From Data to Value: turning First-Party Data into competitive advantages

In the context of digital marketing, the concept of First Party Data takes a central role, defining itself as the set of data collected directly by the company through direct interactions, both online and offline. Thus, a user’s personal information, their transactional history, their navigation on my site, the feedback they leave, the products they prefer, are all first-party data, directly and knowingly released by the user through consent.

The ability to collect them, group them in one place, apply AI algorithms on them to segment and enrich them, enables businesses to gain a deep understanding of their customers, offering consistent competitive advantages through all the triggers and signals that can be used in marketing strategies.

Every professional aspires to decipher and anticipate the behavioral dynamics of their customers, to penetrate the essence of their inclinations and needs. Through meticulous analysis of the signals left by consumers during their interactions, valuable data revealing individual desires, preferences and passions can be distilled. This information becomes the basis on which to build personalized experiences, decide what content to show, what products or offers to offer.

Predictive Customer Lifetime Value (PCLV) analysis is an essential component in understanding the economic value a customer can generate for the company over the course of his or her interaction with it. Through analysis of purchasing behavior, personal interests and comparison with similar profiles with different Lifetime Value values, an individual’s spending potential can be estimated. This allows companies to adopt proactive and personalized strategies, treating the customer based on his or her expected value as if he or she had already made significant purchases.

In parallel, the concept of Time to Push emerges as a key determinant of the effective impact of marketing strategies. This time indicator, generated through predictive analytics, signals the most appropriate time to initiate direct marketing actions. Identifying a more pronounced purchase intention, perceived through the customer’s transactional behavior, enables engagement mechanisms, maximizing their effectiveness.

The ability to anticipate such moments through artificial intelligence has made possible actions with high conversion rates, even before the intent has manifested itself, offering a huge competitive advantage to those players who were first and best able to ground timely algorithms in prediction.

Within the retail sector, predictive analytics has a long history, although it has traditionally focused more on consumer behavior within physical stores, partly neglecting the digital realm. With the advent of large e-commerce and entertainment platforms such as Amazon or Netflix, the opportunities for personalization and the quality of algorithms have reached unimaginable heights.

These analytics practices, aimed at predicting trends and purchasing behavior, were only accessible to a small number of companies. This limitation was mainly due to the high infrastructural and technological costs, as well as the complexity of artificial intelligence algorithms, which required specialized skills found only in a few centers of excellence. As a result, most companies remained excluded from the benefits of these sophisticated insight techniques

In the current digital scenario, we observe a positive transformation characterized by the evolution from the wide availability of data, the reduced computational costs brought by the cloud and the wide availability of artificial intelligence algorithms, contribute to a significant improvement in the management and automation of first-party data. This development has made the martech landscape extremely dynamic, enriching it with opportunities but, at the same time, increasing the complexity of technology stacks. In this context, marketers are choosing the tools they feel are best suited to their activities, thus narrowing the previously existing gap between data processing and practical marketing applications.

In the face of this evolution, organizations are faced with the challenge of delivering optimized data flows to well-established industry tools such as Mailchimp, Google Ads, Salesforce or HubSpot. This data activation process is taking place in a context that is increasingly focused on respecting user privacy by adopting a privacy by design approach.

The increasing emphasis on anonymization and data security raises complex issues related to the collection, storage and aggregation of information, which must be managed in full compliance with the user’s expressed consent.

The implementation of effective data integration processes, which is essential to enable first-party data-driven strategies, presents significant complexities, especially when these processes are set up from scratch. In this context, the adoption of a composability model applied to enterprise data warehouses emerges as the preferred solution. This approach involves the optimized use of existing data warehousing infrastructures, integrating them with modular components that specifically address operational and strategic needs, without the need to implement new tools or platforms that could be redundant or duplicate existing data resources.

In addition, the emphasis on finding “out of the box” integration solutions facilitates the connection between different elements of the technology stack, ensuring a cohesive and integrated data flow. This paradigm aligns with the Modern Data Stack concept, proposed by Snowflake, which promotes a flexible, scalable, and easily managed data ecosystem. Adapting this vision to the specifics of marketing, the notion of the Modern Customer Data Stack has evolved, which takes the principles of the Modern Data Stack and applies them to optimizing customer data management strategies. This evolution reflects the intent to maximize the effectiveness of first-party information by leveraging advanced technologies for deep data analysis and the development of targeted and personalized marketing actions.

Numerous organizations have highlighted a recurring issue: the application of advanced analytical models-such as, RFM, scoring and interest analysis-which, despite their long history and proven effectiveness, often resulted in usage limited to simply reading statistical data. Instead, companies need to turn these analytics into concrete actions, converting loyal customers into targeted segments for Facebook Ads campaigns, personalized dimensions on Google Analytics or tags on CRM systems for sending targeted communications. This implies the need to synchronize audiences with advertising channels, enrich customer profiles and adopt value-based bidding strategies.

