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Predicting Your Customers’ Behavior and Getting Sound Recommendations Based on That

Predicting Your Customers’ Behavior and Getting Sound Recommendations Based on That

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.

 

Predictive and Prescriptive Analytics

These two are taking off in the business world, promising to bring businesses to the next level. 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 can be of good not only in marketing. Procurement department need to know how much 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.

 

Difference from other forms of analytics

For years companies have been making use of analytics tools and software — for identifying 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 companies can gain more value, as they get an overview showing how customers would behave in stated conditions with 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                                                                                                                                      Source: a Gartner report

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.

 

Building a successful predictive analytics tool

The core premise of predictive analytics being so crucial and effective today is that calculations of a staggering amount of data has become available and the advancement in machine learning technology.

Creating a predictive model can be broken down into a 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 inspires 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, and achieved an 15 times greater improvement in predicting gratefulness of clients than expected.

 

BENEFITS

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 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 behaviour, 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.

 

Behavior-Based Customer Insight for Banking and Finance

Understanding customers is the foundation to a sustainable competitive advantage in banking. Basing on the clients’ purchase habits, such 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 banking industry, a predictive system can be specifically customized in order to take the right action at the right time, e.g. provide a client with special offerings on their birthday. Another example — the system can analyze customer spending behaviour, reveal when they don’t spend much (e.g. during spring), and recommend putting down a deposit when an opportune time comes.

 

Predictive Analytics for Insurance Fraud Detection

In their battle with fraud, insurance professionals use software products designed to scan for likely-to-be-fraudulent data, and notify when found. Such systems require a human professional to set the predefined criteria(fraudulent behaviour) of what to look for.

Predictive analytics tools 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.

Our case study

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.

 

Conclusion

The rise of analytics presents a world of almost limitless potential for companies across many industries. Today’s analytics applications are designed in such a way, that they can be used by many business users, not only by highly skilled specialists. Leveraging software that is able to find patterns in new situations, and 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 can offer for the organization.

Are you thinking of leveraging a system that predicts human behavior? Call us at +1 (973) 597-1000 or fill out the form below for a free consultation.

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