Decoding the challenges of MMM in-housing
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Seeking control and transparency, brands adopt in-house Marketing Mix Modeling (MMM). Yet, the journey from expectations to reality proves complex. This analysis uncovers stark disparities, execution gaps, and scalability challenges hindering in-house MMM success.
In the pursuit of control and transparency, many brands venture into in-housing their Marketing Mix Modeling (MMM) and anticipate a surge in analysis and optimization capabilities. However, the reality often falls short, leading to questions about the return on investment and the actual impact on marketing decisions. The potential of fully integrated MMM to drive sales and enhance results is vast, but several key factors determine its success.
In this two-part series, we explore the strategic and structural roadblocks hindering the success of in-house MMM programs. In this first installment, we scrutinize the high expectations versus the often disappointing reality, the gaps in execution, and the scalability challenges.
1. Are you maximizing your measurement?
The journey to in-house marketing measurement aims for control over data, transparency, regular access to new insights, and the ability to simulate and optimize under constraints. However, the actual achievement of these goals becomes a pivotal question. MMM is not merely a tool; it requires meticulous change management, impacting governance and processes. Questions around governance timing and decision-making authority between your business and data teams must be addressed. Moreover, with the evolving nature of MMM, strategic investments to meet changing needs become crucial, leading many companies to adopt a hybrid approach.
2. Don’t overlook the gaps!
The challenges of in-housing are often underestimated, contributing to underperformance in transformation. What are the potential stumbling blocks that may hinder successful execution?
- Your data foundation is not solid: Establishing ownership and processing granular data from various sources can be overwhelming. Data governance, although initially challenging, is crucial for a robust marketing measurement program.
- Have you got the right skillsets? Probably not: Balancing data science and marketing mix expertise is a niche skill. Recruiting and retaining professionals with dual proficiency is a challenge that impacts your project’s run phase.
- Your data platform is not useful, usable, or used: Adoption becomes a hurdle when your platform lacks consensus, hindering the ability to answer new questions and adapt to changing scenarios.
- Is your marketing measurement project as scalable as you think? Not necessarily: The assumption that a successful pilot project can be seamlessly duplicated across your entire business often overlooks the specificities of product categories, countries, and brands.
In-housing Marketing Mix Modeling is not just a data project but a multifaceted endeavor involving business and human aspects. A robust data foundation, collaboration between data and business teams, and high-performance data teams integrated into your business’s reality are crucial.
This initial exploration highlights the intricate challenges brands face when in-housing MMM. It sets the stage for our next installment, where we’ll delve into the best ways to improve the run of a MMM project. The focus will be on growing marketing effectiveness and achieving accurate measurement at scale.
Discover how to successfully implement a large-scale marketing performance measurement ecosystem: Download our dedicated white paper > Scaling Marketing Effectiveness (Increasing impact through a hybrid approach – and being smart about balancing in-housing capabilities)
