International Institute for Analytics

Google has only delayed the inevitable – the end (of third-party cookies) is still coming. Despite Google announcing that it would stop tracking third-party cookies back in January 2020, many analytics teams, both Marketing Analytics and Enterprise Analytics teams, are still working on ways to manage this immense change. We have more time, but we need to use it wisely. Join Sara Stevens, Vice President, Digital Capabilities at Epsilon as she shares some proven approaches, with a focus on privacy first, identity over identifiers, and leveraging first-party data.

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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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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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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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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?

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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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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How do other companies perform A/B testing at scale for their mobile apps? We’d like to work on optimizing user experience performance.

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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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We have a wide variety of systems that create and store customer data. What’s the best way to bring these records together to improve the customer experience?

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