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What is Feature Exploration Data & How to Use It for Triggering Campaigns?

Feature exploration data

Wondering what is feature exploration data and whether it is just another tech gimmick or is really valuable?

One of the most effective tools for engaging your users and boosting your conversion rates is feature exploration data. Understanding it can help you understand the story behind the clicks – what features your customers are exploring, how they use them, and the context of their interactions. This insight lets you tailor your marketing efforts, especially cold email campaigns, to match user behavior and preferences.

As you dive deeper into this article, we will see how you can collect this feature exploration data and use it for personalized, timely, and effective marketing strategies for your campaigns.

What is Feature Exploration Data?

Feature exploration data is an analytical tool that offers deep insights into how users interact with specific features within a software application. SaaS businesses should make informed decisions about which aspects of their service provide value to users and which may require redesign or improvement.

How can Feature Exploration Data Help You Improve Conversion?

Feature exploration data serves as a pivotal resource in optimizing product features to boost user conversion rates. It can be instrumental in improving conversion rates by: 

  • Identifying High-Performance Features: Data gathered from user interactions can highlight the most engaging and beneficial features. Focusing efforts on these high-performance features can attract more users, encourage deeper engagement, and help you attain higher conversion rates.
  • Optimizing Onboarding Processes: By understanding which features trial or new users interact with during their initial use, you can streamline the onboarding process with tailored introductory guides or tutorials.
  • Uncovering and Addressing Pain Points: Feature exploration data often reveals areas of a product that users avoid or use less frequently. Investigating the reasons for this underutilization lets you make tailored improvements to enhance user satisfaction and conversion.
  • Enhancing Feature Visibility and Accessibility: Insights from feature usage can inform design changes that make important features more visible or accessible to users.
  • A/B Testing to Validate Changes: Once you know what to improve through feature exploration data, you can do A/B testing to test different versions of feature presentations or workflows. This method will provide concrete evidence about which changes most effectively increase user engagement and conversion rates.

What Data Should You Collect from User’s Feature Exploration?

The goal of gathering feature exploration data is not just to see where users click but to understand the context and impact of their interactions. It helps you refine and align the product roadmap with actual user needs. The key types of data that should be collected:

  • Quantitative Usage Metrics: This data type includes click rates, time spent on specific features, and feature frequency. These metrics help identify the most and least popular features and offer a baseline understanding of user engagement.
  • Qualitative Feedback: Collecting feedback through user surveys or interviews is crucial. This feedback explains the reasons behind user preferences and dissatisfaction, providing context to the quantitative data.
  • User Journey: Mapping out the sequence of actions that users take within the application can reveal common navigational patterns and pinpoint areas where users face difficulties or disengage.
  • Event Logs: Detailed records of user interactions, such as clicks, scrolls, and mouse hovers, should be logged. Analyzing these logs helps you uncover detailed user behavior patterns and the effects of any product changes.
  • Contextual Data: Gathering information about the user’s device, operating system, geographic location, and usage time can provide insights into how different conditions affect feature use. This data is valuable for segmenting users and customizing the product experience accordingly.

How to Trigger Campaigns Based on Feature Exploration Data?

Let’s go through a step-by-step guide on triggering campaigns based on the feature exploration data from your users.

Step 1.  Collect Feature Exploration Data

Collecting feature exploration data involves tracking and analyzing user interactions with your software or app. This data provides quantitative metrics like click rates and time spent on features and offers qualitative insights through user feedback and behavior patterns. For instance, if you notice a high engagement rate on a new feature, you might consider launching a targeted email campaign to highlight this feature to users who may not have tried it yet.

The key is to use this data to segment your user base. For example, you can identify users who frequently use a particular feature and those who don’t. By understanding these usage patterns, you can create tailored messages that speak directly to how users interact with your product.

You should integrate tools that automatically track and analyze feature usage and integrate this data with platforms to effectively implement this approach. This automation ensures that your campaigns are triggered by real-time data, enhancing their relevance and impact.

Step 2. Analyze the Data

You should first segment the data to identify distinct user behaviors and trends. Look for patterns such as which features attract the most engagement or which are frequently ignored to increase your understanding of the data.

For example, if data shows that a significant portion of users frequently use a task management feature but rarely use calendar integration, it could indicate an opportunity to educate users about the benefits of this underused feature.

You should use advanced analytics platforms to dive deeper into the data. These tools can help you understand the factors driving user engagement. For example, users who engage with certain features have a higher conversion rate. 

An illustrative example of this process in action could involve a SaaS project management tool where analysis of feature exploration data reveals that many users frequently add tasks to their lists but need to utilize the tool’s collaboration features. A targeted email campaign to offer a free training session or guide on optimizing team collaboration can be helpful. The emails should encourage users to engage with these underused features fully.

Step 3. Segment Your Audience Based on Collected Data

After analyzing the data, you should identify distinct groups within your user base. These groups can be formed based on several criteria, such as frequency of feature usage, user pathways, and engagement levels. For instance, you might segment users into “power users,” who fully use all features, and “minimal users,” who only use basic functions.

Suppose a SaaS company offers a learning management system. The company can tailor its communications to match specific user learning behaviors by analyzing users’ interactions with different educational modules. 