Three main challenges emerge in the face of this need:

• Identity Resolution
• Data Enrichment and Segmentation
• Data Activation

Having already discussed Identity Resolution, we will focus on data enrichment, segmentation and activation. These aspects are critical to the effective implementation of digital marketing strategies, as they allow data to be structured in a way that can be easily interpreted and used for marketing initiatives, as well as ensuring that information is activated through the most appropriate channels to maximize engagement and return on investment.

Data Enrichment & Segmentation

In the context of customer data enrichment and segmentation, we have focused on four main areas of analysis: interest analysis, RFM (Recency, Frequency, Monetary value) analysis, lead scoring, and Predictive Lifetime Value calculation. These approaches represent fundamental tools for in-depth understanding and segmentation of customers, based on different aspects of their behavior and interaction with the brand.

Interest Analysis

Interest Analysis aims to delineate customers’ fields of interest by observing their activities on corporate digital platforms, such as websites or applications. The starting point of this analysis is the pages visited by users. Using advanced models, based on Large Language Models and embedding techniques, specific labels indicative of a “topic” covered in that particular URL can be associated with each URL visited.

In this area, Bytek has implemented three different types of interest classification:

• IAB Classification: a multi-level classification system designed by the Interactive Advertising Bureau (IAB) to standardize content categorization in order to facilitate audience comparison and integration, enabling a common language among different market players.
• Personalized Classification: offers customers the ability to define and customize specific interests relevant to their business.
• Product Classification: associates each URL visited with one or more labels that identify the product presented on the page.

Each URL is assigned one or more labels according to these criteria and, through the application of sophisticated algorithms, an interest profile is assigned to each user based not only on his or her own actions but also on the overall user behavior of the analyzed site. Interest in a product is not deduced solely from a visit to a specific page, but is contextualized with respect to users’ overall activities, considering parameters such as the number of pages visited, time spent and actions taken. This approach makes it possible to attribute interest more accurately and representatively to actual user engagement.

RFX Analysis

The second model is RFX analysis, a clustering process aimed at segmenting the user base into homogeneous groups based on their purchasing behavior characteristics. The analysis uses three key variables: Recency (R), Frequency (F), and a third variable (X) representing a specific value, typically monetary. The purpose of this analysis is to categorize users based on when their last purchase was made, frequency of transactions, and a value metric, which may vary based on business needs. Traditionally known as RFM (Recency, Frequency, Monetary) analysis, the name RFX was chosen to reflect the flexibility in adapting the third variable to metrics other than the monetary value of transactions-such as profit margin-thus offering greater customization in interpreting the data.

This methodology is based on the use of clustering algorithms to identify and classify customers into categories such as “best customers,” “loyal customers,” or “loss-prone customers,” depending on their interaction with the company and the value generated through their transactions.

Predictive customer lifetime Value

Once these categories have been identified, it is useful to know the value of the individual customer by conducting predictive analysis in order to obtain Customer Lifetime Value (CLTV). This allows one to determine the potential economic value a customer can bring over the course of his or her relationship with the company, strategically targeting investments in marketing and retention initiatives.

The calculation of Predictive Customer Lifetime Value begins with the analysis of Recency, Frequency and Monetary (RFM) metrics. This process involves a detailed exploration of the distribution of these metrics across the entire customer base and the subsequent training phase of the predictive model. In the training phase, the model is calibrated using a historical dataset during which the actual results are already known. This allows the accuracy of the model’s predictions to be evaluated by comparing them with actual events that have occurred.

Once the model proves to provide reliable predictions that are in line with historical data, the actual prediction phase is conducted. In this phase, an attempt is made to predict customer behavior over the following months. Experience shows that extending forecasts beyond six months significantly reduces their accuracy and usefulness. This stems from the very nature of forecasting, which tends to be more accurate in the short term, while projecting long-term scenarios introduces a greater degree of uncertainty and variability.

In summary, the Predictive CLV calculation process is based on an examination of key customer interaction metrics and the application of predictive models trained on historical data. This approach makes it possible to generate reliable estimates regarding future customer value, providing companies with a solid foundation on which to build targeted marketing and business strategies.

Lead Scoring

In our customer interactions, there frequently emerges a request to implement lead scoring systems, a marketing practice that assigns each user a value that represents the likelihood that they will convert into a customer. This approach offers significant benefits by expanding the scope of marketing and sales strategies, particularly when managing large volumes of leads, allowing for optimization of resources by prioritizing the most promising opportunities.

The importance of lead scoring also extends to the advertising industry, where it can effectively influence budget allocation and campaign personalization.

The calculation of lead scoring presents a fairly high degree of complexity as it is necessary to integrate and analyze heterogeneous data, including behavioral information, previously identified interests, and other data, typically located in the CRM, such as the company where the user works, the size and industry of the company in question, the individual’s job role, etc.

