The problem with marketing is perception. People perceive it as a field that relies only on creativity, gut-feeling, and intuition. However, marketing analytics provide us with perfectly reasoned decisions that evolve with your business. There’s nothing better than numbers to generate budget allocations from your CFO and Executive team. Pay attention to certain marketing analytics to make data-driven decisions that back up your intuitions.
Analytics provide you with the tools to understand big-picture marketing trends. Understanding your programs over time allows you to measure each program’s effectiveness and determine which programs should be run at certain times. Some programs complement each other while others are substitutes and are unnecessary in the grand scheme of things.
Revenue attribution is the key to understanding your marketing analytics. Each one is valuable in their own respect and key to understanding your customer’s journey. There are a few different models:
-First or Last Touch
One of the most common applications of marketing program analysis is single touch attribution models. This allocates all the revenue credit in the opportunity to the first or last interaction that a lead has with your marketing content. As the names would imply, first touch attribution gives all the credit to the first touch, regardless of how far it has progressed, and last touch gives all the credit to the last touch. Last touch assumes the last marketing touch was the most valuable in the lead conversion process.
Unlike single touch, linear attribution accounts for the marketing touches in between the first or last touch. Linear attribution spreads revenue credit evenly across all marketing programs a lead has encountered but doesn’t acknowledge varying influence within the time period.
A step above linear, the time decay model spreads revenue credit across all marketing programs but weights them according to the lead’s most recent interactions. Not all programs are made equal, and this program assumes that the most recent activities are the most valuable ones.
On the other hand, position weighted attribution weights the first and last touches most heavily while everything in between splits the rest of the revenue credit. Though this is a fair approach, the first and last touch have a large impact on the resulting data.
The last multi-touch attribution model credits revenue to marketing programs based on the influence it had on the customer’s purchasing decision. By using lead scoring, it determines the increase that resulted from the lead’s interaction with each subsequent program. Score based attribution is difficult to do if you don’t already use lead scoring but certainly worth your while.
Check out our presentation on Dynamic Revenue Attribution on our Resource page.
Sales forecasts usually get most of the attention from the higher-ups; however, forecasting can be just as valuable and influential to marketing decisions. Use marketing attribution and the customer journey to build a model of the projected revenue cycle. Along with that, ROI and other metrics allow you to project how many new leads the marketing strategies will gather through the revenue cycle. Together, models and projections build a pattern of the lead conversion cycle throughout the year that estimates revenue. The more data you accrue, the better your forecasts will be, so time is invaluable to the forecasting process.
Marketing Analytics Matter
Relying on analytics gives marketing an edge to decision-making and program success. Marketing analytics and attribution recreate the customer journey and give insight on the strengths and weaknesses of programs. If you prove why your programs are successful, it’s that much easier to show executives the value of your plan and budget. Here at ORM Technologies, we recommend you aggregate marketing data over time to make the best-informed decisions possible. If you have any questions about how we can help improve your marketing analytics, please let us know at email@example.com.