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The stories data can tell about sales, customer behavior, marketing, inventory, and products for a business or entrepreneur are endless. This information can help you make better, more informed decisions on what to do next, where to pivot your strategy, and how to better serve your customers. But to do that, you have to sift through a lot of information, which can be time-consuming.
There have been significant advancements in technology to help with manual data management and storytelling, including artificial intelligence (AI) analytics. AI analytics is a broad set of solutions designed to help users understand specific data sets. Like other data processing tools and resources, AI analytics is meant to help inform users and help them take actionable steps.
This guide will break down what you need to know about AI analytics, including some of the benefits and challenges of these tools, how they differ from traditional analytics, and the best way to use them to your advantage.
What is AI analytics?
AI analytics is a set of data analytics that uses artificial intelligence to extract and process data. It uses systems like machine learning (ML), natural language processing (NLP) and specific data mining techniques and strategies to gather information and process it for a user faster than one human could.
AI analytics is especially beneficial for handling extremely large volumes of data. It’s meant to sift through whatever data it’s fed (e.g., customer sales information) and turn its predictions and analysis into a tidy model, chart, or spreadsheet of all the relevant information you need.
How does AI analytics work?
AI analytics works relatively similarly to any other data collection and processing technique. It’s simply faster and has the ability to predict and sort information due to AI technology.
To use AI analytics, you’ll need to have the raw data available to process and an end output in mind. For example, a small business owner needs to understand the performance of their paid ad marketing efforts. They’ll need to organize that data somewhat before feeding the information to the AI analytics tool. Like any other AI tool, you’ll also need to give it a task or prompt so it understands what to do with the data you’re feeding it.
Once that’s complete and the information is in the system, the tool will then process it and identify any patterns or predictions that would be useful to the query.
AI analytics vs. traditional analytics tools
Traditional analytics and AI analytics both use technology to better understand specific sets of data. A key thread distinguishing traditional vs. AI analytics is which data gets used: real-time or old. Consider these other key differences between the two data strategies.
Forecasting: In traditional data analytics, old data is often used to create future models. Rather than basing predictions on old information, AI analytics can use current data and algorithms to forecast anything from customer behavior to market changes.
Insights: Traditional data uses old information (e.g. what happened in the quarter a year ago) to understand what the story might be now. AI analytics can use real-time data that can potentially allow you to make faster, immediate decisions.
Scale: AI analytics are designed to scale and handle vast amounts of information. Traditional data strategies often don’t, and while they do use technology to process and present data, there’s still a human element to it that can slow it down.
Automation: Automation is one of AI’s essential appeals. Traditional data analytics requires human intervention to complete tasks. AI analytics can automate many tasks, which helps to reduce labor or time investment.
What are the different types of AI analytics?
For AI to work well in any capacity, you must provide it with a very specific task. AI needs to have clear parameters around it to be effective. There are three different types of AI analytics that you can use: descriptive, predictive, and prescriptive.
1. Descriptive
Descriptive AI is useful to understand what past data can tell you about historical trends. This is useful for compiling sales numbers from the past to understand where you’ve exceeded or fallen short on goals.
This type of AI will take whatever information you’ve collected and put similar data points together to identify patterns.
2. Predictive
Predictive AI is essential to forecasting. This AI type relies on data to help generate new models and data stories about what could happen next. However, AI analytics algorithms can identify new events or possibilities based on trends that the technology can find and produce.
This type of AI is incredibly useful to better understand customer buying behavior, particularly targeting those who are likely to be repeat buyers.
3. Prescriptive
Prescriptive AI tells you what the outcome should be based on your data set. Rather than just make a prediction, like the previous AI type, prescriptive AI will use both old and real-time data to suggest the best possible action or option based on what it predicts will happen.
GPS apps do this already to recommend you the best routes. For ecommerce, this could be useful to assess productivity or customer service strategies, or even marketing or sales plans.
How small businesses and entrepreneurs can use AI analytics tools
For small businesses and entrepreneurs, AI analytics provides you with an opportunity to understand how well your business is doing, what might need to shift to achieve your goals, and any future predictions that are useful to help you prepare now.
Customer behavior and insights: Knowing behaviors and buying trends helps you better serve your customers and optimize your conversions and sales. Use data and trends from AI analytics to get underneath their buying habits, like when they spend and purchase history, to uncover product bundle ideas or ideal promotion periods. Use website traffic to identify popular products or topics.
Inventory management: AI analytics are very good at helping you keep tabs on what you have available to sell. For future planning, especially if you sell more during peak buying seasons like the holidays, AI analytics can help you prepare your inventory so you’re not under- or overstocked.
Website: AI analytics can help you optimize your online store. AI can review website traffic to quickly illustrate how customers travel through your site, like where your customers go, how long they stay on a page, and at what point they exit. Data pulled from AI analytics can even help you create better, more engaging copy for your website if you find visitors are skipping over buttons or exiting pages quickly.
Benefits and challenges of AI analytics
AI is both an established and emerging technology. New advancements and iterations are constantly available. There are many examples of AI working smoothly for many of our day-to-day needs. It benefits small business owners and entrepreneurs of all kinds, automating and streamlining tasks that free up more time for others. However, there are a number of challenges or risks that can come with using AI. It’s important to understand both.
Benefits of AI analytics
The majority of the benefits of using AI for data analytics are centered on efficiency—whether time, output, or financial.
Speed: AI is fast. It can take an incredible amount of data and process it and present its findings in a timeframe a person couldn’t.
Efficiency: Because AI is so quick to look at large swaths of data, it can also be more efficient for your business overall. Its sole use is to quickly free up more time—including for the person who’d normally do your data analysis—identify gaps, and recommend improvements.
Cost-savings: AI analytics can be a cost-saving tool. From automations and reducing service fees to identifying gaps and waste in the data it processes, AI is a good way to find ways to save money.
Challenges of AI data analytics
Concerns around using AI for data analytics are similar to concerns about AI tools generally, centered on accuracy and the potential for missed nuances.
Bias: AI doesn’t understand nuance the way that we do. If your data is biased in any way, the AI output will reflect that. That’s why it’s valuable to have a person to organize and check any data and results.
Accuracy: Data inaccuracies can occur with AI, which can leave errors in your results. Ensure you’re double checking the prompt or type of AI analytics you’re using, your desired outcome, and the data’s accuracy.
Ethics: There are a lot of ethical considerations when it comes to AI. Sometimes an AI tool is trained on incomplete, inaccurate, or biased data. With a significant amount of data being fed into an AI tool, there are also legal and security risks, especially when it comes to customer information. Be mindful of using quality tools, checking assumptions, and securing your data.