Predictive Analytics: Next Big Thing In App Development

App development, these two words evokes fear in the heart, of almost anyone. Why so? Because mobile is tough, the pace is rapid, users needs are undefined and the tech transforms at every tick-tock of the clock….tenacious to tackle all at once right? With the ocean of apps, sinking seems so obvious. Now, even with lifeguard, your app gets drowned. Something is possibly going wrong. But what? Design? Checked. Content? Checked. Functionality? Checked. User’s queer? Got it…this is where you fall back.

Well, as discussed earlier, the app world is zippy and being mobile app development company, you are under the spotlight to incorporate a go-getter attitude and take an impetuous decision. You are not equipped with the stand in need statistics to come up with a congenial decision. That’s when predictive analytic enter the picture. It can convoke statistical algorithms to catalog pattern of the past data and assist you in having up to the mark forecast on which way app development will turn its way to in future. This resource will drive your actions into the right path based on the calculated data when your mind is in blank space.

Predictive Analytics In App Development

Predictive Analytics

How Does it Work?

It basically does future forecasting of upcoming challenges and threats in the app market, ideas, and inputs to combat them, hunting the upcoming opportunities and getting full-fledged benefit from the opportunity and the upcoming needs of the user and raising the quality bar to cater them. In general, the predictive approach is a roadmap for developers to propound qualitative output in limited time frame lowering the risk level with convalescing results.

Criteria for Predictive Analytics

Similar to the traditional calculation approach, app prediction also uses the basic criteria:

  • Estimated time for app development

  • App testing time frame

  • Coding pattern of the app

  • Bugs on the testing level

  • Post-launch marketing tactics

Based on the data of past behavior, this approach will draft a course of action to be followed when the zero hour decision locus comes at the doorstep. Historical data and current data are mingled and evaluated in depth to make the prediction. This data can also be used again if it is formulated by a trained algorithm expert by incorporating a reliable data set.

One or more algorithms are used and a perfect model is evolved during the lifecycle configuring the past trends, latest movements, competitors strategies and users response. It’s a verbose process embroiling model training, the model mutation at the specified time and vomiting the future events possibility based on the current scenario. Prediction can be used the two-way round to generate the exact output or to assist you in generating any decision. Predictive analytics is not an arrow in the air, there are these predefined set of tools used for running the predictions and availing the reliable outputs. Orange, Apache mahout and RapidMiner top the list of tools used for calculating algorithms and define data patterns to come up with impeccable data behavior. There is also a selective set of an algorithm used by mobile application development for coming up with candid predictive models which are:

  • Time series

  • Neutral Network

  • Regression

  • Decision Trees

  • Clustering

Final Thoughts

To come up with a newfangled app which gives users the anticipatory experience, developers have to be in constant tune with past track records and future predictions which are right here with predictive analytics. A rulebook for developers to be in tune:

  • Know how of who the users actually are.

  • Exploring the on the go intent of the users you target.

  • Collaborating content, design, and functionality to accommodate the user’s intent.


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