“The Goal is to turn Data into Information and Information into Insight”

~ Carly Fiorina

If you gaze in the past, the role of DATA has evolved from a tool for support to the tool for increasing profitability. The reason for the paradigm swing is the introduction of technologies that allows businesses to mine and process the data leading to data monetization.

Traditional statistical techniques when shakes hands with predictive modeling, machine learning and data mining technologies, the merger can answer any question that leads to increased profitability. Such application is called predictive analytics; a digital genie that grants your wish.

But, this digital genie cannot be summoned with some cliché or by rubbing a lamp, you need to hire expert data scientists to curate and cleanse the data to get your wish granted. According to the report from Gartner, more than half of all analytics projects fail because they aren’t completed within budget or on schedule, or because they fail to deliver the features and benefits that are agreed on at their outset.

So, it all boils down to your knowledge about the solutions that prevails in the market and which solution will be suitable enough to provide your desired outset. Adopting analytics as ‘the next step’ might not be a painful process if you select the right approach and espouse it with real-time necessity. Below is the information about the current and potential applications of predictive analytics bifurcated based on diverse departments within an organization:

So, let’s find out, WHAT’S NEXT FOR:

1. Sales and Marketing:

Direct Marketing:

Solutions based on predictive analytics can aid in direct marketing by identifying effective combination of product versions, marketing material, communication channels and timing to target a given consumer. By deploying the descriptive modeling algorithm on the database, grouping of units on basis of their attributes, age-wise preferences, consumer purchasing behavior etc. can be measured for strategic business outcomes. The outset you can expect from such solution is significant reduction in cost per order or cost per action.

Cross-selling:

Marketing is all about pitching the right product to the right customer at right time. Predictive analytics can analyze the past data of the customers' spending and their behavioral pattern to identify which other product can be pitched to the current customer. The study of consumer behavior can be done by using the predictive modeling where the behavior of the target sample is applied to the out of the sample units. The outset here is obvious, increased profitability and strong brand image with better customer relationship.

2. Customer Relationship Management (CRM):

The most common application of predictive analytics is in CRM. There are many solutions that enables Analytical Customer Relationship Management. But to implement analytics in CRM, to have that holistic view of entire consumer life cycle, a combination of various techniques that are used in various departments are integrated creating one master system. It can be applied to the entire consumer life cycle such as

Acquisition

Relationship

Growth

Retention

Win-back

The data from various departments provides insights that can help in taking both strategic as well as operational decisions.

3. Finance:

Finance department or financial institutions have managed to gain the most from the predictive analytics. Effectively addressing the thin-line between perceptions and realities, accurate predictive analytical solutions help strategize action.

Fraud Detection:

Predictive analytics can provide vital information about the candidate’s possibility of making a fraudulent transaction. Statistical methods like risk-scoring methods when applied on learning algorithms, it can be used to identify fraud from both online as well as offline transactions. Such solutions are developed to get an outset of reduced losses and bad-debts.

Collection Analysis:

Liquidity is the blood flow of any organization. All the activities are planned based on the estimated inward and outward of the cash and if that estimate goes wrong, it can trigger a chaos in the organization. With predictive analytics solution to identify the possibility of return by ranking the audit candidates on the basis of their past behavior using the predictive modeling, collection agents can get vital insights that can not only increase the recovery of cash but also aid in reducing the collection cost.

4. Product/Project management:

Product Predictions:

Predictive analytics can help in making predictions about the demand and consumption of the product ranging from store level demand to nation level demand using various time series techniques. It can also be used for effective inventory management by analyzing the huge data bases using machine learning or different learning algorithms.

Project Risk Management:

When business undertakes any project or investment plans, they are based on assumptions of the return it will yield based on the positive future scenarios. But to make sure you are investing by taking a manageable risk, predictive analytics can make prediction using various statistical techniques such as Probabilistic Risk Assessment (PRA) or Delphi Techniques.

Apart from all these applications, predictive analytics solutions can be applied in various other departments. Customer retention algorithm can be used to predict employee retention. Product Prediction techniques can be used to identify the consumer behavior of service industry. Apart from that, is has some specialized application in child protection, clinical trials, neurology etc.

Now that you are aware of the possibilities and potential of predictive analytics, what are you waiting for? What you might love to categorize as fairy-tale or too complicated or futuristic vision, is soon becoming a REALITY! To get your digital genie of success and increased profitability, make your wish come true by clicking on inquiry@cygnetinfotech.com