Nowadays digital marketing is becoming more and more AI-driven. Thanks to artificial intelligence (AI), marketing has propelled content and advertisement personalization into a totally new level. Moreover, AI technologies also helped to enhance the marketing analytics practice.

Understanding marketing analytics is crucial since it helps marketers to measure, manage and analyze marketing performance to in order to maximize its effectiveness and optimize return on investment (ROI). As a result, marketing analytics allows marketers to be more efficient. However this process can be enhanced even more with the use of artificial intelligence.

What are the main trends driving marketing analytics?

AI marketing tools for various marketing disciplines, such as content curation, email marketing or content generation have already “turned to AI”. This has helped marketers to become more efficient in their daily work by eliminating the need of focussing on repetitive tasks, such as reporting, performance monitoring, organizing and analyzing marketing data.

Marketing analytics have changed as well over the years, especially by introducing predictive analytics into the digital marketing world.
With predictive analytics, marketers can see trends and get estimations of how their marketing campaigns will most likely perform. This information can be very beneficial to marketers because their efforts won’t be wasted. They will have the chance to implement the necessary changes in order to increase the likelihood of reaching the set goal for a marketing campaign on time. Thus, artificial intelligence helped to simplify and at the same time optimize marketing analytics practice.

Historical data is the key to predictive analytics

historical data in predictive analytics

Predictive analytics algorithms are based on historical data gathered from various marketing channels and tools. This data is fundamental to building up good quality algorithms and getting high quality predictions.

There are various ways in which a predictive analytics algorithm can be implemented and the methods that can be used. It can be either sophisticated artificial intelligence/deep learning models or more simple functions. The quality of the end results depends heavily on the data and the selection of the best model for it.

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Now we’ll give you a short guidelines into the general steps on how you can improve your marketing analytics:

The process of implementing predictive analytics:

1. Data Collection & Cleaning

In the first step, we need to gather historical data. Most data used in predictive analytics is time-based, certain numbers which occur over time. It is also known as a “time-series”. At this stage, we need to make sure we have all the data in one place and clean it accordingly.

2. Transformation & Selection

In the second step, we transform all the different time-series into the same format and then select the ones which make sense in order to combine them. It is sensible to combine multiple metrics in order to predict one certain metric. This is called multivariate prediction and will most of the time allow us to increase the quality of our predictions.

3. Model Selection & Training

As the next step, we select the best model for our predictive analysis. The model choice heavily depends on the use case and how your data “looks like”. At nexoya, we automatically detect the pattern for your time-series and dynamically select the best model for your predictive analytics depending on the measurement itself (i.e. Is it the number of opened emails or impressions on your blog) and various other factors like trends or weather.

4. Model Evaluation

As the last step, we measure the quality of our prediction and start to constantly improve the algorithm or adjust the model/ if needed.

To learn more about the preparation of data and how an AI Model is built, check our previous blog on ”How to create an AI model?”and ”Learn how to make your data ready for AI with 5 simple steps”.

Why do marketers want to use predictive analytics?

Why are predictions so important for marketing analytics? Marketers have access to an enormous amount of historical data. Savvy marketers want to understand and take advantage of all the available data and get the best out of it. That includes getting insights into the future development and trends of their marketing efforts and channels.

With the support of predictive analytics, marketers can better understand what channels work and how their numbers evolve. This allows them to anticipate customers’ needs and their digital behaviour.

8 Benefits of predictive analytics in marketing (listed)

Below you can find a short list of the Benefits of Predictive analytics in Marketing:

Predictive analytics in marketing
The Benefits of Predictive analytics in marketing (Infographic)

With predictive analytics marketers can obviously benefit a lot and base their decisions on arguments that, again, are based on data. Nexoya offers predictive analytics for digital marketers, helping them to optimize their marketing while being able to identify trends of their most important KPIs early on.

An integrated digital marketing strategy is highly beneficial to your business, as it allows you to spread your message across channels.
But how can artificial intelligence help you to execute your integrated marketing plan?

In our newest article, you will find out how to start planning an Integrated Marketing Strategy and where AI can support you in executing your strategy.

Infographic (Text version)

1. Enhanced marketing performance. Thanks to the increased data-driven decisions (rather than a gut feeling)

2. Better Marketing Budget planning

3. Optimized campaigns and performing CPC-models

4. Getting more leads thanks to channel optimizations

5. Boosted customer intelligence

6. More precise Customer Lifetime Value

7. More precise, personalized content

8. Predicted churn