As we know, marketing initiatives tend to be abstract, in both concept and payoff. However, a good marketing strategy can improve customer satisfaction, increase basket size, and even decrease the need for markdowns. All of these things can improve sales & profitability and have a positive impact on the company. However, in today’s world of information and data, marketing departments are being asked to justify every dollar spent and provide an ROI for every campaign that’s run. So how can marketers show a quantifiable benefit?
With the increasing prevalence of, and access to, customer data, marketing executives can now utilize this information to quantify the effectiveness of a program. Now that you have the data that can prove your worth, you have to decide what metrics to use, and furthermore, how to analyze them.
Every company has its own strategy for data collection, and almost every move a customer makes is tracked. Deciding which pieces of information are most important is up to each company. Some value foot traffic over basket size. Some feel that customer satisfaction is a better indicator of success. The decision on which data metrics to use should not be taken lightly. You have to make sure the information aligns with your company’s culture, while at the same time providing value to you. Once you know what you are looking for, you have to figure out how to look at it.
There are 3 common types of data analytics that can be used: descriptive, predictive, and prescriptive. Descriptive analytics look at what has happened. They are a great way to measure past performance. Predictive analytics tell you what could happen. They are the best way to determine how an initiative will perform. Prescriptive analytics actually tells you what you should do based on your data. They give you specific actions to ensure visible success. To me the answer seems simple. Eliminate the guess work and tell me what I should be doing.
In this fast paced world of increased competition, marketers must play the part of both mind reader and master manipulator. Instead, take hold of your data and decide how you are going to use it. Will you use your data to tell you what you’ve done, how you may perform in the future, or do you want it to to provide you with instructions on how you can be the most successful? If I had a choice I would go with the data analysis that showed me how to be the most successful, because then when I’m faced with the dreaded question of “why did you do this?” I can simply respond, “the data told me to,” allowing me to both justify my actions and show the ROI.
