Marketing Mix Modeling: Cut Costs, Boost Sales, And Improve ROI

Marketing Mix Modeling… Hmm, what can I say – it sounds like a very technical term. And it is! 

Unfortunately, many things around us wouldn’t exist without technology, including your info product. 

Of course, everything I discuss here is meant to advance and benefit your info product business, so let’s dive in without further ado. 

Marketing mix modeling is sometimes referred to as media mix modeling or MMM. It is a data-driven technique that helps businesses measure and optimize the impact of their marketing tactics on sales and other key performance indicators (KPIs). 

In other words, MMM helps businesses recognize how marketing inputs like advertising, pricing, promotions, and distributions affect metrics like sales and profitability. It helps businesses allocate budgets effectively and efficiently by identifying which channels bring the best return on investment (ROI). 

Media mix modeling is oftentimes confused with multi-touch attribution (MTA). While both techniques help businesses measure the effectiveness of their marketing efforts, they differ in data requirements, use cases, and approach. 

Today, our focus is MMM; however, I will expand on the differences between MMM and MTA lower.

Marketing mix modeling relies heavily on data-driven marketing. It uses historical data and real performance insights to optimize spending and scale your business. MMM wouldn’t be possible without structured and accurate data.  

MMM VS MTA: Key Differences

As promised, here are the key differences between marketing mix modeling (MMM) and multi-touch attribution (MTA). 

• Data Type: MMM relies on historical spend and results. MTA tracks and collects data from individual user journeys.
  
  
• Timeframe: MMM needs long-term data (months and years) to produce accurate insights. MTA, on the other hand, analyzes short-term data (real-time and weeks).
  
  
• Marketing Channels: MMM captures data from both online and offline channels, whereas MTA is digital-only.
  
  
• Cross-Channel Analysis: MMM combines data from multiple channels. MTA looks at each channel independently.
  
  
• Best For: MMM is best for tracking channel effectiveness and budget allocation. MTA is best for tracking user journeys and digital ad-spend return on investment (ROI). 
  
  

How Marketing Mix Modeling Uses Data-Driven Marketing

MMM collects marketing and sales data, applies statistical and machine learning models, makes data-based budget suggestions and decisions, and incrementally measures each channel’s impact. Let’s explore the details! 

• Collects RAW Data: MMM collects data from all of your marketing efforts and their results: how much you spend on ads, where you run those ads, how many people watch your ads and how many click on them, how many sign-ups you got, and how many converted into sales, how many people visit your website, open your emails, etc.
  
  
• Applies Statistical And Machine Learning Models: MMM uses regression models or machine learning to help you identify patterns and relationships between your marketing efforts and determine the return on investment (ROI) of each marketing channel.
  
  
• Improves Budget Decisions: MMM identifies what channels perform best, which in turn can help you reduce your ad spend on channels that don’t perform and redirect it towards those that do.
  
  It can also simulate different scenarios, such as “What if you reduce your Instagram ad spend by 30%?” or “What if you double your ad spend on YouTube?”
  
  
• Accounts For Multiple Sources: Data-driven marketing often examines multiple things simultaneously. MMM pulls the data collected together and shows how everything works together.
  
  
• Improves Continuously: MMM updates its insights as you run more campaigns, release more products, and collect more data.
  
  

What Data Is Needed For MMM To Work?

Marketing mix modeling relies on specific data types to understand how and which marketing efforts of yours drive results. It needs factual information about what’s happening around you, what you are doing, and how everything affects your info product business. 

• Marketing Data: This data revolves around your activities, including ad spend, channels, timing, and campaign details.
  
  
• Outcome Data: This is the data that shows your results after running ads and posting content. It shows all the conversions and sales, customer actions like website visits, clicks, or downloads, and the timing of those sales and conversions.
  
  
• External Data: Things that are happening around you, like what season it is, what trends are relevant, and, if possible, any data on competitor activity, like their campaigns and promotions.
  
