[{"disk":70,"module":"spend_personality","description":"Spend personality refers to the characterization of an individual's spending habits.","cpu":4,"_id":{"$oid":"68304d532a153f472aa8dc46"},"uuid":"ecosystem-module","version":"0.98","long_name":"Spend Personality","date_released":"2025-05-22","status":"released","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/sp-small.png","icon_path":"https://modules.ecosystem.ai/public/sp-small.png","name":"Spend_Personality_0.09.8","module_owner":"ecosystem.Ai","description":"Spend personality refers to the characterization of an individual's spending habits based on their bank transactions. This type of analysis, leveraging AI and Machine Learning, provides valuable insights into customer behavior, preferences, and spending patterns over time. By analyzing the frequency, amount, and type of purchases, a bank can categorize their customers into different spending personality types. \n\nFor \"Money Personality\" analyzing customer transaction patterns against profiles, banks can better understand their customers\u2019 money habits and financial management strategies. This can then drive personalized product recommendations, improve customer engagement, and help in more effectively managing financial risks.\n\nFor example, some customers may be identified as big spenders, frequent shoppers, savers, or even impulse buyers based on their demonstrated spending behaviors. These spending personalities can then be utilized for developing personalized marketing strategies, providing tailored financial advice, or enhancing customer service. It can help banks understand their customers better, predict their needs, and build stronger, more profitable relationships with them.\n\nProcess spending personality using Feature Engineering enrichment option:\n1. Use customer transaction data and perform category enrichment.\n2. Then run Ecosystem Spending Personality to generate new collections with scores assigned to customers.\n3. API returns scores per customer.","categories":"banking, personality, spend, spending, money, transactions","created_by":"ecosystem.Ai","version":"0.09.8","fact_sheet_path":"https://ecosystem.ai/spend-personality/","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":70,"module":"spend_personality","description":"Spend personality refers to the characterization of an individual's spending habits.","cpu":4,"_id":{"$oid":"68304d532a153f472aa8dc4c"},"uuid":"ecosystem-module","version":"0.97","long_name":"Spend Personality","date_released":"2023-10-01","status":"deprecated","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/sp-small.png","icon_path":"https://modules.ecosystem.ai/public/sp-small.png","name":"Spend_Personality_0.09.7","module_owner":"ecosystem.Ai","description":"Spend personality refers to the characterization of an individual's spending habits based on their bank transactions. This type of analysis, leveraging AI and Machine Learning, provides valuable insights into customer behavior, preferences, and spending patterns over time. By analyzing the frequency, amount, and type of purchases, a bank can categorize their customers into different spending personality types. \n\nFor \"Money Personality\" analyzing customer transaction patterns against profiles, banks can better understand their customers\u2019 money habits and financial management strategies. This can then drive personalized product recommendations, improve customer engagement, and help in more effectively managing financial risks.\n\nFor example, some customers may be identified as big spenders, frequent shoppers, savers, or even impulse buyers based on their demonstrated spending behaviors. These spending personalities can then be utilized for developing personalized marketing strategies, providing tailored financial advice, or enhancing customer service. It can help banks understand their customers better, predict their needs, and build stronger, more profitable relationships with them.\n\nProcess spending personality using Feature Engineering enrichment option:\n1. Use customer transaction data and perform category enrichment.\n2. Then run Ecosystem Spending Personality to generate new collections with scores assigned to customers.\n3. API returns scores per customer.","categories":"banking, personality, spend, spending, money, transactions","created_by":"ecosystem.Ai","version":"0.09.7","fact_sheet_path":"https://ecosystem.ai/spend-personality/","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":30,"module":"ab_testing","description":"This module implements a traditional A/B testing methodology, allowing users to compare two distinct versions of an element (Version A vs. Version B) to statistically determine which one more effectively achieves predefined objectives or key performance indicators.","cpu":2,"_id":{"$oid":"68304d532a153f472aa8dc4f"},"uuid":"ecosystem-module-abtesting","version":"0.1","long_name":"AB Testing","date_released":"2025-05-22","status":"released","ram":4,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/ab-small.png","icon_path":"https://modules.ecosystem.ai/public/ab-small.png","name":"AB_Testing_0.1","module_owner":"ecosystem.Ai","description":"The AB Testing module facilitates traditional A/B tests to compare two versions (A and B) of a single variable, typically by testing a subject's response to variant A against variant B, and determining which of the two variants is more effective.