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Perspectives from Eric Perz | Brunner

Vice President, Data Science

Eric Perz

In an era defined by data-driven decision-making, marketing intelligence is the cornerstone of strategic success. Discover insights from our team.

At Brunner, marketing intelligence is the foundation of data-driven decision-making. Explore our insights to learn how to leverage data effectively, target your audience, and stay ahead in a competitive landscape.

Our Thoughts on Marketing Intelligence Solutions | Brunner

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Stay updated on the latest business intelligence trends and strategies to make data-driven decisions for growth and success.

Unlock the potential of data with our Business Intelligence team, combining tailored consulting with automation and statistical expertise to deliver valuable insights directly to you. Stay updated on the latest trends and strategies to make data-driven decisions for growth and success.&nbsp;

Our Thoughts on Business Intelligence | Brunner

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Last year, a healthcare client told me that they would need to stop tracking their paid advertisements due to new rules from the U.S. Office of Civil Rights. Many other healthcare organizations made the same decision.&nbsp;&nbsp;The OCR guidance aims to protect patient privacy by strictly enforcing the Health Insurance Portability and Accountability Act — widely known as HIPAA — when it pertains to tracking user activity. This means that healthcare organizations cannot use the usual tracking technologies, like cookies and pixels, that could potentially reveal personal information about patients. As a result, legal departments in healthcare organizations are acting to eliminate digital media tracking, at least until they can sort out what kind of tracking is acceptable.&nbsp;This creates a challenge for healthcare marketers who need to generate demand but now have lost their ability to track and report on their campaigns.&nbsp;&nbsp;<h2>Building a New Way to Measure Marketing Impact&nbsp;</h2>To deal with this challenge, a collaborative team at Brunner has started building a system to replace media performance reporting. We have been working closely with our healthcare clients to identify the right key performance indicators to create a new model for measuring marketing performance.&nbsp;&nbsp;&nbsp;&nbsp;For this work, we are using machine learning and artificial intelligence including something very new — explainable artificial intelligence, sometimes referred to as XAI. An additional key piece of our new system is a knowledge base built from a well-established marketing mix model.&nbsp;&nbsp;Here’s how our team is approaching it:&nbsp;<ul><li>Since we can no longer rely on digital conversions, we focus on aggregate values that comply with HIPAA, such as call volume or insurance leads.</li><li>We set up models to generate new results regularly, focusing on the most recent campaign performance.&nbsp;</li><li>We collaborate closely with Brunner’s media teams, who are running campaigns on various channels.&nbsp;</li><li>We encourage our media teams to question the model scores and suggest new ways to evaluate the performance of ad units.&nbsp;&nbsp;</li></ul>The collaboration between data science, media, and strategy teams is crucial for improving the quality of the marketing mix models and the usefulness of the outputs.&nbsp;<h2>Keys to Success in Using XAI&nbsp;</h2>If there is one thing you take away from this article, it should be this: use XAI with caution and only when you have an established knowledge base for marketing’s bottom-line impact on the business. This understanding can come from a marketing mix model, previous marketing experiments, or a combination of both. But without this knowledge base, it’s difficult to evaluate the AI/XAI models you are using, and you may end up with unreliable results.&nbsp;In our work with Brunner clients, we incorporate insights from the marketing mix model directly into our processes that deal with AI/XAI outputs. This establishes guardrails to keep the results and interpretations of AI/XAI models in check. You may have heard how generative AI can “hallucinate.” Similarly, other varieties of AI can generate misleading results. We have to steer clear of those.&nbsp;There are pros and cons to each situation. One weakness of marketing mix models is their lack of granular insights, such as those at the ad or creative level. That limitation exists for very good reasons, but we see AI as the best tool to resolve it. That is because one of the great traits of AI is its ability to accommodate highly granular data as inputs. However, when you use AI, you are typically left with a black box: something that produces helpful results but does not give you insight into its internal workings. That is where XAI comes in. XAI, while still a developing field, is a collection of approaches that probe these black boxes to measure the relative importance of the original inputs, which are easier for an analyst to interpret.&nbsp;A good example comes from image classification. An XAI model would explain which pixels from an image were most important in determining that the image depicts, for example, a frog. Now apply that same concept to advertising. Just as XAI might help us identify the pixels that let us know that the image shows a frog, XAI might help us understand which ad units, or creative executions, are most effective at improving some healthcare marketing key performance indicators. We also just need to have that knowledge base in place to keep us from misinterpreting something.&nbsp;<h2>Forging the Future of Measuring Marketing Impact&nbsp;</h2>At Brunner, we’re excited to collaborate with our healthcare clients to build a new approach to measuring marketing performance in a way that complies with the OCR rules. We understand how important it is for marketing teams to know how each campaign and channel is performing and where we can identify opportunities to improve ROI.&nbsp;If you’d like to learn more about new ways to measure marketing performance and impact, don’t hesitate to reach out. We’d be happy to chat.&nbsp;&nbsp;

