Introduction
Marketing analytics is the empirical implication of marketing data. It is the analysis of data collected from marketing campaigns in order to identify trends/behaviors such as how a campaign led to conversions, customer behavior, geographic preferences, creative preferences, and much more. The aim of marketing analytics as a methodology is to use these trends and discoveries to refine potential strategies based on what worked in the past.
Marketers and customers also benefit from marketing analytics. This research enables marketers to maximize the return on marketing investments. Analytics suggests what works best in terms of conversions, brand recognition, or both. For instance, due to Analytics one can deduce, rather than mass communication, customers see a greater number of tailored, personalized advertisements that speak to their individual needs and interests.
Importance of Marketing Analytics
Accurate analytics are more critical than ever in today’s marketing environment. Consumers have become extremely picky about which branded media they associate with and which they avoid. If marketers want to capture the attention of the ideal consumer, they must use analytics to create tailored personal advertising. Such tailored advertising is based on individual preferences rather than large demographic associations. This enables marketing teams to serve the right ad, at the right time, on the right platform, to drive customers down the sales funnel.
Challenges of Marketing Analytics
Data Quantity
During the digital era, big data evolved, allowing marketing teams to track every customer click, experience, and view. This volume of data, however, is meaningless if it cannot be structured and analyzed for insights that allow for in-campaign optimizations. As a result, marketers are wrestling with how to better arrange data in order to determine its value. In fact, research indicates that experienced data scientists spend the majority of their time haggling and processing data rather than analyzing it.
Data Quality
Not only is there an issue with the enormous amount of knowledge that companies must sift through, but this data is often regarded as untrustworthy. According to Forrester, low data quality resulted in a 21 percent waste of respondents’ media budgets. This suggests that one out of every five dollars was inefficient. Over the course of a year, these dollars can add up, resulting in $1.2 million dollars and $16.5 million dollars of wasted budget for mid-size and enterprise level firms. Organizations require a mechanism for maintaining data accuracy so that workers can use reliable information to make sound decisions.
Lack of Data Scientists
Even if a company has access to the right data, it sometimes lacks access to the right people. According to the CMO, only 1.9% of businesses believe they have the right people to fully utilize marketing analytics.
Selecting Attribution Models
Choosing the right model to provide the right perspectives can be difficult. For example, media mix modeling and multi-touch attribution provide completely different perspectives – aggregate campaign-focused data versus person-level customer data. Marketers’ models of choice will determine the types of insights they obtain. When it comes to selecting the right model, analyzing engagement through too many platforms can lead to uncertainty.
Correlating Data
Similarly, when marketers gather data from so many different sources, they must find a way to normalize it so that it is equivalent. For instance, it’s particularly difficult to compare online and offline engagements. Online and offline engagement have different attribution models. This is where centralized marketing measurement and marketing analytics systems really shine, by bringing together data from diverse sources.
Article By: Saurav Giri
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