Research

Inquiry Response: Moving Beyond Membership Analytics

By Steve Stone, Jun 28, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We have extensive loyalty club data and perform extensive membership analytics. What should we be thinking about when it comes to data monetization? What types of data governance are necessary for our current state? Beyond membership analytics, what are some common use cases we should consider focusing on next?

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Inquiry Response: Data Collection For The Digital User Experience

By Cory Underwood, Jun 21, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We are focusing on improving our digital user experience and would like to speak with someone who can help us better understand what data is available for us to collect at this point in time.

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Inquiry Response: Graph Database For Customer Data

Mar 22, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We’re building our own customer data platform and are considering a graph database to handle a 360-degree view of the customer. Would a graph database be beneficial and also scalable for us?

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Inquiry Response: Ways To Improve Customer Relationship Management

Mar 08, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We supply propensity models to the marketing team to help predict customer behavior for our various brands. Beyond this, what can our analytics team be doing to support them?

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Inquiry Response: Graph Databases for Customer Data Complexity

Feb 08, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We’re developing a customer data platform and are challenged by the wide variety of ways our customers interact with us. How are other organizations trying to solve this problem?

Response:

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Inquiry Response: Foundations For A Digital Customer Experience

Jan 18, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We’re developing an advanced digital customer experience strategy that needs to be able to support B2B, B2C, and B2B2C. What should we look out for as we prepare our organization and our data for this evolution?

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Value and Opportunity: An Executive Guide to Procurement Integrity

By JEN DUNHAM, Laurent Colombant, Robert Morison, Jan 13, 2021

Procurement Integrity (PI) represents a broader problem and bigger opportunity than most businesses recognize. Comprehensive PI programs continuously validate purchasing transactions, using data and analytics to trace patterns, spot anomalies, and reduce fraud, waste, and abuse. The problems uncovered range from occasional opportunistic fraud to ongoing organized fraud, from duplicate invoices and other improper payments to regular kickbacks, from conflicts of interest to ongoing collusion with suppliers. Continuous monitoring of anomalies in procurement and supplier due diligence processes reveal potential problems, including data issues and process breaches, and help focus the efforts of audit and other investigative staff.

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Inquiry Response: Measuring Promotion Success

Dec 07, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

How can we measure the lift from coupons used and what’s the best way to deploy the models to do it? We know we have lift coming in from various combinations of promotions, but we need a more granular view. Unfortunately, we don’t have access to web data, although we can track the coupons used in our brick-and-mortar stores. What do you think?

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Inquiry Response: Maturing Beyond Outlier-Based Fraud Detection

Oct 12, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re exploring ways to mature our fraud detection models. At the moment we rely on outlier-based detection, but we don’t have a ton of label data. How can we advance our outlier testing until we generate enough labels to build ML models? What else might you suggest?

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Inquiry Response: Tips for Small Data Science Teams and Targeted Marketing

Aug 24, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We have a small, centralized data science team that performs machine learning (ML) analysis for marketing insights into our various brands. Our data scientists are overwhelmed with their workloads. Do you have any organizational or Azure architectural tips that could help us to help them? Also, for marketing activities, how can we improve our individual targeting?

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