Expert insights from Kochava webinar with Spotify Advertising and M&C Saatchi Performance

Since its emergence in the early 2000s, podcasting has experienced exponential growth, prompting businesses to adopt marketing strategies to leverage the rapidly evolving medium. In a webinar showcasing podcast advertising, acclaimed industry players—Charles Manning, CEO of Kochava; Adrienne Rice, Director of Media Investment at M&C Saatchi Performance; and Matt Drengler, Director of Marketing Research and Intelligence for Spotify Advertising—shared their insights into the opportunities and best practices within this channel. The insightful session examined the breakthroughs of podcast advertising, its efficacy for advertisers, and its future.

Why Podcast Advertising?

The panel established the impressive scale of the podcasting landscape, emphasizing the medium’s growth and the opportunities this affords advertisers. Millions of podcasts cater to a global listener base projected to exceed half a billion people in 2024. At the same time, podcast advertising revenue is heading upward of $4 billion. This is no longer a nascent, niche medium, but a burgeoning channel with yet-untapped potential.

Rice shared key insights into the demographics and behavior patterns of podcast listeners that marketers might do well to consider, pointing out that 66% of US internet users listen to podcasts (in most cases at least once a week), with the majority of listeners aged 45 or younger and earning higher-than-average household income. As for the dominant podcast genres—spoiler alert, but perhaps not a surprise—comedy and true crime are well established as top listener favorites.

Evolution of Podcast Advertising

The panel recounted the transformative journey of podcast advertising, from its early implementation to the innovative solutions shaping its future. Traditionally, podcast advertising was predominantly purchased directly from shows and embedded within the podcasts themselves, often in the form of host-read ads. This posed significant limitations in scalability, as each ad insertion was manually placed within and inseparable from the episode. Producers and advertisers also had to consider the ongoing relevance and permanence of ad content; after the show was aired, the ad might be forever “baked in.”

The podcast industry embraced technological innovations to solve these challenges, and over the past several years, the landscape has significantly evolved. Drengler took participants through industry advancements that directly addressed the limitations of embedded ads and revolutionized the podcast advertising space, most notably dynamic ad insertion (DAI). This technology, now accounting for 90% of ad volume, enables advertisers to place relevant ads into a designated spot within a desired podcast episode, seamlessly stitched in at the time of download and refreshable as needed. This marked a significant advance toward resolving issues such as scalability, measurability, and systematic targeting.

Automated programmatic ad placement is rapidly taking hold, and there is still much room for growth in this approach. And Spotify’s streaming audio insertion (SAI) represents a cutting-edge breakthrough, leveraging the shift toward streaming podcast content rather than downloading it. This technology has further enhanced ad integration, real-time targeting, dynamic content delivery, and ad measurement capabilities, in particular the ability to measure on real-time impressions, leading to a more engaging ad experience for listeners while offering greater effectiveness and efficiency in optimization for advertisers.

New Possibilities

Manning emphasized that these significant shifts now enable us to comprehend the podcast consumer journey holistically, essentially having blown open the doors for the medium to become fully viable for performance advertising. The webinar panel agreed that new technologies are rapidly driving equivalence with digital ad formats, fully democratizing podcasting as a reliable advertising channel.

Taking full advantage of these advancements, however, necessitates new paradigms in measurement. Spotify’s SAI offers advertisers a more precise measure of reach, impressions, and audience targeting. While this allows for sophisticated metrics, the greater podcast adtech world is still catching up. Case in point—in a digital environment where clicks and downloads are often misleading, distinguishing between podcast downloads and streams is key to tracing listeners’ post-impression actions.

To facilitate such measurement capabilities, Spotify partnered with Kochava to process and analyze a more dimensional profile of podcast stream data in real time. Advertisers on this platform are no longer subject to the limitations posed by engagement ambiguity as revealed solely by tracking downloads or one-touch attribution. The Spotify-Kochava collaboration has enabled third-party verified measurements that open the way for further performance-based initiatives. One actionable metric has revealed that up to 95% of attributed events take place within 14 days of podcast download or exposure.

Effective Campaigns and Best Practices

These insights derived from enhanced measurability reinforce the importance of understanding the customer journey and the role of podcasts in this journey, from introduction to final conversions. Podcast advertising is more than just another channel, but a uniquely immersive experience that provides a focused and uninterrupted space for advertisers. The conversation revealed a bombshell outcome takeaway: One in five listeners who visit an advertiser’s site after exposure to a podcast ad ends up making a purchase. Ponder that!

The panel delineated key practices for devising and executing successful podcast campaigns:

Leverage listeners’ heightened attention: Advertisers need to comprehend the medium’s perceived authenticity and credibility for effective education and audience engagement over a wide range of topics, resulting in a loyal, receptive listener base. The felt connection between host and listener fosters trust in the medium and by extension the advertisers who directly speak to this audience engagement. High-quality, vivid creative is a must to engage podcast listeners who are primed to embrace relevant, compelling ads and brands/products that complement their listening experience.

Deploy a robust measurement strategy: Advertisers need to leverage the wealth of data now available through podcast analytics. Understanding listener behavior, such as when and how they tune in, listen to or skip ads, and engage with content, is fundamental for optimizing campaign performance. Contextual-based targeting, including seamless, real-time topic and conversation-specific ad placements, is a powerful means to tailor creative to podcast contexts and/or home in on audiences by demographic or behaviors and interests. Data derived from such practices can be used to inform and optimize subsequent initiatives relative to desired key performance indicators.

