Marketing Analytics

Marketing Analytics

Marketing analytics is the analysis of data collected through marketing efforts in order to find trends in things like how a campaign influenced conversions, customer behaviour, regional preferences, creative preferences, and so on. Marketing analytics as a practise aims to leverage these trends and discoveries to improve future campaigns based on what worked in the past.

Marketers and customers both benefit from marketing analytics. This research enables marketers to get a better return on their marketing spending by determining what works best in terms of conversions, brand awareness, or both. In addition, analytics ensures that customers see a greater number of targeted, individualised ads that appeal to their specific needs and interests, rather than the annoying mass communications.

The Importance of Marketing Analytics

Accurate statistics are more vital than ever in today's marketing scene. Consumers have become quite picky about which branded material they engage with and which they avoid. Brands must use analytics to produce tailored personal ads based on individual interests rather than broad demographic associations if they want to attract the attention of the perfect buyer. Marketing teams will be able to serve the correct ad, at the right time, on the right channel, to move customers through the sales funnel.

How Organizations Use Marketing Analytics

Data from marketing analytics can assist your company in making decisions about product upgrades, branding, and more. To avoid a fragmented perspective, data from several sources (online and offline) should be used.

The Challenges of Analyzing Data

While marketing analytics are essential for effective campaigns, the analytical process is complicated by the vast amount of data that marketers can now access. This means that marketers must figure out how to organise data in a way that is easy to understand so that actionable insights may be derived.

Some of the biggest marketing analytics challenges faced today are:

Data Quantity: During the digital age, big data allowed marketing teams to track every consumer click, impression, and view. This volume of data, however, is meaningless if it can't be organised and analysed for insights that may be used to improve campaigns in real time. As a result, marketers are dealing with the best way to arrange data in order to assess its meaning. In reality, research suggests that rather than evaluating data, skilled data scientists spend the majority of their time wrangling and structuring it.

Data Quality: Not only is there an issue with the large amount of data that corporations must sort through, but this data is frequently regarded as untrustworthy. According to Forrester, poor data quality resulted in a waste of 21% of respondents' media budgets. This means that one dollar out of every five dollars was being wasted. Over the course of a year, these costs can build up to $1.2 million and $16.5 million in lost budget for mid-size and enterprise-level businesses, respectively. Organizations require a method for maintaining data quality so that employees may make the best decisions possible based on correct facts.

Selecting Attribution Models: Choosing the proper model to deliver the relevant insights might be difficult. Media mix modelling and multi-touch attribution, for example, provide completely distinct insights — aggregate campaign-level data and individual-level consumer data, respectively. The types of insights marketers receive will be determined by the models they use. When it comes to choosing the correct model, analysing engagement across so many platforms can be confusing.

Correlating Data: In a similar vein, when marketers acquire data from a variety of sources, they must find a means to standardise it so that it can be compared. Comparing online and offline encounters is particularly difficult because they are often measured using distinct attribution models. This is where unified marketing measurement and marketing analytics tools prove their worth by bringing different data sources together.