[et_pb_section fb_built=”1″ _builder_version=”4.16″ custom_padding=”0px|0px|0px|0px” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row module_class=”enbref” _builder_version=”4.27.5″ custom_margin=”||5%||false|false” custom_padding=”0px|||||” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text module_class=”enbref-content” _builder_version=”4.27.6″ custom_padding=”||12%||false|false” hover_enabled=”0″ inline_fonts=”Roboto” global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n
In brief<\/strong><\/p>\n Anticipate departures, optimize recruitment, identify skills needs before they become urgent: predictive HR analysis transforms the HR function from a reactive center into a strategic pilot.<\/p>\n Discover the tools, concrete use cases and success factors for deploying predictive analytics in your organization.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ custom_padding=”0px|||1px||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.6″ hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″ custom_padding=”||5%||false|false”]<\/p>\n Predictive analytics is a tool that allows you to anticipate employee behaviors and future events within the company.<\/p>\n By 2026, it’s no longer the preserve of large organizations: 77% of HR managers say they have already integrated or are in the process of integrating AI tools into at least one HR process. And among the most popular use cases, predictive analysis of turnover and detection of weak attrition signals top the list. <\/p>\n These issues are directly in line with the process of developing a data-driven culture<\/a> andacculturation to HR data<\/a> that we detail in our dedicated articles.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″ custom_padding=”||5%||false|false”]<\/p>\n The objectives of predictive analytics are to assist in decision making, to adjust and to develop strategies adapted to real situations. To do this, it relies on data mining and statistics. It works with automation and parameterization solutions. In concrete terms, using an identified database, this methodology consists of cross-referencing predefined variables with variables taken at a given point in time.<\/p>\n In this way, predictive analytics can be used to identify performance indicators that can then be interpreted by human resources departments. However, to extract usable indicators, the predictive method is based on different working modes:<\/p>\n The benefits of HR predictive analytics are numerous:<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″ custom_padding=”||5%||false|false”]<\/p>\n When it comes to improving recruitment processes, predictive analysis can find the best profiles to match job vacancies. Complemented by artificial intelligence, machine learning can study information from CVs and professional social networks to extract the right candidates for a position. This optimizes the recruitment process, both in terms of time and cost. <\/p>\n According to the OpinionWay Barometer for Kelio 2026, 51% of HR managers still express reservations about the use of AI in recruitment, believing that this process must above all remain human. The key lies in an augmented approach: AI processes the volume, the human decides. <\/p>\n What’s more, the CNIL has listed recruitment as one of its priority control themes for 2026, so organizations using sorting algorithms must inform applicants and carry out a DIA (Data Protection Impact Assessment).<\/p>\n For talent management, the company’s human capital can be enhanced by cross-referencing personal data with the results of appraisal interviews. This approach highlights training and skills development needs. HR departments can then put in place customized training plans or closely support employees in acquiring new skills. <\/p>\n This data is also a powerful lever for attracting and retaining talent, thanks to HR data<\/a>.<\/p>\n To optimize team turnover, human resources management must be able to identify the reasons behind turnover and absences. Thanks to data and AI, it is possible to anticipate resignations or assess the social climate in a company before the situation becomes critical. <\/p>\n Turnover prediction solutions are now achieving significant levels of reliability: Bizneo HR announces a reliability of at least 80% in predicting the risk of departure and absenteeism. These models analyze millions of data points from the employee lifecycle: absences, performance, job changes, engagement, to generate early warnings with concrete recommendations. To find out more, our articles onturnover analysis<\/a> and preparing a turnover analysis<\/a> detail the indicators to track and the approach to adopt. <\/p>\n Generative AI adds a new dimension to traditional predictive analysis. Where traditional models project trends from structured data, generative AI cross-references structured and unstructured data (assessments, interviews, comments) to bring out unexpected correlations and generate action scenarios. <\/p>\n According to Sigma-RH \/ Culture RH (secondary source, June 2025), generative AI no longer simply identifies a risk, it proposes personalized responses based on profiles, contexts and career paths. For example: <\/p>\n A classic predictive model detects a rise in turnover in a team This adaptive approach is at the heart of our HR Data & AI offering<\/a>.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″ custom_padding=”||5%||false|false”]<\/p>\n The digitalization of human resources consists in using powerful HR solutions and big data to exploit the available data. Before choosing a tool, you need to clarify your needs: what will predictive analysis be used for? Will it evolve over time?