To create a more customer-centric organization -- and improve sales, market share, and margins -- you need to know what your customers want. In this course, you'll use the statistical method of conjoint analysis to uncover the product attributes most influential to your customers. By simulating the market, you'll run relevant scenarios to answer questions such as: What would happen if we lowered our price, or offered quality improvements? Which customers should we go after? And, if we give our customers more of one attribute, can we give them less of another?

In this course, you'll use the statistical method of cluster analysis to meaningfully segment and target your market based on customer needs and preferences. Through interactive, applied activities, you'll analyze how customers naturally segment themselves within your market -- and how to predict and target the most profitable segments for your business. Customer data analyzed are similar to what is typically commissioned from market research firms.

To improve sales and market share, knowing what consumers want isn't enough. You also need to know what they believe your product or service, and your competitors', provides. In this course, you'll create and use perceptual maps to identify which dimensions consumers use to differentiate among products, and how they perceive your products relative to competitors'. These maps are valuable for identifying opportunities to introduce and position new products, repositioning existing products, and identifying your true competitors.

Successful customer relationship management encompasses thousands of transactions and impressions, over many years. But which customers are most worth your time and resources? How do firms determine how long they need to keep customers before they become profitable? Analyzing data (such as Big Data) allows marketers to make smarter predictions using the Customer Lifetime Value (CLV) model, which scores current and potential customers based on characteristics such as churn rate, discount rate, retention cost and forecasts of remaining customer lifetime. In this course, you'll use the CLV model to segment and target customers based on their potential long-term value, and build corresponding retention and divestment strategies.

Segmentation and targeting is the tip of the iceberg for implementing a successful marketing strategy. Markets can be sliced and diced in infinite ways; the goal is to focus your marketing activities on customers you identify as most likely to respond and buy. In this course, you'll use statistical market response modeling to develop the right marketing mix: Determine when -- and where -- to spend money on advertising and trade promotions, and how to better forecast demand for your product or service among different customers.

Digital advertising campaigns are an increasingly important element of most brands’ marketing mix and are designed to achieve specific goals: increase brand awareness, drive traffic to the advertiser’s website, and achieve consumer conversions. And although digital advertising generates a huge amount of data, not knowing how to interpret it could result in inefficient spending and missed opportunities.

This course introduces the use of analytics and data to measure the extent to which the goals of digital campaigns are being achieved, and thereby provides a roadmap for you to make more informed spending decisions. Through the application of various analytical tools, such as effectiveness and efficiency metrics, attribution modeling, and the design of randomized controlled trials, you—as a buyer or seller of digital advertising—will be more successful at monetizing digital assets.

You explore this content through a mix of input from industry experts, a hands-on course project, and the presentation of best practices by Cornell University Professor Sachin Gupta. Your fellow students and your instructor will also help broaden your understanding of digital advertising analytics and its impact on your advertising strategy.

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