Relational and behavioral data

Relational data categorizes the relationship between seller and buyer. Beyond the simple act of buying, it qualifies the "quality" of the relationship: number of contacts, buyer status (VIP, exceptional), number of interactions in the year, propensity to open communications...

This data is mainly fed by the buyer's behavior on digital media (website, social networks...). The challenge with this type of data is to be able to link behavior to a specific contact. This is what Data Management Platforms (DMP) are all about.

Transactional data

As the name suggests, transactional data corresponds to information relating to a transaction, and is characterized by at least two key elements: an amount and a date. The most common transaction is the act of purchase, but it can also correspond to inventories, salaries, sales... This type of data is often hosted in ERP systems.

Socio-demographic data

This is all the data that qualifies the contact's profile: age, gender, place of residence, socio-professional category, job title...

This is generally the easiest data to obtain and the one that will be used for basic customer segmentation or to build a file of prospecting targets. In B2B, this data will be completed by information about the contact's company: sector, size, location. This is typically the type of data that can be found quite easily in open data.

Unstructured data

All data whose format is not predefined. This can be text (date, e-mail, forum comment, etc.), video, images...

Structured data

Data with values known in advance. This is predictable data, like most form data.

Example: age, zip code, socio-professional category, etc.

Duplicate

The notion of duplicate refers to data present in two distinct records within the same marketing file.

Data Enrichment or Data Appending

Data enrichment is the process of adding new information to a database. In short, it involves adding value to existing data. Data enrichment is one of the steps involved in Data Quality.

Estocade file

A file that enables you to detect in your customer database all recipients who have moved, who have been notified by La Poste (mail forwarding contract expired) and who have consented to the distribution of their former address, in compliance with regulations on the protection of personal data.

Geomarketing

Geomarketing is a marketing discipline. It involves visualizing and analyzing data on a map.

0 party data

Declarative data voluntarily shared by contacts via forms, polls or surveys. Data often includes communication preferences, centers of interest or personal information.

1st party data

Browsing data and information on online and offline interactions and transactions (website, application, points of sale, etc.) of visitors, prospects or customers.

2nd party data

A company's internal data pooled with that of another company or brand through a partnership.

Example: Integration of proprietary 1st party data with that of a partner.

3rd party data

Data purchased or rented from a third-party company. Example: contact, socio-demographic and behavioral data.

Generative AI

Generative AI is a form of artificial intelligence capable of creating new content autonomously, using training data to imitate human creations (machine learning).

Generative AIs are used in many fields, such as art creation, content creation, data generation, video game content generation, chatbots, content creation for social networks and automatic code generation.

Example: ChatGPT, Bards...

Master Data Management (MDM)

Master Data Management means ensuring that an organization is always working with a reliable, up-to-date version of its data, often referred to as the "golden record", and that it bases its decisions on this version.

Master data" is data whose definition is standardized. It defines and describes the company's main activities. This data should not be confused with "reference data" linked to datasets used to classify or categorize other data (such as units of measurement, exchange codes, currencies and country codes).

Open data

Open data are digital data that are freely accessible and usable by users. They can be of private or public origin, produced by a local authority or public institution. They are disseminated in a structured manner using an open method and license, guaranteeing free access and reuse by all, without technical, legal or financial restrictions.

On the one hand, access to data is intended to enable citizens to better control the administration, and on the other hand, to exploit this data, which implies that this right of access is accompanied by a right to re-use the data. Open data is thus a philosophy of access to information, a movement to defend freedoms and a public policy.

Predictive analysis

Predictive analysis is a branch of statistical analysis. It consists in analyzing a series of data in order to develop predictive hypotheses. This helps companies to develop and react rapidly to change. It involves several stages:

1. Step that allows you to focus on a specific category of data to be collected.
2. This stage focuses on data collection. A very large quantity of data is required for high accuracy.
3. Data processing stage.

In business, predictive analytics can be used to establish patterns and models that determine trends, detect opportunities, and make optimal decisions to take the right action at the right time. Data science and Machine Learning are key technologies for companies wishing to take advantage of massive amounts of information.

Data qualification

Database qualification is a data marketing technique that consists in increasing the value of your data. To do this, you need to "enrich" your database, by gathering valid, relevant and functional information about the contacts in it.

Data qualification is one of the steps involved in Data Quality.

Single Customer Reference (SCR)

It's a database containing all the data collected by a company on one of its customers. It centralizes information from different departments (marketing, after-sales service, etc.) to provide 360° customer knowledge.

Restructuring, Standardizing and Validating Postal Addresses (RNVP)

Abbreviation for the procedure (usually computerized) used to Restructure, Standardize and Validate the postal addresses contained in a file. This procedure is essential in postal direct marketing, not only to limit the number of undeliverable mailings for one reason or another, but also de facto, to limit the cost of this undeliverability. In concrete terms, this involves firstly (restructuring) ensuring that the information describing the recipient's address is presented in the correct sequence: name of recipient, additional address if any, street, PO box, zip code, town. The second step (standardization) involves checking the length of the lines of text, as well as the use of standard abbreviations, punctuation and the required use of capital letters for the name of the locality...

La Poste's national address service manages the file of road references for France's 36,000 communes, as well as localities, zip codes and cedex numbers...

General Data Protection Regulation (GDPR)

The General Data Protection Regulation (GDPR ) is a European regulatory text that frames data processing equally throughout the European Union (EU). It came into force on May 25, 2018.