AI-Driven Insights for Developers: Using SMS Data to Improve Software UX

Nov 5, 2024 · 9 min read
AI-Driven Insights for Developers: Using SMS Data to Improve Software UX

Cultivating a good software experience is one of the most value-adding steps a development team can take for its customers. It enhances conversion rates, improves customer loyalty, and helps increase customer retention rates.

Focusing on user experience right from the start of the development process is a marker of a team that cares about its customers and prioritizes their needs more than anything.

But implementing good UX is easier said than done. It takes countless discussions, numerous iterations, and critical client feedback to get to a point where the UX is smooth and intuitive. One of the ways developers can gather customer data, and a particularly powerful one, is through SMS. The potential of SMS data to transform the user experience is immense, and it’s in our hands to harness it.

Let’s explore how SMS data can improve software user experience and how developers can capitalize on AI technology, such as natural language processing and machine learning algorithms, to make data-driven decisions.

The Critical Value of User Experience

User experience, or UX, encompasses the entirety of a user’s interaction with any given software—from the moment they enter the app all the way through the entire customer journey. It’s not just a buzzword but a cornerstone of successful software development. Understanding and enhancing the user experience can inspire us to create software that truly resonates with our client base.

Ideally, a customer’s user experience should be smooth, intuitive, and relatively simple. Processes that take a customer through various hoops and loops to achieve a certain action contribute to a negative UX and should be avoided whenever possible.

According to a Forrester business report, investing $1 into cultivating positive user experience nets a company $100 in return, on average. That’s a fantastic ROI on something many companies fail to capitalize on.

If you think about it, which company are you more likely to patronize, the one with an app that takes forever to load, has a clunky interface, and doesn’t have pathways of action, OR the one with an app that can take you to any action within three clicks, has a smooth interface, and runs fast even on less optimized devices?

Although UX design has certain rules of thumb that make designing for customers easier, a large chunk of it is still highly dependent on what an app’s audience prefers—which is where customer SMS data comes in quite handy.

Using SMS Data for Valuable UX Insights

SMS data can be an excellent conduit for valuable user information. SMS campaigns can be used to:

  • Highlight what resonates with clients;
  • Identify the best times for engagement;
  • Examine how customers prefer to interact.

Let’s dive deeper into more specific ways SMS data can contribute valuable insights for UX design:

Engagement Metrics and User Interest

Getting customers to click on promotional material is not easy—in fact, click-through rates are often pretty low percentages of the total number of messages sent. However, when customers engage with promotional content, it helps signal to developers that users are interested in that type of topic, feature, or software update.

It also doesn’t help that phishing scams have been through the roof in the last couple of years, making customers even more wary about interacting with any type of promotional content.

By analyzing customer engagement patterns during SMS campaigns, developers can gauge user interest and identify features that should be more prominently showcased or more accessible within the software.

Devs can use this information and the data they can gather from the software’s existing user base (i.e., which app features are most frequently used). Let’s look at some examples:

  • Click-Through Rates (CTR): High CTR on SMS promoting a budgeting tool suggests making it more prominent within the app dashboard.
  • Response Rates: A high response rate to SMS offering weekly tips indicates user interest in ongoing in-app guidance or tips features.
  • Conversion Rates: High conversions from SMS promoting a daily planner trial show demand, prompting developers to integrate similar personalized features in the app.
  • Opt-Out Rates: A high opt-out rate from SMS notifications implies users feel overwhelmed, signaling a need to adjust in-app notification frequency.

Timing and Frequency Insights

Any campaign can benefit from the appropriate timing of content delivery. It is in an organization’s best interest to know when users are most likely to engage rather than interrupt them at inconvenient times.

Aside from timing, SMS campaigns can also glean some insight into the optimal frequency for reaching out. For instance, if customers engage more positively with bi-weekly content rather than daily messages, SMS frequency should be adjusted accordingly.

User Segmentation and Personalization

SMS data can also help developers appropriately group customers according to their behavior. For instance, users who engage with brand content more frequently might receive more frequent and detailed content, whereas customers who engage less might receive simpler materials.

Integrating a telecom BSS solution can further streamline this process, enabling businesses to efficiently manage customer relationships and personalize experiences based on user interactions.

In doing so, users receive an experience that is more suited to their level, making the overall software experience more personalized.

Content and Tone Relevance

SMS campaigns are a testing ground for what features and functionalities will be featured on the app. SMS data can be utilized to identify the tone and the type of content that users resonate with the most.

Collecting Feedback

Doing SMS campaigns can be one way to invite users to provide feedback on specific features or services. Sometimes, these campaigns have additional perks to incentivize users to leave feedback.