<\/p>\n Several analytical techniques are available, each with its own specific features:<\/p>\n Recruitment and talent assessment :<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ background_color=”#f7fbff” custom_margin=”||12%||false|false” custom_padding=”5%|5%|5%|5%|true|true” hover_enabled=”0″ border_radii=”on|23px|23px|23px|23px” inline_fonts=”Roboto” global_colors_info=”{}” theme_builder_area=”post_content” locked=”off” sticky_enabled=”0″]<\/p>\n AI Act compliance: what you need to know before you deploy<\/strong><\/p>\n The European AI Act classifies AI systems used in recruitment, selection, evaluation and promotion as high-risk systems. This implies obligations for human supervision, traceability of decisions, employee information and impact analysis. <\/p>\n Before deploying an HR predictive analysis tool, systematically involve your DPO and check that the chosen solution is compliant.<\/p>\n Our articles onimproving HR data quality<\/a> and HR data quality<\/a> give you the keys to securing this base. <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″ custom_padding=”||5%||false|false” locked=”off” hover_enabled=”0″]<\/p>\n According to ConvictionsRH’s AI & HRIS Panorama 2025 (primary source, 40 publishers surveyed), the conditions for a successful predictive HR analysis project rest on five pillars:<\/p>\n To avoid the classic mistakes of this type of project, our article on 5 mistakes to avoid in HR data projects<\/a> is essential reading before you get started.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″ custom_padding=”||5%||false|false” locked=”off” hover_enabled=”0″]<\/p>\n Predictive HR analysis<\/strong> transforms the HR function from a reactive center into a strategic pilot capable of anticipating risks, optimizing decisions and creating value for the entire organization. But this transformation cannot be decreed: it requires a reliable database, clear governance and support for teams. <\/p>\n Would you like to deploy predictive analytics in your organization? Our HR Data & AI consultants can support you from scoping to production. Contact us for an initial discussion, or find out more about our Data Analytics offer. <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding_last_edited=”on|desktop” module_class=”cta-bleu” _builder_version=”4.27.5″ background_color=”#2029a0″ background_image=”https:\/\/www.sqorus.com\/wp-content\/uploads\/2023\/12\/Bandeau-LB-Data-RH.png” background_size=”contain” background_position=”center_right” width=”100%” width_tablet=”90%” width_phone=”90%” width_last_edited=”on|phone” custom_margin=”||3%||false|true” custom_padding=”50px|400px|50px|55px|true|false” custom_padding_tablet=”50px|55px|50px|55px|true|true” custom_padding_phone=”40px|55px|40px|55px|true|true” background_last_edited=”on|phone” background_enable_image_tablet=”off” border_radii=”on|23px|23px|23px|23px” global_module=”25038″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text module_class=”Titre_40 Titre_blanc” _builder_version=”4.23.1″ _module_preset=”default” header_2_font_size=”30px” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text module_class=”FondBleu” _builder_version=”4.23.1″ _module_preset=”default” text_font=”||||||||” text_text_color=”#d6d8f4″ text_line_height=”30px” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Optimize your HR strategy with the most effective management tools on the market, and give your company a head start.<\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/www.sqorus.com\/en\/ebook-optimize-hr-data\/” button_text=”Download” button_alignment=”left” button_alignment_tablet=”center” button_alignment_phone=”center” button_alignment_last_edited=”on|tablet” admin_label=”Bouton de t\u00e9l\u00e9chargement” module_class=”Bouton Fond_blanc” _builder_version=”4.27.0″ _module_preset=”default” custom_button=”on” button_text_size=”15px” button_text_color=”#f36145″ button_bg_color=”#FFFFFF” button_border_width=”1px” button_border_color=”#f36145″ button_border_radius=”50px” button_font=”||||||||” button_use_icon=”off” custom_margin=”20px||||false|false” custom_padding=”13px|25px|13px|25px|false|false” custom_css_free_form=”selector:hover{|| background-color: #FFF6F5 !important;||}” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Section – Contactez nous” module_class=”Fond_bleu Fond_icone” _builder_version=”4.26.1″ custom_margin=”2rem||5rem||false|true” custom_margin_tablet=”4rem|40px|4rem|40px|true|true” custom_margin_phone=”|20px||20px|false|true” custom_margin_last_edited=”on|phone” custom_padding=”3rem||3rem||true|false” global_module=”31205″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row make_equal=”on” admin_label=”Text block” module_class=”LargeurBox” _builder_version=”4.25.0″ _module_preset=”default” animation_style=”fade” locked=”off” collapsed=”off” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.25.0″ _module_preset=”default” custom_padding=”|9rem||9rem|false|true” custom_padding_tablet=”|0rem||0rem|false|true” custom_padding_phone=”|0rem||0rem|false|true” custom_padding_last_edited=”on|desktop” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text admin_label=”Sous-titre” module_class=”SousTitre” _builder_version=”4.25.0″ _module_preset=”default” text_font=”||||||||” text_orientation=”center” custom_margin=”||10px||false|false” text_orientation_tablet=”” text_orientation_phone=”center” text_orientation_last_edited=”on|tablet” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Contact<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Titre – H2″ module_class=”Titre_40″ _builder_version=”4.25.0″ _module_preset=”default” text_orientation=”center” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Texte” _builder_version=”4.25.1″ _module_preset=”default” text_text_color=”#d2d4ec” text_font_size=”21px” text_line_height=”35px” text_orientation=”center” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Contact us today and find out how we can work together to make your company’s digital future a reality.