Magic Quadrant for Marketing Mix Modeling Solutions

Marketing Mix Modeling Solutions Vendor Product Comparisons 2024

Key features and pros and cons of top Marketing Mix Modeling Solutions products:

Analytic Partners - Leader

Pros:

Breadth of approach: Analytic Partners' MMM solution delivers what it calls Commercial Intelligence, encompassing marketing, finance, supply chain and other enterprise functions. This enterprisewide engagement accelerates adoption, especially for nonmarketing recommendations, such as timing budget increases to maximize impact (finance) or making inventory and distribution changes (supply chain).
Robust software platform: Analytic Partners' software is one of the most powerful tools in this research. GPS Enterprise enables capabilities not offered by other platforms in this research, such as in-platform commenting and notifications for scenario planning and enterprisewide decisions, as well as extensive data processing, lineage, quality and metadata management with ROI Genome.
Complex marketing analytics: Prospective clients with sophisticated data science and finance teams benefit from GPS Enterprise's in-platform support for many advanced analytics. These analytics include weighting multiple key performance indicators (KPIs) in a single model, calculating halo effects for channels brands and lines of business, estimating the long-term impact of brand, and triangulating results of incrementality tests on models.

Cons:

Limited private cloud support: GPS Enterprise does not support deployment in client-managed private clouds. Prospective clients whose policies or industry regulation require containing their data in self-managed cloud environments will need to investigate if a vendor-managed private instance will meet their requirements or work with another provider.
High price: Analytic Partners' MMM solution costs are among the highest in this research. Contract cost often highly correlates with engagement complexity. Prospective clients should assess the incremental value provided by Analytic Partners' specific differentiators against the value provided by other potential providers.
Model latency: Analytic Partners requires human oversight for data transformations and model validation for all model updates. Therefore, clients may wait longer for model updates to complete compared to other providers in this research (typically four to seven days longer). Prospective clients should ensure model updates can be executed to support the cadence of all their key decisions.

Circana - Niche Player

Pros:

Retail data and consumer goods assets: Circana has access to store-level data for a range of supermarkets and major consumer goods and general merchandise retailers. Compared to aggregated data feeds, this depth increases the number of observations available and improves the robustness of results in ways that would be costly to replicate with alternative data feeds or first-party data strategies.
Comprehensive marketing measurement capability: Circana's platform provides the capabilities of MMM, multitouch attribution and lift studies to quantify the impact of creative elements and in-flight campaign measurement.
Global coverage: Circana's worldwide presence enables it to service projects from the closest local country office for a project implementation and supplement with local expertise. Based on first-party data, or data secured by clients' data-sharing agreements, Circana can cover a broad range of countries and categories.

Cons:

Services-intensive approach: Circana's approach to MMM might not be the right fit for prospective clients looking to support model development and calibration via internal data scientists. Prospective clients should ensure that Circana will engage their data scientists during model construction.
Impact of Acquisition Integration: In August 2024, Circana announced its acquisition of Nielsen's MMM business. Gartner expects that integration work associated with bringing together two large organizations, platforms, people and measurement methodologies will be a significant effort.
Low expected return: Circana's website mentions an expected measurement ROI of 9-to-1 (increase in media effectiveness versus cost of measurement). That expected measurement ROI is low when compared to Leaders in this study. Prospective clients should work with Circana to understand the costs and expected returns associated with critical measurement components.

Ekimetrics - Visionary

Pros:

Platform modularity: One.Vision offers a module-based approach that offers its clients financial flexibility by letting them pay for only the module-specific functionality they need. With 12 modules offering specific functionalities (such as a customer analytics module that supports understanding the impact of cross-brand upsells on overall loyalty), potential clients can realize incremental savings by selecting only the modules that support their intended use cases.
Data science engagement: Ekimetrics offers strong support and integration for its clients' data scientists. Client data science teams receive complete details on the underlying models created, including a list of features, type of model, data transformations applied and all statistical outputs.
Diverse engagement models: Ekimetrics uses different engagement models for different relationships (i.e., in-house, hybrid, partner-led). It also supports a variety of use cases, from big-budget advertising to enterprise mix modeling and house of brands. Clients have the flexibility to manage the total cost of ownership of their MMM program.

