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Month: January 2023

What data strategy when media budgets are at half mast?

Since the summer of 2022 and for the first half of 2023 at least, media budgets, including digital, are like the economy: gloomy. How can we continue to work with data in this context? On which pillars should we rely to get the full value of interactions with audiences? Here’s an overview.

A low tide that is slow to rise. This is the picture that emerges from the forecasts for the evolution of the digital advertising market. Yes, digital is in better shape than the overall advertising market, but the ebb is there. In France, the players (publishers, trading desks) consulted by JDNet at the end of 2022 confirm a decline in programmatic since the summer of 2022 and anticipate a dull first half of 2023.

Globally, advertising growth estimates have been revised downwards: GroupM forecasts an increase of 5.3% in global advertising investments in 2023 (the estimate was 6.4% six months earlier). If we take a look at the US market, and more specifically at ad spending on social networks, there too a cold wind is blowing on the forecasts. For Insider Intelligence, nearly $10 billion disappeared between the December 2022 estimates and those published in March 2022. As the analysis goes on, a consensus is emerging: the slowdown should continue during the first half of 2023. At least.


Digital media buying in poor shape


This small form of digital media investment can be explained. In a very uncertain global context, brands anticipate a drop in consumption and modulate their investments accordingly. “And when you have to cut back, digital media buying is easily deactivated/reactivated, unlike investments in content or SEO. There, cutting budgets can result in regressions “, says the digital strategy manager of a major industrial player. In this context, with less media investment, how can you continue to develop your data?


Focus on organic levers


Unsurprisingly, efforts are focused on organic levers, for both acquisition and retention. On the acquisition side, those who have already built up an “SEO income” (i.e., SEO work that guarantees regular traffic), will at least maintain it, and even develop it by supporting their content production. This traffic is especially interesting if it is generated by content that covers the different types of search intentions (informational, navigational, commercial, transactional). The visits generated by this means therefore represent qualified traffic that can be worked on, at least in part, as a result of consent management.


Another channel that is the object of all the attention during these times of media dearth, emailing. In all its forms: from the “acquisition cold email” to the regular newsletter that intends to maintain a lasting relationship with an audience. These emails are precious supports to enrich the data: beyond the classic click and opening rates, other indicators such as the level of loyalty, the typology of the consumed contents allow to refine the knowledge of its audience to better activate it later.


More than ever, think omnichannel


Making the most of the traffic gained through these types of levers means optimizing the “playbooks”. To develop consideration and conversion, these automation sequences often combine emails, but brands have every interest in thinking about them on an omnichannel scale. For example, to trigger them on the basis of an in-store action (purchase, delivery of a loyalty card) or to develop them by integrating into the scenario an interaction with the call center (to provide advice and confirm an interest).


Whatever the organic levers used, they all have one thing in common: unlike media buying, the aim here is not to “overpressure an audience” but to address it with appropriate messages, at a measured pace and in full compliance. A delicate balance which, to be maintained, requires the support of 3 pillars:


Pillar #1: Consent Management


Now an essential part of the martech stack, the CMP (Consent Management Platform) must help identify the formula that guarantees a consent rate consistent with the benchmarks of your business sector. A formula to “ABtest” by varying design elements but also the language used, which must be a happy compromise between brand tone, pedagogy and legal imperatives.


Pillar #2: Identity Resolution


Equally important is the ability to reconcile interactions around a unique ID for each user. Without this identity resolution, it will be difficult not to over-solicit audiences and, even more so, to send them content adapted to their expectations. If technical prerequisites, especially in emails (read our white paper “a world without cookies”) are necessary, it is also important to think of campaigns to multiply the opportunities to associate emails and cookies.


Pillar #3: Audience segmentation


Finally, to fully capitalize on organic levers and first-party data, the ability to segment audiences holds the keys to effective activation. The objective is to capture as many signals as possible in order to create segments of varying degrees of complexity and to personalize messages. And, once again, by thinking on an omnichannel scale, therefore capturing signals from all horizons and on a large scale, in order to give teams the material to enrich the dimensions of these segments.


Backed by these three pillars, the teams in charge of acquisition and retention will be able to improve customer knowledge. And gain precision in the allocation of media budgets for which, mechanically, brands can expect an increased return.


What is the difference between a CDP and a CRM?

A Customer Data Platform (CDP) and a Customer Relationship Management (CRM) system are both tools used by businesses to manage customer data and improve customer relationships. While they have some similarities, they serve different purposes and have distinct functionalities. Here’s an overview of the differences between a CDP and a CRM: 

From customer management to customer unification:

A CRM system focuses on managing customer interactions and relationships. It primarily stores data related to customer interactions, such as contact information, sales activities, customer support tickets, and communication history. CRM systems often have features for sales pipeline management, lead tracking, and customer service. 

Meanwhile, a CDP is designed to collect, unify, and organize customer data from multiple sources, such as transactional data, online behavior, marketing interactions, and more. Its primary focus is to create a unified customer profile or a “single source of truth” that combines data from various touchpoints. 


Data Integration and Unification

   – CDP: CDPs are built to handle large volumes of data from various sources and unify it into a centralized customer profile. They employ data integration techniques to collect and synchronize data from different systems, such as e-commerce platforms, email marketing tools, social media platforms, and more. CDPs create a comprehensive view of the customer across multiple channels. 

   – CRM: CRM systems typically focus on capturing and managing data related to customer interactions within the organization. While they may integrate with other systems, their main purpose is not to unify data from multiple sources but to provide a centralized repository for customer-related information generated within the organization itself. 

