Over the past decade, predictive analytics technology has been introduced into the mainstream. This application of predictive analytics has some useful features, including eBook recommendations based on your reading history and cutting-edge matchmaking systems that leverage societal trends and specific user information to identify potential soulmates. And while the end user may not realize a particular mobile app feature is powered by a predictive analytics engine, this technology’s impact can go a long way toward helping companies achieve their goals and guiding customers to products and solutions that actually interest them.
Whether you’re looking for ways to increase sales, boost user retention, lower company costs or improve user experience, predictive analytics can offer a solid solution—all while helping you increase app revenue.
Using Predictive Analytics to Increase Sales
Predictive analytics can be extremely effective for increasing sales by presenting shoppers with relevant product recommendations and improved search results. These recommendations or results are based on the user’s browsing history, purchase history and even their demographics, which can be captured in a member or user profile. A savvy developer can include an interface that prompts the user to input information about their age, gender, location, interests and any other relevant data as part of the app set-up process.
A predictive analytics engine can also be a useful component of an ecommerce app, as shops tend to see higher per-order value when the shopper is presented with related products in a “shoppers also bought” or “recommended products” section. For instance, a shopper may purchase a mop that uses disposable wipes. The shop can increase sales by displaying a couple different varieties of floor wipes. Similarly, if a shopper is purchasing an MP3 player, there’s a good chance they’ll also purchase headphones—that is, if you serve up a recommendation at the right point in time. This is precisely where predictive analytics come into play, as you’ll have the ability to engage the shopper further while potentially increasing their total purchase value.
Leveraging Predictive Analytics to Increase User Retention
User retention is a major pain point for many mobile apps, but predictive analytics can work to your advantage by creating a better user experience. This can be achieved by anticipating the user’s needs and making adjustments, recommendations or customizations based on solid data.
Predictive analytics give mobile app developers the power to create an application that is highly responsive and user-friendly. For example, you may design a predictive analytics engine that monitors GPS data for the device, making note of which stores and restaurants the individual appears to frequent. Then, based on this data, the app may offer recommendations for a restaurant or shop that the user will likely enjoy. This can result in an app that’s more useful and more profitable since you’ll have a larger pool of long-term users.
The same goes for ecommerce apps that use predictive analytics engines to generate product recommendations. Shoppers will get the items they need, without having to perform a search. This improved convenience spells greater success for the app since you’ll have better customer loyalty and a higher per-sale value.
Predictive Analytics to Lower Costs and Reduce Trial and Error
While the aforementioned benefits primarily apply to consumer-facing apps and some B2C apps, this particular advantage applies to enterprise apps which are used within your company. In this usage, a predictive analytics engine can help your company make better, data-driven decisions. This can save money while driving revenues upward and toward the right.
For instance, let’s say you manage a cosmetics manufacturing venture. Predictive analytics could be implemented to foretell which types of products—or product variants, such as scents and colors—are most likely to be popular. The beauty industry is one where you’ll see distinctive trends surrounding specific product types, ingredients, colors and scent families, so it’s advantageous if a cosmetics company can clearly identify these trends as soon as possible.
You can also use a PA engine to determine the ideal timing for a production run for a particular product so as to ensure maximum freshness—a concern for cosmetics companies and any other business that makes products with an expiration or “best by” date. Plus, it’s possible for a retailer to use this technology to predict demand, thereby enabling the shop to acquire the appropriate amount of stock.
Data-driven decisions are heavily favored in today’s business world, so this is where technology can give you a competitive edge and a financial advantage. What’s more, predictive analytics engines get more powerful over time as they receive more and more data. Weather prediction models are a wonderful example of this ever-improving accuracy. This makes a predictive analytics engine a wise long-term investment.
If you’re ready to integrate predictive analytics, you’ll need a development team with experience developing and integrating PA engines. At SevenTablets, we specialize in predictive analytics along with other types of emerging technology, such as augmented reality, artificial intelligence and natural language processing.
Headquartered in Dallas, SevenTablets also maintains regional offices in Austin and Houston. But if you’re situated outside of Texas, you’ll be in good company as our clientele spans the entire United States. If you’re ready to get started with the app development process, contact the team at SevenTablets today.
Reach out to our team today!
- 3 Ways Predictive Analytics for eCommerce Can Help You Destroy the Competition
- What is a Predictive Analytics Engine?
- Three Ways to Use Predictive Analytics For Mobile Applications
- Know Your Users: Predictive Analytics Best Practices
Adam cultivated the creation of an industry leading $300M affiliate program and also worked as a marketing consultant on the start-up team of a now publicly traded commercial energy brokerage firm. He was one of the first media buyers on Facebook, and also among the first to work in the SAG-AFTRA New Media (WebTV) industry, serving the online commercial and content needs of major Hollywood studios.
Adam holds a BA from Southern Methodist University and a MS in International Marketing Management from Boston University.
Latest posts by Adam Rizzieri (see all)
- What Are the Pros and Cons of Machine Learning? - February 20, 2018
- 3 Factors That Influence Augmented Reality App Prices - January 4, 2018
- Three Ways Predictive Analytics Can Increase App Revenue - December 11, 2017