Data Analyst

When Does It Pay Off To Invest In Projections?

As marketers, we are continually faced with decisions of how to best spend our time and money. We make those decisions by doing our best to answer simple questions such as: “What does the output look like for my investment?”. Answering these questions can be difficult and complex, but are critically important to inform decisions to maximize ROI. Unfortunately, in a fast-moving world, we don’t have the luxury of modeling out the expected impact of every decision we make. In this post, we will cover how predictive analytics can help answer some of marketers’ most important questions.

Most questions involve decisions about how to spend media dollars to achieve goals:

  • What return can I expect if I spend $X on media?
  • How much can I spend and keep an ROAS over $X or CPL under $Y?
  • How should I budget between channels, tactics, time periods, and service lines to maximize return?

Answers to these questions can range from basic, directional predictions to more in-depth, complex budget recommendations. There are a few factors that will impact how much you should invest in using projections: the level of historic data available, the complexity of your business structure and goals, and the volume of media that the projections would help optimize. So, how do you decide when a basic analysis will suffice, or when you are better off investing dollars upfront to allow for smarter media allocation at scale? We’ve put together a quick guide to help navigate these decisions.

Tracking your media needs against your metrics required

To determine which level of projection investment is right for you, below are a few example circumstances:

Basic: Limited data, lower volume of media, simpler business questions. Example: Forecasting demand to inform spend for the next month based on recent performance.

Intermediate: Have more historic data, spending a large volume of media, and operate in an environment where budget can be planned for and moved fluidly among tactics and channels. Example: Planning annual, monthly, and weekly budget.

Advanced: Managing complex business structures that involve multiple brands and service lines with multiple goals, and with multiple data sources (in addition to criteria for intermediate). Example: Setting annual, monthly, and weekly budgets across multiple goals, brands, and service lines. Advanced projections can also provide recommendations on a daily level or for a specific promotional period. For instance, projections can inform retail brands that run specific holidays or one-day promotions with recommended spend levels.

It’s also important to plan for the frequency of a projections exercise. Re-running projections quarterly or bi-annually helps to measure and adjust the model to better predict future performance. If you would like to learn more about how data-driven projections can help drive results for your business, contact Rise today.

03/19/2018 at 07:04