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Canal+ Churn Prediction Models Explainable with Machine Learning Intelligibility",{"rendered":506,"protected":507},"\n\u003Cp>Discover how we transformed Canal+ existing churn prediction model from a &#8220;black box&#8221; into a \u003Cstrong>relevant and meaningful solution\u003C\u002Fstrong>. Now our client&#8217;s marketing teams are able to know \u003Cstrong>why their customers churn\u003C\u002Fstrong> and \u003Cstrong>take appropriate actions\u003C\u002Fstrong> to retain them.\u003C\u002Fp>\n\n\n\n\u003Cp>The existing Canal+ approach could only output the probability of a customer to churn within six months. However, the likelihood of a Canal+ customer to churn was predicted without any concrete reasons or causes. In short, the model was just spitting out a number. This naturally led to a relatively low adoption rate by the international marketing teams.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-context-amp-key-challenges-from-numbers-to-insights\">Context &amp; Key Challenges: From Numbers to Insights\u003C\u002Fh2>\n\n\n\u003Cp>Canal+ Group, a leading French audiovisual media group present in 40 countries and serves more than 22 million subscribers with television channels, the MyCanal streaming platform, and original content production.\u003C\u002Fp>\n\n\n\n\u003Cp>Their marketing teams had been \u003Cstrong>using a &#8220;fragility score&#8221;\u003C\u002Fstrong> for some time now—a simple percentage from 0% to 100%&nbsp;indicating how likely a customer was to churn within six months. This score was use to spot an number of customers with a higher risk to leave in order to retain them through specific commercial offers. While the model worked, it left teams with a critical gap: they knew \u003Cem>who\u003C\u002Fem> might leave, but not \u003Cem>why\u003C\u002Fem>.\u003C\u002Fp>\n\n\n\n\u003Cp>Therefore, our client was facing three main challenges:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>No explanation for model’s decisions:\u003C\u002Fstrong> Marketing teams couldn&#8217;t understand why specific customers were flagged as &#8220;fragile&#8221;\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Low adoption across regions:\u003C\u002Fstrong> International entities hesitated to use the score due to concerns about country-specific behavior (i.e., sensitivity to the training data) and lack of transparency\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Limited improvement opportunities:\u003C\u002Fstrong> Few indicators and metrics were available to enhance the model or identify weaknesses\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>The result? Telemarketers were making random retention attempts without clear, relevant arguments to keep customers engaged.\u003C\u002Fp>\n\n\n\n\u003Cp>Our client&#8217;s goal:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Deep understanding\u003C\u002Fstrong> of their existing model and churn predictions\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Concrete insights\u003C\u002Fstrong> about why customers might leave, enabling targeted retention strategies\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Increased trust \u003C\u002Fstrong>in the model across international marketing teams through explainability\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-our-approach-churn-prediction-model-explainable-with-machine-learning-intelligibility-mli-implementation\">Our Approach: Churn Prediction Model explainable with Machine Learning Intelligibility (MLI) Implementation\u003C\u002Fh2>\n\n\n\u003Cp>We implemented a comprehensive 8-week Machine Learning Intelligibility solution. With a holistic approach that aims to bring clear and relevant explainability, interpretability, and intelligibility of a Machine Learning (ML) model.\u003C\u002Fp>\n\n\n\n\u003Cp>In close collaboration with Canal+ IT and Architecture Teams, we went through these three key phases:\u003C\u002Fp>\n\n\n\n\u003Col class=\"wp-block-list\">\n\u003Cli>Model &amp; Data Quality Assessments\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cspan style=\"color: initial;\">Explainability (XAI) Indicators Implementation\u003C\u002Fspan>\u003C\u002Fli>\n\n\n\n\u003Cli>Operationalization of the explained model\u003C\u002Fli>\n\u003C\u002Fol>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-1-model-amp-data-quality-assessments\">1. Model &amp; data quality assessments\u003C\u002Fh3>\n\n\n\u003Cp>We conducted \u003Cstrong>a thorough evaluation\u003C\u002Fstrong> of the ML model&#8217;s performance and the training \u003Ca href=\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fboosting-business-decisions-with-modular-data-quality-management-and-machine-learning\">data quality\u003C\u002Fa>\u003Cstrong> \u003C\u002Fstrong>to identify potential biases that could impact predictions (such as geographic location factors).