Analyzing the results of a customer feedback survey can help developers identify which areas of the current project require improvement, helping them plan the next steps more efficiently.

A lot of the time, these feedback surveys will feature questions that relate to how satisfied a user is with a certain feature. As more users respond, developers can track customer satisfaction over time.

Anticipating User Needs

Proactive software development will always be better than reacting to existing user complaints. It’s about anticipating user needs and delivering solutions before they even realize they need them. This forward-thinking approach can set us apart in the competitive software market.

One way developers can anticipate user needs is by analyzing past interactions with SMS campaigns. If campaigns that promote educational content, such as tips and tricks, constantly get more engagement than other content types, then it might be best for developers to develop a dedicated “tips” section within the software or a dedicated onboarding experience for new users.

Here are more examples of anticipating user needs in action:

  • Feature Interest Signals: If an SMS survey asking, “Would you like a dark mode option?” gets highly positive responses, it suggests prioritizing a dark mode feature in the app.
  • New Feature Requests: An SMS campaign promoting a beta version of a calendar sync feature has strong engagement, indicating users would appreciate more productivity-focused tools.
  • User Segmentation: Responses to SMS prompts about preferred activities (e.g., “Are you more interested in tracking steps or calories?”) allow developers to tailor the app experience to fitness or nutrition-oriented users.
  • Seasonal Interests: Positive responses to SMS messages promoting seasonal features (e.g., holiday-themed workouts or budgeting for holidays) show when users are more receptive to timely, seasonal content.
  • Product or Service Preferences: When users engage more with SMS promotions related to specific app features (e.g., personalized playlists), it indicates areas where personalization within the app could be enhanced.

Using AI to Analyze SMS Data for UX Improvements

Natural Language Processing

Natural Language Processing, or NLP, is a field of computer science that utilizes machine learning to aid computers in understanding and communicating using human language. It’s a fantastic area of interest that has direct implications for how humans interact with software.

For example, on the productivity tool Asana, when you type a new task, such as “run tests on Monday,” it automatically sets the task deadline to Monday. Small things like these are an application of NLP and can significantly improve software user experience.

NLP can help developers understand user sentiment based on how customers respond to or engage with SMS campaigns. So, when a particular campaign generates negative interest, it might indicate a need to adjust the app’s offerings or tone.

Predictive Analytics for Behavior Forecasting

Predictive analytics can be a powerful tool that helps organizations more accurately anticipate user actions by monitoring past behavior. In this case, through SMS data.

1. Engagement Predictions

Predictive analytics can help teams identify high-value users and anticipate future feature usage. Through AI analysis of SMS data patterns, businesses can determine which users frequently interact with certain types of messages and take that even further by identifying which users are likely to continue engaging with the app. This helps businesses understand their clientele even deeper and provides developers with more information on feature prioritization and building.

2. Churn Prediction

Churn prediction is a crucial prediction tool used to identify which customers are likely to stop using a product or service. SMS data can be useful for detecting these customers by categorizing which users have stopped engaging with SMS or clicked any opt-out links. Analyzing SMS data using AI can also be helpful in understanding drop-off points or at which point during a campaign a user tends to stop using an app. All this information can be of use to developers as they adjust their re-engagement strategies and ensure users are engaged at all the critical points.

3. Behavior Pattern Recognition

AI can also be a massive help for analyzing any other user behavioral pattern. For example, AI can use SMS data to find out which users are more active in specific seasons.

Personalization with AI

Hyper personalization is one of the best ways any brand can pique a customer’s interest. As individuals, we are bombarded daily with an influx of marketing material telling us to get this or subscribe to that, and it’s incredibly easy for any campaign to get lost in the noise.

With proper user segmentation based on SMS data, it is much easier to create in-app personalized experiences. For example, if an SMS campaign talking about productivity tips gets positive responses, then developers can work towards adjusting in-app tools to provide more productivity-related content.

Artificial Intelligence also allows developers to feature more dynamic content to individual users—ensuring that each user gets features that align with their interests.

Final Thoughts

Software success hinges on numerous factors, one of the most significant being how customers interact with and experience the app—otherwise known as user experience. Some more traditional organizations may hesitate to invest in UX because of its nebulous gains (in that it isn’t always easily translated to measurable results), but they are very clearly missing out.

Prioritizing user experience can be a fantastic way for businesses to stand out to their customers and gain an edge over competitors. Plus, with the help of artificial intelligence, using SMS data as a way to improve software user experience has never been easier.

Cover photo by drobotdean on Freepik