Here is your Tip of the Week from EngageClass! We sincerely hope that we will once again help you do more with WebEngage with our latest tip. Enjoy reading and implementing this one!
Understand how your users interact with your platform using Paths
As a Product Manager or Marketer, you continuously analyze the crucial steps your users take on your app/website to figure out what's not working or can be improved/fixed for the ideal user flow. You get to know where and why your users drop-off in their journey. However, it's challenging to visualize and explore all the different ways in which users navigate your product or what they do before or after dropping off.
So, how can you eliminate uncertainties and pinpoint where your users drop-off in their journey?
We’ve got you covered! 🙂
Using Paths, you can analyze your complete user journey and pinpoint the steps where your users drop-off. Paths also helps you answer questions like "What are my users doing:"
- after signing up?
- before uninstalling the app?
- before purchasing products from a particular category?
How to use Paths to understand how users navigate and interact with your platform?
Step 1: Specify a time frame.
Step 2: Select your Anchor Event.
Step 3: Set a Lookahead Window based on your business use-case.
Step 4: Click on Show Path.
The image above shows a Paths analysis of an E-commerce store after users perform the event 'Cart viewed.'
You can measure these conversion steps with Funnels as well and build a hypothesis around the possible reasons for drop-offs at each step. But with Paths, you get to know exactly what your users do after a specific action (event). Once you identify the event, you can then create journeys to get them back to the main flow.
Though Paths complement Funnels, both of them don't have the same functionality.
Let's understand how Paths supports user engagement and retention efforts across industries:
If you run a Gaming business, you can improve retention by identifying behavioral patterns before your players churn. Churn can mean Subscription cancellations, App uninstalls, Downgrades or anything else that best defines churn for you can become your Anchor Event.
For example, you might discover that your players churn after playing a specific game level for multiple days.
What can you do in such a case?
You can dig deeper to see if offering a bonus on the 7th day or a free power-up to your players stuck at that level can help you retain them.
If you run an E-commerce business, you can find the best path to conversion and replicate it for other users.
An ideal E-commerce funnel looks like this:
Product Searched → Product Viewed → Added To Cart → Checkout Started → Product Purchased
But in reality, it’s oversimplified. Using Paths, you can conduct an analysis to see what actions your users take after they search for a product on your app/website.
Perhaps, your users:
- Go back to the search page to view more products
- Start a fresh search
- Discover a new product by navigating the site through the site hierarchy
- Check out the new product launch
If you run an OTT platform, you can use Paths in different contexts to optimize and grow your metrics. You can split a Path by different event attributes and compare how users interact with your app.
For example, you can split your Anchor Event by Country and compare Paths by users acquired from different geographies. You can discover what makes each group stick and customize the experience for different users and versions of your product.
Common examples of path comparisons include:
- Mobile app vs Web app
- High-involvement purchase (mobile phones) vs Low-involvement purchase (case covers)
- The United States vs Rest of the world
- Paid vs Organic users
- iOS vs Android
If you run a BFSI business, you can track user journeys over different time-frames for desired results. You can set different time-frames before or after users perform the Anchor Event. This time-frame is the Lookahead Window.
You might want to assess drop-offs from lead generation forms. To do this, you can set the Lookahead Window to 4 hours to see where your users drop-off and what they do on your platform after viewing the form within the 4-hour window.
Or you might want to see your overall site conversions. For this, 4 hours would be a small window to look at because users easily take a month to convert, owing to a series of verification and approval processes. You can then set the Lookahead Window to 30 days and include the necessary events to analyze Paths.
If you run an EdTech business, you can understand the impact of your new releases and feature roll-outs. Let’s say you release a feature that lets students record a video and share it with their teachers for doubts. Using Paths, you can analyze the different actions your students perform before or after recording the video.
You can also dig deeper by excluding the event ‘Video uploaded.’ This will show if users who aren’t familiar with the feature hit roadblocks or dead ends during their navigation. You can use this signal to improve the onboarding flow by providing tips and guides to using the feature through in-app cards or more.
Media & Publications 📇
If you run a Media & Publications business, you can evaluate the engagement level in different sections of your app. You can perform A/B testing and analyze the same through Paths to validate the hypothesis on your product. Paths analysis is a great way to answer questions like:
- Which are the most commonly viewed sections before a subscription or a purchase?
- What series of actions lead users to leave the app?
- How do the paths to purchase vary for people who select Basic and Premium plans?
- How is the top user flow different, based on the users’ device type, region, or app version?
- What kinds of ‘odd’ journeys do users take to perform a specific event, which can give me a new perspective on a feature or product?
• • •
So, what are you waiting for?
Use Paths Now
Got questions? Please feel free to drop in a few lines at firstname.lastname@example.org or get in touch with your Account Manager in case you have any further queries.