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Marketing Mix Modeling (MMM) provides a monumental new approach to assessing effectiveness of your marketing. It aids businesses to make data-informed decisions across their marketing landscape. This approach transcends classic attribution models to provide an up-to-date understanding of the effectiveness of campaigns, while offering recommendations on where a budget should be invested or strategically placed.
Budget Allocation Optimization
Resource distribution as revealed by advanced analytics
Optimizing budget allocation with MMM calls for detailed statistical modeling that quantifies the ROI of your marketing investments across your channels. This technique employs sophisticated optimization techniques to model thousands of possibilities of where the budget could be spent. It returns the most optimal combination that fulfills business goals within certain operational constraints.
Real-World Performance Improvements
The advantage of MMM for optimization is demonstrated using case studies. A key omni-channel retailer realized an impressive 32 percent boost in incremental sales by reallocating its budget according to MMM findings. Meanwhile a gaming company acquired 8% more additional revenue and lowered customer acquisition costs by 6%, when reallocating spend out of saturated bottom-funnel channels into effective top-funnel brand campaigns.
Optimization Methodologies and Implementation
Today, we will discuss the three primary methods used by MMM platforms. One, as budgets increase marketers can spend more while losing less efficiency by identifying the channels with the most rapid growth growth. Second, in the middle of budget cuts, MMM assists to optimize efficiency by shifting spend towards those media channels driving best. Third, even under fixed budgets, MMM reveals opportunities for optimization that can improve efficiency at zero added cost.
Campaign Performance Measurement
Comprehensive Evaluation Beyond Last-Click Attribution
Marketing mix modeling enables comprehensive campaign analysis that extends beyond the easy last-click attribution. It covers the full spectrum of marketing influence. This approach acknowledges that customer journeys are nuanced, with various touchpoints serving as different marketing tactics work in tandem towards conversions.
Brand Awareness and Integration of Offline Marketing
MMM is very good at judging brand type-awareness campaigns where you may not be event driven but it increases the bandwidth and the quality of other marketing activities. Traditional digital attribution almost always comes up short in valuing these upper-funnel activities because we don't have blanked tracking of conversions from something directly related to them. MMM, in other hand, uses statistical modeling to demonstrate how overall conversion rates increase when brand awareness campaigns are active.
Long-Term Impact Assessment
One of the major benefits of MMM is measuring the sustained profits ON going sales activity years after initial exposure as a result of marketing investment. This "adstock" effect is important, particularly for branding efforts that may not manifest immediately but yield long-term advantages by raising brand awareness and purchase consideration.
Cross-Channel Attribution and Incrementality
The MMM methods of today focus on incrementality — the additional sales created by a marketing effort, not sales that would have occurred anyway. This entails setting the baseline sales quantity and measuring the lift attributed to either marketing channel, which better indicates 'real' effectiveness of marketing.
Seasonal and Event Planning
Optimizing High-Impact Period Performance
Ecommerce is largely subject to seasonal demand spikes, and certain times of the year—Black Friday / Cyber Monday / the holidays—are make-or-break moments for your annual revenue objectives. MMM enhances marketing effectiveness in these critical moments with granular demand forecasts and strategic planning.
Demand Forecasting and Timing Optimization
MMM has more sophisticated forecasting features that allow companies to understand optimal timing for increasing marketing, among other things. MMM can predict shifts in demand completely when you analyze historical trends (inside the business) and external variables (economic indicators, weather patterns, etc.).
Channel-Specific Seasonal Performance
There are different trends of performance on marketing channels in seasonality. MMM analysis indicated that social media ads work best at an early stage of seasonal campaigns, while search advertising reaches its peak effectiveness the last 48 to 72 hours prior to significant shopping days.
Inventory and Operational Capacity Planning
MMM's prediction extends beyond marketing and helps in managing inventory and operational capacity. For companies to stock appropriately, with customer service available to help manage demand, it's paramount that the forecast increase in sales due to marketing is known.
Measuring Incrementality of Seasonal Promotions
Ideally, when it comes to monthly planning with MMM, one needs to measure the incremental effect of Promotions compared to the natural growth in demand. During holidays, some sales might increase due to holiday spending regardless of marketing.
How trivas.ai improves the success of market mix modelling
trivas.ai is a full-end marketing analytics and attribution system that takes the implementation of MMM to the next level by providing strong data infrastructure, analytical tools required to obtain most efficient modeling outcomes. The solution has been designed to overcome several of the major roadblocks organizations experience in attempting to take on MMM.
Data Integration and Quality Management
trivas.ai is particularly good at aggregating marketing data from everywhere into an easy-to-analyze format. Necessary hard historical data of MMPs in all marketing channels for 2-3 years, and trivas.ai's next-generation data connectors seamlessly integrate all ad, CRM, email marketing and offline media information. This single point of integrated data truth is critical to achieving the most accurate MMM results, because incomplete or poorly-integrated data can seriously erode the reliability of a model.
trivas.ai's MT attribution features can verify MMM results by providing granular CJ data for the comparison with MMM predictions. This validation exercise increases trust in MMM outputs and points out parts where the model has to be improved.
