Demand testing for B2B SaaS products is difficult. At HVL, we know this first-hand. You’re pitching products that are still in development. You’re attempting to capture the attention of a market that has studied every tactic and learned every trick in the book when it comes to cold outreach. That’s why, in reality, 90% of your growth experiments will fail. And as a result, iterations and pivots will become not only necessary but an everyday task. So, how do you find time to create, set up, execute, and track all these experiments? One word: Automation.
By automating your email marketing, you can quickly evaluate what works for your product and make necessary changes. Automation not only saves you time, but it also frees up your attention for more important tasks.
Early Demand Generation at HVL
Demand generation is an essential part of the new product development process. As a SaaS founder, you should be testing demand long before you build the product, and long after you launch. For the HVL Studio team, experiments and speed are the name of the game.
It’s critical to know what works and what doesn’t when it comes to testing assumptions, assessing opportunities, and creating demand for a new product. And it’s critical to understand this quickly.
We design these experiments to test individual variables such as targeting, messaging and positioning, value propositions, and channel strength. Cold email campaigns, when automated, can help speed up the process considerably.
Cold Email Automation at HVL
After we prioritize our unknown variables and experiment backlog (we recommend the ICE Scoring Model for this), it’s time to start designing, building, and running them.
For cold email experiments, our process typically follows these steps:
- Hypothesize: What do we believe to be true that this test will help us validate or invalidate?
- Set key metrics: What are we measuring? How will we know if the results are valid or invalid?
- Design: This includes a detailed breakdown of the experiment flow from first impression through completion of key metrics, including tools that will be used for execution.
- Build: This is when copy, creative assets, and any other needed content are drafted – including list prospecting (we’ve had success with Hunter and ZoomInfo). Experiment materials are then moved over to the tools and platforms we defined in the design phase and configured to follow the experiment flow.
- Monitor: It’s important to check on experiments, especially those with multiple steps, as they’re running. Keep an eye out for early insights or red flags.
- Analyze: Compare results against key metrics and benchmarks to determine if your hypothesis was validated or invalidated. Was there anything surprising? Based on the analysis, what iterations could be made to improve results going forward?
Our team builds email sequences, or campaigns, based on the experiment design and begin adding and organizing contacts from prospecting.
After enrolling our contacts in their respective campaign, we monitor open, unsubscribe, and bounce rates in the campaign report. Each campaign report allows us to see each contact’s status within the campaign and filter contacts based on a number of actions.
When you’re ready to take your early demand generation tests to the next level, using a cold email automation tool is a great solution. Not to mention that access to analytics is crucial for optimizing your cold email campaigns further.
Learn more about validating SaaS startup ideas here.