Users who frequently interact with modules on business management can receive notifications about upcoming webinars or new courses in this domain. In contrast, those engaging in IT skills development might receive recommendations for advanced certification programs. This approach will enhance personalized learning experiences and user engagement.

Step 4. Design Your Personalized Campaigns

Designing personalized campaigns tailored to specific user segments enhances user engagement and increases the likelihood of conversions by delivering messages that resonate personally with your audience. When campaigns are designed with a deep understanding of user behaviors and preferences, they meet and often exceed the recipients’ expectations.

Personalize your emails with SalesMix based on Feature Exploration Data

Personalize your emails with SalesMix

To begin designing your personalized campaigns, you should utilize the segments you’ve identified in your data analysis. Create unique campaign messages that address each segment’s specific needs and interests. 

For example, for users who heavily utilize a particular feature, craft messages that delve deeper into advanced uses or introduce complementary features that could further enhance their experience.

An effective personalized campaign should also consider the user’s journey. New users will benefit from introductory content that helps them get the most out of your product, while long-term users might appreciate advanced tips or insights into new features. This approach ensures that each message is relevant and timed perfectly to match the user’s familiarity and engagement level with your product.

Step 5. Implement an Automatic Trigger-Based Cold Email Campaign

To set up an automatic trigger-based cold email campaign, start by defining the triggers based on user actions. These triggers could be anything from a user signing up, using a specific feature for the first time, or returning to the application after a period of inactivity.

Each trigger should align with a specific cold email campaign to address the user’s engagement with your product. For instance, if a user explores a new feature but doesn’t fully engage, a follow-up email can provide additional information or a personalized tutorial to guide them further.

The next step involves crafting tailored email content that resonates with the context of the trigger. This means creating messages that acknowledge the user’s recent activity and offer value, such as tips, guides, or even promotional offers related to their interests. Ensuring these emails feel personal and directly relevant to the user’s actions is key to increasing engagement rates.

For example, consider a SaaS platform where a user experiments with a data visualization tool but doesn’t utilize its advanced functionalities. An automated email triggered by this action could include case studies illustrating the benefits of these advanced features or offer a free consultation session to help them maximize the tool’s potential.

Step 6. Analyze Your Campaign Performance & Optimize

The final and perhaps most critical step in leveraging feature exploration data for campaign management is analyzing your campaign performance and optimizing future efforts. This phase is essential because it closes the feedback loop, lets you measure your campaigns’ results, and makes informed decisions about adjustments needed to improve outcomes.

You need to track key performance indicators (KPIs) such as open rates, click-through rates, conversion rates, and other metrics relevant to your goals. It’s crucial to not only look at aggregate data but also to drill down into the performance of specific segments and triggered emails. 

For example, suppose a specific campaign designed to re-engage users who have abandoned a feature shows unusually high open rates but low conversion rates. In that case, it might indicate that while the subject line is effective at grabbing attention, the content of the email does not successfully persuade users to take the desired action. In such cases, A/B testing variations of your email content can help you pinpoint more effective messaging strategies.

How can SalesMix Help You Trigger Campaigns with Feature Exploration Data?

Imagine having a smart assistant that lets you act on the feature exploration data you’ve collected – this is what SalesMix is designed to do.

Once you gather feature exploration data through the analytics tools of your choice, SalesMix can use important data from the tool to launch your email campaigns. It seamlessly integrates with some of the best analytics tools, such as Mixpanel, Google Analytics, Amplitude, Heap, Pendo, UXCam, and Logrocket.

Integration in SalesMix

Integration in SalesMix

How this works is whenever your users have any interaction that you define as trigger events – be it a user who interacts with a feature for the first time, shows signs of frustration, or uses a key feature that has led to conversions in the past. After the user does any of these actions, SalesMix is ready to send personalized emails triggered by these specific actions. This proactive approach ensures that your communication is timely, relevant, and highly personalized, increasing the chances of engaging users effectively. 

SalesMix allows for the scheduling of follow-up messages. You can set these emails to go out after several days. This approach helps you keep the conversation going and gradually build value around your services.

Follow-up Sequence in SalesMix

Follow-up Sequence in SalesMix

SalesMix enhances your email campaigns through advanced personalization capabilities. It integrates data from your CSV files, enabling dynamic content insertion, like adding a recipient’s first name or customizing terms related to their workplace. It also excels in scheduling by letting you time your emails perfectly, considering your recipients’ time zones, and avoiding US public holidays to increase open rates.

Email Scheduling in SalesMix

Email Scheduling in SalesMix

The platform also offers an automated warm-up service, which can be activated with a single button press. It helps your emails maintain a high deliverability rate, avoid the spam folder, and strengthen your email campaign’s effectiveness. A pre-send testing feature is also there to check whether your emails are landing in the inbox or the spam folder.

Conclusion

Diving deep into feature exploration data is about connecting the dots between what your customers do and what they need. This data is a goldmine for any SaaS founder looking to understand and actively engage their user base.

You can use the ideas discussed in this article to start campaigns that feel personal to your users and prompt them to consider your service. You should think of your users as more than data points and understand them to take the next step in pursuing them as paying customers.