What is AI Analytics?

Our 2023 Marketing Trends Report Found that data-driven marketers will win in 2023. It’s understandable, but data analysis can be challenging and time-consuming for many businesses.

AI Analytics

Enter AI analytics, a time-saving process that gives marketers the answers they need to build data-driven campaigns. In this post, we will discuss:

As is the case with all machine learning, AI analytics becomes more accurate and precise over time, especially when trained to learn industry preferences in order to contextualize results to individual business needs.

AI analytics is sometimes referred to as augmented analytics, which Gartner defines as “The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation for how people explore and analyze data in analytics and BI platforms.”

How to use AI in Data Analytics

AI analytics differs from traditional analytics in that it is machine-driven. Its scale is more important, data processing is faster, and algorithms deliver accurate outputs.

AI analytics can do what humans do, but be careful not to view it as a total replacement. If you use AI in data analytics, consider leveraging it to complement your team’s capabilities and expertise.

For example, an AI analytics tool can process the results of an A/B test and quickly tell which version had the highest ROI and conversion rate. A marketer can take this information, identify what exactly impacted the performance of each version, and apply this information to future marketing practices.

Benefits of using AI Analytics

The major differences between human-powered data analysis and AI analytics are the three main advantages of using AI analytics: scale, speed, and accuracy:

1. scale

AI analytics tools can take advantage of huge amounts of data at once. Its scale also brings a competitive advantage, as machines can seek publicly available data from other sources, run comparative tests, and help you learn more about competitor performance and how you measure up. .

2. speed

Machines don’t require the downtime that humans require, so data processing can happen instantaneously. It can simply be fed a data set and left alone to process, learn and generate insights.

3. accuracy

Machine learning algorithms are better at understanding data while processing data, bringing comprehensive and accurate results.

You can also train the algorithms on industry language and standards so that the results are contextually relevant to your business goals.

Some additional benefits include:

  • bias reduction: The algorithms do not have confirmation bias or general bias that teams can (unwittingly) have when analyzing data, so the results are unbiased.

  • new insightAI: Since the scale of the data is much larger than human capabilities, AI analytics can shed light on trends and patterns that might otherwise go unnoticed by the limited capabilities of human researchers.

Business Applications of AI Analytics

Machine learning and AI work together to help businesses make data-driven decisions. Marketers can gain deep insight into consumer behavior and marketing performance. Possible applications include:

  • Testing: Run your usual marketing tests and uncover the version most likely to maximize key marketing metrics like ROI and conversions.

  • Campaign Segmentation: AI tools use data to discover consumer preferences so you can create segmented campaigns to maximize the potential for conversions and ROI.

  • SEO: Machine learning algorithms can understand the search intent behind queries and help you learn more about the types of content to generate and identify new keyword opportunities.

  • eCommerce Analytics: Get insights about page conversion rates and learn what may be driving customers along the way to purchase.

  • Identify problem areas: A huge benefit of AI data analytics is uncovering new data points that you might not have found through your own processing. You can discover hidden variables affecting performance and adapt your strategies to address them.

AI analytics is also beneficial to other areas of business, including:

  • Sales forecast: Teams can use AI analytics to forecast revenue and sales based on historical data.

  • Customer Experience MonitoringData helps service teams understand customer satisfaction levels and learn how to build customer loyalty and reduce churn.

  • Internal Display: Business leaders can use AI analytics to understand internal team performance, from win rates to customer satisfaction scores, to understand what’s going right and identify opportunities for improvement.

Limitations of AI Analytics

The most important limitation of AI analytics is that computer is not human. While machines can sort through significantly more data in less time, a human knows a business and its processes better than a computer.

Be careful of treating AI tools as a replacement for human understanding. Teams can use the insights (and will greatly benefit from the insights) with their relevant understanding of the business requirements before making decisions.

The limitation comes down to this: You can’t replicate human understanding and experience, so this needs to be considered when leveraging AI tools.

AI analytics gives businesses a competitive advantage

Overall, using AI analytics gives businesses a competitive advantage. Machine learning algorithms generate data-driven insights from which marketers can make data-driven decisions.

Take a look at your current data analysis process to see where it fits in and reap the benefits.

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