Designing Integrated Solutions

We bring the world-class expertise of CMU faculty into collaborative conversations with industry professionals in management, R&D, and operations from major retailers and service providers across the U.S. The result is a vibrant community that can clearly articulate today’s challenges and develop solutions that work across technical, managerial, operational, and consumer dimensions.

Adapting the Organization

We are experts in technologies such as artificial intelligence, data science, robotics/automation, and Internet of Things — and we know that technology alone is not a business solution. We understand critical aspects of retail and services, such as virtual assistants and clienteling, pricing and procurement, operations and logistics — and we know that the organization itself must evolve in order to deliver better services from new technologies.

Developing New Competencies

Whether you represent a brick-and-mortar store, an e-commerce presence, a competitor in the hospitality industry, a technology service provider, or supply chain logistics company, we can support your entry or evolution into omnichannel and new markets from the perspectives of organizational structure, technology competence, and user experience.

Combining Technology, Business, and Design

We invite you to participate in our unique integration of world-class practices in technology, business, and design. All three must smoothly braid to effectively address the rapid changes in market demands and consumer behavior that is upending today’s retail and service businesses.

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Predictive Analytics Under Data Shift

The coronavirus pandemic has disrupted the usual cycle and efficacy of predictive models. Old models based on old data are increasingly less accurate as the pandemic unfolds. How can previous prediction models incorporate new shocks to the data generating process, aided by human expertise?

Social Media Content Engineering

What content in social media engages users better? What content is appropriate for long-term brand marketing vs. immediate lead to purchase? Are there data-driven strategies?

Multiview (multi-source) CRM

Novel methods are available for customer purchase prediction and churn management using a multi-view bayesian model. This model is flexible enough to incorporate multi-faceted data sources (clickstream, history, Salesforce data, chatbot data, emails, and customer complaints) to predict customer churn and purchase behavior. Multiview CRM could be employed when some aspects of consumer data are missing.

Unintended Consequence of Recommenders

Explainable recommenders: Recommenders are known to increase purchase probabilities and revenues. But do they have unintended consequences? What is the impact on sales diversity and niche items vs. popular items? How do product attributes and reviews interact to influence efficacy? Can we build a recommender system that tells both the users and retailers why certain products are being recommended?

Review Content Analytics

What content in customer-read reviews actually cause customers to purchase? Is there specific information that increases short-term conversion and decreases return rate? What is the best way to present reviews?

AI For Retail With Human-in-the-Loop Approach

How do we incorporate domain expertise into an AI algorithm? When can AI and humans working together help? When do they harm?

AI Scheduling for Promotion

Can we automate promotional calendars? The algorithm should be aware of cross-elasticity among products and seasonality.