Cons:

Narrow industry expertise: Ekimetrics' customer base skews heavily toward consumer products, banking and retail. Prospective clients in other industries (e.g., healthcare or manufacturing) may find it difficult to apply relevant benchmarks during model construction and validation. Potential clients should investigate insights and benchmarks within their specific industry to determine the sample size and model capabilities.
Low utilization outside of marketing: Ekimetrics scored near the bottom of vendors analyzed for adoption outside of marketing and the lowest percentage of total users within finance. Potential clients should work with Ekimetrics to develop a roadmap to ensure finance adopts the methodology, metrics, process and reports.
Limited North American (NA) focus: Ekimetrics has some of the smallest North American MMM customer counts and employee counts in this research. Prospective NA clients could experience cascading delays due to coordination across time zones and should work with Ekimetrics to mitigate that risk by developing a support plan and clear SLAs that meet their specific needs.

Fractal Analytics - Niche Player

Pros:

Tailored solutions: Fractal offers customized MMM solutions built to each client's specific requirements. It addresses clients' unmet needs in multiple ways, including by creating custom unified measurement across MMM and multitouch attribution (MTA). It brings together data scientists, subject matter experts and industry professionals to support MMM implementation and execution, adapting to each client's unique needs.
Large enterprise success: Fractal has a track record with large clients, consistently delivering substantial projects for clients that meet their complex needs. It has the largest percentage of customers with over $1 billion of enterprise revenue in this research.
Business scenario planning: Fractal offers robust business scenario planning with data enrichments in pricing, finance and supply chain, such as competitive pricing and inflation rate. This support helps its clients generate accurate simulations, informed decision making, better resource allocation and optimized marketing strategies.

Cons:

Slow implementation: Fractal's solution takes several months more to implement compared to other vendors in the research due to its purpose-built nature. This time frame is a steep trade-off against the potential for customized model outputs. Prospective clients should conduct an initial assessment to clarify overall financial commitments and set realistic expectations for timelines and value realization.
Roadmap limitations: Fractal's technology development roadmap lagged other vendors in the research. Priorities like the automated modeling workbench are considered common features by other vendors. This position may limit client access to advanced features and further MMM innovation.
Delayed data management capability: Fractal lacks out-of-the-box, self-serve workflows for data integration and model management, limiting clients' ability to independently manage these tasks. Clients can compensate for this weakness through additional headcount and support, but doing so will incur higher costs and reduce agility. Prospective clients should identify a workflow plan and establish strong communication with Fractal's support team for smooth integration and management.

Ipsos MMA - Leader

Pros:

Enterprisewide C-suite perspective: Ipsos MMA can accelerate executive buy-in and cross-functional adoption of MMM. Its strong network of client C-suite relationships gives Ipsos MMA deep understanding of and experience influencing strategic business priorities.
Highly engaged customer experience: Ipsos MMA applies a structured comprehensive client engagement model to define client success requirements. Clients maximize their time spent with Ipsos MMA in standard business touchpoints, such as weekly status, quarterly executive governance, and biannual functionality and reporting meetings, as well as senior executive peer group meetings and sessions.
Support for large advertising budgets: Ipsos MMA helps advertisers understand when creative has become "worn out," benchmark their strategies with similar companies and manage the complex data taxonomies for larger advertising budgets. It accomplishes this by combining its Spark AI creative scoring tool with its Activate platform scenario planning capability, and by using a systematic approach to change management, data taxonomy and media strategies for large advertisers.

Cons:

Limited support for smaller advertising budgets: Ipsos MMA provides large global advertisers with fully managed (not self-service) programs. Prospective clients with working media budgets of less than $25 million should carefully evaluate the relevance and utility of Ipsos' benchmarks from lower-spend markets and countries given its basis in high-budget, global advertising strategies.
Premium pricing: Ipsos MMA's MMM solution contract values are among the highest in this research. Prospective clients should weigh Ipsos MMA's costs and longer time-to-production relative to other Leaders against its faster model refreshes and overall value-add.
Limited in-platform support for complex analytics: Ipsos' Activate platform supports scenario planning, but does not currently include capabilities such as weighting multiple KPIs to address multiple business outcomes. This gap limits real-time access to specific, complex insights and forces clients to conduct or request custom analyses and off-platform work.

