We’ve all heard the buzz surrounding agentic AI agents. What’s missing for many of us is how they can help our business. What is an AI agent? Can it really perform tasks and get things done?
I asked those questions and more to Anthony DelPizzo. He’s with Triple Whale, the Shopify-backed ecommerce analytics platform that has launched its own AI agent called Moby. It responds to ChatGPT-like prompts, suggests marketing channels, and even composes emails.
The entire audio of our conversation is embedded below. The transcript is condensed and edited for clarity.
Eric Bandholz: Who are you, and what do you do?
Anthony DelPizzo: I lead product marketing at Triple Whale and have been here for about nine months. Before that, I spent nearly four years at Klaviyo.
Triple Whale is an analytics platform for ecommerce brands. We merge fragmented data across marketing and sales into a single system and dashboard to help merchants make strategic decisions. To date, we’ve processed over $65 billion in gross merchandise volume.
We launched Moby, an agentic AI agent, about a month ago after a long testing phase. Moby is a set of AI tools that interact directly with merchants’ data. Think of it as ChatGPT focused on the platforms you already work with. Merchants can ask Moby both simple and complex questions and get answers tailored to their own data.
Moby Agents take it a step further. They’re akin to autonomous teammates that can analyze information, generate insights, and even take actions across ad platforms, marketing channels, operations, and more. The result could be much higher conversions or lower overhead.
Moby is built on Triple Whale’s massive data warehouse. It draws on those benchmarks and works natively with metrics such as CAC and ROAS. By using the data, Moby can connect cleanly with large language models such as Anthropic and OpenAI for each type of query.
Moby is embedded within the Triple Whale platform. It doesn’t just analyze; it can also perform tasks such as activating ads or drafting emails.
Bandholz: Do you share customer data with those LLMs?
DelPizzo: We have privacy agreements with all LLM partners. Data stays within Triple Whale’s private environment. We’re not sending entire datasets to Anthropic, OpenAI, or any other company. Instead, Moby provides context to the LLMs based on the prompt, allowing our customers to use the LLMs securely.
For example, a prompt could be, “How should I prepare for BFCM to grow revenue 30%?” Moby’s Deep Dive feature breaks requests like this into multiple steps, with each acting as an agent examining a different aspect of the business. The result is a structured plan merchants can use to prepare for Black Friday and Cyber Monday.
Merchants use Moby for general prompts and analysis, not just seasonal planning. We provide a prompting guide to help start with effective questions and then refine the queries through follow-ups.
Bandholz: Say I prompt Moby to analyze my sales, margins, and ads, for guidance. What then?
DelPizzo: Moby would connect to your data as a Triple Whale client — product margins, SKUs, ad performance, Klaviyo, Attentive, logistics, and more. By analyzing these inputs, it can identify growth levers, such as which products or channels drove profit last year and which ones are trending now. For instance, if a brand has started performing well on AppLovin, the mobile ad platform, Moby might suggest scaling there for BFCM.
Triple Whale’s platform includes eight attribution models, along with post-purchase surveys, to track what’s driving results. We’ve also added marketing mix modeling to measure the impact from click and non-click channels, including Amazon. Moby can run correlations at a statistically significant level, which gives merchants confidence in the conclusions.
Based on that, it forecasts likely outcomes tied to business goals. If a brand wants to grow revenue by 30%, Moby highlights which levers — spending, channels, creative — are likely to help reach that target. Merchants can even see Moby’s reasoning step by step, like watching strategists think through a plan.
Moby’s analysis isn’t limited to numbers. Using AI vision, it can review ad creative, such as color choices, hooks, and copy. It also analyzes email performance by scanning HTML, subject lines, and preview text. It can draft email copy informed by this analysis, giving merchants ideas to test.
Bandholz: Can you cite anonymous customer wins from Moby?
DelPizzo: We rolled out early access to Moby and Moby Agents in February, five months ago. In April, a $100 million global brand used it during a four-day giveaway. On the final day, the team asked Moby, “What should we adjust in our plan?”
Moby responded with a detailed budget allocation by channel and predicted the revenue impact. They followed it exactly and ended up having their highest revenue day ever — 35% above their previous record, more than $200,000 higher.
Another example is LSKD, a fitness apparel brand in Australia with more than 50 stores. They used Moby to analyze the performance of their marketing channels. One agent uncovered over $100,000 in fraudulent spend from an influencer’s self-bidding, which saved the company that money. Since adopting Moby Agents, LSKD’s ROAS has grown about 40%.
Bandholz: How can merchants go wrong with Moby?
DelPizzo: The most common challenge is trying to adopt too much at once. Success usually comes from starting small. We provide a library of 70 pre-built agents, but using all of them right away can feel overwhelming.
The best outcomes are from teams that begin with a single agent, adapt it to their business, and build confidence with steady results. From there, they expand to other areas — maybe they start with the conversion rate optimization team, then retention, then other steps in the funnel. That gradual approach tends to be more sustainable.
Bandholz: Why use Moby instead of building a custom data tool with an LLM such as DeepSeek?
DelPizzo: One factor is the dataset it draws from. Moby is trained on $65 billion in GMV and has access to broad ecommerce benchmarks. It’s not about sharing brand-specific data but rather using aggregated insights to provide context — like knowing typical CAC or ROAS levels in different industries, or, say, margins for apparel versus skincare.
Another piece is the infrastructure. Building from scratch requires a unified schema for orders, events, and performance data. At Triple Whale, our large team of engineers has worked on this for years, and it’s still evolving. Without that groundwork, it’s hard to achieve the same level of ecommerce-specific intelligence.
Custom setups are possible, but Moby combines benchmarks, context, and infrastructure in a way that’s difficult to replicate.
Bandholz: Where can people support you, follow you, reach out?
DelPizzo: Our site is TripleWhale.com. Our socials include X and LinkedIn. I’m on LinkedIn.
