By fneedle@hubspot.com (Flori Needle)

Our 2023 Marketing Trends Report found that data-driven marketers will win in 2023. It makes sense, but data analysis can be challenging and time-consuming for many businesses.

Enter AI analytics, a time-saving process that brings marketers the answers they need to create data-driven campaigns. In this post, we’ll discuss:

• What is AI analytics?
• How to Use AI in Data Analytics
• Benefits of Using AI Analytics
• Business Applications of AI Analytics
• Limitations of AI Analytics

As with all machine learning, AI analytics gets more precise and accurate over time, especially when trained to learn industry preferences 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 to augment 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-led. Its scale is more significant, data processing is faster, and algorithms give accurate outputs.

AI analytics can do what humans do, but be mindful of viewing it as a total replacement. If you use AI in data analytics, consider leveraging it to supplement your team’s capabilities and expertise.

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

Benefits of Using AI Analytics

The key differences between human-run data analysis and AI analytics are the three main benefits of using AI analytics: scale, speed, and accuracy:

1. Scale

AI analytics tools can leverage large amounts of data at a time. 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 need, so data processing can happen instantaneously. It can simply be fed a data set and left alone to process, learn from, and bring insights.

3. Accuracy

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

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

Some additional benefits include:

• Bias reduction: Algorithms don’t have the confirmation bias or general biases that teams might (unintentionally) have when analyzing data, so results are unbiased.

• New insights: Since the scale of data is …read more

  Source:: HubSpot Blog

        

  

  Author: Aaron