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A Guide to Marketing Analytics

What is Marketing Analytics

Marketing analytics is the study of data garnered through marketing campaigns in order to discern patterns between such things as how a campaign contributed to conversions, consumer behavior, regional preferences, creative preferences and much more. The goal of marketing analytics as a practice is to use these patterns and findings to optimize future campaigns based on what was successful.

current state of marketing measurement and optimization

Marketing analytics benefits both marketers and consumers. This analysis allows marketers to achieve higher ROI on marketing investments by understanding what is successful in driving either conversions, brand awareness, or both.  Analytics also ensures that consumers see a greater number of targeted, personalized ads that speak to their specific needs and interests, rather than mass communications that tend to annoy.

Marketing data can be analyzed using a variety of methods and models depending on the KPIs being measured. For example, analysis of brand awareness relies upon different data and models than analysis of conversions. Some popular analytics models and methods include:

  • Media Mix Models (MMM): Attribution models that look at aggregate data over a long period of time.
  • Multi-Touch Attribution (MTA): Attribution models that provide person-level data from across the buyer’s journey.
  • Unified Marketing Measurement (UMM): A form of measurement that integrates various attribution models including MMM and MTA into comprehensive engagement metrics.

The Importance of Marketing Analytics

In the modern marketing landscape, accurate analytics is more important than ever. Consumers have become highly selective in choosing the branded media they engage with and the media they ignore. If brands want to catch the ideal buyer’s attention, they must rely on analytics to create targeted personal ads based on individual interests, rather than broader demographic associations. This will allow marketing teams to serve the right ad, at the right time, on the right channel to move consumers down the sales funnel.

Marketing analytics data can help your business make decisions on matters including product updates, branding and more. It’s important to take data from multiple sources (online and offline) to prevent a fragmented view. Using this data, your team can gain insights into the following:

Customer Trends and Preferences

Product intelligence involves taking a deep dive into the brand’s products as well as how those products stack up within the market. Typically done by speaking to consumers, polling target audiences or engaging them with surveys, organizations can better understand the differentiators and competitive advantages of their products. From there, teams can better align products to the unique consumer interests and problems that help drive conversions.

Customer Trends and Preferences

Analytics can tell a lot about your consumers. What messaging / creative resonates with them? Which products are they buying and which have they researched in the past? Which ads are leading to conversions and which are ignored?

Product Development Trends

Analytics can also offer insight into the types of product features consumers want. Marketing teams can pass this information on to product development for future iterations.  

Customer Support

Analytics also helps uncover areas of the buyer’s journey that could be simplified or improved. Where are your clients struggling? Are there ways you can simplify your product or make the check-out process easier?

Customer Support

Analytics also helps uncover areas of the buyer’s journey that could be simplified or improved. Where are your clients struggling? Are there ways you can simplify your product or make the check-out process easier?

Messaging and Media

Data analysis can determine where marketers choose to display messages for particular consumers. This has become especially important because of the sheer number of  channels. In addition to traditional marketing channels such as print, television and broadcast, marketers must also know which digital channels and social media networks consumers prefer. Analytics answers these key questions:  What media should you be booking? Which are driving the most sales? What message is resonating with your audience?

Competition

How does your marketing efforts compare with your competition? How can you close that gap if there is one? Are there opportunities your competitors are capitalizing on that you may have missed?

Predict Future Results

If you have a thorough understanding of why a campaign worked, you’ll be able to apply that knowledge to future campaigns for increased ROI.

The Challenges of Analyzing Data

While marketing analytics are critical to successful campaigns, the analysis process poses key  challenges because of the immense quantity of data marketers are now able to attain. This means that marketers must determine how to best organize the data into a digestible format to derive actionable insights.

