Last week we retrofitted your old website’s blog with  AI.  Now it’s a beacon to potential customers. Let’s focus this week on your customers who have interacted with you.

In today’s data-driven world, small business owners have a powerful tool: AI-powered data analysis.  You don’t need to have taken statistics in college or have an MBA anymore to understand how your customers interact with your business when they contact you, and what questions they ask can significantly enhance your customer service and marketing efforts. Even if you have sales receipts with detailed info like time of day, products, etc you can prove your intuitions correct.

For my wedding business, I had ten years’ worth of forms filled out with customer information.  Not all that filled out a form became customers, and many were spam, but I could export them into a .csv file on WordPress that I could upload to Chat GPT.

From there I could ask all sorts of questions, such as what day of the week do most of my inquiries come?  Turns out it was Wednesday.

Even if you don’t have a form, maybe you just have emails or spreadsheets with leads and conversions it doesn’t matter, AI can decipher the date, especially if you include as much detail as possible in your prompting.

Using my case as an example. Here’s how you can leverage AI to gain actionable insights from your customer data easily.

Getting Started with AI for Data Analysis

1. Understanding Customer Interaction Patterns

Once you have given AI all the data, all the emails, all the receipts you have, one of the simplest yet most impactful ways to use it is to analyze when and how customers interact with your business. Here are some queries to get you started:

Queries by Time of Day

• Analyze Response Count by Hour of the Day: This helps you understand which hours are most active for customer responses, I’m a one-man show for the most part but if you have a small office even, knowing when customers come and spend their money. allows you to optimize staffing and response times.
• Peak Hours for Different Days of the Week: Identify peak submissions or peak times for each day of the week, enabling you to plan in-store, or online promotions or support more effectively.  For example, if your dinner business drops mid-week you can offer a promotion like half-priced burgers or similar based on the data.  Or maybe it’s better to close on Wednesdays and open Mondays.

Queries by Location

• Identify Geographic Patterns: If you have location data, analyze response counts by city, state, or region to see where your customers are most active. Then adjust your advertising.  Change your Google ads to ensure you are hitting areas where people are looking.
• Cross-Reference IP Addresses with Locations: Match IP addresses to geographic locations to determine where most referrals are coming from.  Not everyone is going to have an IP address but if you do it’s, going to spit out the information in seconds versus you needing to go one by one through the data which could take days.

Queries by Referrer

• Top Referrers by Weekday: Determine which referrers (e.g., social media, search engines) are most effective on different days of the week.
• Referrer Effectiveness Over Time: Analyze how the effectiveness of different referrers changes over months or years. We went from the web to Facebook to Instagram, and understand that often growth and even survival, comes from the trends .  I was mostly relying on intuition. It’s still a little nebulous but figuring out where people are referring you is one of is not the most important item to focus on.

2. Delving Deeper into Customer Messages

AI can also help you analyze the content of customer messages to uncover common themes, questions, and sentiments.

Queries by Message Content

• Common Keywords in Messages: Perform text analysis on messages to find common themes or keywords, which can inform your product development and customer service strategies. What are the frequently asked questions? Maybe you can address them on your website or profiles before they contact you to create a better funnel.
• Sentiment Analysis of Messages: Determine the overall sentiment (positive, negative, neutral) of the messages to gauge customer satisfaction and address issues promptly. How can you improve the mood of your customers before they even interact with you or your product?

3. Visualizing Data for Better Insights

Visualizing your data can make it easier to identify trends and patterns, helping you make data-driven decisions.  You can create charts and graphics within the chat.

Visualization Queries You Can Try.

• Heatmap of Response Times: Create a heatmap showing response counts by day of the week and hour of the day to pinpoint peak activity periods.
• Trends Over Time: Plot the number of responses over time to identify trends or seasonal patterns that could influence your business strategy.

By leveraging AI for data analysis, small business owners can gain valuable insights into customer behavior and preferences. These insights can drive better decision-making, improve customer satisfaction, and ultimately, boost your bottom line.

Would you like to explore a specific query from the list above or have any other specific analysis in mind? Let us know how I can help you get started with AI-powered data analysis today!