies in the market offering a similar product to yours, and therefore, the customer needs you to provide a different experience with your company. Thus, personalization becomes crucial for that customer to decide to buy your product/service instead of that of your competitor.

For example: if two customers do not want the same thing from your company, one buying a product online and the other in the physical store, do not send discounts for your e-commerce to the one who wants to go to the store. Simply show him/her the new products and offer him/her a discount in the physical store through email marketing, so that he/she gets what he/she wants from your brand.

4. Optimize your marketing strategies

Marketing strategies are determined by the CLV, and therefore a detailed and accurate analysis of the CLV will allow your company to optimize these strategies. This way your marketing strategies will have an efficient budget that will boost your ROI since the tendency will always be to receive revenues above the costs of the campaign.

In addition, a CDP will help you to optimize these campaigns, avoiding unnecessary expenses or even losing customers due to invasive or excessive advertising. With campaigns measured to the millimeter as a CDP allows you to know the effect of the campaign on each customer, thus being able to predict the result with the help of AI/ML, and knowing the expenditure that you should or can make.

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 maximise 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.