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The marketing and media landscape is evolving at an unprecedented pace. With digital channels proliferating, consumer behavior shifting, and marketing budgets facing increasing scrutiny, businesses are under mounting pressure to maximize the return on investment (ROI) of their marketing spends. This has led to a growing reliance on data-driven decision-making and marketing analytics to ensure optimal resource allocation.
The increased requirement for data-driven decision-making has brought marketing analytics front and center for brands. It has now become essential for measuring performance, optimizing spend, and quickly adapting to shifts in consumer behavior. Fortunately, marketing teams now have access to vast amounts of data from multiple sources, including digital advertising, traditional media, social media, and direct-to-consumer interactions. While this data explosion presents opportunities, it also poses a challenge—extracting meaningful insights to drive effective marketing strategies
This is where Marketing Mix Modelling (MMM)—which has seen a resurgence in recent years—has a key role to play by helping businesses measure and optimize marketing effectiveness. By providing insights into the impact of various marketing activities, MMM empowers brands to make informed decisions, allocate budgets efficiently, and drive improved performance.
The insights they provide often become outdated by the time they are processed, limiting their utility in a fast-changing market.
Furthermore, implementing traditional MMM requires a dedicated data science team capable of running complex models and interpreting results. This dependency makes it feasible primarily for large corporations with the resources to invest in such expertise, leaving smaller and mid-sized businesses struggling to leverage MMM effectively.
While MMM can be a valuable analytics technique, traditional MMM solutions come with significant limitations. They are typically slow, resource-intensive, and reliant on external vendors.
Cloud-based, automated SaaS MMM solutions are helping address these challenges, and revolutionizing the field by offering a more cost-effective and flexible approach. These platforms allow companies to access advanced analytical tools through a subscription-based model, reducing the need for extensive in-house technical expertise. This makes SaaS MMM an easy, hassle-free and affordable MMM option even for smaller companies with limited marketing budgets.
SaaS MMM solutions leverage the cloud and AI-driven automation at various steps of the MMM process to offer a easily accessible, fast, no-code approach to MMM:
Equally, it is also an attractive option for start-ups, digital-first brands and smaller companies that need frequent model updates for real-time adjustments to their marketing campaigns. Using a SaaS model to run MMM in-house allows them to do this with only a few skilled resources with basic statistical knowledge and industry expertise.
SaaS MMM is beneficial for companies managing multiple brands and operating in diverse markets, as it enables frequent updates and analytics at a fraction of the cost of traditional MMM.
SaaS MMM delivers a range of advantages that make it a game changer for marketing teams:
SaaS MMM is transforming the way businesses measure and optimize marketing effectiveness. By offering real-time insights, scalability, and cost efficiency, it enables brands to make faster, more informed decisions in an increasingly dynamic marketing landscape. For companies seeking to maximize the ROI of their marketing spend, SaaS MMM is a game changer, providing the flexibility and power needed to stay ahead of the competition.
Analytic Edge, a part of C5i, is a global analytics company that leverages technology and advanced analytics to help companies make data-based marketing decisions. The company’s flagship platform Analytic Edge Qube offers a suite of marketing analytics solutions with a Software as a Service (SaaS) model. The solutions include DemandDriversTM for always-on Marketing Mix Modelling (MMM), SynTestTM for AI powered Test and Learn, PriceSenseTM for pricing and promotion analytics, and PowerViewTM for analytics visualization. Analytic Edge works with clients across industry verticals such as e-commerce, mobile apps, gaming, consumer packaged goods, retail, automotive and many others.