Use Cases and Functionality:

   – CDP: CDPs excel in creating a holistic view of the customer by combining data from various touchpoints. They enable businesses to analyze customer behavior, personalize marketing campaigns, segment customers based on specific attributes, and deliver consistent experiences across different channels. CDPs often provide capabilities for customer analytics, audience segmentation, and data activation for marketing purposes. 

   – CRM: CRM systems are primarily focused on managing customer relationships and interactions. They provide tools for sales teams to track leads, manage opportunities, and monitor the sales pipeline. CRM systems also assist in customer service and support by organizing customer inquiries, tracking customer interactions, and enabling efficient issue resolution. They often include features like contact management, sales automation, and customer support ticketing. 

User Roles:

   – CDP: CDPs are typically used by marketing teams, data analysts, and data-driven professionals who leverage customer data for segmentation, targeting, and campaign optimization. They help marketers gain insights into customer behavior and preferences to deliver more personalized experiences. 

   – CRM: CRM systems are commonly used by sales teams, customer service representatives, and account managers. They provide tools and functionalities to streamline sales processes, manage customer interactions, and enhance customer service. 

 In summary, while both CDPs and CRMs deal with customer data, their primary focus, scope, and functionality differ. CDPs specialize in unifying and analyzing data from various sources to create a comprehensive view of the customer, enabling personalized marketing and segmentation. On the other hand, CRMs are primarily used to manage customer interactions, sales activities, and customer service within the organization. 

To know more about the CDP, contact us!

Improving data quality, a key issue for companies

In a world where data is increasingly valuable to businesses, ensuring its quality is essential to guaranteeing the effectiveness of campaigns and therefore maximizing marketing investments. This is where our platform comes into play.

Why is data quality so important to businesses?

Data quality is crucial for businesses because it directly impacts their ability to make informed decisions, analyze data accurately, and achieve their business goals. When data is of poor quality, it can lead to errors in analysis and decision-making, which can have a negative impact on marketing campaign ROI, data analysis, and campaign analytics. For example, if a company uses poor quality data to target its advertising campaigns, it may end up reaching people who are not interested in its products or services, which can lead to wasted budget and lower campaign performance. Similarly, if a company uses poor quality data to analyze its performance, it may end up making decisions that are not based on accurate data, which can be detrimental to the company’s long-term growth and success.
In summary, data quality is critical for businesses as it is the basis for informed decision making and accurate analysis.

What is Data quality or Data Integrity?

Data integrity refers to the accuracy and consistency of data throughout its life cycle, from collection and storage to analysis and dissemination. Without data integrity, companies risk making decisions based on inaccurate information, which can lead to lost revenue, damaged reputations, and even legal issues. Ensuring data integrity is a complex and difficult process, especially for organizations that handle large amounts of data from multiple sources. It requires the implementation of a series of controls and processes, including quality control, validation, duplicate removal, integrated delivery control, real-time alerts, preservation and backup, cybersecurity and advanced access controls. These measures ensure that data is accurate, complete and consistent, and that any data integrity threats are identified and quickly addressed.

Improve data quality with our platform

Our platform aims to give companies the confidence in their data in a very simple way. We offer a standardized datalayer interface that allows users to define their data schema and define validation rules that feed their data quality workflow.
Moreover, our Data Cleansing feature allows users to transform/correct their events in real-time in a simple and intuitive way, thanks to our no-code approach. However, the more technical among us are not forgotten since we also offer a low-code module (or even just code for the more daring).

Manage data errors with our platform

We have several features to manage data errors. First, we have a data quality dashboard that allows users to see specification violations at a glance and quickly correct them at the source or in real-time with the Data Cleansing feature.
We also offer real-time alerts so that users can react quickly to data errors. These alerts can be sent by email, messaging (Slack, Teams, …), webhook or via notifications in the interface. An alert can be configured in 3 clicks, with a slider to choose the trigger threshold and the communication channel.

How our product helps work with the same data across the enterprise

Our standardized datalayer interface allows users to define the schema of their data and define validation rules to ensure that all data conforms to that schema. This way, all teams can work with the same data and ensure that it is of high quality. In addition, we have a single data dictionary that allows users to define and share their data definitions across the enterprise.

What is Data Cleansing and how does it work?

The Data Cleansing feature allows users to transform/correct their events before sending them to their destinations. We have several types of transformations available, such as event renaming, event derivation, property modification and event filtering, which can be created in a simple and intuitive way thanks to our no-code approach based on basic formulas and operators, very similar to what one would find in a spreadsheet like Excel. For those who prefer a low-code approach, it is also possible to add custom JavaScript code to create custom transformations. The Data Cleansing feature is particularly useful for ensuring that data sent to destinations is of high quality and complies with required specifications.

What about the quality of the data transmitted to destinations?

We have an event deliverability tracking interface that allows users to check if data is reaching its destination or if there have been any problems in sending. This interface includes quick and easy to read metrics such as the percentage of events not sent, a visualization of the evolution of correctly sent and failed events over a given period of time, and an error summary table. The latter gives an overview of the different types of errors encountered and how to resolve them. In case of sending problems, we also offer an alert system to notify users immediately.

How our platform simplifies complex technical errors when sending data to partners

First of all, the errors are not always technical, they are often missing or badly formatted data and our platform generates explanations in natural language that are very easy to read. And as for technical errors, whether they come from a partner’s API feedback or an unavailability of its servers, it was important for us that each error be very simple to understand. We use a natural language generator (NLG) to transform these unreadable errors into explanations that are perfectly understandable by a non-technical profile with resolution paths. That’s the magic of AI 🙂
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