Some of the biggest marketing analytics challenges faced today are:

  • Data Quantity: Big data emerged during the digital age, enabling marketing teams to record every consumer click, impression and view. However this quantity of data is irrelevant if it cannot be structured and analyzed for insights that allow for in-campaign optimizations. This has left marketers grappling with how to best organize data to evaluate its meaning. In fact, research shows that experienced data scientists spend the majority of their time wrangling and formatting data, rather than conducting analysis.
  • Lack of Data Scientists: Even if companies have access to the right data, many don’t have access to right people. In fact, according to a survey by The CMO, only 1.9% of companies believe they have the right people to fully leverage marketing analytics.
  • Selecting Attribution Models: Determining the model that provides the right insights can be tricky. For example, media mix modeling and multi-touch attribution offer entirely different insights – aggregate campaign-focused data and person-level consumer data respectively. The models that marketers choose will dictate the types of insights they receive. Engagement analysis across so many channels can create confusion when it’s time to choose the right model.
  • Correlating Data: In this same vein, because marketers are collecting data from so many different sources, they must find a way to normalize it to make it comparable. It’s especially challenging comparing online and offline engagements, as they are typically measured by different attribution models. This is where unified marketing measurement and marketing analytics platforms demonstrate true value, organizing data from disparate sources. 

What is a Marketing Analytics Software Used For?

Marketing analytics software combats these challenges by collecting, organizing and correlating valuable data quickly, allowing marketers to make real-time campaign optimizations.

Modern marketing platforms are valuable for the speed at which they can store and process massive amounts of data. One of the major drawbacks of having access to so much data is that marketers cannot possibly parse through it all in time to make real-time optimizations. That’s where the processing power of advanced analytics platforms comes into play, enabling marketers to adjust creative or ad placement as needed before the campaign ends, enhancing potential ROI.

Additionally, many platforms now leverage unified marketing measurement, to normalize and aggregate marketing data from across various channels and campaigns, simplifying analysis.

Finally, advanced analytics platforms go beyond measuring consumer engagements to offer insights into brand perception and how certain audience segments react to creative elements. This helps marketers better determine brand-building ROI, as well as how to further personalize branded experiences.

Skills That Marketing Analytics Managers Need

As marketing teams seek to conduct quality analysis that lead to more engaging, profitable campaigns, they must focus on employing analytics managers who can:

  • Conduct Quality Analyses: First and most obvious, an analytics manager must have experience evaluating large data sets to discern insights including buying patterns and engagement trends within the target audience.
  • Make Optimization Recommendations: Once data insights are gained, the ability to come up with recommendations to improve underperforming campaigns based on trends is crucial. For example, data may show that one consumer engaged with branded content only in the evening, informing a strategy shift to serve the ad on the consumer’s commute home, rather than the morning commute.
  • Understand Consumer and MarTech Trends: Analytics managers should also stay abreast of consumer and MarTech trends. Understanding consumer demands for a seamless omnichannel experience and how buyers are engaging with augmented and virtual reality will certainly play a role in determining next steps for optimization opportunities.
  • Work with Analytics Tools: Next, analytics managers must be onboarded and comfortable with various automation tools and analytics platforms, because of the vital role these tools play in reducing the time from consumer engagement to consumer insight.  
  • Collaborate with Stakeholders: Finally, members of the analytics team must be able to use the data they work with to tell a compelling story to stakeholders, and demonstrate the ways other departments, such as sales or product development, can use these findings to drive engagement and conversions.

How to Begin the Marketing Analytics Process

If you’re looking to enhance analytics capabilities, here are four steps to take at the outset of your program:

  • Understand What You Want to Measure: There are many aspects to a marketing campaign you can measure: conversion rates, leads captured and brand recognition, to name a few. Understand the problem you are trying to solve or insight you are trying to glean when beginning to analyze your data.
  • Establish a Benchmark: What does a successful campaign look like? This will determine the types of data and metrics marketers collect. For example, if the goal is to increase brand awareness – the success benchmark might be an increased percentage of brand loyalty demonstrated in a customer panel, rather than an online click or impression.
  • Assess Your Current Capabilities: What is your company doing today? What are your weak spots? Whether assessing offline campaign results or identifying media most likely to convert, understanding these weak points can help you strengthen your program.
  • Deploy a Marketing Analytics Tool:  Marketing analytics tools will  increase in importance as consumers become more selective and datasets grow. An advanced platform, such as our Marketing Measurement and Optimization Platform uses unified marketing measurement to help marketers identify the messages that resonate and media that converts. This provides a holistic view of which campaigns are successful and which are underperforming in real time.

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