Kantar - Niche Player

Pros:

Brand, creative and media data approach: Kantar's comprehensive approach to MMM incorporates the impact of brand equity, creative quality and media. Kantar's proprietary creative and brand datasets work in conjunction with its MMM to provide insights that are particularly valuable for improving investments in brand equity and high-quality media.
Organizational viability and execution: Kantar is one of the largest vendors analyzed in this study. The scale of its organization, along with the fact that its customers sign multiyear contracts at a high rate, affords prospective clients confidence in the long-term health of Kantar's MMM solution.
Model update speed: Kantar's model update and refresh speed were among the fastest of all vendors researched, driven by capabilities that assist ongoing data prep and ingestion into their models. Kantar clients can respond swiftly to market changes, optimize their campaigns in real-time and make data-driven decisions with the latest insights.

Cons:

Product roadmap and pace of evolution: Kantar's cloud-hosted MMM platform has a short-term roadmap focused on functions and features that many competitors have already deployed. These features include data-upload optimizations, model development streamlining and multicriteria optimization, among others. This short-term focus on feature development may lead to downstream impacts on longer-term roadmap items, like the potential introduction of additional self-service options.
Time to production: Kantar's approach to model development and validation resulted in the longest time to production of all vendors evaluated for this research, particularly the duration of data onboarding and model development/validation stages. Prospective clients should estimate the incremental costs for onboarding marketing data themselves and incorporate it into their total cost of ownership calculations.
Client data science limitations: Kantar clients do not have the ability to change model design or design incremental tests directly within the tool. Prospective clients with strong data science teams should work with Kantar to get their own data scientists involved early to increase visibility into model construction and performance.

Keen Decision Systems - Niche Player

Pros:

Innovation: Keen consistently innovates, with initiatives like its patent-pending Marketing Elasticity Engine (MEE), which provides Bayesian prior coefficients for use in model estimation. Its roadmap for innovation includes incorporating customer journey modeling to MMM as an extension of its multibrand portfolio modeling.
SaaS capabilities: Keen offers an end-to-end SaaS experience that gives clients access to marketing mix models. This approach may be most suitable for prospective buyers who have well-organized data, who do not have very complex customization needs and are prepared to leverage software to support their decisions.
Smooth onboarding: Keen's patent-pending QuickStart capability offers a smooth onramp for users. This path allows users to start receiving value from the platform, even though they may have limited data, and see some of the benefits of Keen's solution without having to deploy a full model.

Cons:

Primarily SaaS delivery model: Keen's solution focuses on being a SaaS product, with features and workflows geared toward self-service. Keen names one account manager and one analyst for clients at its highest pricing tier. Only in this highest tier, Keen structures onboarding and support around consistent, predetermined touchpoints. As such, prospective clients looking for a high-touch or customized full-service vendor should review whether the highest pricing tier will suit their needs.
CPG focus: A high proportion of Keen's clients are in the consumer products industry. Whereas Keen's MEE has had relatively low application in other industries, prospective clients from other industries should assess whether Keen's MEE can successfully recommend marketing activity optimizations for their business.
Midmarket focus: Focused primarily on North America, Keen tends to target advertisers with revenue between $10 million and $500 million. At a larger scale, media plans prioritize different channels and organizational complexity increases, which affects the service demands on an MMM provider. Prospective clients with large media budgets should discuss with Keen their expectations and complexity of needs, as well as assess whether the software addresses their specific requirements.

Nielsen - Niche Player

Pros:

Portfolio of solutions: Nielsen's TV, streaming TV, audio and digital ad audience measurement solutions are available as complementary audience measurement tools alongside its MMM solution. Mass-market brands with large investments in these channels benefit from improved measurement and recommendations based on Nielsen's extensive model inputs from these data sources.
Sport sponsorship and fan insights: Nielsen offers a full suite of sponsorship and fan insights solutions. Brands with investments in these highly indirect, often unmeasured channels will benefit from the granular data and company expertise.
Global coverage: Nielsen provides coverage for all of North America, while also covering Western and Central Europe, APAC and most of South America, among other geographies. Brands with significant global presence can simplify the onboarding of global teams and streamline the number of vendors by working with Nielsen.

Cons:

Market viability: Nielsen has completed multiple rounds of layoffs and job relocations, from in-market locations to global capability centers, in India, Poland and Mexico, according to news reports. Prospective clients should consider the possibility of future disruptions and Nielsen's capacity to deliver on future roadmap items.
Minimal information publicly available: Nielsen offers a single webpage, a promotional video dated January 2019 and a single webinar from 2024. This scarcity limits prospective clients' ability to research capabilities in advance, making it especially difficult to build internal cross-functional consensus.
Lack of cross-functional support: Nielsen focuses on marketing and supporting marketers. Given the planned sale of the business to Circana and the effort and complexity of integrating two separate business units, software and commercial relationships, it's highly unlikely that Nielsen will expand its cross-functional support in the near future.

