Marketing mix modeling (MMM) is an advanced analytics approach that companies use to better understand how their marketing activities are driving key business outcomes, and more efficiently optimize their marketing spend.
Although data-driven companies understand that a mathematical, data science-driven approach will consistently outperform more simplistic analytics (or worse, gut-driven decisionmaking), traditional MMM approaches possess many limitations that have significantly impeded their adoption by brands. A successful MMM model that will generate 15-20% improved ROI requires multi-data source collection, unification, processing, wrangling, modeling and reporting, which can take companies quarters or years to complete. As a result, MMM solutions have historically been surface level, manual and onerous (both in terms of long setup times, and cost), which has slowed adoption.
Thankfully, analytics operating systems like Redbird are ushering in a new era where MMM workflows can be no-code, automated, always-on, robust and maintained at a lower cost. This means companies are able to build and run workflows rapidly, with extensibility into unlimited new data sources and lightning speed.
Companies currently have 3 options when it comes to implementing their MMM strategy:
1. Legacy MMM providers. Companies that specialize in MMM solutions and services
2. DIY Custom Build. Utilizing internal engineering / data science resources
3. Analytics Operating Systems. No-code, automated, always-on tools
With the emergence of always-on, automated, lower cost analytics operating systems like Redbird, MMM has become a no-brainer from an ROI perspective. Once baseline MMM needs are being met, given their flexibility and power, these tools also afford companies the ability to easily enrich their analyses with more granular data that can extend beyond basic marketing sources into broader experiential moments customers are experiencing across different touchpoints. We call this Experience Mix Modeling.
Introducing the Experience Mix Model - a full business CX optimization engine
Traditional marketing mix models factor in external variables (e.g. macroeconomic data), marketing activities (e.g. paid, owned, earned for both online and offline) and combine these with basic non-marketing business metrics (e.g. pricing, discounts, seasonality). Once data has been collected and standardized, models can be trained to predict key business metrics (e.g. sales).
Given the reduction in time and custom-coding effort required to ingest and process granular data from a variety of sources, an analytics operating system like Redbird is capable of expanding MMM into a more robust, insightful view which we call the Experience Mix Model (EMM). In addition to traditional MMM data sources, EMM includes a robust set of customer insights sources such as surveys, ratings, call center conversations, customer journey touchpoints, reviews, ecommerce funnel metrics, A/B testing, supply chain, CSAT/NPS, post-purchase interactions, and many more. Through advanced data science modeling, always-on reporting and custom interactive web applications, teams can seamlessly understand which parts of the customer experience are driving business results, and optimize the customer journey accordingly.
Through more automated, always-on, no-code approaches, an analytics operating system like Redbird can help customers generate >20% improvements in media spend efficiency across all touchpoints. Once baseline MMM models are in place, customers can expand their understanding of business drivers using Experience Mix Modeling, which incorporates new data sources and identifies additional opportunities for business optimization across all customer journey touchpoints.