Q&A: Why advertisers are leveraging marketing mix modeling to quantify external sales factors
The factors influencing sales in the digital marketplace represent an intricate tapestry extending far beyond traditional advertising. In a multifaceted environment, where any event, development and trend can affect sales, advertisers must employ a sophisticated approach to understand and attribute these myriad influences.
At the heart of this challenge lies marketing mix modeling (MMM), a powerful tool bringing clarity to the often chaotic and interconnected world of sales drivers.
In this Q&A, the Custom in-house agency at Digiday spoke with Gary Danks, general manager of AIM by Kochava, about often-overlooked factors driving sales and how advertisers can use MMM tools to quantify the elements likely to shape consumer behavior and affect sales.
What are the common factors influencing the purchase journey?
Gary Danks: While advertising is a crucial sales driver, it’s far from the only one. The idea that a consumer will spontaneously download an app and make a sale is increasingly obsolete. Such an oversimplified view fails to acknowledge the complex web of elements that work in tandem with advertising to influence consumer behavior. Consider the impact of seasonal effects such as weather or one-off events like sporting events or even a Taylor Swift concert. These factors often work subtly, shaping consumer mood and behavior in ways that are not immediately apparent.
Which of these factors do sales and advertising teams most often overlook?
With mobile advertising, the direct sequencing of purchase-journey steps — having a consumer click on a mobile banner, download an app and then make a purchase — is few and far between. Despite this, the smartphone is a hub of activity. Most consumers have dozens of apps and use them regularly for tasks ranging from mundane purchases to planning entire holidays. This leads to an intriguing question: what influenced them to download these apps? The answer lies in a combination of advertising influences and what we term ‘external events.’
How can advertisers use these external events to their advantage in their marketing campaigns?
Gary Danks: These external events encompass a range of occurrences, from weather changes to major public events, each leaving an indelible mark on consumer decisions. It’s often a synergy of advertising precisely timed with an external event that nudges one toward downloading an app or making a purchase. This synergy is subtle yet powerful, highlighting the need for a nuanced understanding of sales influences.
MMM emerges as a beacon in this complex landscape. It doesn’t just quantify the direct impact of advertising; it delves deeper, unraveling the intertwined influences of external events, seasonal variations and more. By harnessing this tool, marketers can gain a comprehensive view of what truly drives sales in the digital age — a view that acknowledges the diverse and interconnected nature of consumer behavior.
Can you share an example of how using MMM to play up various external events has worked especially well for a brand?
Gary Danks: Kochava’s marketing mix modeling platform recently collaborated with a prominent ridesharing firm in North America to develop a comprehensive, custom model. This process incorporated historical and real-time marketing data, allowing for dynamic updates to the model. Traditional factors like seasonality, weather patterns, political events and sporting events were integrated as well. This approach yielded a robust and highly accurate predictive model.
However, the model’s capabilities were truly showcased when we introduced an unexpected variable: Taylor Swift’s North American tour dates. Upon integrating these dates into the model, we observed a remarkable 5% increase in predictive accuracy. The model demonstrated a clear correlation between the tour dates and an uptick in sales conversions (ride bookings) for our client. This enhancement was not trivial; it signified a more comprehensive understanding of consumer behavior. Our model could now attribute a portion of previously opaque ‘organic conversions’ directly to the influence of Taylor Swift’s tour dates.
Moreover, including the tour dates revealed greater insights into the effectiveness of different marketing channels. Both branding and performance media channels were more impactful than we had initially thought. Our MMM enabled the client to see the direct influence of these channels on the Taylor Swift audience.
The experience with this client highlighted a remarkable aspect of marketing: the unseen impact of external events on sales. Through analysis, we uncovered a significant, albeit previously unrecognized, increase in sales coinciding with Swift’s tour dates. This discovery was not just a testament to the power of MMM but also a prompt to ponder the potential of such insights.
How can MMM complement advertisers’ existing campaign tactics?
Gary Danks: Imagine the possibilities if businesses could proactively harness the power of external events known to positively influence sales. If a Taylor Swift concert can markedly boost sales, think about the numerous other events that could have similar effects. Understanding the ebb and flow of external events and their impact on sales opens up new strategic avenues. It empowers marketers to pull previously unrecognized levers, where they can turn insights into actionable plans to capitalize on these opportunities.
This insight should prompt businesses to look beyond touch attribution methods and consider next-generation MMM tools to quantify how external factors like popular events can be pivotal in shaping consumer behavior and driving sales.
The journey to understanding what influences sales in the digital world is intricate and multi-dimensional. It’s a journey that requires us to look beyond the immediate and obvious to consider the subtle interplay of factors that shape consumer decisions. Marketing mix modeling is then an essential ally, guiding us through the nuanced landscape of factors affecting digital sales.
Sponsored by Kochava
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