\n\nThis module allows users to set up experiments, define variations, and measure the impact on key metrics to identify the better performing option. It's commonly used for optimizing websites, applications, marketing campaigns, and other user experiences. By analyzing the results, businesses can make data-driven decisions to improve conversion rates, engagement, or other desired outcomes.\n\nProcess for using the AB Testing module:\n1. Define your hypothesis and what you want to test (e.g., button color, email subject line).\n2. Create two versions: Control (A) and Variation (B).\n3. Configure the test parameters, such as audience split and duration.\n4. Run the test and collect data on user interactions.\n5. Analyze the results to determine statistical significance and identify the winning version.","categories":"testing, analytics, optimization, comparison, marketing, experimentation","created_by":"ecosystem.Ai","version":"0.1","fact_sheet_path":"https://ecosystem.ai/ab-testing/","contact_email":"support@ecosystem.ai","status":"released"}},{"disk":60,"module":"website_recommender","description":"Recommends banners, colors, and other key objects on a website to enhance user experience and engagement.","cpu":4,"_id":{"$oid":"68304d532a153f472aa8dc52"},"uuid":"ecosystem-module-websiterecommender","version":"0.02","long_name":"Website Recommender","date_released":"2025-05-22","status":"deployed","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/wr2-small.jpeg","icon_path":"https://modules.ecosystem.ai/public/wr2-small.jpeg","name":"Website_Recommender_002","module_owner":"ecosystem.Ai","description":"Recommend banners, colors and other key objects on a website. This module helps personalize website content dynamically, aiming to improve user engagement, conversion rates, and overall site effectiveness by tailoring visual elements and calls-to-action to individual user preferences or segments.","categories":"website, recommender, personalization, ux optimization, banner recommendation, dynamic content","created_by":"ecosystem.Ai","version":"0.02","fact_sheet_path":"https://ecosystem.ai/website-recommender/","contact_email":"amy@ecosystem.ai","status":"deployed"}},{"disk":50,"module":"customer_agent","description":"Enhances customer engagement using the ecosystem.Ai agentic journey management framework for personalized experiences.","cpu":2,"_id":{"$oid":"68304d532a153f472aa8dc55"},"uuid":"ecosystem-module-customeragent","version":"0.0.2","long_name":"Customer Agent","date_released":"2025-05-22","status":"review","ram":4,"module_metadata":{"reviewed_by":"Ramsay Louw","image_path":"https://modules.ecosystem.ai/public/ca-small.png","icon_path":"https://modules.ecosystem.ai/public/ca-small.png","name":"Customer_Agent_002","module_owner":"Ramsay Louw","description":"This module enhances customer engagement by leveraging the ecosystem.Ai agentic journey management framework. It enables the design, execution, and monitoring of personalized customer journeys, with intelligent agents guiding interactions and optimizing outcomes based on real-time data and customer behavior. This framework allows for dynamic adjustments to journeys, ensuring a relevant and adaptive experience for each customer, ultimately aiming to improve satisfaction and loyalty.","categories":"jms, journey management, customer engagement, agentic framework, personalization, automation","created_by":"Ramsay Louw","version":"0.0.2","fact_sheet_path":"","contact_email":"ramsay@ecosystem.ai","status":"review"}},{"disk":40,"module":"engagement_messages","description":"Dynamic message generator based on spend personality.","cpu":2,"_id":{"$oid":"68304d532a153f472aa8dc58"},"uuid":"ecosystem-module-engagementmessages","version":"0.3.0","long_name":"Engagement Messages","date_released":"2025-05-22","status":"validation","ram":4,"module_metadata":{"reviewed_by":"ecosystem","image_path":"https://modules.ecosystem.ai/public/em-small.png","icon_path":"https://modules.ecosystem.ai/public/em-small.png","name":"Engagement_Messages_0.3","module_owner":"ecosystem","description":"Dynamic message generator based on spend personality. Use a contextual recommender when using these