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Customer Churn by 15% With Predictive Analytics",{"rendered":494,"protected":495},"\n\u003Cp>A large German direct bank with several million customers had an elevated churn rate that was increasing over the years. Here is how we reduced it by 6% after 10 months, and by 15% after two years.\u003C/p>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-key-challenges-rising-customer-churn-in-consumer-credit\">Key Challenges: Rising Customer Churn in Consumer Credit\u003C/h2>\n\n\n\u003Cp>The bank had an elevated churn rate that was increasing over the years. More specifically, they had to deal with frequent early redemptions of consumer credits. To address this, they initiated a project to systematically:\u003C/p>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Define and identify churners of consumer credits.\u003C/li>\n\n\n\n\u003Cli>Understand their behavior.\u003C/li>\n\n\n\n\u003Cli>Identify early warning indicators.\u003C/li>\n\n\n\n\u003Cli>Forecast them as precisely as possible in terms of time.\u003C/li>\n\n\n\n\u003Cli>Derive suitable preventive CRM measures to reduce the churn rate significantly.\u003C/li>\n\u003C/ul>\n\n\n\n\u003Cp>The behavior of more than 450,000 consumer credit customers who redeemed their loans early for various reasons was analyzed over a period of more than twelve years.\u003C/p>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-our-approach-to-predicting-and-preventing-customer-churn\">Our Approach to Predicting and Preventing Customer Churn\u003C/h2>\n\n\n\u003Cp>We covered the full scope of the project, from initial analysis to productive operation:\u003C/p>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>“Customer Analytics” status quo analysis as a prerequisite for project success.\u003C/li>\n\n\n\n\u003Cli>Early conception and development of customer win-back campaigns, in cooperation with the Digital Marketing department and the responsible campaign managers.\u003C/li>\n\n\n\n\u003Cli>Profitability calculation of the win-back campaigns in the first year.\u003C/li>\n\n\n\n\u003Cli>Analysis of customer churn behavior and customer-specific churn forecast.\u003C/li>\n\n\n\n\u003Cli>Support through the preparation and implementation of the campaign, as well as monitoring and success control in the first two subsequent years.\u003C/li>\n\n\n\n\u003Cli>Transfer of the prototype into regular productive operation.\u003C/li>\n\u003C/ul>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-benefits-measurable-reduction-in-churn-rate-and-upselling-effect\">Benefits: Measurable Reduction in Churn Rate and Upselling Effect\u003C/h2>\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-\">\u003C/h3>\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Determination of key contract termination indicators for consumer credit customers\u003C/li>\n\n\n\n\u003Cli>The monthly updated, time-accurate termination forecast enabled customer-specific marketing measures to be taken for the first time to prevent terminations before they occur\u003C/li>\n\n\n\n\u003Cli>6% reduction in churn rate (in pilot operation after 10 months)\u003C/li>\n\n\n\n\u003Cli>15% reduction in churn rate (after two years of regular operation)\u003C/li>\n\n\n\n\u003Cli>Upselling effects:\u003Cstrong> \u003C/strong>Identification of a customer group that even takes out a new consumer credit with a higher credit volume after having been contacted\u003C/li>\n\u003C/ul>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"team-involved-on-this-project\">Team Involved on this Project\u003C/h2>\n\n\n\u003Cp>The project team brought together a Product Owner, a Scrum Master, a Data Engineer, a Machine Learning Engineer, a Data Scientist, a Management Scientist, a Digital Marketer, and a Campaign Manager.\u003C/p>\n",false,{"rendered":497,"protected":495},"\u003Cp>This case study article details how we managed to create a churn prediction solution for a Direct Banking company. Learn how we achieved to reduce the churn rate by 6% after 10 months, and by 15% only two years.\u003C/p>\n",43,13902,"open","standard",{"_acf_changed":495,"content-type":8,"inline_featured_image":495,"footnotes":8},[43],[],[506],55,[508],129,[510,489,511,512,513,514,515,516,517,518],"post-13899","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies","cbtw_industries-banking-finance","cbtw_services-ai-data-platforms",{"authors":8,"article_authors":495,"direct_external_link":8,"short_name":8,"short_description":8,"layout_builder":520},[521],{"acf_fc_layout":522,"title":198,"ui_v2":523,"short_description":524,"cbtw_related_group_filters":525,"items":495,"link":531,"hubspot_form_id":8,"mainPostId":483},"block_insights",true,"Access related expert insights",{"cbtw_related_category_filter":526,"cbtw_related_tag_filter":528,"cbtw_related_industry_filter":529,"cbtw_related_service_filter":530},[200,527],"case-studies",[],[],[],{"title":532,"url":533,"target":534},"View all insights","https://cbtw.tech/insights","_self","\u003C!-- This site is optimized with the Yoast SEO Premium plugin v25.4 (Yoast SEO v25.4) - 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