🥁 Prerequisites
Before diving into Meza AI, it's essential to identify your goals, key activities, the metrics you want to monitor, and the data sources.
Goals
If you wish to gain insights into sales trials, automate intelligence gathering, track sales signals, and implement automated user/account engagements, start by reading B2B Product Trials/PoCs 101.
Alternatively, if your focus is on gaining visibility into customer adoption of your product, automating intelligence gathering, monitoring retention signals, and implementing automated user/account engagements, refer to Customer Success 101.
Identifying Customer Activities to Track
Meza AI can monitor both product and non-product activities of users and accounts. Create a list of activities that represent significant milestones indicating progress towards a successful trial or product adoption. Here's an example of customer activities in the context of a product with features similar to GitHub:
| Activity Name | Description | Data Source |
|---|---|---|
| New account sign up | Account creation in the product | DB |
| New user sign up | New user registration | DB |
| New repository | Creating a new private/public repository | DB |
| New issue | First bug, feature request, or user story | DB |
| Delete repository | Removing a private/public repository | DB |
| Create pull request | Submitting a pull request for code changes | DB |
| Commit code | Committing code to a branch or master | DB |
| Merge pull request | Merging a pull request to implement changes | DB |
| Chat conversation | User chat with customer support | Intercom |
| Create support ticket | Creating a support ticket to report an issue | Zendesk |
| File a feature request | Submitting a feature request | Aha |
| Product crash | Occurrence of a product crash during use | Grafana |
💡 Note
Identifying Usage Metrics to Track
Whether in a sales trial or a product adoption scenario, the focus shifts from product discovery to active usage. Here are example usage metrics for a product offering GitHub-like features:
| Metric Name | Description |
|---|---|
| # of user sign-ups | Number of users using the product in an account |
| # of repositories created | Total repositories created in an account |
| # of repositories created (per month) | Monthly count of created repositories |
| # of pull requests (per month) | Monthly frequency of pull requests |
| # of lines of code committed | Quantity and frequency of code commits |
| # of tickets closed/open | Average ticket resolution rate |
| # of new feature requests | Growth or decline in feature requests |
| # of P1 crashes (per month) | Active disruptions per month |
| Average chat sentiment | Monthly average sentiment in chat conversations |
✅ Tip
Defining a Journey Funnel
Now, it's time to establish a Journey Funnel. This involves defining constituent stages and outlining the associated activities and metrics. In the Meza AI platform, you can specify what it means for an account to reach a particular stage in the Journey Funnel.
Here's an example of journey funnel stages for a product similar to GitHub, based on the activities and metrics defined above:
| Journey Funnel Stage | Activity Condition | Metrics Condition |
|---|---|---|
| Acquisition | New user signup, New account signup | # of users > 0, # of accounts > 0 |
| Activation | New repository, New issue | |
| Usage | Create pull request, Merge Code, Commit code | # of repositories > 5, # of pull requests (per month) > 90 |
| Value | Create a support ticket, Create a chat | Positive chat sentiment, # of pull requests (per month) > 300 |
| Growth | File a feature request | # of users > 10, # of lines of code committed > 50 million |
💡 Note
Defining Lifecycle Conversations and Nudges
At this stage, define the lifecycle conversations and nudges that you want to automate using the Meza AI platform.
What Are Lifecycle Conversations?
Lifecycle conversations are a series of interactions between a company and a user. In the context of Meza AI, these conversations refer to interactions during product use. They can be triggered by activities within or outside the product. The goal is to create a positive customer experience, leading to user satisfaction and value realization.
What Is a Behavioral Nudge?
A behavioral nudge is a subtle suggestion or encouragement to persuade a person to take a specific action, both inside and outside the product. The aim is to influence decision-making and encourage specific actions, such as making a purchase.
Here are examples of lifecycle conversations and nudges that can be used with a product offering GitHub-like features:
| Activity Name | Lifecycle Conversation (LC)/Nudge | Destination |
|---|---|---|
| New account sign-up | Welcome Email to admin (LC) | N/A |
| New user sign-up | Welcome Email to the user | N/A |
| New repository | Nudge to create a new issue (after 5 days) | New issue |
| New issue | Congratulations (LC) | N/A |
What's Next?
If you've followed along so far, you're ready to get started with Meza AI.