The initial stage of the process involves a careful assessment of the quality of the data collected, followed by cleaning and, where necessary, reduction operations if the quantity is too high in relation to the available observations, requiring the application of advanced statistical techniques to prepare the data for analysis.

The choice of the most suitable model to calculate lead scoring varies depending on the specifics of the dataset. There is no universally applicable model, but several techniques such as neural networks, shrinkage methods, and ensemble models (which include techniques such as bagging, boosting, and stacking) must be evaluated to identify the most effective approach. The selection of the final model is based on its predictive ability, choosing the one that provides the highest accuracy.

It is crucial to take a critical and personalized approach when choosing the lead scoring model, avoiding standardized solutions that do not take into account the peculiarities and complexity of the data under consideration. Only through detailed analysis and careful model selection can lead scoring and predictive strategies be optimized, ensuring effective results tailored to the specific needs of the company.

Data Activation

The depth and complexity in algorithm processing is critical to establishing robust data reliability. This accuracy is crucial for data activation, as it ensures the effectiveness of forecasts and avoids misallocation of marketing resources. The goal is to optimize existing methodologies without necessarily reinventing them, while customizing them to ensure that the corporate identity is distinctively recognizable. A key element in this process is the acquisition of up-to-date and timely data. The ability to quickly detect customer transitions between different segments is essential. Consequently, an agile and responsive data infrastructure that enables fast algorithmic processing and insight generation is vital.

Marketing Trigger

The concept of triggering, originating in the IT industry, has found wide application in marketing. This methodology is based on the implementation of automatic actions in response to the occurrence of specific events. A practical example would be the entry of a customer into a specific cluster or the making of a purchase, which triggers the sending of a personalized e-mail communication. This approach enables targeted and timely interaction with the customer, enhancing the effectiveness of engagement and retention strategies.

Lookalike Audience

As part of defining targeting strategies, using first-party data to identify top-performing customers is a key step. Instead of simply searching platforms such as Facebook for users with a generic interest in certain product categories, opt for sending an information feed related to the most relevant customers. This approach is based on the premise that the products offered possess unique characteristics, thus making it more effective to search for users with similarities to so-called Top Clients. This methodology facilitates audience expansion by targeting similar individuals, optimizing the effectiveness of advertising targeting.

Enriched Bidding

In the context of a digital advertising campaign, the process of tracking conversions assumes a crucial role. Suppose a tracking system installed on a website detects a conversion attributable to a particular user, reporting that event to the related campaign. The campaign identifies that the user in question has completed a conversion following a click on a banner ad, providing positive feedback on the effectiveness of the campaign and the return on investment.

This mechanism, while effective in immediately assessing performance, may not consider significant qualitative elements related to the user’s profile, such as their Predictive Lifetime Value.

The integration of enriched signals represents a qualitative advance in campaign management. Through the adoption of this strategy, a differentiated value can be assigned to each conversion, optimizing the allocation of the advertising budget according to the potential long-term value of users. This approach makes it possible to move beyond a purely transactional view of conversions, favoring more sophisticated campaign management geared toward valuing user relationships based on their predictive value.

Adopting fully automated campaigns, such as Advantage Plus, can further amplify results. However, using campaigns heavily based on artificial intelligence in the absence of first-party data is not recommended. Because algorithms will try to immediately find the customers that convert most easily giving very underperforming results at first and underperforming over time.

In fact, performance analysis of marketing actions through tools such as the Marketing Mix Model and Lift Experiment reveals that campaigns without a solid base of first-party data tend to show low incrementality, focusing on users already predisposed to purchase. In contrast, integrating carefully selected data on top clients into targeting models forces campaigns to expand to new users, similar to Top Clients, maximizing the effectiveness of advertising strategies and sales incrementality.

CRM Enrichment and CX Personalized

A particularly relevant aspect is the ability to manage and exploit data in real time. The integration of the labels generated by the algorithms in the company database offers the possibility to further customize the communication towards the customer, relying on variables such as lead scoring score or high value customer cluster membership.

Labels can also be imported on Analytics systems, allowing you to assess the impact that a certain label has on the conversion rate of an onboarding path.

In addition, you can customize the user experience by synchronizing data in real time directly on the front end of the website. Our Predictive Marketing Data Hub makes it easy to transmit your user profile to your browser’s local storage for tailored navigation. This allows you to customize content, use chatbots and other advanced features more effectively.

The evolution of cloud technologies and the reduction of computational costs have made real-time customization accessible to businesses of all sizes, democratizing an opportunity that was previously limited to a few market players, such as Netflix. This technological advancement, combined with appropriate methodologies, allows to offer highly personalized experiences, previously exclusive to companies with significant resources.

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