messages.","categories":"dynamic,messages,personality,engagement,marketing","created_by":"ecosystem","version":"0.3.0","fact_sheet_path":"","contact_email":"amy@ecosystem.ai","status":"validation"}},{"disk":70,"module":"digital_personality","description":"Digital Interaction Personality refers to the characterization of an individual's online behavior and the way they interact with digital platforms.","cpu":4,"_id":{"$oid":"68304d532a153f472aa8dc5b"},"uuid":"ecosystem-module-digitalpersonality","version":"0.3.0","long_name":"Digital Personality","date_released":"2025-05-22","status":"released","ram":8,"module_metadata":{"reviewed_by":"","image_path":"https://modules.ecosystem.ai/public/dp-small.png","icon_path":"https://modules.ecosystem.ai/public/dp-small.png","name":"Digital_Personality_030","module_owner":"","description":"Digital Interaction Personality refers to the characterization of an individual's online behavior and the way they interact with digital platforms. Users can exhibit different tendencies online based on a variety of factors such as their comfort with technology, personal preferences, or other situational variables.\n\nIntentional behaviors are those in which the user has a clear goal in mind, such as going online to make a specific purchase or find a particular piece of information. These users tend to have targeted digital interactions, often using familiar platforms and taking direct actions to achieve their desired outcome. \n\nExperiential behaviors, on the other hand, are more exploratory in nature. These users tend to browse platforms more freely, discovering new information, products, or services in a more fluid, less goal-oriented manner. They might spend more time engaging with interactive content, trying new platforms or features, or following their curiosity rather than working towards a pre-determined outcome.\n\nThese different behaviors can vary greatly between users, and understanding them can greatly help companies tailor their digital strategies. By leveraging AI and Machine Learning, companies can analyze these behaviors and then personalize their offerings, enhance user experience and engagement, and ultimately, drive greater customer retention and growth.","categories":"digital behavior, user profiling, online interaction, personalization, analytics, customer intelligence","created_by":"","version":"0.3.0","fact_sheet_path":"","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":60,"module":"dynamic_recommender_convergence","description":"This module shows the concept of how the Ecosystem Rewards algorithm converge under different conditions.","cpu":2,"_id":{"$oid":"68304d532a153f472aa8dc5e"},"uuid":"ecosystem-module-drc","version":"0.04","long_name":"Dynamic Recommender","date_released":"2025-05-22","status":"review","ram":4,"module_metadata":{"reviewed_by":"Jay","image_path":"https://modules.ecosystem.ai/public/drc-small.png","icon_path":"https://modules.ecosystem.ai/public/drc-small.png","name":"Dynamic_Recommender_Convergence_04","module_owner":"Jay","description":"This module shows the concept of how the Ecosystem Rewards algorithm converge under different conditions. It shows a basic set of offers that are presented using an API for slow tests. There is also a simulation where uptake etc is tweaked for review purposes.","categories":"dynamic,interactions","created_by":"Jay","version":"0.04","fact_sheet_path":"","contact_email":"jay@ecosystem.ai","status":"review"}},{"disk":70,"module":"intelligent_sales","description":"Help guide your Sales journey using AI.","cpu":4,"_id":{"$oid":"68304d532a153f472aa8dc49"},"uuid":"ecosystem-module","version":"0.2.0","long_name":"Intelligent Sales","date_released":"2025-05-22","status":"released","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/isales-small.png","icon_path":"https://modules.ecosystem.ai/public/isales-small.png","name":"Intelligent_Sales_020","module_owner":"ecosystem.Ai","description":"Intelligent Sales refers to the use of AI technologies to enhance and optimize the sales process. This can include a variety of applications, such as predictive analytics, customer segmentation, lead scoring, and personalized marketing. By leveraging AI, businesses can gain deeper insights into customer behavior, preferences, and needs, allowing them to tailor their sales strategies accordingly.\n\nFor example, AI can analyze historical sales data to identify patterns and trends, helping sales teams prioritize leads and focus on the most promising opportunities. It can also automate routine tasks, such as data entry and follow-up emails, freeing up sales representatives to spend more time on high-value activities.\n\nAdditionally, AI-powered chatbots and virtual assistants can provide real-time support to customers, answering questions and guiding them through the sales process. This not only improves customer satisfaction but also increases conversion rates by providing timely assistance.