Agenda Las Vegas 2020

From market research to direct mail metrics to web analytics to Big Data, the job of “marketing” has changed dramatically over time. We have arrived at a fundamental shift in marketing that is as impactful as the advent of the Internet: Artificial Intelligence and Machine Learning.

This workshop introduces marketing professionals of all ranks to the theory, the language and the practical application of these disruptive technologies.
This workshop will not teach you how to be a data scientist.

It will teach you enough about the language and implications to speak cogently with your colleagues, and determine where to apply this innovative technology first. You will also get a firm grasp on how these new tools will change your job and what you can do to remain relevant in tomorrow’s marketing department. 

Savvy marketers understand the value of data they have about their customer and their behavior. They use this data enhance their website, develop campaigns and marketing programs that drive positive outcomes. A key component of being data driven is the ability to quickly collect data, manipulate it and draw conclusions. However, they are often blocked by other priorities of SQL developers.   The course is developed to help Marketers become self-sufficient to extract and manipulate the data without a SQL developer.

This course will teach everything marketers and marketing need to pull and combine the data to generate deeper marketing insights.
What you will learn:

1. Fundamentals of the relational database and table structures
2. Query writing to pull the required data, update data, add new data and delete data
3. Data integration from various sources such email, CRM, website logfile etc.
4. How to export data from the databases to flat files for further analytics  in Excel or Tableau
5. Identifying the most loyal and most profitable customers
6. Scoring your customers based on their purchase behavior
7. Customer categorization based on various criteria in your data sources – develop customer segments for better targeting
8. Conducting cohort analysis
9. Combining offline and online data to create remarketing lists in Google Ads
10. Using data from the database to augment Google Analytics or Adobe Analytics reports – Will walk you through the process with Google Analytics

By the end of this workshop, you will:

• Understand databases and SQL
• Learn how to harness data from various sources to become a better marketer.
• Write SQL queries to pull the right data from database yourself so that you don’t have to be dependent on your developers.
• Add a valuable new skill to your arsenal that will immediately make you a better data-driven marketer, marketing analysts and growth hacker.
• Have readymade SQL scripts and recipes that you can use right away.

Jim Sterne, Co-Founder of the Digital Analytics Association and author of 12 books on online marketing and analytics, sets the stage. Jim explains the rules, lays out the expectations, and gives you your marching orders. Sit back, relax, and enjoy the show? Not even close. Tune in, turn on, and hang on with both hands!

Board Member David McBride challenges you to participate beyond these few days by spelling out what the DAA stands for (hint: Digital Analytics Association) and how to can get involved to help advance our profession – and your career.

Vidya’s analytics journey is one of passion, grit, and hard work. Vidya demystifies the common jargon associated with Data Science, pointing out digital trends and their impact on your career. Capturing, integrating, storing, and mining data are the nuts and bolts of gleaning meaning. But all of those steps are undergoing massive changes in the face of Machine Learning. Learn key strategies and tactics to help direct your choices of tools, techniques and career paths.

You walked into a roomful of strangers, but in a matter of minutes, you’re going to discover some amazing people. Jim introduces you to experts, practitioners, and people just like you. These are people you’ll want to talk to during the break.

From Behavioral Analytics, to Enterprise Reporting, to Voice of Customer and now In-Store tracking, Gary has spent almost three decades pushing the frontier of analytics. Along the way, he’s failed at almost every aspect of doing, explaining, and operationalizing analytics which leads to a lot of wisdom. Gary looks at some of his key learnings along with what good, state-of-the-art analytics looks like today. He offers up what not to do (and, yes, some of what to do) when tackling Customer Journey Mapping, integrating Voice of Customer with Behavioral Data (digital or in-store), building behavioral segmentations, analyzing Customer Lifetime Value, doing (or forgetting about) cohort analysis, setting KPIs, building enterprise reports, organizing your teams, and talking to C-Suite executives. This is an entire analytics PHD in 45 minutes.

Gabi and Leah share their radical approach to building data culture at  high-growth companies, fueled by their belief that data is often the least important part of building a data-driven organization. If, for example, you woke up tomorrow and your organization’s data was suddenly perfect and every single employee was a SQL expert, would anything change? Would your colleagues know how to identify the most impactful problems, or ask the right questions of their data? Simply making data accessible isn’t enough; you need to create an active community of employees who are empowered and motivated to use data to solve problems. They tell the tactical story of how their grassroots data innovation program grew from ad-hoc onboarding support to a global network of data evangelists, and some lessons learned along the way.

Yep – they pay the bills.