Prioritize privacy issues: With privacy becoming an increasingly important concern, advertisers need to be cognizant of how they collect and use listener data. Ensuring compliance with privacy laws and being transparent with listeners about data usage can help maintain trust and reinforce positive brand image.

Catch the Full Webinar on Demand

The complete on-demand webinar, Capitalizing on Podcast Advertising in 2024, is available now! The discussion is full of fascinating insights on podcast advertising, effective measurement approaches, and future trends, with a fun addition of some of the speakers’ own favorite podcasts. The overall takeaway from this informed panel of industry experts: It is abundantly clear that the podcast medium will continue its upward trajectory, and savvy marketers will be eager to leverage this golden opportunity to apply these webinar insights directly into their digital marketing strategies for a marked competitive edge.

Enhancements to MMM make it a powerful tool for advertisers as privacy regulations evolve

What Does MMM Stand For?

Known as marketing mix modeling or media mix modeling, MMM is a statistical data-analysis methodology that gives marketers a better understanding of the optimal mix of marketing strategies across media channels to positively impact sales and other key performance indicators (KPIs). MMM seeks to take into account all advertising channels—print, social, and online advertising (e.g., search, display, video) as well as offline channels.

How Does MMM Work?

Marketing mix modeling relies on aggregate data from marketing and non-marketing sources gathered over an extended period of time. Typically, a minimum of three or more months of historical data (ideally 12+ months) are necessary to reach data significance and account for seasonality shifts. This large volume of data is used to create an accurate demand model to give marketers insights into the most effective channel strategies and forecast the best omni-channel allocation of future ad spend for greatest impact and return on investment (ROI). From these insights, marketers can adjust ad spend allocation across channels and partners for future optimization.

Seems a little abstract? Let’s look at an example.

A CMO at a major fintech company wants to zoom out from granular campaign- and creative-level performance reporting to capture the bigger picture. The CMO’s goal is to understand the incrementality of ad spend across channels and overarching performance peaks and valleys throughout the year. They work with an MMM platform and after plugging in historical data are able to arrive at new recommendations for how much to spend across channel partners at different times of the year. The marketing director and UA manager now can reallocate spend across their various channel partners to drive better incrementality and reduce unwanted oversaturation on any given channel.

And that’s essentially MMM—collecting and processing a lot of data, then presenting it at a high, aggregated level so marketers can glean broad insights into advertising effectiveness, transcending individual outliers and skewed averages.

As digital marketing evolved in the early 2000s, MMM largely took a back seat to direct-response attribution modeling, which relies on user-level interactions on websites and mobile apps. Unlike MMM aggregated data, attribution data is inherently granular—useful for marketers in focusing their efforts on specific users and customers via direct response marketing. This approach facilitates insights derived from customer-level engagements, enabling marketers to drive creative optimization, A/B tests on messaging and creatives, and other personalized marketing tactics tailored toward unique persona profiles.

In recent years, however, MMM has seen a renaissance owing to the data processing and analysis potency enabled by AI and machine learning. Companies and their marketing teams have adopted the advanced analytics and predictive insights afforded by MMM to fuel growth. At the same time, recent developments in user privacy and data use have eroded the availability of granular, user-level attribution data. As a result, marketers are relying more on aggregated data and rediscovering the potential of MMM to inform their marketing strategy. MMM enables them to optimize budgets across channels while respecting privacy policies.

How MMM Is Evolving to Help Advertisers

With the revival of marketing mix modeling, how marketers interact with it has evolved to support the dynamic needs of today’s user acquisition teams. In the fast-paced digital advertising landscape, quarterly or semiannual MMM reports are quickly outdated and lack actionability. Traditional MMM is time-consuming and laborious to manage, making it accessible only to large organizations that have the resources to maintain it in-house or the budget to outsource it.

While historically only such corporations have been able to afford fully leveraging MMM, automated data flows, cloud computing, and machine learning have made MMM more accessible, accurate, nimble, and easily updated. Cutting-edge software as a service (SaaS) next-generation MMM solutions, now accessible to companies of all sizes, have been developed to fit the needs of today’s advertisers. AIM (Always-On Incremental Measurement) by Kochava, a real-time MMM tool, maximizes the effectiveness of the marketer’s budget by providing advanced control over incrementality, channel saturation, and seasonality. AIM utilizes a sophisticated learning system that ingests new data daily and continuously updates and enriches its models. This always-on approach ensures that the insights it produces never go stale and are always ready to use—providing marketers who must make confident decisions with turnkey recommendations for optimized budget allocations.

As user privacy continues to weave itself throughout the adtech ecosystem, next-generation MMM tools will become increasingly indispensable for advertisers in determining the effectiveness of their omni-channel media strategy.

The Conclusion on Marketing Mix Modeling

Next-generation MMM is at the forefront of a marketing revolution, offering actionable recommendations for data-driven decision making in an increasingly privacy-conscious adtech landscape.

Have questions or want more information on AIM and MMM? Check out our Marketing Mix Modeling 101 ebook and explore even more helpful content in the AIM Resource Center.

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