  
• Baseline Data: Sales that happen without any marketing efforts. Things like organic sales (ones you get without marketing, like from word of mouth) and historical sales data.
  
  

Benefits Of Media Mix Modeling

Why go through the trouble of learning what marketing mix modeling is and how to implement it? Info product businesses rely heavily on digital and social media marketing. 

MMM can help you identify the most profitable channels and marketing tactics for your business. Let’s explore all the benefits! 

• Identifies What Drives Sales: Do you know exactly which channel brought you leads and sales, or are you just happy that you got some new business?
  
  Well, marketing mix modeling can help you identify whether it was the Facebook ad you ran last week or the micro-influencer collaboration that brought the most results for your business.
  
  
• Saves Money On Ads: Following the previous point, once you identify what brings the most effective results, you can stop running ads through the channels that are not performing.
  
  For even more results, you can increase your ad spend on the performing channels.
  
  
• Improves Funnel Efficiency: Most info product businesses have a sales funnel that looks something like this: Ad > Landing page > Free / low-ticket lead magnet (Webinar, PDF, e-book) > upsells and main product/service.
  
  Marketing mix modeling helps you identify which marketing approach or tactic better moves your audience through your sales or conversion funnel. This allows you to either tweak what isn’t working or eliminate it altogether.
  
  
• Measures Long-Term Value: MMM can help identify whether free or low ticket lead magnets actually lead to future sales and customer retention. This helps your business prepare for what can and should come after the first sale.
  
  
• Adjusts For Seasons And Trends: MMM uses data to identify what is working now and can help predict what may or may not work in the future.
  
  As technology evolves and audience preferences shift continually, it is vital to stay on top of the latest trends and be present on the most relevant platforms.
  
  
• Enables Forecasting: Sometimes, launching something new (a course, an addition to software, or starting a community) can feel like a gamble. MMM can analyze past results and help you predict which marketing efforts will and will not perform. 
  
  

Can MMM Work For Small Businesses?

The short answer is – yes. 

However, it depends on your budget and how much historical data you have. 

MMM will work for your small business if you track marketing performance and want to optimize your ad spend. However, for best results, you need at least several months of historical data and a substantial ad spend amount. 

How much? According to multiple sources, such as Google Search, Chat GPT, and Grok, the minimum ad spend for small businesses should be at least $10,000/year. The more you spend, the more data media mix modeling has to work with. 

Here is an example of how MMM can help your small business:

Let’s say you sell a $50 course. You spend $100 on Facebook Ads, $40 on email marketing, and religiously posted free informative content. After a month, you got 15 sales. 

MMM can help you identify how many buyers Facebook brought compared to emails. It can also help you identify what type of content drove traffic that converted later. 

How To Build A Marketing Mix Model?

Building a marketing mix model might seem too technical and complex, but I’ll try to explain it in the most beginner-friendly way possible. 

Essentially, it is all about collecting as much data as possible, finding patterns, and using that to make smarter and more effective marketing decisions. 

Here are the steps you can follow to create your own marketing mix model:

1. Define Your Goals: Determine what you want to measure, whether it’s which channels (emails, ads, blogs) drive your course sales or how much revenue comes from each dollar you spend. The simpler and clearer – the better.
   
   
2. Gather Your Data: Gather you marketing inputs (dates, ad spend, and your activity volume), your conversions and sales (the total of your conversions and sales, and their frequency), and any external factors that may have affected your results ( holidays, trends, promotions, organic sales, etc).
   
   You can do so manually with a spreadsheet or use tools like Google Analytics or ad platforms to assist with this process.
   
   
3. Organize Your Data: You can organize your data in a table so it is easier to view. The longer your timeline and the more data you have, the easier it will be to identify trends and patterns. 
   
   
   
   
4. Identify Patterns: Compare weeks and months. Did your sales increase when your ad spend increased? Did specific seasons get better results? Did sales drop when you posted less content?
   