Brunner is using explainable artificial intelligence in our marketing mix model to help clients comply with OCR guidance. 

How Can Healthcare Marketers Navigate Reporting with Post-OCR Guidance

How Can Healthcare Marketers Navigate Reporting in a Post-OCR Guidance World that Aims to Protect Patient Privacy? 

Unlock the power of your data with insights from Brunner’s data management and analytics team.

Unlock the power of your data with insights from Brunner’s data management and analytics team. Explore how we transform complex data into actionable insights that drive informed decisions and uncover new growth opportunities.

Data Management and Analytics Content and Perspectives | Brunner

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Speed versus depth of insight is a fundamental trade-off marketing strategists often face when working with analyst teammates. Both sides contribute to this reality. Strategists often ask nuanced or difficult questions (what is the ROI of a sports sponsorship?), and analysts or data scientists, reacting to a wide variety of these questions, tend to approach each new project from scratch.This conundrum can be solved when convening agency and client strategists and data scientists. We believe the conversation should cover:<ul><li>What prior analytical work was most helpful to strategists?</li><li>What new features would strategists like to see from analytical output, and what compromises are acceptable?</li><li>Which data sources are reliable and are refreshed often?</li></ul><h2>Automated Systems Can Drive Efficiencies</h2>Data scientists, now understanding the value of their past work, as well as having clear direction and an approved list of data sources, can then work on automated systems.An example of what such a system could do is calculate and compare marketing ROI across detailed paid channels while accounting for the diminishing returns effects of increased marketing investment. These are sophisticated, non-linear models – the kinds marketing strategists associate with weeks or months of turnaround for each request. To be fair, it can take a few months to get this system up-and-running. But once that upfront work is completed, the outputs are automated and the insights valuable and frequent. A few thousand lines of code will import raw data, transform it as needed, test different models, pick the best one, and push the results to the strategist team.<h2>Strategists Can Leverage Output to Inform What's Next</h2>Strategists can dig into the data and review fresh modeling outputs to inform thinking and next steps. Here are some use cases:<ul><li>The client wants to increase this week’s non-branded search budget by $10,000 to meet a business goal? OK! According to the outputs of the program, the first $2,000 should be in Exact Match and focus mostly on mobile in region A, the next $1,000 should be desktop Broad Match in region B, etc.</li><li>How is our new display vendor doing? Easy! We are seeing great returns on low levels of investment but it really flattens out after that. There seem to be reach limitations and we should modify our spending accordingly.</li></ul><h2>Automation Supports Agile Strategies</h2>An automated system like this allows strategists to deploy budgets fluidly and optimize across search engine or publisher, device type, region, and other dimensions. In contrast, most strategists still must pre-ordain budgets for each channel and try to optimize within them based on experience and simple reports. If they are lucky, they have access to marketing mix model results that optimize budgets by channel at a high level, and maybe only annually. Fast analysis is not limited to reporting, and sophisticated analysis does not have to be slow. But analysis, insights and reporting can make the difference – and should be a fundamental part of any marketing strategy and partnership.

