Predictive analytics in Enterprise Mobility

Predictive analytics in Enterprise Mobility Img
0 shares facebook twitter linkedin

Predictive Analytics is a term that is used to forecast activities, behavior and trends based on either the current or historical data. It is a form of advanced analytics involving the application of analysis techniques, statistical analytics queries and automated machine learning algorithms to data sets to create predictive models. These predictive models could give a numerical value on the likelihood of happening of some specific event. Nowadays, enterprises are keenly interested in finding out ways as to how analytics could help in improving their mobile app to facilitate enterprise mobility for their business.

Predictive analytics can provide that extra edge to the enterprises which could help them in enhancing the mobility and in keeping them a step ahead from the peers.

How to get started with Predictive Analytics for Enterprise Mobility?

There is no rocket science involved in getting started with predictive analytics.  It is simply information driven from the data collected either in recent past or at present. There are sundry tools available in the market that can help you with predictive analysis in a better manner. Let us walk you through the simple steps that could be followed to initiate the process of predicting the future and deploying the methods for the best outcome.

1. Analysis
  • Make a thorough analysis about what exactly you need to know about the future based on the data collected. For instance, if your organization is working on eCommerce platform, then it would be good to collect customer information, their gender, their likes & dislikes; so that, you can predict their behavior based on the data collected. After analyzing what can be predicted for the business, suggest suitable actions that are required to be taken at the end.
2. Collection
  • Collect both structured and unstructured data to prepare a predictive model for the enterprise. Structured data generally refers to the one which has a defined length and format. For instance, numbers, dates, group of words, strings etc. On the other hand the unstructured data include e-mail messages, word processing documents, audio/video files, photos, webpages and other kind of business documents.
3. Deployment

Take necessary actions for the refinement of the predictive model. The actions can include various techniques like;

  • scoring the model, where the score value is provided to the business for enhancing the operational effectiveness.
  • Integration according to the reporting, where various business intelligence tools are used for reporting and are used as reference for collaboration and consultation.
  • Application integration, where the model is integrated with various applications, for instance, mobile app; and is used in the business operations.
4. Achievement
  • Turn the analysis into real business results, so that the objective of predictive analysis is achieved. For this, after the deployment of the model, monitor the process keenly and consistently. This would let you know about the discrepancies in the model, if any. It will help you in framing a robust model for your business operations, after analysing the flaws and removing them from the model.

Most benefited sectors from predictive analytics in enterprise mobility

E commerce:
  • The technological advances made in the field of data sciences have proven to be beneficial for the ecommerce and retail industry.
  • Nowadays, consumers rely more on using technology, which could make their lives easier. Therefore, to impress the customers, it is better to know their likes and dislikes. Most of the eCommerce store owners keep a record of the past purchases of the customers. This would allow them to know about the likes and dislikes of the customers; thereby, making their shopping experience more delightful.
  • Ecommerce stores can provide contextual information on mobile, which will make it easier for the customers to make a better decision while buying the products.
Music and Entertainment:
  • Predictive Analytics is used in Music and Entertainment industry to make a particular mobile app popular. There are numerous players that are playing really good in the music streaming market, such as Apple Music & iTunes, Spotify, Tidal, Google Play Music etc. They all offer almost same things- with little difference based on the availability of the artists or on the presentation of the content. But, the ponderable point over here is to know how they win over the customers from other services? Or how they convince the customers that they are better than the other services? Well, the answers to these questions lie in the fact how efficiently they make use of predictive analytics.
  • Most of the service providers take help of predictive analytics tools to know about the users’ interest and give recommendations of the various music tracks that might seem interesting to them. The customers feel less troubled as they are given a choice of discovering music they would enjoy. The service giving best results to the customers based on their preferences, mood etc., wins their loyalty.
Banking, Finance and Insurance services:
  • Predictive Analytics has a significant role to play in banking industry. It has been used widely to determine the attributes, such as credit risk calculation, fraud detection, liquidity planning, customer lifetime management etc. based on the data collected. Also, by connecting the banks and ATMs, banks can get and analyze location specific data, trends followed in usage of ATMs and other banking services, etc. With the help of predictive analytics based on different data collected, banks can attract and retain customers as well as identify the unsatisfied customers.
  • For instance, Bancolombia, a Colombia based financial institution, used predictive analytics model and the results were surprisingly good. They were able to achieve 40% improvisation in the quality of suspicious transaction reporting along with the productivity savings of nearly 80%.

Why predictive analytics for Enterprise Mobility? Predictive analytics can prove to be extremely beneficial for your Mobile App Business. It allows you to learn from the past experience to predict the future events, thereby, improving the effectiveness of the process and driving successful business outcomes.

Organizations applying predictive analytics are 2.2% more likely to outperform their peers.

According to an estimation by Gartner, predictive and prescriptive analytics will attract 40% of enterprises’ net investment in BI and analytics.  

Let us now explore how predictive analytics can impact your Enterprise mobility app:

  1. Enhancing Productivity: Predictive analytics is used for predicting the future methods to be followed for the best results. This will allow you to think apart from traditional presumptions, reducing the planning time and enhancing the overall productivity.
  2. Cost effective: Prediction helps in the acknowledgement of the process that is faster than the one which is already being followed. Thereby, saving the cost involved in the delayed processing methodologies.
  3. Less number of Resources: With the help of predictive analytics, the outcome of the process can also be predicted. Therefore, it saves on the misallocated resources, if any, further saving cost and time.
  4. Faster Results: The organization can easily capitalize on the future trends with the help of predictions based on new developments and customer acquisition model. This will help them in gaining faster results.
  5. Improvised Operational Efficiency: Predictive Analytics is beneficial to the industries that offer different prices on daily basis. For instance, airline and hospitality industries make use of predictive analytics for their decision making process. This will improve the quality as well as functionality of the process.
  6. Early Detection and Prevention of Fraud: The advancements in the cyber threats and various criminal activities have become the kingpin issue being face by different businesses. Therefore, analysis done in multiple layers could help in detecting the fraudulent activities. This will further prevent any sort of vulnerabilities.
  7. Risk Assessment: Predictive analysis in real time business helps in timely response to the challenges before they actually come.
  8. Optimizing Marketing Campaigns: The organization can effectively plan their marketing campaigns after determining the customer behavior based on the predictive analytics.

Wrapping Up:

Building mobile apps based on the predictive analytics is a way forward in the coming year. It is not always a straight path to walk upon, but if done in a proper manner can give businesses a good head start to be ahead of competition.

0 Shares facebook twitter linkedin

Leave a Reply

Your email address will not be published. Required fields are marked *

Hire Expert
WordPress Developers
Hire Now... Hire WordPress Developers