\u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-2-explainability-indicators-implementation\">2. Explainability indicators implementation\u003C\u002Fh3>\n\n\n\u003Cp>Although this model was from the Random Forest Models (a family of models that already possess a form of built-in explainability), it was&nbsp;necessary to&nbsp;\u003Cstrong>determine what indicator would suit best the use case’s context\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Before-After.png\" alt=\"Before and after comparison diagram showing churn prediction model improvements through MLI\u002FXAI implementation, demonstrating how explainable AI transforms telemarketing from blind calls with 77.4% churn probability to informed, relevant calls with same probability but clear reasoning: equipment age, long-standing client status, and low service usage over 30 days.\" class=\"wp-image-10288\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Before-After.png 1280w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Before-After-800x450.png 800w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Before-After-1024x576.png 1024w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Before-After-100x56.png 100w\" sizes=\"auto, (max-width: 1280px) 100vw, 1280px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Based on our experience with similar use cases, we chose to use Shapley values for the local explainability, and the PDP &amp; PFI methods for the global explainability. The difference between them is the explanation scope:\u003C\u002Fp>\n\n\n\n\u003Cp>A \u003Cstrong>Local Explainability Indicator (Shapley values)\u003C\u002Fstrong> explains individual customer predictions—why does John Doe have a 77.4% churn probability? While a \u003Cstrong>Global Explainability Indicator (PDP &amp; PFI methods)\u003C\u002Fstrong> reveals overall model behavior—which variables most influence predictions?\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Before: “John Doe has a 77.4% chance to churn. (No context provided)”.\u003C\u002Fli>\n\n\n\n\u003Cli>After: John Doe has a 77.4% chance to churn because he subscribed long ago and his decoder is likely outdated. (Actionable insight: offer decoder replacement and loyalty rewards).\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Global-Explanation.png\" alt=\"Table showing global explanation of churn prediction model variables with importance and incertitude percentages, featuring Age of Equipment (33% importance), Time to end subscription (12%), Subscription to specific service\u002FFeature (8%), Customer Service contacts about contract termination (7%), Customer Seniority (4%), and Customer Service problem resolution contacts (2%).\" class=\"wp-image-10287\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Global-Explanation.png 1280w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Global-Explanation-800x450.png 800w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Global-Explanation-1024x576.png 1024w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FChurn-Prediction-Model-Global-Explanation-100x56.png 100w\" sizes=\"auto, (max-width: 1280px) 100vw, 1280px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>The telecom marketing department can now create a \u003Cstrong>tailored response \u003C\u002Fstrong>to this client&nbsp;such as offering to replace their decoder and a special promotion to reward their loyalty.\u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-3-operationalization-of-the-churn-prediction-model\">3. Operationalization of the Churn Prediction Model\u003C\u002Fh3>\n\n\n\u003Cp>Once the XAI indicators were implemented, it was crucial to make them available to all the business teams, no matter if they were using the churn model directly or not.\u003C\u002Fp>\n\n\n\n\u003Cp>Canal+ DevOps and MLOps teams to operationalize our developments into their existing infrastructure. We ensured that all business teams could access the enhanced explanations and ensure we provided a code that fully complies with the technical requirements of the IT infrastructure of our client.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-benefits-actionable-churn-insights-and-improved-model-performance\">Benefits: Actionable Churn Insights and Improved Model Performance\u003C\u002Fh2>\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>10% improvement\u003C\u002Fstrong> in churn prediction performance\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Concrete retention strategies\u003C\u002Fstrong> usable by end-user of the model (telemarketers and marketing teams)\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Increased \u003Ca href=\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fensuring-ai-adoption-through-model-understanding-and-explanation-xai\">model understanding and trust\u003C\u002Fa>\u003C\u002Fstrong> across international business teams\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Complete explainability of the model\u003C\u002Fstrong> through both global and local explanations\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Indicators operationalization\u003C\u002Fstrong> into existing MLOps pipelines\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Marketing teams can now understand that a customer is &#8220;fragile&#8221; because they haven&#8217;t used the service for 31 days, or their equipment needs updating.