The Best MMM Software for Agencies
Consumer privacy changes have greatly limited the use of personally identifiable information in marketing measurement, causing issues for traditional approaches such as multi-touch attribution and other forms of consumer tracking to measure campaign performance. For agencies that are under pressure from brands to prove the value of the marketing efforts they drive for the brand, a once-traditional measurement approach called marketing mix modeling (or “MMM” for short) is now back in vogue. With advances in AI and ML, cloud computing and modern software and data science, agencies now have more modern MMM options available. But a new challenge has arisen: how to select the best MMM solution for the brand, especially when agency analytics resources are limited?
How Marketing Mix Modeling Software Can Help Agencies
The good news for agencies is that MMM overcomes many of the disadvantages of multi-touch attribution. MMM can measure offline conversions, as well as traditional media including TV, Radio, Print, OOH and other channels. And because MMM doesn’t rely on PII, it can be used safely in today’s era of consumer privacy and enhanced state-by-state privacy regulations.
MMM can help an agency in many ways:
- One of the most overlooked values of MMM for an agency is that a good MMM model strengthens the trust with a brand and unlocks strategic advisory services for the agency. Since the role of an MMM model is to help guide to better decisions, the MMM model puts the agency squarely in the middle of decisioning with the brand.
- Likewise, the preparation, planning and data onboarding to support an MMM model helps the agency drive additional services for the brand and build more services revenue.
- Of course, a good MMM model will measure the incremental value being driven by advertising.
- MMM helps the agency plan media and determine the most optimal allocation of an advertising budget across all available options.
- MMM doesn’t “over-attribute” the value of marketing like last-touch or MTA approaches and doesn’t “under-attribute” other forms of media that do not have clicks.
Traditional vs. Modern MMM
For the uninitiated, traditional MMM has severe pitfalls:
- Manual modeling: One of the main problems is that nearly all MMM approaches are manual, meaning a highly skilled and trained data scientist must craft the models by hand.
- Slow, Delayed Measures: Because of the manual approach, traditional MMM is extremely slow and typically provides measures many weeks or months after the period being measured, which is of no help to the brand.
- Expensive: Manual approaches cost a lot more, and MMM models are expensive even with offshore resources.
- Lacking Detail: Manual MMM models also lack detail, depth and granularity. As a result, the guidance doesn’t help brands much, and misses ROI and performance lift in the details of each marketing channel.
The Challenges of MMM Software for Agencies
There are many challenges to consider when an agency is looking for an MMM solution:
1. Traditional Vendor Solutions Don’t Work for Agencies
Many solution vendors exist in the market providing MMM. But since most also use manual approaches and rely on consulting revenues to make their business models work, they effectively compete with the agency directly. For any consulting services that the agency provides to the brand, those services are taken away from the MMM vendor, and as a result, the MMM vendors don’t want to reduce their own consulting revenues. It’s a major conflict that prevents agencies from using many of the leading MMM vendors in the space, and preventing leading vendors from building agency partner-friendly offerings.
2. New Open-Source Solutions Miss the Mark
Open source MMM solutions appear attractive initially until the many tradeoffs and considerations are taken into account. They are “free” to use. But they still require highly skilled modelers with deep experience. The risk of damaging a brands’ business with a poorly built model is very high.
3. Open-Source Solutions Are Missing Important Tools & Capabilities
None of the open source MMM solutions have ready-made planning tools, dashboards, reports and 24/7 user interfaces for the agency’s clients to use directly. The result is that the agency must still manually craft models and build PowerPoints, all while getting lapped in the market by faster, more agile solutions.
Also, Open-Source MMM solutions lack granular details that brands need to compete effectively in their markets. The same issues that plague traditional MMM vendor solutions also reside in open-source MMM solutions too.
The Best MMM Option for Agencies: OptiMine
OptiMine’s approach to MMM is modern and agile – high-scale modeling, cloud-based automation and scale, machine learning to drive improved measures all delivering major benefits to agencies and their brands:
OptiMine is the fastest MMM solution to deploy in the market, generating ROI faster and lowering solution risks.
Detailed, Granular Measures
OptiMine delivers the most actionable, granular and detailed guidance delivering more ROI with higher confidence and transparency.
Fast, Flexible Solutions
Stay ahead of your clients’ needs with more speed and flexibility delivered by OptiMine.
Ready-Made Tools for the Agency and their Brands
PowerPoints are great, except when your client wants more. OptiMine delivers a wide set of reports, dashboards, secure data feeds and real-time planning tools – for your agency use, your clients’ use, or both.
No Business-Model Conflicts
Only MMM solutions from OptiMine do not compete with your agency’s services model. Our goal is to support your successful client relationship, not compete with your services.
Also, OptiMine is unique with our own Agency Partner Program. OptiMine delivers major advantages to agencies through preferred support, training, lower partner fees and total transparency. Agencies can get world-class MMM and build consulting services offerings around high-scale MMM solutions, thereby building deeper client relationships, higher brand retention rates and higher revenues.