You’ve developed the perfect app concept. The market research has revealed promising data, and you’ve come up with some killer branding. You feel pretty confident going into the development process—except when it comes to the predictive analytics engine. That’s a region of uncharted territory, as you’ve never integrated this technology into a mobile app interface before. So, how do you ensure you’re on a path toward success?
Predictive analytics hold the power to truly transform your mobile app, whether it’s by increasing engagement, improving accuracy and reliability, empowering better decision-making, or driving sales. A predictive analytics engine can be integrated into a wide variety of app types, but integration can be complex, so it’s important that your development team gets it right the first time.
When it comes to implementing predictive analytics, best practices can include everything from verifying you have a solid, relevant data set to ensuring your user interface provides an accurate visual representation of the prediction—whatever it may be. If you follow this approach, you’ll be well-positioned to glean the maximum benefit from this technology.
Be Sure You Have a Large, Valid Base Set of Data
Each predictive analytics engine is unique, pulling data from specific sources, processing that data and generating a certain type of results. Over time, predictive analytics engines get more and more accurate, but the initial set of data your engine is built around is extremely important as it can dramatically impact the PA interface’s efficacy and accuracy.
To build a predictive analytics engine, you must start with a data set that is large enough to generate an accurate baseline. Otherwise, the predictions may be skewed and inaccurate. This could lead to a negative user experience and stifled revenue generation, which are the last things you want when launching a new mobile app. Therefore, it’s critical that you carefully evaluate that initial data set, ensuring it is truly representative of the norm. Generally, the larger the data universe, the better.
Identify and Draw From the Best Data Sources
Nearly all predictive analytics engines are dynamic, meaning they’re constantly evolving their recommendations or predictions as the interface takes in new data. For example, if you’re integrating a PA engine into an e-shopping platform and have found that a particular product has been especially popular in recent weeks, then it would be beneficial for the engine to recommend that product more often.
This means you need to identify high-quality data streams that you can feed into your predictive analytics interface, including any new data sources that arise over time.
Make Predictions Visually Clear
How are you going to represent and convey the predictions or forecasts your predictive analytics engine generates? Some visual representations are fairly standard. For instance, if your PA engine recommends products to a shopper, then it’s customary to have a “recommended products” or “shoppers also bought” section on the app.
However, not every case is this straightforward. Let’s say your predictive analytics engine predicts which local restaurants and bars are likely to appeal to a user. How are you going to represent this in a visual manner? Sure, you could offer a simple list of restaurants, but the more engaging solution might to be offer an interactive map with pins that the user can tap to view details about an establishment.
Take some time to carefully consider what visual representation will be most effective for your specific users and objectives.
Monitor Performance to Ensure Continued Relevance
Once you’ve implemented predictive analytics in your mobile app, it’s vital that you routinely monitor performance to ensure the predictions or recommendations are accurate and relevant.
User expectations can shift periodically, so it’s important to update your predictive analytics engine to account for these shifts, if and when they occur. A change may be outwardly apparent—like a sudden uptick in sales following the launch of a new advertising campaign—or more subtle, such as a gradual evolution in user preferences. Any divergences will be reflected in your user analytics, which will indicate reduced engagement (i.e. a shrinking user base, fewer app launches, shorter in-app interactions, fewer sales and more app uninstalls).
Overall, the key to making the most of your predictive analytics lies in your data and the manner in which you convey the recommendations or predictions derived from that data. If you can address these issues effectively, then you’ll maximize your chances of seeing a significant benefit from this technology.
Once you understand PA best practices, you’ll need an experienced development team that can turn your app concept into a digital reality. At SevenTablets, we specialize in some of the most rapidly-advancing areas of the tech world, including augmented reality, artificial intelligence and, of course, predictive analytics. This means we’re well-positioned to integrate such cutting-edge technology into your app interface.
With headquarters in Dallas, SevenTablets also maintains offices in Austin and Houston, although we work with clients across the United States. If you’re ready to begin the mobile app development process, reach out to the team at SevenTablets today.
Reach out to our team today!
Lacey earned a B.A. from Baylor University. Sic'em!
Latest posts by Lacey Williams-McGhee (see all)
- Undergoing a Digital Transformation in Retail - January 18, 2019
- The Top Uses for Augmented Reality in Education - December 6, 2018
- 3 Ways Predictive Analytics Can Combat the Retail Apocalypse - November 29, 2018