\u003C\u002Fp>\n\n\n\n\u003Cp>Now, our client is able to see that a customer is ‘fragile’ because they have not used the service for the last 31 days, or because their equipment is too old, for instance—enabling targeted, effective retention campaigns.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-team-involved\">Team Involved\u003C\u002Fh2>\n\n\n\u003Cp>The project was successfully delivered through the collaborative efforts of our specialized team over an 8-week engagement. The compact timeline required focused expertise and efficient coordination between our specialists and the client&#8217;s team.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Data Scientist:\u003C\u002Fstrong> Our data science specialist brought comprehensive expertise in statistical analysis, data modeling, and machine learning methodologies. They were responsible for designing \u003Ca href=\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fhow-to-successfully-drive-your-csrd-esg-initiative-with-a-data-driven-approach\">data-driven solutions\u003C\u002Fa>, validating model performance, and ensuring the analytical framework met the project&#8217;s specific requirements.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>XAI Expert:\u003C\u002Fstrong> The Explainable AI specialist played a crucial role in developing transparent and interpretable machine learning solutions. They focused on implementing XAI methodologies, developing explanation mechanisms, and ensuring stakeholders could understand and trust the AI-driven outcomes.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>\u003Cstrong>Collaborative Partnership:\u003C\u002Fstrong> The 8-week engagement fostered intensive collaboration between our specialized team and the client, ensuring rapid knowledge transfer and efficient project delivery within the compressed timeline.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-technologies-used\">Technologies Used\u003C\u002Fh2>\n\n\n\u003Cp>The project implementation leveraged a carefully selected technology stack designed for efficient data processing, analysis, and explainable AI capabilities. Each technology was chosen for its specific role in delivering a comprehensive and transparent solution:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Python\u003C\u002Fstrong> served as the primary programming language, providing the foundation for all data science and machine learning development.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Amazon Redshift\u003C\u002Fstrong> was utilized as our cloud-based data warehouse solution, providing scalable and high-performance analytics capabilities.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Atlassian Bitbucket\u003C\u002Fstrong> provided our version control and collaborative development platform, enabling secure code management, peer review processes, and seamless collaboration between team members.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Ca href=\"https:\u002F\u002Fhubs.la\u002FQ03GWjjG0\" target=\"_blank\" rel=\" noreferrer noopener\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1200\" height=\"627\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FWHite-Paper-Banner-Website-Customer-Analytics_B_Insights_01.png\" alt=\"Hyper-personalization with Modern AI: The Next Generation of Customer Orientation - White Paper Banner Website Customer Analytics\" class=\"wp-image-10298\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FWHite-Paper-Banner-Website-Customer-Analytics_B_Insights_01.png 1200w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FWHite-Paper-Banner-Website-Customer-Analytics_B_Insights_01-800x418.png 800w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FWHite-Paper-Banner-Website-Customer-Analytics_B_Insights_01-1024x535.png 1024w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FWHite-Paper-Banner-Website-Customer-Analytics_B_Insights_01-100x52.png 100w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003Ca id=\"_msocom_1\">\u003C\u002Fa>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003C\u002Fp>\n",false,{"rendered":509,"protected":507},"\u003Cp>CBTW transformed Canal+&#8217;s churn prediction model from an unexplainable &#8220;black box&#8221; into a powerful solution that marketing teams can actually use. Serving 22 million subscribers across 40 countries, Canal+ needed more than just churn probabilities—they needed to understand why customers leave.