What if your employee engagement app could not only produce a more engaged and effective workforce but also help you predict things like employee turnover? People and organizations have always learned from past history (or claimed to), but today that past history can be dissected and analyzed to an extent never before imagined. Two reasons account for this phenomenon: big data, and the tools that know what to do with it.
Predictive analytics are not solely for apps. The term refers to a broader phenomenon of collecting often-massive quantities of data and using new and powerful techniques to glean insights from it. Companies use predictive analytics for many purposes, including managing HR data, predicting customer actions, and yes, improving employee engagement. Here is what you should know about predictive analytics and how they can improve your organization.
Reasons Companies Use Predictive Analytics
The ultimate reason any organization uses predictive analytics is to make smarter, more strategic decisions. They can be regarded as a tool (or toolkit) in much the same way you now consider mobile devices and tablets to be business tools.
On the marketing side of a business, predictive analytics can be used with app and social media data to learn the finest details about how customers and potential customers engage with the brand. This, in turn, can be used to help design individual marketing campaigns as well as broader marketing strategies.
Internally, companies may use predictive analytics to identify employee trends, reduce employee turnover, and generally understand the workforce better, with an eye toward improving performance and retention. Companies that create employee engagement apps tap into a massive trove of data that can be used to identify trends and produce predictive analytics, and this can be tremendously useful to departments like HR and IT.
How Predictive Analytics Work
Predictive analytics is, to oversimplify perhaps, a way of using data to make sound predictions, much as a meteorologist uses atmospheric data to make weather forecasts. Data, analysis, statistics, and machine learning are all components of predictive analytics, and it is already being used in a variety of creative and useful ways. For example, machine learning can be used to answer questions like, “How long will a machine like this run without needing maintenance?”
Predictive analytics begins with a business goal, whether that is to cut costs, save time, or increase employee retention. Specific steps vary depending on goal and industry, but in general, a predictive analytics workflow consists of the following steps:
- Importing data from a variety of sources, like apps, web archives, and databases
- Cleaning the data of outliers and conglomerating multiple data sources together
- Developing a predictive model based on data statistics, machine learning, or both
- Integrating the predictive model into a forecasting system to provide continuous insights
Predictive Analytics and Mobile Apps
Mobile apps and predictive analytics go together. If, say, you have developed a game app, the data collected by game players can help you fine-tune the app and learn how, and how often people use it. If you introduce a new gameplay element, you can immediately begin collecting data that will inform you of how the new element affects players and app usage.
By the same token, a company that develops an employee engagement app has a wonderful opportunity to gather data that can offer important insights about the workforce and help with business decisions. One straightforward example may be pushing a quick poll to the app asking employees to check off benefits they most value in their employee health insurance plan, and then using the data gathered to help select next year’s plan options.
You can do countless other things with predictive analytics and employee engagement apps too. Data may tell you that employees access video content at higher rates than other forms of content, allowing you to tailor future app content to maximize user engagement.
Barriers to Adoption of Predictive Analytics
Perhaps the largest barrier to adoption of predictive analytics with employee engagement apps is simple lack of knowledge about their value on the part of people at upper levels of management. When C-level executives are enthusiastic about the possibilities for predictive analytics in the context of an employee engagement app, and when you show them the business value of those analytics, you help ensure general support for the concept before deciding exactly how to implement it.
Another barrier to adoption of predictive analytics is that the concept can be overwhelming. Do not think that your organization has to go full speed ahead with predictive analytics from the beginning. Often it is better to define a single, circumscribed predictive analytics goal and pursue it successfully before expanding expectations. Another risk is the tendency to collect data for the sake of collecting it. Know what your goals are before determining what data to collect and which products to use to analyze it. Otherwise, you could end up with a large, but ultimately disappointing mass of data.
Why There Is No Reason to Wait
Predictive analytics as part of an employee engagement app makes perfect sense. Never before has it been so easy to collect and analyze data in order to make important (and often unexpected) insights from it. If you are considering developing an employee engagement app, you should plan for the capability for data collection and analysis right from the start, even if you do not use all that gathering and analyzing capacity right away. Smart data makes for smart decisions, and that confers a definite competitive edge.
HubEngage is an employee engagement app platform that allows organizations like yours to create fully customized employee apps that do what you want and collect the data you want. Companies use employee engagement apps to quickly disseminate announcements, solicit feedback, provide employees with valuable, relevant content, and deliver training modules. The hubEngage platform includes powerful analytics that offer fast “big picture” insights while having the ability to drill down and get more detailed insights that lead to better business practices and a more engaged workforce.
Best of all, you can try the hubEngage app for free and see for yourself how easily you can put the power of mobility, content, and employee participation to work to create a more engaged, productive workforce. Employee talent is strong currency in today’s business world, and employee engagement is the key to ensuring you make the most of your workforce’s strengths.