   You can also calculate your return on investment (ROI) manually. For example, if you spent $200 on ads and received $600 in sales, your ROI would be 3x. Do this for each channel that you believe is driving your sales.
   
   
5. Build Your Model: You can do it yourself with a spreadsheet or use a beginner-friendly tool like Cassandra (more free tool suggestions below).
   
   Spreadsheet: honestly, Grok explained it best, and I will absolutely take advantage of that!
   
   Use a tool like Google Sheets or Excel. Try a linear regression (a statistical method that uses a straight line to identify the relationship between two variables).
   
   Your dependent variable is your sales – list them as the “result”.
   
   Your independent variables are your ad spend, emails, blogs, etc. – list them as the input.
   
   Run your regression.
   
   Your output is the numbers showing how much each input affects sales.
   
   Personally, however, I wouldn’t do this manually. Instead, here is a tutorial on how to build your marketing mix model with Cassandra. 
   
   

Is MMM AI-Powered?

Yes, the modern marketing mix modeling is AI-powered. 

Artificial intelligence enhances and supercharges everything else, and it can absolutely do the same with MMM. AI can make it faster, smarter, and more adaptable, especially for businesses that have tons of complex data. 

AI-powered MMM user algorithms to learn from and combine current and historical data, instead of basic statistics like regression. 

AI provides flexibility and more clarity. It can handle incomplete, messy, and real-time data, which is especially helpful if your business is still small and new and doesn’t have years of data yet. 

AI also automates tasks like processing and analyzing numerical data, adjusting for current seasons and trends, and predicting outcomes. 

Here is how AI enhances traditional MMM:

• Enhanced Pattern Detection: AI can provide more detailed insights when analyzing patterns.
  
  Traditional MMM might point out that you get more sales when you run more ads, but AI will show you exactly what happens (e.g., Facebook ads on weekends after a blog post boost sales 30% more.)
  
  See the difference?
  
  
• FIlls Data Gaps: AI can estimate certain data based on trends, holidays, or historical data, so you don’t need a perfect database for everything.
  
  
• Provides Real-Time Updates: If you adjust anything in your campaigns, AI can adjust the model in real time, so you don’t have to wait for an extended period of time to recalculate.
  
  
• Improved Predictions: AI can use past data to simulate situations to help you make better future budgeting decisions.  
  
  

AI-Powered Marketing Mix Modeling Tools

If you want to test MMM on a budget, here are some tools that you might find useful. 

• Cassandra: A beginner and user-friendly MMM tool that does not require any code to build marketing mix models. Once your model is built, it can suggest a budget plan to increase your sales.
  
  It works best with a substantial amount of data that can be uploaded via a CSV file, for example.
  
  
• Google Meridian: An open-source MMM tool from Google that helps you calculate marketing impact across channels. It does not rely on cookies, helps you see how all your marketing channels affect your conversions and sales, and is very customizable.
  
  This tool requires a technical setup – Python (or a similar programming language) knowledge is needed.
  
  
• R or Python With Open Source Libraries: You can use these free programming languages with MMM libraries like PyMC or Lightweight MMM. It is free and entirely customizable.
  
  If you decide to go this route, there are many online tutorials to assist you. 
  
  

Closing Remarks 

Marketing mix modeling is an effective way to figure out how well your marketing works. MMM looks at all of your marketing efforts, analyzes them, and points out what you can do better to increase your conversions and sales. 

MMM uses data like how much you spent, which channel you used for advertising, and how much your return on investment (ROI) was to determine what is worth your time, energy, and, of course, money. 

Look at marketing mix modeling as a map to achieve the best results for your info product business. It helps you identify the things (channels, ads, content, etc) that get your audience to buy your products and services. 

On that note, subscribe to Vavoza Insider to receive effective and actionable marketing tips in your inbox weekly.

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– Steph