Agenda

Marketing Analytics Summit 2025
Sheraton Phoenix Downtown Hotel, Phoenix, AZ

While many focus on generative AI’s ability to automate tasks like email writing and workflow management, its greatest potential lies in amplifying human creativity and ingenuity. Jim is excited about how to leverage Large Language Models as creative partners rather than mere tools. He presents how to use AI for ideation, strategic planning, and novel problem-solving. This is a practical approach for using generative AI to unlock new perspectives, make unexpected connections, and drive innovation in ways traditional computing never could.

You could be home watching old presentations on YouTube – OR – you take full advantage of being in the same room with other, living, breathing analytics enthusiasts. You never know whom you’ll meet, newbies, or people who have been coming to the Marketing Analytics Summit since 2002! Important notes:

1. You’ll only have 2 minutes to tell the person you are face-to-face with your entire life story so practice your elevator pitch.
2. Bring business cards. No, really. If you meet 100 people this week and your only record is a LinkedIn connection, how on Earth are you going to remember who’s who?? A note on a physical business card is a superb memory hack.

Josh explores how combining campaign performance data with user behavior data, you can improve conversion rates, enhance personalization, and increase customer lifetime value. Ideal for marketers, product managers, and analytics professionals seeking to maximize their analytics tech stack. Josh then shares how to build a cohesive data infrastructure, including architecture patterns, common pitfalls, and some practical integration strategies for a scalable analytics foundation.

Tired of getting pulled into endless ad-hoc reporting requests that derail your day? Hassan delivers a hands-on session with real-world strategies to streamline your reporting, reclaim your focus, and empower your team to deliver impactful insights. He shows how to automate recurring reports, build intuitive self-service dashboards for stakeholders, and align reporting schedules with business needs. Learn how to manage stakeholder expectations, cut down on low-value data requests, and create a structure that frees your team to focus on meaningful analysis.

From the start (2002) the Marketing Analytics Summit has been the place to discuss and share our common problems. These are your people – they understand your situation. Often rated the best part of the Marketing Analytics Summit, sharing your problems with like-minded professionals is your path to answers, a little empathy, and a stronger professional network. Come prepared with an issue you are facing, a problem you are solving, or a question that needs answering. And then, be ready and willing to help others in this, their hour of need. (Homework assignment! At the end, email Jim Sterne with three pieces of advice that you are most grateful for. He’ll compile the results and feed them all back to everybody.)

Creating a robust Next Best Action (NBA) model can be tricky. Emma demonstrates some tips and tricks through the strategic use of analytics. Through real-life examples, Emma describes some of the challenges in both analysis and the implementation of a Next Best Action model and shares:

• Guidance on how to approach the analytical side of NBA
• Tips and tricks for a successful model
• Real-life examples of NBA in action
• Pitfalls to watch for in the implementation of an NBA model in the front line.

Now that you know what MMM is thanks to Jim Gianoglio (see previous session), learn how to apply the knowledge gleaned from MMM to your marketing decisions. Skeptical? You should be! Jeff will walk through how to use MMM flexibly and in tandem with other methods with the aim of building trust. Worried about how to get your organization to lean in? Jef delivers tips and tricks for being an agent of change.

Once paired with another MAS attendee, you have 5 minutes to ask them all about their company, their job, their responsibilities, a dream project they’d love to tackle, an unusual skill or hobby they have, their spirit animal, and what they want to be when they grow up. Need help with questions? Maybe ChatGPT can look at their LinkedIn profile and give you some pointers. Then the tables turn, and you will be in the hotseat to reveal your true self. Next, we share what we’ve learned about our interviewee. This is what sets an in-person event apart from social media.

Starting in March 2011, a web analytics group that jokingly called itself “Team Evil Forces” spent four years trying different ways to communicate in a less public way than Twitter. They eventually settled on Slack that had over 100 people in January 2015. Today, there are roughly 25,000 digital analytics denizens asking questions, helping each other out, and being the gravitational center of the digital analytics community. Today, we honor Lee and Michele for their dedication to the industry.

