[ Updated Nov 17, 2023 ]
And that’s a wrap—our first-ever SuperSummit has officially ended.
Twelve spectacular speakers, including representatives from Meta, Google, LinkedIn, Uber, and more. Two dazzling hosts and more than 8,000 of you from all over the world.
Thanks, everyone, for joining us, keeping the conversation going in the chat and sharing your SuperSummit moments online. We loved seeing how much you enjoyed the event.
This year, we discussed the significant shifts affecting marketing measurement, including privacy, big data, and generative AI.
The half-day event was packed with insights and interesting discussions from the panelists. But if we have to pick, here are the top key takeaways from SuperSummit.
Navigate this post:
- Use data as headlights to guide decision-making
- Marketing mix modeling has become more popular as we transition into a privacy-first world
- Marketers need to be comfortable with using data to make decisions
- To maximize your organization’s data potential, you need to pair the right people with the right tools
- Companies need to start investing in a first-party data strategy
- You’re not competing with AI; you’re competing with marketers using AI
Takeaway 1: Use data as headlights to guide decision-making
Data is a tool to help marketers create amazing customer experiences and actively drive business results. However, most marketing teams only use data to review past performance. In this new era of marketing, looking at past outcomes alone isn’t enough to improve marketing ROI.
Data should be used to influence not only decision-makers but also product marketers and engineers. Dorothy Advincula, Head of Analytics and Insights at Uber, says, “As the world democratizes its data functions, you can influence product marketers and event engineers.”
To make the most of the massive amount of data, marketing teams need to shift their mindset from viewing data as a rear-view mirror to using it proactively to stay ahead of the curve.
Takeaway 2: Marketing mix modeling has become more popular as we transition into a privacy first-world
Marketing mix modeling (MMM) is a statistical analysis technique that quantifies the impact of various marketing inputs to measure the effectiveness of marketing campaigns and activities. Automation and new technology have made MMM more accessible, faster, and more effective.
As third-party cookies fade away, many companies are looking into MMM as an alternative to multi-touch attribution. If you want to build a successful model, it’s important to:
- Have a data infrastructure that can support MMM.
- Make sure your data is clean.
- Start small and iterate the model as you go.
- Collaborate with experts in the field.
As with everything, as important as building a good model, you should use the results to improve your marketing. Igor Skokan, Marketing Science Director at Meta, says, “MMM is only worth as much as the positive actions that you can take using the model.”
Takeaway 3: Marketers should be comfortable with using data to make decisions
Technical skills are becoming increasingly important for marketers. Working with data can be overwhelming, but rather than mastering a million data tools or languages, it’s more important for marketers to know the foundation of marketing data management.
Khrysyna Grynko, Cloud Customer Engineer at Google, shares, “We know that we need data to do AI. We know that we need data to personalize the customer experience. We know that we need big data to analyze our ads. But sometimes, we just don’t know where to start.”
If you’re getting started with your first data project, whether it’s building your first report in a spreadsheet or improving your current dashboards, it’s good to follow these steps:
- Think about use cases.
- Think about the place, for example, a data warehouse, to consolidate your data.
- List the data sources needed for your use cases.
- Define how to get the data from these sources to your data storage place.
- Define whether the data is usable as it is or if it should be cleaned/prepared.
- Choose your visualization tool.
- Define what reports you need to create to understand your data.
- Find how to leverage your consolidated data in your marketing tool.
Takeaway 4: To maximize your organization’s data potential, you need to pair the right people with the right tools
As your company’s analytics capability matures, there will be different data needs and skills. It’s important to recognize that data is about people and should be used to educate and inspire different stakeholders, such as decision-makers, product marketers, and engineers.
Because of this, if you want to build and nurture a data-driven culture, it’s important to give your team access to the data and tools they need to succeed. Anssi Rusi, Co-CEO at Supermetrics, says, “For companies to measure the return on investment, you need the right tools.”
For example, use spreadsheets and Looker Studio for ad-hoc analysis and campaign optimization analysis. Meanwhile, a data warehouse is great for storing and managing data for trend analysis or more advanced measurement methods.
Analytically matured companies need tools for both use cases.
Takeaway 5: Companies need to start investing in a first-party data strategy
Compared to B2C, B2B measurement is much more difficult because of the longer sales cycle, and because the majority of tools and platforms aren’t built for B2B. Most existing tools focus on shorter lookback windows and frontline metrics rather than the longer sales cycles and business outcomes of B2B marketing.
With increasing privacy regulations and signal loss, B2B measurement will continue to be more challenging. That’s why it’s important to layer multiple types of measurement to get a comprehensive view of your performance.
Emily Gustin, Senior Business Development at LinkedIn, says, “Where there’s true value exchange with customers or potential customers, you should aim to collect first-party data in a secured way so you can use that data to inform smarter models and decisions.”
Additionally, it’s equally important to start building a solid first-party data strategy. This will help marketers:
- Comply with privacy regulations since you’ll need customers’ consent when collecting first-party data.
- Get accurate data since the data comes directly from your audience.
- Run targeted and personalized campaigns.
- Build a collection of data that can be used to power advanced measurement methods such as marketing mix modeling.
Takeaway 6: You’re not competing with AI; you’re competing with marketers using AI
The way consumers search is evolving, and paid search and the way marketers work need to reflect that change.
The human component is crucial in creating the most value with generative AI. Marketers and AI create a flywheel where both learn from each other. For example, marketers supply creative goals and conversion data. In turn, AI takes care of optimization and delivers insights back to the marketer.
Feeding your AI solution with first-party data is critical, as that’s much more durable than any third-party data set.
As Matz Lukami, Product Lead of Performance Max and Attribution at Google, says, “You’re not really competing with AI. You’re competing with marketers using AI.”
Now that the first SuperSummit is done and dusted, what’s next?
SuperSummit will definitely come back next year with more interesting topics and amazing speakers. Keep an eye out for more updates on our social media—LinkedIn and Instagram. All SuperSummit sessions are now available on demand. You can watch them at your convenience.
About the author
Joy is the Content Strategist at Supermetrics. With internal and external experts, Joy helps businesses eliminate the data chaos and turn marketing data into opportunity.
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