\u003C\u002Fp>\n",43,10290,"open","standard",{"_acf_changed":507,"content-type":8,"inline_featured_image":507,"footnotes":8},[43],[],[518],60,[520],129,[522,501,523,524,525,526,527,528,529,530],"post-10187","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies","cbtw_industries-telecom-media","cbtw_services-ai-data-platforms",{"authors":8,"article_authors":507,"direct_external_link":8,"post_region":532,"layout_builder":533},"global",[534],{"acf_fc_layout":535,"title":190,"ui_v2":536,"short_description":537,"cbtw_related_group_filters":538,"items":507,"link":545,"hubspot_form_id":8,"mainPostId":496,"region":532},"block_insights",true,"Access related expert insights",{"cbtw_related_category_filter":539,"cbtw_related_tag_filter":540,"cbtw_related_industry_filter":541,"cbtw_related_service_filter":543},[379],[],[542],"telecom-media",[544],"ai-data-platforms",{"title":546,"url":547,"target":548},"View all insights","https:\u002F\u002Fstaging.cbtw.tech\u002Finsights","_self","\u003C!-- This site is optimized with the Yoast SEO Premium plugin v25.4 (Yoast SEO v25.4) - https:\u002F\u002Fyoast.com\u002Fwordpress\u002Fplugins\u002Fseo\u002F -->\n\u003Ctitle>Churn Prediction Model with MLI for Canal+ - CBTW\u003C\u002Ftitle>\n\u003Cmeta name=\"description\" content=\"Discover how we made Canal+ churn prediction model explainable. They now get to know the concrete and real reasons making the customer churn.\" \u002F>\n\u003Cmeta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \u002F>\n\u003Clink rel=\"canonical\" href=\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility\" \u002F>\n\u003Cmeta property=\"og:locale\" content=\"en_US\" \u002F>\n\u003Cmeta property=\"og:type\" content=\"article\" \u002F>\n\u003Cmeta property=\"og:title\" content=\"Making Canal+ Churn Prediction Models Explainable with Machine Learning Intelligibility\" \u002F>\n\u003Cmeta property=\"og:description\" content=\"Discover how we made Canal+ churn prediction model explainable. They now get to know the concrete and real reasons making the customer churn.\" \u002F>\n\u003Cmeta property=\"og:url\" content=\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility\" \u002F>\n\u003Cmeta property=\"og:site_name\" content=\"CBTW\" \u002F>\n\u003Cmeta property=\"article:published_time\" content=\"2025-09-04T16:29:20+00:00\" \u002F>\n\u003Cmeta property=\"article:modified_time\" content=\"2025-09-04T16:31:57+00:00\" \u002F>\n\u003Cmeta property=\"og:image\" content=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fglenn-carstens-peters-churn-prediction-model-unsplash-scaled.jpg\" \u002F>\n\t\u003Cmeta property=\"og:image:width\" content=\"2560\" \u002F>\n\t\u003Cmeta property=\"og:image:height\" content=\"1703\" \u002F>\n\t\u003Cmeta property=\"og:image:type\" content=\"image\u002Fjpeg\" \u002F>\n\u003Cmeta name=\"author\" content=\"Victoire Goarin\" \u002F>\n\u003Cmeta name=\"twitter:card\" content=\"summary_large_image\" \u002F>\n\u003Cmeta name=\"twitter:label1\" content=\"Written by\" \u002F>\n\t\u003Cmeta name=\"twitter:data1\" content=\"Victoire Goarin\" \u002F>\n\t\u003Cmeta name=\"twitter:label2\" content=\"Est. reading time\" \u002F>\n\t\u003Cmeta name=\"twitter:data2\" content=\"6 minutes\" \u002F>\n\u003Cscript type=\"application\u002Fld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\u002F\u002Fschema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility#article\",\"isPartOf\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility\"},\"author\":{\"name\":\"Victoire Goarin\",\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002F#\u002Fschema\u002Fperson\u002Ff5aafe48b3af0222f5bfb95e227643c2\"},\"headline\":\"Making Canal+ Churn Prediction Models Explainable with Machine Learning Intelligibility\",\"datePublished\":\"2025-09-04T16:29:20+00:00\",\"dateModified\":\"2025-09-04T16:31:57+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility\"},\"wordCount\":1050,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002F#organization\"},\"image\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility#primaryimage\"},\"thumbnailUrl\":\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fglenn-carstens-peters-churn-prediction-model-unsplash-scaled.jpg\",\"articleSection\":[\"Case Studies\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility\",\"url\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility\",\"name\":\"Churn Prediction Model with MLI for Canal+ - CBTW\",\"isPartOf\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002F#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility#primaryimage\"},\"image\":{\"@id\":\"https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fmaking-canal-churn-prediction-models-explainable-with-machine-learning-intelligibility#primaryimage\"},\"thumbnailUrl\":\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fglenn-carstens-peters-churn-prediction-model-unsplash-scaled.jpg\",\"datePublished\":\"2025-09-04T16:29:20+00:00\",\"dateModified\":\"2025-09-04T16:31:57+00:00\",\"description\":\"Discover how we made Canal+ churn prediction model explainable. 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