Mocke: AI Email Campaign Simulation

This podcast introduces Mocke, an AI agent designed to simulate email campaigns, allowing users to predict outcomes like open, reply, and unsubscribe rates without sending actual emails. This tool addresses common challenges in email marketing, such as the slow testing process, lack of feedback on campaign failures, and the risk of burning through real customer lists. Mocke provides detailed insights into why emails might be ignored or reported as spam, offering an unprecedented level of analysis for optimizing message-market fit. The creators highlight its ability to deliver rapid results and precise reasons for campaign performance, thereby revolutionizing how businesses approach cold email outreach. Initial user feedback and creator responses affirm the tool’s potential as a “game-changer” for A/B testing and understanding email campaign effectiveness.

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Frequently Asked Questions

What problem does Mocke aim to solve for email marketers and sales teams?

Mocke addresses several critical issues in cold email campaigns: the slow feedback loop (2 weeks to a month for results), the “guess game” of not knowing why campaigns fail (no clear reasons for low engagement), and the waste of “burning through” real customer lists for testing, which can harm reputation. It particularly focuses on the challenge of “messaging-market fit” – knowing if an email’s content will resonate with a specific audience before a live send.

How does Mocke work to simulate email campaigns?

Mocke utilizes an AI agent to mock cold email campaigns. Users upload their campaign’s CSV file, and with a single click, the “mocked” campaign is launched. The AI then simulates the sending process and generates results in as little as one minute. This simulation provides insights into how real leads might interact with the email without actually sending it.

What kind of results and insights does Mocke provide that are not typically available in real-world campaigns?

Beyond standard metrics like predicted open, reply, and unsubscribe rates, Mocke offers critical, often hidden, information. This includes how many people reported spam, how many forwarded internally, how many ignored/archived/deleted the email, and even the leads’ simulated thoughts behind their actions (e.g., why they opened, read a second time, or replied). It also provides an AI-driven analysis of the campaign, offering unseen insights instantly.

How does Mocke claim to change the game for cold email outreach?

Mocke transforms cold email by offering significantly faster feedback, changing a 2-week waiting period into a 1-minute result. It replaces the “guessing game” of campaign failure with exact reasons for low engagement. Crucially, it allows users to test messaging against mocked customers as many times as desired, eliminating the risk of harming their reputation or burning through real customer lists during the testing phase.

How reliable are Mocke’s simulated results compared to real-world campaigns?

Mocke aims for strong self-consistency, meaning the simulation will provide similar results when testing the same group and message. It also injects variations to mimic real-world scenarios. While Mocke cannot guarantee real-world results due to external factors like deliverability infrastructure (which it doesn’t control), its back-testing against internal campaigns has shown similar outcomes. The primary goal is to instill confidence in users that their messaging and lead targeting are correct, allowing them to focus on other aspects of cold outreach.

What kind of cold email volumes and performance outcomes have the creators of Mocke experienced themselves?

The creators of Mocke, having a startup journey involving significant cold emailing, have sent up to 1,000,000 cold emails daily. They’ve observed a wide range of performance, from a best-case scenario of 80% open, 25% reply, and 10% interest, to a worst-case of zero interest or replies. They highlight that even with advanced tools, the “messaging-market fit” remained a persistent challenge.

Who created Mocke, and what is their background?

Mocke was created by co-founders Matt Sun and Daniel Wasserlauf, who met in 2021 at a previous company. They started building their current business in 2022 as part of the YCombinator S22 batch. Over the past 3+ years, they’ve built multiple products, made a major pivot, interacted with numerous investors, and served a diverse range of clients from large public companies to pre-seed startups. They operate with a globally distributed team across four time zones.

What are the main benefits of using Mocke for A/B testing and campaign optimization?

Mocke provides immediate, in-depth feedback on messaging effectiveness without the time and reputational costs of live testing. It allows users to A/B test different email copies, CTAs, and targeting strategies against a simulated audience in minutes. This enables rapid iteration and optimization of campaigns, ensuring that only the most effective messages are sent to real prospects, saving time, money, and preserving the quality of customer lists.

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