OptiMine - Leader

Pros:

Granular tactic recommendations: Although many MMM vendors focus on channel-level optimizations, OptiMine's data precision offers insights at a more granular level. This strategy allows clients to optimize more components of their campaigns, including tactics, content and audiences, more frequently to achieve greater impact.
Production and deployment speed: OptiMine's automated data management and modeling technology enable shorter production and deployment timelines compared to most vendors in this research. This approach allows customers, on average, to achieve production in two months, with most deployments fully implemented within three months.
Partner network: OptiMine leverages a vast partner network of agencies and consulting companies to deploy its MMM solutions via white-label and hybrid approaches. OptiMine offers support with a dedicated partner channel support team, sales group and extensive partner training programs.

Cons:

High conversion volume requirement: OptiMine's solution needs at least 500 daily conversions for accurate modeling and analytics, which could be challenging for some businesses. Prospective clients should assess their ability to meet this requirement and determine if more limited recommendations, from aggregating conversion data weekly, are still valuable.
Limited in-platform support for complex analytics: OptiMine's platform has limitations, including a lack of direct UI support for adstock and halo effects analysis. These analyses are provided via custom reports. Prospective clients should plan to integrate these insights into workflows to avoid delays and ensure comprehensive evaluation.
Sales team reduction: OptiMine recently shifted its go-to-market strategy, focusing on a more senior sales executive and partner-based sales approach, which led to a significant reduction of its internal sales team. Clients may experience heightened risk of an unsatisfactory sales process. Prospective clients should ensure OptiMine's updated value proposition and roadmap meet their needs.

TransUnion - Leader

Pros:

Identity capability: TransUnion leverages its core consumer data and identity capabilities to offer person-level incrementality experiments and attribution as part of its solution. Clients with first-party data assets can benefit from these lift studies.
Market responsiveness: TranUnion's strong client retention rates result in part from a diverse portfolio of customer engagements, including quarterly business reviews to customer advisory boards, hosting an annual conference, and regular lunch-and-learn sessions and webinars. These touchpoints allow TransUnion clients to stay abreast of developments and provide feedback for solution enhancements.
Comprehensive model validation: TransUnion offers comprehensive criteria for assessing model deployment and validation. With a relatively fast time to deployment at two months, its completed validation for model deployment is 12 months, which demonstrates a commitment to proving model validity through the annual planning cycle.

Cons:

Customer-data-centric: TransUnion primarily serves organizations with robust and granular first-party consumer data, which means that organizations lacking these first-party customer data assets may not see as much benefit from the individual-level experimentation and MTA.
No support for proofs of concept (POC): TransUnion does not offer any POC process, which may make it more challenging for clients that need a more cost-effective method to validate a provider's ability to cater to certain requirements before purchasing. Prospective TransUnion clients have access to benchmarks and expected ROI ranges, so prospects should plan to scrutinize this information to determine timelines and requirements.
Multiscenario comparison: TransUnion's MMM cannot compare multiple media planning scenarios within the platform or export model-generated media plans directly to media platforms. Prospective clients should validate whether alternative approaches, such as comparing via screenshots or transferring via Microsoft Excel files, will still be sufficient for self-service media plan scenario comparisons.

FAQs

Q: Which vendors are Leaders in 2024?

A: Analytic Partners, Ipsos MMA, OptiMine, TransUnion

Q: What does "Leader" mean in MQ terms?

A: Leaders possess capabilities that enable them to provide deep support across nearly all aspects of MMM, particularly cross-functional adoption support, business scenario planning, marketing adoption support and media optimization. They demonstrate the ability to address the needs of large enterprise clients, in part, due to their capabilities to support MMM adoption throughout the enterprise. Leaders' roadmaps balance continued investments in software capabilities for mix models with generative AI methods supporting self-guided user engagement with knowledge bases and reporting tools.

Q: How do I interpret a Gartner Magic Quadrant?

A: https://www.gartner.com/en/research/methodologies/magic-quadrants-research

Report:

Top Vendor Products for Marketing Mix Modeling Solutions in 2024

Marketing Mix Modeling Solutions Vendor Product Comparisons 2024

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FAQs

Q: Which vendors are Leaders in 2024?

Q: What does "Leader" mean in MQ terms?

Q: How do I interpret a Gartner Magic Quadrant?

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