The primary role of a CMO is to get the most out of their marketing dollars. But proving the efficiency of marketing campaigns and showing a return on investment (ROI) can be challenging. For CMOs in B2C companies, the feedback loop from campaign to sales is faster, and it’s easier to calculate ROI.
But for CMOs working in B2B companies the time-lag between campaigns and sale performance can be months. This is particularly true of companies selling complex solutions or products that have long sales cycles.
As a CMO in this type of B2B company, establishing the value of a marketing campaign to validate your role, avoid probing questions and achieve bigger budgets, can be difficult.
This is where advanced marketing analytics is important. It can help CMOs visualise the timelag of their marketing campaigns, and assist in providing accurate performance data to their Boards.
Because advanced marketing analytics helps to accurately establish the efficacy of campaigns across a longer time period, it is easier to get a true calculation of ROI and overall marketing performance. Moreover, it helps avoid the trap of pulling the plug on campaigns too early.
Let’s take a deeper look at advanced marketing analytics, and why this is so valuable to a CMO.
What is Advanced Marketing Analytics?
Advanced marketing analytics is a pretty broad term. It refers to various advanced methods and tools that marketers can use to get more from their data. By using advanced analytics, you will be able to predict trends more accurately and gather a stronger behavioral forecast.
For marketers, using advanced marketing analytics means getting more value from your campaigns. Companies use this to improve their ROI and generate greater success.
When looking at advanced marketing analytics, different types of analytics are included.
- Regression Analytics: Regression analytics looks into the relationships between a dependable and an independent variable. This is a good way to find trends in data because the relationships found in your sample will also be present in the larger population.
- Predictive Analytics: Predictive analytics is a key component of advanced analytics, as this helps to find a solution to the unknown. This type of analysis uses various techniques from other data processes (such as data mining, AI, machine learning, and modeling) to make a deep analysis of available data to create a prediction for the future. Predictive analytics is used to create accurate forecasts.
- Prescriptive Analytics: Prescriptive analytics is the final stage of business analytics. This is the process of using technology to look at the raw data and make decisions based on the existing descriptive and predictive analytics to find the best possible outcome.
Learn more about how and how it can help your marketing strategy.
Ways to Apply Advanced Marketing Analytics
Businesses can use advanced marketing analytics in all stages of their marketing. As opposed to basic marketing analytics, with advanced marketing analytics can help businesses to automate various marketing processes, and optimize them for stronger results.
One of the key aspects to implementing this type of analytics is gathering data from a wider variety of sources, and not just social media channels. In order to get the most from your data, and make more informed decisions, a wider spread of data needs to be analyzed for greater accuracy. No company already has all of the data it needs for making informed decisions. Instead, businesses need to further extend their data reach for broader industry knowledge.
Look towards complimenting your own data with larger external data providers. This can help to scale your business more accurately and create more insightful models.
You also need to take more of a forward-looking approach to data. Past analytics and marketing modeling are most likely irrelevant for upcoming campaigns. Instead of using historical data, you should uncover further relationships between market factors and influences – both online and offline. Taking a more in-depth approach to customer behavior is necessary for an analytics model that helps achieve more impactful actions.
Taking a top-down approach to data is also important. This aggregated, market-level approach helps to find a wider spectrum of decision points for more predictive data. Granularity is a good thing in advanced analytics.
When putting together data models, including a wider range of skill sets in their creation can also add more value. The people who build data models should cover different positions and skillsets. Varying areas of expertise can help create data models that are more accurate and practical for real-world use.
Advanced marketing analytics techniques perform better with large quantities of varied data. For this reason, a business should clean up its existing data and carefully prepare the framework for the new analytics model.
How Does Advanced Marketing Analytics Help CMOs?
Compared to basic marketing analytics, advanced analytics offer data that closely approximates what happens in the real world. You get a stronger overview of reality from the data that can be used to produce more meaningful actions.
Advanced marketing analytics helps businesses to better predict future events, and gain a more accurate forecast on trends. This type of analytics also achieves more trustworthy perspectives from a wider set of accurate and credible data.
For a CMO, this means more useful information that can help campaigns to generate a greater impact.
As a CMO, your marketing results are only as good as the data you have to back them up. With the right data, you will be able to produce more impactful campaigns, finding more meaningful solutions to real-life problems. Advanced technologies and processes allow for smarter analytics to help take marketing to a new level of effectiveness.
Learn more about advanced marketing analytics and with us today to see how our platform can help bring more value to your business and improve your marketing strategies. Empower your business with the knowledge to do better.