\n\nOverall, Intelligent Sales leverages AI to drive efficiency, improve decision-making, and ultimately boost revenue for businesses.","categories":"sales, interactions, journeys","created_by":"ecosystem.Ai","version":"0.2.0","fact_sheet_path":"https://ecosystem.ai/intelligent-sales/","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":70,"module":"interaction_science","description":"Interaction Science helps you deliver on those personalization, next best action and recommendation goals","cpu":4,"_id":{"$oid":"684ac8188dc4950c4138cf73"},"uuid":"ecosystem-module","version":"0.5.0","long_name":"Interaction Science","date_released":"2025-06-12","status":"released","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/is-small.png","icon_path":"https://modules.ecosystem.ai/public/is-small.png","name":"Interaction_Science_050","module_owner":"ecosystem.Ai","description":"Interaction Science helps you deliver on those personalization, next best action and recommendation goals.","categories":"sales, interactions, journeys","created_by":"ecosystem.Ai","version":"0.5.0","fact_sheet_path":"https://ecosystem.ai/interaction-science/","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":70,"module":"interaction_science","description":"Interaction Science helps you deliver on those personalization, next best action and recommendation goals","cpu":4,"_id":{"$oid":"68337d8461f23964737c8b16"},"uuid":"ecosystem-module","version":"0.3.0","long_name":"Interaction Science","date_released":"2025-05-22","status":"deprecated","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/is-small.png","icon_path":"https://modules.ecosystem.ai/public/is-small.png","name":"Interaction_Science_030","module_owner":"ecosystem.Ai","description":"Interaction Science helps you deliver on those personalization, next best action and recommendation goals.","categories":"sales, interactions, journeys","created_by":"ecosystem.Ai","version":"0.3.0","fact_sheet_path":"https://ecosystem.ai/interaction-science/","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":70,"module":"interaction_science","description":"Interaction Science helps you deliver on those personalization, next best action and recommendation goals","cpu":4,"_id":{"$oid":"68382387312d5eee95f33a6f"},"uuid":"ecosystem-module","version":"0.4.0","long_name":"Interaction Science","date_released":"2025-05-29","status":"deprecated","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/is-small.png","icon_path":"https://modules.ecosystem.ai/public/is-small.png","name":"Interaction_Science_040","module_owner":"ecosystem.Ai","description":"Interaction Science helps you deliver on those personalization, next best action and recommendation goals.","categories":"sales, interactions, journeys","created_by":"ecosystem.Ai","version":"0.4.0","fact_sheet_path":"https://ecosystem.ai/interaction-science/","contact_email":"amy@ecosystem.ai","status":"released"}},{"disk":70,"module":"intelligent_sales","description":"Help guide your Sales journey using AI.","cpu":4,"_id":{"$oid":"684c3db20bd5511494f90ca9"},"uuid":"ecosystem-module","version":"0.3.0","long_name":"Intelligent Sales","date_released":"2025-06-13","status":"released","ram":8,"module_metadata":{"reviewed_by":"ecosystem.Ai","image_path":"https://modules.ecosystem.ai/public/isales-small.png","icon_path":"https://modules.ecosystem.ai/public/isales-small.png","name":"Intelligent_Sales_030","module_owner":"ecosystem.Ai","description":"Intelligent Sales refers to the use of AI technologies to enhance and optimize the sales process. This can include a variety of applications, such as predictive analytics, customer segmentation, lead scoring, and personalized marketing. By leveraging AI, businesses can gain deeper insights into customer behavior, preferences, and needs, allowing them to tailor their sales strategies accordingly.\n\nFor example, AI can analyze historical sales data to identify patterns and trends, helping sales teams prioritize leads and focus on the most promising opportunities. It can also automate routine tasks, such as data entry and follow-up emails, freeing up sales representatives to spend more time on high-value activities.\n\nAdditionally, AI-powered chatbots and virtual assistants can provide real-time support to customers, answering questions and guiding them through the sales process. This not only improves customer satisfaction but also increases conversion rates by providing timely assistance.\n\nOverall, Intelligent Sales leverages AI to drive efficiency, improve decision-making, and ultimately boost revenue for businesses.","categories":"sales, interactions, journeys","created_by":"ecosystem.Ai","version":"0.3.0","fact_sheet_path":"https://ecosystem.ai/intelligent-sales/","contact_email":"amy@ecosystem.ai","status":"released"}}]