Predictive Analytics for Business Growth

Differentiate between the three types of analytics—descriptive, predictive, and prescriptive—by exploring their unique functions and use cases. Analyze and articulate the strengths and limitations of predictive analytics in driving business outcomes. Identify and evaluate case studies with the business drivers that align with your own professional goals and challenges.

Communicate the range of data sources available in the marketplace to your team. Evaluate internal and external data sources effectively for applications such as prospecting, consumer marketing, customer segmentation, and lead qualification. Formulate data-driven questions that align with revenue goals and establish metrics for measuring success.

Assess the need for audience segmentation based on business objectives. Evaluate the strengths and limitations of off-the-shelf segmentation solutions. Design a marketing campaign utilizing rule-based and k-means segmentation techniques.

Communicate to analysts, in actionable terms, the key factors distinguishing successful from unsuccessful campaigns. Explain the calculation of return on ad spend (ROAS) and effectively convey how data informs ROAS for marketing campaigns to sales or marketing teams. Assess when audience segmentation offers a more effective strategy than a "one-size-fits-all" approach for enhancing customer interaction, retention, and engagement.

Develop equations to score and rank prospects based on their likelihood to purchase or projected purchase value. Establish optimal audience sizing and cutoff points aligned with KPIs like ROI and ROAS to meet business objectives. Identify appropriate scenarios for using linear or logistic regression and confidently perform and interpret these techniques.

Evaluate strategies for making the optimal offer to targets when multiple products are available. Communicate to analysts or the data science team the necessity of using multiple predictive models, including segment-based and probability-based models, and explain their significance. Guide analysts or the data science team in measuring campaign results and directly linking outcomes to the model structure.

Explain Customer Lifetime Value to clarify its function and purpose for all stakeholders. Plan campaigns with CLV as a component and explain to the marketing team how to operationalize such campaigns. Differentiate between industries that benefit from CLV and those where it is not crucial.

Apply the knowledge and skills gained in this course to develop comprehensive annual plans for your organization. Safeguard your organization and customer data by adhering to best practices in data ethics, privacy, and cybersecurity. Prepare for alternative scenarios by anticipating potential risks and challenges in predictive modeling.