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Overcoming Marketing Mix Modeling Challenges
By using Marketing Mix Modeling (MMM), you can have a far better understanding of which marketing channel drives sales and revenue. But there are a number of technical issues that have to be overcome in order to deploy and operationally manage the system.
Data Quality and Integration Issues
Meaning: Data quality and integration problems arise if marketing data does not live in the same location, or is not formatted consistently, with sales data. This makes difficult to compile a normalized dataset for the purpose of modeling.
When data are fragmented in advertising platforms, CRMs, POS systems and offline channels, three issues typically occur:
- Divergent data schemas: Platforms may refer to resources with differing names, formats of dates or definitions of metrics. This involves a lot of ETL (Extract, Transform, Load) one must do just to standardize the data.
- What you don't have or is incomplete: Offline/legacy channels tend not to be tracked at a granular enough level for the model, generating holes of information that can mess up what comes out of a model if they're not well-estimated or filled in.
- Misaligned attribution windows: Some standard cookies-based attribution windows (7-day, 30-day) may not always match up to the real consideration period for higher value things and can therefore misrepresent how much credit each different channel is due.
- Cross-device tracking restrictions: Mobile searchers converting on desktop create muddled user journeys that legacy MMM solutions cannot handle.
Key Takeaway: A robust data pipeline with automatic schema mapping, intelligent treatment of missing values and cross-device linking is critical to creating clean, integrated datasets that will lead to accurate MMM results.
Model Complexity and Resource Requirements
Definition: Model complexity and resource need refers to the level of statistical or machine learning methods applied in MMM, and demand for computational power as well as specific expertise to build and manage these models.
Sophisticated MMM methods such as, for example, Bayesian structural time series or machine-learning-amplified regression, yield fine-granular insights but also pose limitations:
- Need for model agility: Static models built using historical trends could be obsolete in dynamic markets. This necessitates automatic retraining and real-time parameter adaptation.
- Granular insights are limited: Aggregate MMM outputs could hold back a more detailed customer behavior patterns (such as response curves by segments) which is required to make smart budget decisions.
- Complexity of mixed-media integration: Dialing in digital metrics (impressions, clicks, engagements) with traditional media spend parameters (TV GRPs or outdoor impressions) takes expert calibration and custom model tweaks.
- Infrastructure and skills: The cost of MMM projects is also amplified where high-performance computing environments, distributed cloud-based processing and advanced statistical capabilities are required.
Key Message: Striking the optimal balance between model complexity (leveraging automated workflows and modular architectures) is what will keep insights both actionable and cost-effective.
Organizational Adoption and Change Management
Definition: Organizational adoption & change management refers to human as well as cultural consideration that must be in place for broader organization acceptance, understanding, and taking action with data driven insights produced through MMM.
Even the best models can flounder if stakeholders don't support it or do not have the skills to understand and act on its findings:
- Sponsorship: It's the leadership endorsement of MMM as a core planning platform which allows for budget and resources.
- Cross-functional alignment: Ensuring the marketing, finance and analytics teams are aligned around goals, metrics and reporting deadlines is necessary for insights to be used in practice as an opportunity to evaluate correct media plans.
- Continued learning and knowledge: Interactive dashboards, playbooks, and workshops allow users to walk through scenarios and comprehend the relationships between drivers and responses.
- Iteratively refining the model: Continuously fed-back performance data post-campaign improves the accuracy of models and faith in using them.
Key Point: By incorporating MMM into the decision process, with executive backup and collaboration tools in place, then it is maintained for the long-term and a greater return that can be made from modelling efforts.
Why E-commerce Analytics Platform is Best for MMM Success
trivas.ai has a full-stack solution to addressing these challenges, making MMM faster, more accurate and easier to scale:
- One-click Data Ingestion: With built-in connectors and intelligent schema adaptation, data aggregation from ad platforms, CRM, POS and offline sources is automated with no manual ETL required.
- Adaptive Modeling Engine: Powered by Bayesian and machine learning hybrids, trivas.ai will iteratively retrain models in the cloud, meaning up-to-date information and fresh new insights without the need for expensive on premise infrastructure.
- Granular Segmenting: Unlock customer segmentation through native tools to dissect demographic, geographic and behavioural segments for highly precise channel optimisation at all levels.
- Integrated mixed-media support: Digital and traditional media metrics are integrated within one analysis structure, with automatic adaptation for TV GRPs, print reach and online impressions.
- Work as a Team: Create shared, interactive dashboards or use scenario simulators and role-based access to enable marketing, finance and analytics teams to collaborate on finding meaning in results and refine models.
- Executive Dashboards and Alerts: Personalized executive scorecards with up-to-the minute KPI monitoring and alerts keep leadership informed and responsive.
Combining automated on-demand data operations, advanced data modeling, and shared workspaces, trivas.ai reduces the time it takes to deploy MMM and delivers actionable enhancements in marketing effectiveness. This enables companies to maximize budgets and prove ROI, while also being able to track with confidence against changes in their target market.