“Culture eats strategy for breakfast,” goes the saying and it’s true for experimentation in any size organization. Anil bolsters his deep knowledge by reaching out to the most experienced testing and online marketing experimentation practitioners to reveal what companies large and small learned from mistakes made along the way. He brings back answers to the most common questions:

• Who were involved?
• How do you involve other groups and get them on board?
• How do you get C level support?
• Where do you start?

Whether you use Data Studio, Excel, Google Sheets, Tableau, R, or (most likely) some combination of platforms, data visualization is a core mechanism for communicating with your stakeholders. The difference between a mediocre illustration and an excellent visualization is whether a stakeholder struggles to internalize the information and fails to put it to use or understands and acts upon it. Tim walks through the neuroscience and psychology that underpins data visualization best practices in. He’ll gift you with platform-agnostic, effective, tactical tips that put you in the data illumination driver’s seat.

Hundreds of Bachelors and Masters Degree programs, Certificate programs, and bootcamps have sprung up to teach the science behind Data Science, including machine learning algorithms, statistics, and coding in python, R, and SQL. Yet data scientists quickly discover that it takes more to be a data scientist than knowing the science and data doesn’t always cooperate with the analyst! Dean describes five things that are critical to success in building predictive models and creating solutions with machine learning in a business context that are rarely taught in school. Real-world examples show how these principles matter operationally. Hint: as cool as Gradient Boosted Trees, Random Forests, and Deep Learning networks are, none of these principles are related to algorithms.

Many of us have bought a lottery ticket just for fun. Or maybe when there’s a big jackpot. How we play the Lottery is one thing; why we play the Lottery is another. If you sell Lottery tickets, you need to understand both so you can communicate most effectively with the right message in the right place at the right time. Neil, back by popular demand, divulges how the Belgian National Lottery created behavioral segmentation models for their online customers to understand opportunities for upsell, cross-sell, and reactivation. Neil dives into combining attitudinal and behavioral data to build communication strategies based on an individual’s playing behavior, allowing the Belgian National Lottery to be more relevant and timely – and effective.

These are the people who make this all possible. They are also the people who have unique solutions to problems you may have, not even know you have, or are about to have. Sponsor sessions are not a burden, they are an opportunity.

Pokeman showed the world that extended reality has a truly immersive market. Fast forward to 2020, XR has hit a stage for exponential growth. This new creative palette comes with an emotional impact and a new set of analytics challenges. Prahlad and Harish dive into eye-tracking, custom events, and object tracking that help brands and retailers extend their reach to geographically dispersed participants. It won’t be long until you are faced with the XR analytics challenges of processing large data, specialized platform requirements, and the potential security hazards when retina scans are used for authentication. Be prepared!

From the start in 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.

Are you using every tool at your disposal to find the gems of insight that will make you look like a Rockstar and propel your department into the next level? Tracking granular level prospect/customer events – action level tracking – combined with CRM data (appending demographic data, offline behavioral data, etc.) can drive super-advanced learnings. Matt shares how to leverage that data into a machine learning model or take it into Google’s new Attribution beta. He also shows how to tell when you are too deep in the weeds.

For the second time in a year, the boss said he wasn’t sure about the value of analytics, because ‘the business is so simple,’ and, ‘the execs just didn’t get it.’ It’s budget-cutting time again. We had streamlined reporting, settled on KPIs, and helped them get insight they never had before. What’s not to get?? Melinda describes the over-delivering trap she had built for herself, reveals the warning signs she should have heeded, and gives specific instructions on how to get yourself out of the same hole. The first lesson: stop digging! The second lesson will save your job and the third will make you an analytics rock star.

Reinforcement Learning – rewarding the machine for better outcomes – was front and center for Samsung’s email optimization. Venkata developed an agent that intelligently learns from previous campaigns and prescribes target population to maximize engagement (CTR) and thus conversions. Venkata shares how this Prescriptive Engine agent solves the Multi-Arm Bandit problem/Exploration-Exploitation dilemma. 

Analytics mistakes, large and small, result in bad data, misunderstood reports, faulty analysis, job loss, self-doubt, and thoughts of becoming a pet food taster. Rejoice that your pain is acknowledged and shared and be very grateful that you can dodge a mistake your company is just about to make. Implementation missteps? Yep. Script slipups? Oh, yes. Goals gaffes? More common than you think. Mehwash highlights some of the worst analytics blunders companies have made, how to fix them, and how to avoid making them in the first place. Learn from others’ mistakes is much less painful than making them yourself

ITP updates, Edge’s new rendering engine, Chrome sameSite cookie changes, CCPA, …Oh! and make sure all of those new dashboard updates are done by the end of the week and give me a cross-tab for seasonality by region pegged to competitive ad spend, OK?thanksbye. While Cory totally commiserates with you on that last bit, he can fundamentally help with practical, tactical insights and advice about tag management turmoil, Do-Not-Track and the upcoming California Consumer Privacy Act anxiety, and modern browsers and the destruction of an analyst’s dreams. If you work in the weeds, bring your questions!