<\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/www.sqorus.com\/en\/contact\/” button_text=”Contact SQORUS” button_alignment=”center” module_class=”Bouton fond_orange” _builder_version=”4.25.2″ _module_preset=”default” custom_margin=”30px||||false|false” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”CTA IMAGE BAS DE PAGE RH” _builder_version=”4.16″ custom_padding=”10px|0px|54px|0px” link_option_url=”https:\/\/www.s-h.fr\/landing-page-guide-valoriser-utilisation-sirh-entreprise\/” global_module=”12462″ saved_tabs=”all” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text admin_label=”A lire aussi dans notre dossier data RH” module_class=”Titre_h3″ _builder_version=”4.26.1″ header_2_font=”|700|||on||||” header_2_text_color=”#000000″ header_2_font_size=”18px” header_2_line_height=”1.7em” inline_fonts=”Roboto” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.5″ custom_margin=”10%||||false|false” custom_padding=”0px|0px|0px|0px” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.16″ custom_padding=”0px|||||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.6″ custom_padding=”||5%||false|false” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n [\/et_pb_text][dsm_faq dsm_make_accordion_toggle=”on” dsm_open_faq_item=”on” dsm_enable_schema_markup=”on” _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″][dsm_faq_child dsm_title=”What is HR predictive analytics and how does it differ from traditional BI?” dsm_content=”<\/p>\n Traditional BI (Business Intelligence) analyzes past data to produce reports and dashboards. Predictive analysis goes further: it uses statistical models and machine learning to anticipate future events, a risk of departure, a need for recruitment, a drop in commitment. In 2026, generative AI will further enrich this approach by generating personalized action scenarios from structured and unstructured data. <\/p>\n <\/p>\n ” _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” parentOrderClass=”dsm_faq_0″ sticky_enabled=”0″][\/dsm_faq_child][dsm_faq_child dsm_title=”What are the most common use cases for HR predictive analytics?” dsm_content=”<\/p>\n The most frequently deployed use cases are: predicting turnover and absenteeism, optimizing recruitment (scoring applications, skills-position matching), detecting training needs, workforce planning and simulating payroll scenarios. Turnover analysis is often the first use case selected, as its ROI is directly measurable. <\/p>\n <\/p>\n ” _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” parentOrderClass=”dsm_faq_0″ sticky_enabled=”0″][\/dsm_faq_child][dsm_faq_child dsm_title=”What are the risks of poorly managed HR predictive analytics?” dsm_content=”<\/p>\n The main risks are: algorithmic bias (indirect discrimination on the basis of gender, age or origin), GDPR and AI Act non-compliance, loss of employee trust if uses are not transparent, and HR decisions made on the basis of poor-quality data. With the AI Act classifying HR tools as high-risk systems, compliance is no longer optional. <\/p>\n <\/p>\n ” _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” parentOrderClass=”dsm_faq_0″ sticky_enabled=”0″][\/dsm_faq_child][dsm_faq_child dsm_title=”Do you need an advanced HRIS to deploy predictive HR analysis?” dsm_content=”<\/p>\n Not necessarily. Tools like Power BI make it possible to build simple predictive models from existing data, even without a next-generation HRIS. The key is to have reliable, structured and accessible data. Data support to audit the quality of existing data is often the first step recommended before any deployment. <\/p>\n <\/p>\n ” _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” parentOrderClass=”dsm_faq_0″ sticky_enabled=”0″][\/dsm_faq_child][\/dsm_faq][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Predictive analysis provides HR with actionable indicators, centralizing data and freeing up time.<\/p>\n","protected":false},"author":27,"featured_media":6510,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","_ayudawp_aiss_exclude":false,"footnotes":""},"categories":[393],"tags":[],"metiers":[226],"sujet":[218],"class_list":["post-8461","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hr-data-en","metiers-hr","sujet-data-ia-en"],"acf":[],"yoast_head":"\nPredictive analysis: what are the objectives?<\/strong><\/h2>\n
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Predictive analysis applied to human resources<\/strong><\/h2>\n
Optimizing recruitment<\/h3>\n
Managing talent<\/h3>\n
Anticipating and reducing turnover<\/h3>\n
Generative AI: a new dimension in predictive analytics<\/h3>\n
Generative AI identifies common points between recent departures, extracts weak signals in evaluations, then proposes several intervention scenarios: implementation of targeted mentoring, manager development, reworking of individual objectives.<\/p>\nWhat predictive analytics tools are available to HR?<\/strong><\/h2>\n
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The main HR predictive analytics tools in 2026<\/h3>\n
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Analysis of turnover and commitment<\/h3>\n
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HR visualization and management<\/h3>\n
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Conditions for a successful HR predictive analytics project
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Conclusion: HR predictive analytics, a strategic lever if well prepared<\/strong><\/h2>\n
How can you optimize your HR data?<\/h2>\n
A project? A request?A question?<\/h2>\n
Read more in our “HR Data” feature:<\/h3>\n
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FAQ – HR predictive analytics<\/h2>\n