Predictive analytics (PA) have transformed our online interactions in powerful ways. You’re continuously shown ads for products or services related to what you’ve just browsed or purchased. When perusing profiles on a dating site, you’re offered a list of people who have interests and lifestyles that are likely to be compatible with your own. And, searching the web has never been easier since you’re given suggestions for similar search queries that may lead you to the information you’re seeking.
Naturally, app developers have started integrating predictive analytics solutions in the mobile sphere. This data holds the power to transform your app, improving user experience, increasing engagement and bolstering your bottom line. For mobile applications, you could theoretically add a PA engine at any point in the app’s lifecycle. But ideally, this integration should occur during the pre-development planning phase. So how do you go about integrating predictive analytics into your mobile app?
Does Your App Involve User Choices or Search Options?
Apps that include a search feature or involve an element of choice—such as an ecommerce app or a dating app—can see dramatic improvements with the addition of a predictive analytics feature.
PA technology can be used to collect data on the searcher, from their location, age, gender and interests to their browsing and buying habits. As the user interacts with the app, more and more information is collected and over time, trends and parallels quickly become apparent. Individuals in a certain location may be more likely to buy a particular brand, while another type of user may be more likely to purchase a certain accessory if their purchase history includes a specific item.
The bottom line is this: if your app involves an element of choice or a search function, then you may see a major benefit from a predictive analytics integration.
How (and Where) Will You Collect Data?
Data collection is the first and most important point to address when considering a predictive analytics solution. Predictive analytics require data (and lots of it!). Where are you going to collect the data your developer needs to create an efficient and accurate PA engine?
Data sourcing is a major consideration for the initial PA development process and for the long-term. The most accurate predictive analytics engines receive continual data input. This allows the engine to become more accurate over time and helps it evolve. For instance, if you’re building a rideshare app, you may see a big seasonal fluctuation in demand. Therefore, it’s critical that your PA engine can adapt over time, identifying trends and adjusting its recommendations in a manner that allows you to maintain accuracy.
Uber’s surge pricing model is a wonderful example of how real-time data can be leveraged to improve user experience. Surge pricing maps provide drivers with information on geographical areas that are seeing increased demand. This allows for pre-positioning, so the driver can quickly respond to a rider’s request for pick-up. Surge pricing maps are perpetually evolving, as Uber’s PA engine receives a constant data stream that allows it to make accurate predictions regarding areas of high demand.
How Can You Leverage the Predictions?
You’ll want to be sure you’re equipped to make the most of the data and predictions a PA engine generates. From a UI design perspective, it can be challenging to find a balanced, appealing design since you’re working with limited space in a mobile format. This means you’re restricted in terms of how many options or predictions are presented to users. For instance, if you’re developing a dating app, you may have a predictive analytics interface that recommends potential matches. However, you may only have space to display three profiles, so you’ll need to figure out how to achieve this in a way that delivers maximum effect.
You’ll also want to consider how you can leverage that information in other areas of the app. That dating app may have a section with listings for popular restaurants, venues and date ideas in the user’s region. Ideally, your developer could program the app so it pulls data from the PA engine when generating those listings. For example, you may find that users of a certain age, gender and income level who live in a particular region tend to enjoy restaurant X. Therefore, it may be beneficial to display restaurant X at the top of the list for users who fit that criteria.
In short, there may be multiple areas of a single app that can benefit from predictive analytics, so work closely with your developer to make the most of this technology.
Additionally, you may find that the PA data collected via your app could be useful for other projects, such as app marketing efforts and data-driven decision making. If this is the case, then your developer could include a portal that would allow an administrator to download the latest data set.
As a whole, this technology gives you the ability to predict a user’s actions, likes or dislikes. And the best part? PA engines become more accurate over time, as more and more data is added to the pool. Once you have this knowledge, you’ll be better equipped to achieve your objective, whether it’s helping a user find Mr. Right or maximizing your e-shop’s average per-order value.
At SevenTablets, we have created some sophisticated predictive analytics engines. But our quest for innovation does not end there, as our talented development team has also worked hard to pioneer advances in the area of natural language processing, augmented reality, and virtual reality. This means our developers are well-positioned to provide you with the high-tech solutions your company needs to maintain a competitive advantage.
Based in Dallas, SevenTablets also maintains regional offices in Austin and Houston. But our clientele spreads beyond Texas, as we also work with clients throughout the country. Contact us today to discuss our mobile app development services.
Reach out to our team today!
VK studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India and earned a Master’s Degree in computer science at George Mason University.
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