![Predictive Analytics Tools Empower Decision-Making [Whitepaper]](https://7t.co/wp-content/uploads/2017/09/predicitve-analytics-900x450.webp "Predictive Analytics Tools Empower Decision-Making [Whitepaper]")
As the owner of a small e-commerce shop, you’re always searching for the latest and greatest products. Recently, one product line was featured on a popular show and sales skyrocketed overnight. Your website is one of three that carries this product, so you’re inundated with new customers. But being new to this business, you’re unsure when you should order more stock (and how much to order). It would also be helpful if you had greater insight into whether you should hire new staff and seek additional warehouse space. If only you could predict sales! This got you thinking about something one of your developers recently mentioned: Predictive Analytics (PA).
Predictive analytics hold the power to transform your business, allowing you to gain key insights that can improve your bottom line. You’ll have the data you require to anticipate the needs of your customers, your company’s IoT-enabled equipment and machinery, or your latest marketing campaign. The net effect can be dramatic, as you can save time and money while boosting efficiency, improving user experience and enhancing your employees’ satisfaction levels.
According to a Forbes Insights study, 86% of users report a tangible bottom line benefit as a direct result of predictive analytics. Although this technology is under-utilized because just 13% of those surveyed felt their company was fully taking advantage of advanced predictive analytics. So how can you harness the power of PA to elevate your company to the next level?
What is Predictive Analytics?
Predictive analytics data can be generated using a number of different technologies and approaches, including stats and modeling, data mining, machine learning and artificial intelligence. At the most basic level, this technology collects and analyzes data, then makes a prediction using this information.
Predictive analytics differ from traditional analytics in one major way: timeframe. Traditional analytics are collected in real-time and while you can see past data sets, you can’t predict the future. PA interfaces make a data-driven projection, which can be updated as more data is collected.
How Are Companies Using Predictive Analytics Tools?
One of the most familiar examples of PA technology involves social media networks such as Facebook, which offers friend and page recommendations based on your current social circle and interests. Facebook’s algorithm examines the data, identifies trends and patterns, then makes recommendations.
Facebook has a wealth of data that can be leveraged in some intriguing ways. For example, Facebook data scientist Bogdan State conducted a study examining the relationship status of users between 2008 and 2011. It was determined that pairs who are listed as a couple on Facebook have a high chance of a successful long-term relationship if they remain “Facebook official” for three months or longer. About half of all couples who stayed “in a relationship” for more than three months had a fifty percent chance of seeing their relationship last four years or more.
This study examined just one attribute, but Facebook has dozens of additional data points that could lead to precise predictions (versus a mere probability or “likelihood”). This is where sophisticated predictive analytics algorithms are useful, as they quickly identify the patterns and correlations required to make accurate projections.
Social media is just the tip of the analytics iceberg, as predictive analytics can be advantageous for businesses in a variety of industries and applications. Consider the following uses for PA:
- A manufacturer can issue preventative maintenance directives for IoT-enabled machinery. The system may monitor a filter’s efficiency and note when it decreases over time. The PA system can record this information and once a sizable data set is obtained, the algorithm will be able to effectively predict when efficiency will drop to an unacceptable level. Your system may then issue a filter replacement alert for technicians before efficiency drops off.
- A marketing firm can maintain and grow their reach. Forbes reports that marketers who use predictive analytics are “2.9 times more likely to report revenue growth” at a rate that exceeds the industry average. These individuals are also more than 2.1 times more apt to “occupy a commanding leadership condition in the product/service markets they serve.”
- A video streaming service can generate recommended films or shows. Netflix and Hulu are already using PA technology. This type of algorithm identifies which shows or movies users are most likely to enjoy based on their viewing history, thus increasing engagement and customer loyalty.
- An online shop can offer product recommendations. Your company’s e-commerce platform can be outfitted with a PA system to boost sales. The system collects data on browsing and purchase history and makes recommendations based on this information. This can improve sales when the shopper is shown items he or she finds appealing.
These examples illustrate how predictive analytics can be utilized on a continual, rolling basis, but this data is also useful for making real-time business decisions, both large and small. For instance, Boston Regional Medical Center recently implemented a new system called Hospital IQ, a PA-based interface that collects information such as time of day, staffing levels, patient numbers, room occupancy and wait times—all key indicators when evaluating the hospital’s overall efficiency. The Hospital IQ algorithm processes this info and offers a recommendation on how many rooms, nurses and doctors are required during times of peak demand.
UI Optimization Using Predictive Analytics
If you’re developing a mobile app, you’ll find predictive analytics can be an essential tool for engaging users and optimizing your application. 7T’ predictive analytics engine collects a variety of data points from your mobile app, then analyzes them in order to make determinations such as:
- Are your users engaged?
- How often are your users utilizing the app?
- What are your app’s strong points?
- What are your app’s weak points?
- Which features/functions are the most popular?
- Which features/functions are the least popular?
- How are specific elements of the app interface impacting user experience?
- Is your app helping or hurting your company?
Our PA engine derives and analyzes data on all these points, serving up solid, data-driven projections and recommendations. This info allows you to pinpoint areas of your app that should be modified for enhanced conversions and higher engagement levels. Alternatively, the data may enable you to identify the perfect time for to a flash sale or ideal opportunities to send out news and notifications.
7T’ talented team of mobile app developers are available to integrate predictive analytics into your app in whatever way will best benefit your business. For instance, if you’re seeking an enterprise app for monitoring and managing your company’s IoT-enabled machinery, our developers can integrate a PA interface to monitor key data points. The app can be configured to issue maintenance recommendations, alerts following decreased efficiency or any other notifications your company would find useful.
In addition to working with clients in DFW, where 7T is headquartered, we also maintain regional offices in Austin and Houston, TX, and our clientele spans the nation. Our world-class mobile app developers take great pride in their work, including our STAX open source app development platform, which cuts development timeframe and costs by as much as 35% to 40%. We predict you’ll be pleased with the end result. Contact 7T today to learn more about our service offerings.
