In part 1 of this series on the updated “AI Apps Development Canvas,” I introduced the updated AI Apps Product Development Design Canvas. The AI Apps Product Development Canva is one of the capstone deliverables for my “Thinking Like a Data Scientist” methodology, so getting feedback is critical to ensure that the methodology is relevant and practical.
Several folks and students tested the canvas and gave me great feedback, resulting in this updated design canvas (Figure 1).
Figure 1: AI Apps Development Canvas
I will leverage the canvas in part 2 of this series to create a “Local Events Marketing Effectiveness” AI app. This is not a short exercise and reflects how comprehensively one has followed the steps and design canvases for the “Thinking Like a Data Scientist” methodology.
Example: Local events marketing effectiveness app
Here is my attempt to complete the AI App Development Canvas to develop a “Local Events Marketing Effectiveness” AI app that supports store management and local / field marketing in selecting, designing, managing, and optimizing local events marketing effectiveness.
- (1) User problem and usage scenario: Optimizing the selection, sponsorship, and participation in local community activities and events that can increase brand awareness, goodwill, and customer acquisition. The usage scenario is that the key stakeholders (store management, local marketing, field marketing, corporate marketing) can use the app to explore, evaluate, and execute local events marketing campaigns based on the predicted impact of each event on the targeted customer segments.
- (2) Targeted users and desired outcomes: The targeted users are the key stakeholders involved in local events marketing, such as store managers, local marketers, field marketers, and corporate marketers. Their desired outcomes are to increase store traffic, sales, loyalty, and referrals by sponsoring and participating in local events that can attract and engage potential and existing customers.
- (3) User gains or benefits: The user gains or benefits from using the app are:
- Improved ROI of local events marketing by selecting the most effective events based on the predicted impact on customer behavior and value.
- Enhanced customer experience by creating personalized and relevant customer interactions during and after the events.
- Increased brand awareness and goodwill by supporting local community causes and activities that resonate with customers.
- Reduced operational costs by automating and streamlining the planning, execution, and evaluation of local events marketing campaigns.
- (4) Potential user impediments: The potential user impediments that may hamper the usage and adoption of the app are:
- Poor data quality and availability for some local events or customer segments may limit the accuracy and reliability of the app’s predictions and recommendations.
- Resistance to change from some stakeholders who may prefer to rely on their intuition or experience rather than data-driven insights for local events marketing decisions.
- Privacy and ethical concerns from some customers who may not want their data to be used for marketing purposes or may perceive the app’s recommendations as intrusive or manipulative.
- (5) Key business entities: The key business entities around which data must be captured and analytics must be built are:
- Events: The local community activities and events that the app can recommend for sponsorship and participation, such as sports games, concerts, festivals, school activities, etc. The data attributes for each event may include name, date, time, location, type, category, organizer, audience size, cost, etc.
- Customers: The potential and existing customers who may attend or be influenced by the sponsored or participated events. The data attributes for each customer may include ID, name, age, gender, location, preferences, behavior, value, loyalty, etc.
- Campaigns: The local events marketing campaigns that the app can help plan, execute, and evaluate. The data attributes for each campaign may include ID, name, objective, budget, duration, events list, target customer segments, expected outcomes, actual outcomes, etc.
- (6) Upstream system or application dependencies: The upstream system or application dependencies that are necessary to ensure that the correct data is being provided at the right times for the operation of the app are:
- Data sources: The app will need to access various data sources that contain information about events, customers, and campaigns, such as internal databases, CRM systems, social media platforms, event websites, etc.
- Data ingestion: The app must ingest, validate, and standardize the data from different sources using appropriate methods and formats, such as APIs, ETL processes, CSV files, etc.
- Data integration: The app will need to integrate and harmonize the data from different data sources using common identifiers and schemas, such as event ID, customer ID, campaign ID, etc.
- (7) Downstream system or application dependencies: The downstream system or application dependencies into which the results of the app must be fed or materialized are:
- Campaign management: The app must feed the recommended events and target customer segments into the campaign management system or application that will help execute and monitor the local events marketing campaigns.
