Data analytics has proved to be an invaluable tool for improving security, preventing fraud, and increasing operational efficiency in the BFSI sector by analyzing raw data to uncover trends and insights.

We will examine the top 6 data analytics use cases in this article that are propelling the BFSI sector’s digital transformation.

1. Fraud Detection and Prevention Using Data Analytics in BFSI

Fraud is a constant threat in the quick-paced world of financial services and can cost banks, insurance companies, and other financial institutions a lot of money. It should come as no surprise that cybercriminals frequently target the BFSI sector, searching for vulnerability to exploit, given the amount of money at risk.

BFSI institutions can, however, turn the tables on these fraudsters thanks to the power of data analytics. Financial institutions can identify and stop fraud before it even starts by utilizing advanced analytics techniques like predictive modelling, machine learning, and data mining.

The secret to success is searching through the massive amounts of data produced by BFSI institutions for patterns and behaviors that could point to fraudulent activity. Financial institutions can identify potentially fraudulent activities and act before they cause significant financial harm by developing predictive models based on historical data.

Data analytics is a way to stay ahead of the competition as well as a tool for preventing fraud. BFSI institutions can spot new opportunities and maintain a competitive edge by utilizing the insights gained from data analytics.

BFSI institutions can protect their customers’ funds and open up new doors for growth and success by using the right analytics techniques and a commitment to constant vigilance.

Few Examples

To improve fraud detection and prevention, the BFSI sector can use data analytics in a number of ways. These strategies include, as some examples:

Money laundering

Fraudulent activity may involve moving money through multiple accounts to conceal the source of money that was obtained illegally. Using data analytics tools to identify anomalous patterns in transactional data, which can then be reported to the bank, it is possible to identify potential money laundering activities. While an investigation is being conducted, the bank may take the necessary action, such as alerting the appropriate parties or freezing the affected accounts.

Insurance Fraud

Making a false claim for financial gain constitutes filing a fraudulent insurance claim. Data analytics tools can be used to examine claims data and look for patterns and discrepancies with legitimate claims to find these fraudulent activities. Insurance companies are able to identify and stop the payment of fraudulent claims in this way.

False Credit Card Transactions

Data analytics solutions can identify possible fraudulent activities by examining credit card transaction data, including purchase history, transaction amounts, and location information. This enables banks to recognize such transactions and stop them from being approved, ultimately preventing fraud.

2. Personalized Customer Experience Through Data Analytics in BFSI

Ingenious business models that personalize customer journeys and advance financial inclusion have been developed by BFSI organizations thanks to the advanced capabilities of data analytics technologies like machine learning and Artificial Intelligence (AI). With the help of data analytics, BFSI institutions can use bots to communicate with customers in a variety of languages and dialects, offering individualized and practical branch-like services.

Furthermore, based on customer activity, big data and AI-driven data analytics can analyze customer profiles, behaviors, and needs, enabling institutions to suggest suitable financial services and products. Data analytics solutions have sophisticated natural language processing and machine learning capabilities that allow for accurate understanding of customer intent, facilitating contextual engagement and raising customer satisfaction.

Customer data analytics, for instance, can enable chatbots and voice assistants to give customers wise investment and savings advice. AI-enabled voice assistants can also assess a customer’s loan eligibility, facilitate disbursement, and keep track of equated monthly installments thanks to data analytics (EMIs).

Also Read: Testing a bank application: A Success Story

3. Risk Management Through Data Analytics in BFSI

The BFSI industry is exposed to a variety of risks, including credit, operational, regulatory, liquidity, and market risks, all of which have the potential to endanger their operations. BFSI institutions use data analytics tools to effectively identify and manage these risks to reduce their impact.

Businesses in the financial services industry (BFSI) can learn more about various facets of their operations and spot potential risks by analyzing data. These insights can be used to evaluate risks and create individual mitigation plans for each one. Data analytics, for instance, can be used to analyze customer behavior, spot fraud, keep an eye on market trends, and assess the creditworthiness of customers. Due to their ability to manage risks in real time and make informed decisions, BFSI companies can avert potential problems before they become serious.

4. Predictive Analytics for Investment Decisions in BFSI

Predictive analytics is an essential tool for BFSI companies to use when making informed investment decisions. BFSI companies are constantly looking for investment prospects. These businesses can analyze historical data and statistical models to gain insights into upcoming market trends by utilizing predictive analytics, allowing them to recognize and seize potential investment opportunities.

Here are a few ways that BFSI uses predictive analytics to make investment decisions.

Portfolio management and assessment

Using historical data, predictive analytics can assess the returns & risks related to a specific investment. The predictive analytics model can assist BFSI firms in identifying trends and patterns that may indicate an investment’s likelihood of success or failure, enabling them to decide whether to pursue the investment opportunity or not.

Financial advisor assessment

Firms can analyze the performance of an advisor (internal and independent) by using data analytic techniques. It is possible to predict which advisor is expected to bring in higher revenues by assessing their past performance. Firms can in turn keep these set of advisors highly motivated thus enabling them to beat their past performance and generate higher revenue.

Customer Segmentation

To classify customers based on their investment preferences and behavior, BFSI companies use predictive analytics. Predictive analytics models can identify patterns and trends in customer data through customer analysis, allowing BFSI companies to tailor their investment products to the specific requirements of various customer segments.

5. Regulatory Compliance Through Data Analytics in BFSI

BFSI institutions operate in an environment that is highly regulated, and failure to comply with regulatory requirements can result in costly fines, negative legal consequences, and a damaged reputation.