Discover how automating complex marketing models can provide deeper and more frequent insights for your brand.

Automating Complex Marketing Models for Deep Insights

Automating Complex Marketing Models for Insight That is Deep and Frequent

Many articles have commented on the acquisition of creative talent by consultancies to compete with ad agencies. In hindsight, this was a predictable progression as advertisers shifted more budget into data-intensive digital media and at the same time increased expectations for financial accountability. Reading between the lines, it’s easy to extrapolate the trend to the “inevitable consultancy takeover”. However, agencies may be in a better position than they think. Those with strong data science capabilities should not only be able to retain current business, but be a competitive resource on projects traditionally in the domain of the consultancies. How? By aggregating the right talent and understanding the landscape.<h2>The Right Talent Comes First</h2>Data science groups should be built around talent with deeply technical backgrounds with must-have skills including statistics, machine learning, coding, database and cloud computing know-how, communication, and a strategic mind. Then you have to consider the role of data in daily responsibilities but also within the larger marketing ecosystem.Data scientists spend a great deal of time measuring marketing ROI. It’s the biggest technical challenge in the feedback loop linking plans and budgets, strategy sessions, and campaign execution. For example, if comparing year-over-year sales growth for markets where, say, media was heavy versus where it wasn’t approximates your approach to analytics, then consultancies are a serious threat. And while marketing ROI is a much more complicated calculation than that, the landscape is much bigger. It’s important for agencies to demonstrate knowledge of how non-marketing factors affect a business and its marketing.<h2>Engage Past Marketing</h2>Data scientists can build sophisticated models (commonly referred to as “marketing mix optimization”) to detect and quantify often complex relationships between marketing and their desired effects. Think: cable TV can lift search activity, which in turn lifts store traffic. But don’t forget non-marketing factors!Data must account for KPI changes due to the environment (e.g., macroeconomic conditions, weather, competition), operations (e.g., inventory, pricing), and seasonality (e.g., holidays, elections). These inputs affect brand performance and in most cases, marketing performance can only be measured in the context of these other variables.While agencies are typically approached to answer questions about marketing specifically, having deep knowledge in dynamics such as the relationship between staff turnover and sales, the initial impact when competitors launch a new service, or how much inventory shortages cost the organization creates domain experts in the business. Serving in that role puts agencies in the position to be a valuable resource for departments outside of Marketing.That’s why finding the right talent, and encouraging them to go beyond marketing can increase an agency’s competitive advantage.

Agencies should match consultancies in competitive non-marketing engagements to enhance their value and client relationships. Learn more.

Cultivating Agency Talent to Compete in Non-Marketing Engagements

Agencies Should be as Competitive as Consultancies in Non-Marketing Engagements

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This series covers the elusive topic of data. How to gather it, develop it, and structure it so that you can hone in on valuable insights. Getting closer to your customers requires making the most of the data available to you and understanding where the organization is at in terms of data maturity.Find “Own Your Data: Data Analytics Maturity Series, Episode 1,”&nbsp;<a href="https://www.brunnerworks.com/perspectives/own-your-data-data-analytics-maturity-series-episode-1" rel="noopener noreferrer" target="_blank">here</a>.In our second episode Brunner’s Director of Data Science, Eric Perz returns to our data analytics maturity matrix and dives in to the importance of setting the foundation for success. Eric talks about ensuring your organization’s data analytics structure has the “4 Vs,” of variety, volume, velocity and veracity. Key to building a data-driven organization is cultivating a culture that is bought in to the importance of sourcing, analyzing and using data to transform.

Continue your journey in data analytics maturity with Brunner’s second episode, focusing on building a strong foundation for success.

Eric Perz

Vice President, Data Science

Eric Perz

Vice President, Data Science

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