Which customers are predicted to spend the most in the next year? Which should I select for retargeting? How often should different customers hear from my brand? Answering these requires segmenting customers but instead of applying ad hoc rules, clustering algorithms can reveal the hidden structure in data to group them on their similarity. Dirichlet Process Mixture Modeling (DPMM) is a nonparametric Bayesian method that also infers the optimal number of clusters. Raghav gives a high-level tour of Bayesian methods and DPMM followed by results on customer transaction data. Walk away with a better understanding of nonparametric Bayesian modeling and a greater appreciation for how flexible modeling with these approaches can help uncover the hidden structure in marketing datasets.

Meaningful marketing impact relies on cross-functional alignment in strategy and alignment in measurement. This session will showcase new analyses and learnings from the LinkedIn Insights Team to help teams better plan, execute, and measure their marketing efforts.

Data visualization is a must, but it’s equally important to engage your audience with a narrative – to tell a story with the numbers. Narratives are more powerful than raw statistics, more enduring than pretty charts, and are better at influence decisions and drive change. Brent helps you master the art and science of data storytelling with frameworks and techniques to craft compelling stories with data and shows how to take the three central elements of data storytelling – data, narrative, and visuals – and combine them for maximum effectiveness. Isn’t it time you transform your insights and data visualizations into appealing, impactful data stories?

No matter what tool you use, an A/B testing strategy requires a flow of test ideas to keep the cycle of continuous improvement going. That sounds great, but how do you come up with test ideas that matter? Melanie reveals how to use your available quantitative and qualitative data to find opportunities for experimentation. Learn how to use web analytics, user surveys, heatmaps, user recordings, and heuristics for test ideas that lead to more successful user experiences.

Everyone wants to be a data scientist and build state of the art Machine Learning models. But for most companies, only a small percentage of models actually go into production. While ML can be used for research and discovery, its true ROI is realized when you make real business decisions autonomously.  Kishen describes what to and what not to do when approaching a business problem with an ML lens. He covers steps to take to go from ideation to production and all the fun you can have in between.

After spending a couple of days steeped in lectures, roundtables, workshops, and casual conversations, what is still on your mind? Jim and a few hand-picked experts take the stage to answer your lingering questions. How do I convince my boss? Should I learn R or Python? What is the air-speed velocity of an unladen swallow? Here’s your chance to head back to work with One More golden nugget, or just settle a bet.

Bringing it all together, tying a ribbon around it, and putting a nice bow on top.

“No matter how big your data, you must master storytelling and visualization to influence business decisions.”

Visualization is the new communication medium – not the activity of making charts! Learn how to apply the foundations of visual language, evoke what people really want from their data, and unleash your creativity.

Develop leadership in the processes, people, and culture around visualization. To make big data successful requires you to build new skills in visualization, turn data into stories, and change how teams work together.

This workshop is more than a day of amazing ideas. The exercises and team-based challenges build your confidence with practical ways to immediately improve your visualizations and reveal the “a-ha” insights.

Are you ready to transform how your organization makes decisions and takes action?
 

Having a strong foundation in data-driven thinking can transform the way you work.

The Data Driven Culture Workshop is perfect for anyone who wants become the go-to data person on their team, or transition into roles that are more data-oriented.

No matter your skill level, this 1-day course will help you become a data-driven thinker, storyteller and leader. You’ll learn how to ask the right questions of your data, identify problems that data can help solve, and to quickly build solutions to real problems in the business.

Participants will join a community of employees across industries who are passionate about using data to solve problems. We welcome participants of all backgrounds ー technical experience is not required.

You’ll leave this workshop with:

• A clear understanding of the typical data infrastructure and analytics workflow
• A framework for identifying and quantifying problems that will change how you think about your work
• Tools to bring experimentation and data-driven thinking into your day to day decision-making
• A strong foundation in the tools and languages you can apply to your current role (SQL, Tableau, and Looker)
• A foundation in data visualization and storytelling that will change how you think about presenting information
• Tools to collaborate with your data team productively
• Practical advice on how to use data skills to change your career trajectory