AI-driven insights are revolutionizing marketing campaigns by unearthing compelling narrative possibilities from vast quantities of human and behavioral data. For most businesses, that data resides in Excel documents.
Here is a quick guide to using AI to draw insights from Excel data but remember; any data you upload to an AI platform may be used to train that platform. For this reason, any proprietary information you share should be anonymized to protect it from potential public exposure. Bader Rutter prohibits uploading Excel data to AI platforms without client or agency permission, even if the data have been anonymized.
Got that? Good. Now gather an Excel spreadsheet and give it a try.
1. Define the Objective:
For example, you may want to uncover patterns in customer buying habits over the past year. You will get better results by defining your objective more precisely.
2. Prepare and Upload Data:
Ensure your Excel file is formatted clearly with relevant columns. Each row might represent a transaction or purchase date.. Before uploading, ensure that all data respects privacy regulations and guidelines; scrub or anonymize personally identifiable information (PII). Always handle sensitive data with care, using secure methods for uploading and analyzing.
Example: Your initial Excel sheet contains customer emails and the times of their purchases. Given your focus is on buying habits and trends, “Customer Name” and “Email Address” are not necessary. Remove or anonymize these columns to protect customer privacy before proceeding.
3. Choose the Right AI Tool:
To select an AI tool suitable for analyzing buying habits, consider platforms tailored for that specific functions or ones that are adept at handling diverse datasets. ChatGPT-4, with its advanced language and analytical capabilities, can be useful for gaining insights, particularly when the data requires nuanced interpretation or contextual understanding.
Example: If you’re unsure about some anomalies in your buying habits data, use ChatGPT-4 to review these patterns and gain a clearer understanding of potential reasons or trends. For visualization or deeper analysis, you might want to integrate a tool like Tableau or Power BI.
4. Run Analysis Using AI:
Depending on your selected tool, set parameters or choose an analysis type tailored to buying patterns.
Example: You might choose a “Customer Segmentation” or “Purchase Trend Analysis” feature. The AI might reveal that a sizable portion of customers tend to buy certain products in the first week of every month or that there is a surge in online purchases during holiday seasons.
5. Interpret and Act on Insights:
Once you’ve extracted meaningful patterns from the results, cross-check them with your business realities to confirm them, then decide on any next steps to take.
Example: After discovering that a large customer segment consistently buys organic products online, you might launch a targeted online marketing campaign for organic products or introduce new organic product lines.
Utilizing your team is critical to broaden the context of the problem and provide considerations that the AI cannot. AI output quality relies on having your analysts set proper guidelines. AI cannot replace the analysts, but it can empower them to dive more deeply and quickly into insights that can change the course of your company.
About the Author
As the Manager of Analytics at Bader Rutter, Jon Schotte and his team define client goals and deliver actionable insights around marketing performance. Jon started his career in the travel and tourism and higher education marketing industry as an analyst and data engineer, before he transitioned into the e-commerce industry focusing primarily on data engineering and automation to provide data in real-time dashboards. Jon’s dedicated team is working to provide clients with the latest industry standards for data reporting and insight, focusing on telling the story behind the data.