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.”
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.
The key differences between human-run data analysis and AI analytics are the three main benefits of using AI analytics: scale, speed, and accuracy:
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.
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.
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 much larger than human capabilities, AI analytics can shed light on trends and patterns that might otherwise go unnoticed by human researchers’ limited capabilities.
Machine learning and AI work together to help businesses make data-driven decisions. Marketers can get deep insights into consumer behavior and marketing performance. Potential applications include:
Testing: Run your usual marketing tests and uncover the version(s) 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 type of content to create and identify new keyword opportunities.
eCommerce analytics: Get insight into page conversion rates and discover what might cause shoppers to drop out of the path to purchase.
Identify problem areas: A big benefit of AI data analytics is uncovering new data points you might not find through your 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 forecasting: Teams can use AI analytics to forecast revenue and sales based on historical data.
Customer experience monitoring: Data helps service teams understand customer satisfaction levels and learn how to build customer loyalty and reduce churn.
Internal performance: Business leaders can use AI analytics to understand internal team performance, from win rate to customer satisfaction scores, to understand what’s going right and identify opportunities for improvement.
The most significant limitation of AI analytics is that a computer is not a human. While machines can sort through significantly more data in a shorter time, a human knows a business and its processes better than a computer can.
Be mindful of treating AI tools as a replacement for human understanding. Teams can use insights (and will greatly benefit from the insights) alongside their contextual understanding of business needs before making decisions.
The limitation boils down to this: you can’t replicate human understanding and experience, so it’s essential to consider this when leveraging AI tools.
Overall, using AI analytics gives businesses a competitive advantage. Machine learning algorithms produce 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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