A robust digital marketing plan, a subset of campaign planning, should go beyond simple predictions such as “this much for that” and incorporate elements like measurement, efficiency, effectiveness, cost, profitability, and more. What sets digital marketing apart is its reliance on analysis—be it in terms of measurements, efficiency, effectiveness, cost, profitability, etc. At Dijilopedia, we strive to offer a unique perspective on digital marketing, focusing on predictive planning to make campaigns more tailored to your objectives. This article will delve into the technique behind making foreseeable predictions even at the planning stage. We hope you find it beneficial.
What is Operations Research, and How Does It Relate to Digital Marketing?
Before delving into the technique, let’s provide a brief conceptual overview. Operations Research is a discipline that emerged during World War II, focusing on optimizing the use of military resources (tactically, operationally, logistically, etc.). Inspired by this field, we’ve developed a technique for maximizing profit and optimizing the impact area with minimal cost—a method akin to maximizing returns on limited resources.
Optimizing Advertising Channels
The first step is identifying the platforms where our target audience resides. These may include news sites, social media platforms like Facebook, Instagram, Twitter, blogs, forums, or search engines like Google. Some have fixed costs, while others are variable. Calculate the cost per thousand impressions for fixed-cost platforms and estimate the cost per thousand impressions for variable cost platforms. Analyze past data to understand click-through rates, costs per click, conversion rates, and costs per conversion.
Based on these data, select the platform that minimizes costs and maximizes profits, considering time and budget constraints. It’s crucial to avoid choosing a platform solely based on daily costs, as, for instance, a website demanding $1,000 per day might be more cost-effective than one asking for $1,500 per day.
CS Matrix and Mathematical Modeling
Now, the most anticipated part. Arrange our CS Matrix with selected platforms, publication dates, and costs. Leverage past data to estimate the advertising budget needed to achieve sales targets. For instance, if we aim to sell 1,750 units of product X within 8 days, calculate the estimated advertising budget required by analyzing conversion rates and working backward from sales targets.
Alternatively, predict approximate sales from the given advertising budget. All these optimization efforts, starting from choosing advertising channels, are part of what we term mathematical modeling.
Example Campaign Planning Matrix
Note: A sample CS Matrix is provided below.
In conclusion, without delving into mathematical modeling during advertising channel selection, one might miss out on crucial optimization opportunities. We hope this model proves useful in your planning endeavors.
Best of luck with your planning! 🙂
