Developing Custom Application For Predictive Analytics

Developing Custom Application For Predictive Analytics

Is developing a custom predictive analytics software real? Imagine having the foresight to know which of your customers are prone to buy your newest product, which of the applicants are much likely to submit fraudulent claims, or which of your patients are grateful enough to donate to your foundation… What business would not benefit from such incredible capability? And what if we tell you that nowadays it’s real — with leveraging predictive and prescriptive analytics tools and applications.


Based on survey data, Forbes says that 86% of executives who have been overseeing predictive marketing efforts for at least 2 years report increased ROI as a result of their predictive marketing.

Predictive analytics tool can be good not only in marketing. Procurement department needs to know how many materials and supplies are needed to sustain the business in the future. Sales managers need to understand the most likely prospects for the new offerings. Human resource managers are willing to get to know the upcoming staffing requirements and skills in demand. And predictive analytics can provide all these specialists with data-driven answers to the variety of their questions, and even more. So, yeah, we can say that such tools and applications, or simply – software, can be used in every company for any type of business.


For years companies have been making use of analytics tools and software not only for predictive analysis but for identifying the target audience, evaluating the effectiveness of marketing campaigns, revealing factors that impact sales etc. Former analytics techniques divided the customers into groups based on their demographics, attitudes, or buying behaviors, and then the groups were targeted with the message that will best resonate with them.

By utilizing predictive analytics software companies can gain more value, as they get an overview showing how customers would behave in stated conditions with a certain level of probability. To help distinguish predictive from other forms of analytics and make the competitive advantage of applying it more obvious, we suggest looking at the following scheme:

     Analytics Capabilities Framework     

Predictive analytics raises the more relevant question, requires less human input than other types, and has a more proactive approach. Prescriptive goes even further — it bypasses the human input stage enabling you to take actions instantly, or even to set an automated straight-through process.


The core premise of predictive analytics applications being so crucial and effective today is that calculations of a staggering amount of data have become available and the advancement in machine learning technology. For successful predictive analytics software development, we should develop the model.

We can divide a predictive model creation into few key steps:

  1. Setting a business objective;
  2. Data acquisition;
  3. Data preparation;
  4. Creating a model;
  5. Train, test and re-evaluate your model.

Meanwhile, the causes that inspire a person to action may remain hidden, machine learning offers valuable predictions on human behavior. This is well-proven by the Elder Research company, that used predictive analytics software, and achieved a 15 times greater improvement in predicting gratefulness of clients than expected.


Those businesses that utilize predictive modeling grab the whole plethora of benefits, as the predictive systems enable them to:

  • understand consumers’ attitude towards the new product or service;
  • market the product or services successfully by generating personalized recommendations to each customer;
  • predict customer lifetime value of every customer, i.e. bring the understanding of a long-lasting strategy for every customer — how to engage, convert and sell to them, instead of looking only at the short-term revenue a new customer may bring the company;
  • acquire new and retain the existing customers more effectively;
  • get relevant insights as by synthesizing information from disparate cases and identifying hidden patterns in customer behavior, one may realize the existing bottlenecks in the company’s workflow, discover business opportunities and more;
  • quickly respond and adapt to changes in the living standards, trends, fashion and technological advancements.

Without saying the word, the custom predictive analytics applications  bring benefits that are suitable for every company.


Understanding customers is the foundation to a sustainable competitive advantage in banking. Based on the clients’ purchase habits, such predictive analytics systems can predict future buying trends. In addition, the system can inform about effective cross-selling opportunities of the right product at the right time. In the banking industry, a predictive analysis can be specifically customized to take the right action at the right time. For example provide a client with special offerings on their birthday. Another example — the system can analyze customer spending behavior, reveal when they don’t spend much (e.g. during spring), and recommend putting down a deposit when an opportune time comes.


Insurance professionals use software products designed and developed to scan for likely-to-be-fraudulent data and notify when found. Such systems require a human professional to set the predefined criteria(fraudulent behavior) of what to look for.

Predictive analytics applications turn to be more efficient in tackling the problem. The primary advantage of such systems is that due to their self-learning capabilities they would adapt to new, previously unseen fraud-schemes. The software that is able to discover novel fraudulent activity is a great acquisition for a long-term perspective. And is already taking its place in claim processing and underwriting.

Check our case study on utilizing predictive and prescribing analytics for an insurance provider — how we created the system that predicts whether the claim is fraudulent and is empowered to make recommendations to underwriters.


The rise of analytics presents a world of almost limitless potential for companies across many industries. Today we design predictive analytics applications for many business users, not only by highly skilled specialists. Leveraging software products that are able to find patterns in new situations. It allows organizations to act upon them is a win-win strategy for the future. And the more you use it and accumulate data, the more value predictive analytics software can offer for the organization.

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