AI-Assisted Marketing Mix Modeling 101
Which marketing efforts are the biggest contributors to your sales, market share, and profitability? How can you best use this information to optimize your marketing efforts? Digital marketers have been working to successfully answer this question for a long time now.?
It’s a bigger challenge than ever, thanks to the widespread use of “omnichannel marketing,” especially when you’re selling a B2B long-sales-cycle product or service. There can be quite a few actions customers can take before they finally decide it’s time to talk to a salesperson.
Marketing Mix Modeling (MMM) is the science of using regression analysis, time series analysis, and machine learning algorithms to answer those important questions.?
MMM is rising in importance and is also benefiting from the analytical power of new AI tools. However, it’s important to say here that it is not a perfect tool. As with all analysis, there’s a “garbage in, garbage out” factor. There will be times when your data analyst needs to make a “best guess” entry, especially when you’re trying to assign a numerical value to an aspect of your business that is difficult to quantify.?
Our goal with this article is to make MMM understandable so you can put it to work for your company.?
AI-Assisted Marketing Mix Modeling 101
The term “marketing mix” is usually seen now as the collection of channels and methods (SEO, social, content, email marketing, online advertising, etc.) that you’re using to attract and educate your customers.?
Back in 1949, when the term was introduced by Niel Borden,1 a professor at Harvard Business School, he defined the marketing mix as the set of marketing elements that a firm could use to achieve its objectives, including “product, price, place, and promotion.”?
The goals of Marketing Mix Modeling
You want your marketing to work. You want to see how your marketing efforts are impacting your sales. You want to be able to use reliable data to make solid marketing decisions. MMM can help.
Pros of MMM
When it is done properly, MMM will improve your:
It’s important to note that even the most sophisticated data analysis is not a substitute for interviewing customers who have already purchased your products. Understanding their very specific Mindset when they set out to buy (consisting of their desires, concerns, and questions) will make sure that your messages appeal precisely to your target audience and support the steps in their buying process.?
For that reason, we tend to think of MMM more of a help choosing channels (where your message will appear) rather than the messages themselves.?
Challenges/Cons of MMM
How Marketing Mix Modeling works
OK, time to get into the nitty-gritty.?
What needs to go into the model is much of what we’ve talked about. Marketing activities and their respective costs, pricing info, distribution channel info, and external factors such as economic indicators and competitive marketing activities.?
What you choose for input will be unique to your company, industry, the types of products or services that you sell, and the marketing channels and methods that you are using. Your market might be seasonable. Internal or external issues might come into play.?
Each marketing channel has its own metrics.?
With online advertising, for example, it’s impressions, clicks, engagement, site visitor sessions, the various ways that customers have interacted with your content, and actual sales in a business-to-consumer situation.?
Even in a long-sales-cycle business-to-business buying process, if you have a strong CRM and reliable input from salespeople, you can track a customer’s activity all the way through to a sale to see your ROAS (Return on Ad Spend).
Each type of product or service has its own sales cycle. The speed with which a person decides to buy from you depends on the amount of scrutiny that the person applies to the purchase.?
The entire exercise of identifying the appropriate KPIs will be much easier if you have already mapped out the typical buying process for your type of products and services.?
You will need someone who enjoys this kind of analysis to own the process of building your list of KPIs. Your list will be unique to your company, offerings, and situation.?
Two categories of sales may come into play in your model:?
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Laying the groundwork carefully here will ensure the best outcome.?
What should come out of the model are metrics related to sales, market share, customer acquisition and retention, and profitability. Some products or services may be more profitable than others; being able to tie campaigns to profitability is about as good as analysis gets.?
Inputs include how much you’re spending on various marketing channels, your current pricing and possible changes in pricing, distribution channel allocations, and external factors such as economic conditions, seasonality, weather patterns, and industry trends.?
Outputs include predicted sales over a given period, market share compared to the competition, and customer acquisition and retention.?
The tools MMM practitioners use
Here are just a few of the tools that MMM practitioners use. This is definitely not a complete list, but it will give you an idea of the resources and processes used for MMM.?
There are a number of companies coming on the scene that use AI to take these tools and build them into a campaign mix platform. One we have been pleased with so far is TripleWhale.com .?
Regression analysis
One of the tools used is multi-linear regression (MLR), which models the linear relationship between a “dependent” variable and “independent” variables.?
Dependent variables are the ones that you’re turning into your KPIs, such as revenue, market share, and customer lifetime value, the amounts you’re spending on a marketing channel, the price of your products, etc.?
Independent variables are those that somehow have an influence on the dependent variables, such as the amount you’re spending on advertising, the price of your product or service, competitive activity, economic conditions, and more.?
Time-Series Forecasting
This tool is used to forecast future values derived from historical data points collected over a time period and collected at regular intervals. Basically you will be predicting future values using previously collected data.?
You will be looking for trends, while filtering out the information that is less relevant (the “noise”). Various machine learning models can be used for this.
Applications include forecasting sales, allocating budgets, planning campaigns, predicting ROI, and playing “what if” with different marketing mix combinations to understand possible outcomes.?
As with all MMM efforts, the biggest challenge is compensating for missing data and factoring in external market factors.?
Logistic Regression
This technique can be used:
The larger the sample size, the more you can depend on the results.?
Basic steps to implementing MMM4
Conclusion
Obviously, these methods are rigorous and require deep thinking and proper use of the tools. If there is too much “guessing” on the input side, the output will lead management to make decisions that are too far from reality to be successful.?
Marketing can be data-driven, but you need to hire someone—in-house or as a contractor—to put these tools and methods to work for your company. Or, rely on the companies that have combined these tools on platforms that can be used by marketers without a degree in statistics.?