This is your chance to get out of your comfort zone and prove that you are serious about meeting your peers. Sign up for a Dutch-treat dinner and we’ll tell you at which (very nearby) restaurant to meet for a meal. Get to know your colleagues in a relaxed setting over good food and favorite beverages. You might just meet somebody who can solve that burning issue you’ve been working on or whom you can help with your experience. “This was a little awkward for the first few minutes and then we had a great time!”

Traditional customer journey mapping relies heavily on assumptions and historical patterns. But what if we could let the data tell us what’s really driving customer behavior? Michele shares groundbreaking applications of Bayesian networks and neural architectures that reveal genuine cause-and-effect relationships in customer data. Learn how these techniques can identify hidden influences on purchasing decisions, predict the impact of marketing interventions, and optimize customer experiences – even with incomplete data. Oh – and you can download Microsoft’s Deep End-to-end Causal Inference (DECI) generative model from GitHub and use it for free.

Marketing analysts shouldn’t spend their time wrangling messy data, they should focus on generating insights to make better decisions. Enter the data engineer: the unsung hero who builds pipelines, ensures data quality, and enables scalable analytics. Mayra details the vital role data engineers play in supporting analysts and marketers, while comparing their work to that of data analysts, analytics engineers, and marketing analysts. With the rise of generative AI adding complexity to already massive datasets, Mayra offers practical strategies to optimize your tech stack, free up your analysts, and deliver faster, more reliable insights.

Jayda illustrates AI models that predict CTR and customer behavior across Omni-channel campaigns, with proven performance improvements for engagement and conversion. She taps into AI-driven predictive querying and unravels how models retrieve sponsored – and relevant – products based on customer search. She also explores predictive audience lists, AI customer segmentation, optimizing upsell opportunities, and prospecting strategies. Learn how AI can elevate customer engagement and personalization at scale.

Lisa presents a strategy for aligning top resources with high-value customers by identifying critical content and channels. She showcases how to create a leadership-facing dashboard of top LTV segments, match these with a Marketing Resource Management system, and pinpoint key bottleneck resources. Then, she incorporates weighted averages to align with business values (politics!). It’s all about mining metadata to monetize messaging.

The Canadian Automobile Association (CAA), one of Canada’s largest organizations needed to up level their digital analytics education. The idea was to teach the principles behind data reports, rather than just the platforms. Working with their agency, CAA went beyond a mere Data Literacy program to establish effective processes and position analytics as a go-to tool for decision-making. Anil and friend describe how they turn every report into a learning opportunity to create a culture where data-driven insights are embraced and effectively applied in everyday business decisions.

With analytics, you have data about the performance of your content. With AI, you have a powerful data analyst that can find insights fast. Andy delivers a step-by-step process for combining those two tools. It’s amazing to see AI crunch through a dataset. It’s also fun to see what it suggests. You’ll quickly get insights from your marketing data that you can use for social media, SEO, campaign performance and conversion optimization. Andy is your guide on this tour of the latest in AI-powered data analysis.

• How to export and upload analytics data to ChatGPT 
• Which exports from other reporting tools work well with AI
• How to prompt AI for topics and ideas, analysis and actions

From its creation to its critical role in AI and machine learning, every datapoint has its story. From converting business requirements into data requests and planning data structures to monitoring data flows, Elizabeth shares years of experience battling the trials and tribulations of properly getting the data into shape. She pinpoints best practices, common pitfalls, and key considerations that ensure your data is ready to drive smarter outcomes.

AI and audio can turn complex visualization patterns into memorable stories that resonate with stakeholders on an emotional level. Chalsea delivers demonstrations and practical examples to show how you can identify story-worthy patterns, translate metrics into sonic elements, and create powerful multi-sensory presentations. She has practical templates, AI prompts, and a sound mapping toolkit that will revolutionize how you communicate insights. Check out the future of data storytelling and make your insights unforgettable through the power of sound.

Vidya delivers a comprehensive approach to solving machine learning problems that transcends the usual algorithm-first perspective. She’s here to help you navigate the entire process of machine learning projects—from data preparation and validation to model selection and performance evaluation. Learn how to select the right models and validation techniques based on business problems, data availability, and desired outcomes. Get the scoop on optimal validation techniques, an overview of anomaly detection methods, and practical approaches for prediction and classification. Make informed decisions about machine learning techniques that best suit your business needs.