- Customer engagement: The app must feed personalized and relevant interactions and offers into the customer engagement system or application to help communicate and interact with customers during and after the events.
- Business intelligence: The app will need to feed the actual outcomes and performance metrics of the local events marketing campaigns into the business intelligence system or application that will help analyze and report the effectiveness and ROI of the campaigns.
- (8) Key decisions or actions: The key decisions or actions that the user will take from using the app are:
- Selecting the most suitable events for sponsorship and participation based on the predicted impact on customer behavior and value.
- Segmenting and targeting the potential and existing customers who may attend or be influenced by the sponsored or participated events based on their preferences, behavior, value, loyalty, etc.
- Creating and delivering personalized and relevant interactions and offers to customers during and after the events based on their predicted propensities and responses.
- Evaluating and optimizing the performance and ROI of the local events marketing campaigns based on the actual outcomes and feedback.
- (9) KPIs and metrics: The KPIs and metrics that the user will use to measure the success of the app are:
- Customer acquisition: The number and percentage of new customers acquired due to the local events marketing campaigns.
- Customer retention: The number and percentage of existing customers retained due to the local events marketing campaigns.
- Customer loyalty: The number and percentage of customers who are loyal or advocates as a result of the local events marketing campaigns.
- Customer value: The average revenue, profit, and lifetime value of customers influenced by the local events marketing campaigns.
- Campaign ROI: The ratio of the net profit to the total cost of the local events marketing campaigns.
- (10) Prescriptive recommendations: The prescriptive recommendations that the app must deliver to help the user make the decisions that help them achieve their desired outcomes are:
- Event recommendation: The app must recommend the best events for sponsorship and participation based on the predicted impact on customer behavior and value, as well as the budget and objective of the campaign.
- Customer segment recommendation: The app must recommend the optimal customer segments for targeting based on their preferences, behavior, value, loyalty, etc., as well as the event characteristics and expected outcomes.
- Interaction and offer recommendation: The app must recommend the most effective interactions and offers to deliver to customers during and after the events based on their predicted propensities and responses, as well as the campaign objective and budget.
- (11) Analytic (predictive) scores: The analytic (predictive) scores that will need to be created that power the prescriptive recommendations are:
- Event impact score: The score that measures the predicted impact of each event on customer behavior and value, such as attendance, engagement, conversion, retention, loyalty, revenue, profit, etc.
- Customer segment score: The score that measures the predicted attractiveness and profitability of each customer segment for each event, such as preference, behavior, value, loyalty, etc.
- Interaction and offer response score: The score that measures the predicted propensity and response of each customer to each interaction and offer during and after each event, such as click-through rate, redemption rate, satisfaction rate, etc.
- (12) Data sources: The data sources that support the creation of the ML features that will drive the predictive effectiveness of the analytic score are:
- Internal data sources: The data sources that contain information about customers and campaigns from the organization’s systems and databases, such as CRM, POS, loyalty, etc.
- External data sources: The data sources that contain information about events and customers from external platforms and websites, such as social media, event websites, etc.
- (13) Data transformations: The data transformations that are necessary to prepare the data for consumption by the ML models are:
- Data cleaning: The process of removing or correcting invalid, incomplete, inconsistent, or irrelevant data from the data sources.
- Data encoding: The process of converting categorical or textual data into numerical or binary data that the ML models can use.
- Data scaling: The process of transforming numerical data into a standard range or scale that can improve the performance of the ML models.
- Data aggregation: The process of combining or summarizing data from different sources or levels into a single or higher level that can provide more meaningful insights for the ML models.
- Data splitting: The process of dividing the data into training, validation, and testing sets that can be used to train, tune, and evaluate the ML models.
- (14) Machine learning (ML) features: The ML features that the ML model uses to generate predictions and prescriptive recommendations are:
- Event features: The features that describe the characteristics of each event, such as name, date, time, location, type, category, organizer, audience size, cost, etc.
- Customer features: The features that describe the attributes of each customer, such as ID, name, age, gender, location, preferences, behavior, value, loyalty, etc.
- Campaign features: The features that describe the details of each campaign, such as ID, name, objective, budget, duration, events list, target customer segments, expected outcomes, etc.
