Ben leads Customer Success at AnalysisGPT, passionate about making sure every customer gets real value from the platform. A Dalhousie Commerce grad with a team-first mindset, he can be found bouldering, perfecting his pizza, or talking rugby.
Why Your Business Data Lives in Five Different Tools and What to Do About It For a small business founder or operator, pulling up a weekly revenue report should be simple. But by the time you check Shopify for sales, QuickBooks for cash flow, HubSpot for leads, Google Sheets for
Most startups don’t have a data shortage. They have sales data in Stripe or Shopify, marketing data in HubSpot or Google Ads, finance data in QuickBooks, and extra reporting buried in spreadsheets. At first, one person can pull those numbers together manually to track basics like revenue and marketing
analytics
It’s Monday morning. You see an urgent email from your operations leader asking for “the spreadsheet,” the one that shows last quarter’s numbers. Three people respond immediately with three different files. They all say “FINAL” in the file name. Nobody knows which one is current, or whether any
analytics
Today, we're launching native Shopify connections in AnalysisGPT. Merchants can now connect their Shopify store directly and start asking questions about their business in plain English. No exports, no spreadsheets, no data engineering required. Your Store Data, Answering Back in Real Time Running a Shopify store means sitting
advanced data analytics
You sat through a Tableau demo, and 10 minutes in, you were already lost. It has too many features, too much complexity, and no clear link to how it actually helps your business. Or maybe you skipped the demo and went straight to the pricing page, which was enough to
advanced data analytics
Discover the 7 best AI analytics tools for small businesses. Compare time-to-value, pricing, integrations, and features. No SQL or data engineering required.
advanced data analytics
AnalysisGPT vs Julius AI: Compare features, workflows, connectors, security approach, and pricing to choose the right analytics tool for your team today.
data silos
Enterprise data is often scattered across multiple silos—CRM systems, sales trackers, inventory databases, work orders, and spreadsheets. This fragmentation poses significant challenges for data analysts seeking to extract meaningful insights. Enterprise data typically resides in multiple silos such as: * CRM systems with data on customer interactions and sales leads
analytics
In the rapidly evolving world of data analytics, organizations are constantly seeking ways to streamline processes, reduce complexity, and accelerate insights. Traditionally, data analysis has been synonymous with cumbersome Extract-Transform-Load (ETL) procedures and time-consuming data imports. These steps, while necessary for legacy systems, often create bottlenecks, require specialized skills, and
advanced data analytics
In today’s data-driven world, businesses across every sector are inundated with information. From sales figures and customer feedback to supply chain metrics and security logs, the sheer volume of data can be overwhelming. Yet, within these vast datasets lie the answers to some of the most pressing business challenges.