As the world of digital marketing has exploded, the rise of big data and incredibly technical and complex data sets has been both a blessing and a curse to brands big and small.
While it’s true that detailed data can help businesses understand their consumers and grow their businesses, it’s often the case that the data is overwhelming.
With technology platforms and analytics tools being able to collect enormous amounts of data, brands are often left struggling to get through it all and understand what it is that they’ve gathered.
In order to address the issue of how to manage incoming data and then use that information to make impactful decisions, a clear analytics strategy is necessary for all brands.
Picking the right strategy for your business is the key to making sure you are getting the most out of your planning and marketing activity.
Marketing mix modeling is one example of a marketing analytics strategy that can really help your brand manage data and learn the best places to invest your budget and time on.
Keep reading this post to learn more.
What is Marketing Mix Modeling?
Marketing mix modeling is a statistical marketing method that attempts to determine the effectiveness of marketing campaigns and initiatives by taking apart data and attributing contributions to different marketing tactics and factors to better predict future success.
Put another way, marketing mix modeling looks at different pre-determined factors and the data that has been gathered from marketing campaigns to see which factors have had the biggest impact on return and which factors have contributed the most to success.
Once this data has been collected and organized, the marketing mix modeling system will use the past and historical data to predict or forecast future marketing and sales success.
By looking at the trends that have worked before, the marketing mix modeling will theoretically be able to forecast with more accuracy than other analytical methods.
The 5 P’s of Marketing Mix Modeling
As stated above, marketing mix modeling distributes success from data to different pre-determined factors.
Those factors are often referred to as the 5 P’s of marketing, which are derived from other marketing research and studies. Let’s look at those 5 P’s now.
Product
Product refers to the actual products or services that are created and offered to customers by a brand.
Price
The price takes into consideration any deals, sales, pricing models, and methods of payment involved in a sale.
Place
Place refers to the channels through which products are available to consumers and how consumers are able to find the offers that the brand has.
Promotion
Promotion is the method by which products or services are marketed and shared among audiences.
People
People is the final P, and is sometimes left off of marketing mix modeling. People refers to both the internal staff and the customers that drive sales in a brand.
Marketing Mix Modeling vs. Attribution Modeling
Marketing mix modeling is often compared to another popular model of marketing analytics, attribution modeling.
Attribution modeling is the process of setting up different touchpoints that trigger events on the customer’s journey.
Each touchpoint is assigned a value to help determine which points in the customer’s journey are responsible for bringing in revenue.
While attribution modeling can be helpful to understand data and provide context for ROI, it also has a few major drawbacks.
The biggest problem is that not every touchpoint in a customer’s journey can possibly be tracked and analyzed through collected data.
Another drawback of attribution modeling is that it functions mainly through clicks and clicks alone — other potential data points are put aside in favor of clicks that can “prove” a conversion has taken place at a touchpoint.
Attribution modeling also doesn’t prove the effectiveness of a campaign. After all, a customer will have to pass through the same touchpoints whether they were convinced through an advertisement to make a conversion or not.
That makes it difficult to assign return to specific touchpoints.
Benefits of the Marketing Mix Modeling
Let’s take a deeper dive into the benefits that it can provide to your brand’s analytics and reporting models.
Prove the ROI of Marketing Initiatives
Marketing mix modeling allows marketers to really prove the ROI of their initiatives. By relating data insights back to the factors in each campaign that provided success, it can help brands understand the full impact of their efforts.
Gather Insights
Marketing mix modeling is also great for understanding key insights from business initiatives. Those insights can be used to drive effective budget allocations within marketing and sales departments and convince stakeholders of the benefits of the model.
Create Better Sales Forecasting
Sales forecasting refers to the practice of estimating how much revenue can be generated in the future based on the impact that your sales and marketing efforts have had in the past.
By allocating success to key factors, marketing mix modeling allows brands to have more accurate forecasting.
Understand Historical Data and Trends
Marketing mix modeling is based on understanding the past data that has been collected during initiatives and campaigns.
Many other analytics models will ignore this valuable data or only look at parts of it. The marketing mix system ensures historical data and trends are examined closely for value.
Account for Negative Impacts
Just as marketing mix modeling allows brands to see the positive impacts that their efforts have created, it can also be used to see negative impacts on different marketing factors.
That helps brands know which areas of the business need work and where serious corrections need to take place.
What are the Limitations of Marketing Mix Modeling?
Like all marketing analytics methods, there are drawbacks and limitations to this system.
The amount of data collected means that there isn’t one method of analytics that can address every data set. Here are some of the major drawbacks of marketing mix modeling:
- Infrequent reporting, meaning no real-time data analytics.
- Does not analyze the customer’s experience or journey.
- Doesn’t provide the 1:1 analysis of attribution modeling.
- Doesn’t examine relationships between channels.
- Doesn’t look into brand awareness, messaging, or reach.
- Requires a large marketing analysis budget.
- Harder to implement in B2B businesses than B2C brands.
How to Build the Marketing Mix Modeling
While there are some setbacks, marketing mix modeling can provide major benefits to your brand. Let’s take a look at how you can go about building this system in your own organization.
1. Establish Your Goals
The end goal of any marketing analytics strategy is to parse through and gather insights from your data sets.
That means that marketing mix modeling is meant to help organize your data and your analytics methods.
Therefore, it makes sense that the first step is to establish the specific goals you want to attain through your strategy.
Your goals might center around budgets, marketing campaigns, product pricing, or your brand in comparison to competitors.
2. Create Internal Alignment
In order to succeed, you need to have clear alignment across your organization.
As with most data analytics, marketing mix modeling requires you to pull data from many different systems from different departments.
That requires compliance across different teams and with the key stakeholders in your organization, such as:
- CMO
- Media agencies
- Marketing agencies
- Marketing executives and managers
- CRM managers
- Sales leads
3. Identify What Data is Relevant to Your Goals
As mentioned earlier, there is an incredible amount of data that is collected in different systems across your organization daily.
In order to truly get the most out of your marketing mix modeling, you need to understand which data sets are going to be most relevant to your goals.
You might want to consider going through and cleaning your data regularly if you are not already.
This helps to eliminate irregular data, repetitive data, or data that has errors and missing pieces of information.
You can also try a new way of organizing your data if it isn’t consistent or stored in easy-to-find areas.
4. Understand Both Data Access and Limitations
The final step in getting a marketing mix modeling system set up is to understand exactly what data you have access to.
If you need other permissions or are expecting time delays on gathering specific types of data from some of your platforms, you want to be well aware of that.
Wrap Up
Marketing mix modeling is just one type of marketing analytics strategy that you can implement within your organization, but it’s an important one to consider.
It can help your brand understand data sets and set up a strategy to capitalize on marketing trends that will get you the most return for your investments.
Marketing trends are key to understanding the ways in which the market is moving and how your brand needs to adapt to keep current.
If you are interested in learning more about them, check out our blog on digital marketing trends!
It is a great read if you want to stay ahead of the game and manage the ever-changing aspects of modern digital marketing.