- Interaction and offer features: The features that describe the content and delivery of each interaction and offer during and after each event, such as type, channel, message, offer value, timing, etc.
- (15) Model performance: Accuracy, recall, and precision measures will evaluate how well the ML models generate accurate and reliable predictions and prescriptive recommendations for each event, customer segment, interaction, and offer.
- (16) Model monitoring or observability: Model monitoring involves tracking and analyzing the key metrics and indicators of the app’s performance, such as data quality, data drift, model accuracy, model drift, model bias, model explainability, model robustness, model reliability, etc. These metrics and indicators can help identify and resolve issues or anomalies affecting the app’s functionality and usability.
- (17) App software development requirements: The app software development requirements are the specifications and documentation of the app’s features, functions, interfaces, design, architecture, testing, deployment, maintenance, etc. They also include the tools and frameworks used to develop the app.
- (18) API requirements: The API requirements are the specifications and documentation of the app’s APIs that enable the communication and exchange of data and results between the app and other systems or applications. They also include the tools and frameworks to create and manage the app’s APIs.
- (20) Model feedback metrics: The model feedback metrics measure the actual outcomes and feedback of the local events marketing campaigns, such as attendance rate, engagement rate, conversion rate, retention rate, loyalty rate, revenue rate, profit rate, satisfaction rate, etc. They also include user satisfaction and app adoption measures, such as usage rate, retention rate, churn rate, referral rate, rating score, review score, etc.
- (21) Model feedback methods: The model feedback methods are the techniques and sources used to gather and process the data and information about the actual outcomes and feedback of the local events marketing campaigns and the user satisfaction and adoption of the app. They may include surveys, interviews, focus groups, online reviews, social media posts, web analytics tools, etc.
- (22) App UI / UEX requirements: The app UI / UEX requirements are the specifications and documentation of the app’s user interface (UI) and user experience (UX) design that define how the app looks and feels to the user. They also include the tools and frameworks used to create and test the app’s UI / UX design, such as wireframes, mockups, prototypes, usability testing tools, etc.
- (23) Privacy plans: The privacy plans ensure the proper storage and management of “personal identifiable information” (PII) that follows privacy regulations like GDPR, HIPAA, CCPA
- (24) User feedback and testing: The user feedback and testing specifications for the “Optimize Local Events Marketing” app are:
- Plan for collecting and incorporating user feedback and testing throughout the app development process. This will help ensure that the app meets the user’s needs and expectations and identify and fix any issues or bugs that may arise.
- Methods and sources to gather and process user feedback and testing data include surveys, interviews, focus groups, online reviews, social media posts, web analytics tools, etc.
- Metrics and indicators to measure and evaluate user feedback and testing results, such as usage rate, retention rate, churn rate, referral rate, rating score, review score, satisfaction rate, etc.
- Techniques and tools to analyze and visualize user feedback and testing data include descriptive statistics, sentiment analysis, text mining, word clouds, charts, graphs, dashboards, etc.
- Procedures to incorporate user feedback and testing insights into the app design and development, such as prioritizing user requirements, modifying app features, functions, interfaces, design, architecture, testing, deployment, maintenance, etc.
- (25) Potential Unintended Consequences (added by Neil Raden): A plan for identifying and mitigating the potential unintended consequences of using the app. These may include the negative impacts or risks that the app may have on the users, customers, events, communities, or society at large.
- Monitor and evaluate the potential unintended consequences of the app, such as ethical frameworks, impact assessments, audits, reviews, feedback, etc.
- Metrics and indicators to measure and report the potential unintended consequences of the app, such as fairness, transparency, accountability, privacy, security, etc.
- Techniques and tools to prevent and address the potential unintended consequences of the app, such as data anonymization, data minimization, data quality control, model explainability, model robustness, model bias detection and correction, etc.
- Procedures to communicate and disclose the potential unintended consequences of the app to the users, customers, events, or communities, such as consent forms, privacy policies, terms and conditions, notifications, warnings, etc.
Summary
I’m already over my word count on this baby, so I’ll leave it to the audience (and my students) to review and grade. Have fun!
