Marketing budgets have never been under the microscope as much as they are today. According to Gartner's 2024 CMO Spend Survey, the average marketing budget is down to 7.7 percent of overall revenue. Every marketing dollar counts like never before.
The old adage of allocating the budget based on intuition simply doesn't work anymore. Too many decisions are still being made on gut feelings, not hard facts. P&G notoriously cut $100 million in digital spend because of poor results. Netflix took the opposite approach, increasing ad spend by 54 percent. Same market, different data, different results.
The common denominator of the companies that get it right is that they have a real answer to the question of how to optimize marketing budget allocation.
Before you move a single dollar, you need to have a clear understanding of success. Media Mix Optimization Strategy is all about setting clear goals. Are you looking to gain new customers? Are you looking to increase customer loyalty? Are you looking to speed up the customer purchase process?
Different goals require different metrics. While acquisition costs for customers can give you one type of answer, customer lifetime value is the answer you actually care about.
Most brands use a last-click model. You’re losing all the data before that.
Research shows that ads in one channel can affect how well ads in another channel perform. This is called spillover. You reduce the budget in a channel that is performing poorly in terms of last-click conversion. However, that same channel is driving conversion in another channel. You’ve just made a huge mistake.
Cross Channel Budget Allocation Model is about understanding how channels work together.
Volume without value is theater. Nice to watch. Expensive to produce but completely meaningless. For example, let’s say Company A generated 231,000 installs. Nice. However, these users were 35 percent less likely to convert to a paid membership. Company B generated fewer installs but at a higher CPA. However, these users were twice as likely to convert. The performance marketing analytics framework is where you'll catch this. It's tracking what happens after the click.
The traditional model is backward-looking. You run a campaign. You wait for a report. You make adjustments for the next one.
Predictive analytics for marketing budget shifts that model. Machine learning is used to analyze historical data and market trends to make predictions on where to spend your budget. You learn before you spend.
Some platforms run thousands of simulations to understand where each marketing channel is hitting a point of diminishing returns. Not enough spending and you leave money on the table. Too much spending and you waste money.
The fact is, traditional media and digital media don't live in separate universes. They intersect. In fact, a study of 1,083 global campaigns found that using TV and digital media together can drive a much stronger brand performance.
TV drives trust. Digital drives precision. Together, they drive something much more powerful than either could do on its own. And all of this can happen through simple media planning efficiencies that can drive brand metrics up by over 50 percent.
So, marketing spend efficiency strategies consider all of that.
Brands are wasting money on redundant reach. Brands are working with multiple networks to reach a very limited audience, while missing out on a potential game-changing audience.
Some partners are providing 82 percent unique reach, while others are barely able to crack 18 percent. A network that provides 100,000 conversions and high uniqueness is far more valuable than a network providing 200,000 conversions and low uniqueness.
ROI-based media planning is a function of a few simple disciplines.
First, unified data. If social data is housed in one place, and email data is housed in another, and sales data is housed in a spreadsheet, then you are not measuring ROI. You are just guessing.
Second, a proper attribution model. Using a multi-touch model that takes into account cross-channel influence is far better than a last-click model.
Third, and perhaps most importantly, testing. Using A/B testing to determine what works and scaling that up is a far better model than guessing.
Spatial Access's investment decision framework helps you answer four key questions:
Who has the highest lifetime value?
What media channels reach them with the least overlap?
What's the marginal return on investment for each media channel, including interactions?
Where's the point of diminishing return?
Not to mention, brands that can answer these questions consistently outperform those who can't.
Connect with Spatial Access today.