As a result, these companies must devise creative strategies to guarantee that all legal requirements are met. One such solution that can assist BFSI companies in complying with regulations is data analytics.

Here are some strategies for using data analytics to help BFSI firms comply with regulations:

Reporting

BFSI organizations, as previously mentioned, heavily rely on data. However, it can be difficult to manually generate reports that show compliance with regulatory requirements. In situations like these, data analytics is essential. BFSI companies can use data analytics tools to analyze all data pertaining to compliance activities and produce reports that show regulatory bodies that the company complies with its compliance obligations.

Monitoring Compliance

By examining vast amounts of data related to compliance, BFSI companies can use data analytics to track their adherence to regulatory requirements. This makes it possible for them to spot potential compliance problems and take appropriate action to stop them from developing into serious issues.

Audit Management

By giving auditors the knowledge, they need to assess compliance and pinpoint areas for improvement, BFSI companies can use data analytics to support the auditing process. This reduces the possibility of regulatory fines and helps organizations avoid costly compliance mistakes. Data analytics can offer insights that help auditors in their evaluation process by analyzing data pertaining to compliance activities, such as identifying potential risks and areas of non-compliance. BFSI companies can avoid compliance problems and guarantee that they are successfully adhering to regulatory requirements by doing this.

Read our Success Story on : Real-time collaborative Fraud Analytic solution to combat Identity Theft

6. Cybersecurity Using Data Analytics in BFSI

Cyberattacks and fraud are very common in this sector. An isolated security lapse can result in sizable monetary losses and harm to a company’s reputation. Because of this, data analytics are essential to identifying and preventing cyber threats. Cybersecurity is therefore of the utmost importance in this industry. The BFSI industry can promote cybersecurity through data analytics in the following ways.

Threat Detection

Data analytics can identify potential cyber threats by examining trends and patterns in network traffic or other data sources within BFSI systems. The BFSI company can take appropriate action to eliminate the threat as soon as an abnormal activity is discovered, preventing further damage.

Incident Response

Data analytics helps incident response by supplying real-time data and statistics on cyber threats and security incidents. This allows BFSI organizations to quickly respond to potential security incidents in order to stop them from escalating.

Risk Assessment

Data analytics can be used by BFSI companies to assess the risks of cyberattacks. They can identify areas of risk and create risk mitigation strategies to protect their data from unauthorized access by analyzing data on potential vulnerabilities and cyber threats.

Compliance Management

To make sure that BFSI companies adhere to the various cybersecurity standards and regulations governing their operations, data analytics tools can be used. With the aid of these tools, the company can identify compliance gaps in cybersecurity-related activities and take the necessary corrective action to be following legal requirements and industry best practices.

Wrapping Up

The BFSI sector has always relied heavily on data, but data analytics is pushing that dependence to new heights. BFSI companies can use data analytics to drive digital transformation and open new opportunities for growth by leveraging their data.

BFSI companies can reduce fraudulent activity, personalize customer experiences, increase operational effectiveness, and guarantee regulatory compliance by using data analytics. Furthermore, data analytics can aid in the detection and prevention of cyberthreats, protecting sensitive data from unauthorized access.

BFSI businesses must embrace digital transformation and use data analytics tools in order to stay ahead of the competition. They can accomplish operational excellence by doing this, giving them a competitive advantage in the market.

Our team is here to support BFSI organizations integrate data analytics into their processes as they lead the way in digital transformation. Get in touch with us right away to find out more about how we can support your digital transformation efforts by assisting you in maximizing the power of data analytics. Click here for more details…

The post How Data Analytics Is Transforming the BFSI Sector appeared first on Indium.

Technology is one of the key driving factors in the market for predictive analytics. Newer cloud-native solutions are being continually worked on leaving legacy data analytics solutions behind. This is done so that businesses can derive qualitative and faster intelligence by shifting to cloud-native data solutions.

Discussed below are some best practices that businesses must follow while they leverage their data and analytics resources for better business insights:

Best Practices while Leveraging Data & Analytics Resources

• Source Data with an Ample Strategy: Many companies refrain from adopting the right analytics processes as they believe the quality of the resources are not up to the mark. Data can be sourced or purchased through free open-source resources and other data providers. An organization should balance the cost of acquisition for the resources with what value the data brings to the analytics effort.

• Transition from Analytics Projects to Products: Analytics projects more often than not are to be planned for the get-go and have a defined scope. There needs to be a strategy formed before-hand. Instead, if businesses focus on analytics products, they can generate a considerably higher amount of return on investment (ROI) along with obtaining business insights, thereby improving the overall business performance.

• Maintain a Close Communication Channel with Stakeholders: Engagement and support can be facilitated by enlisting stakeholders onto the initial stages of the analytics processes. The best way to build questions is to clarify assumptions and to get the stakeholder to organically put across their requirements. Simply asking what the stakeholder wants will not suffice, as additional context will have to be provided. This helps to ensure that the key performance indicators (KPIs) and business goals are being met on a regular basis.

• Build High-Performance Teams with Compliance as the Focus: The collection of data needs to be done with compliance being the main focus. Productive teams make for more efficient teams, as they work to integrating analytics into the company’s daily workflow. There needs to be a specific importance given to how compliance affects different factors such as internal business rules, industry standards, and government regulations.

• New Infrastructure Technology with Advanced Analytics: There is a need to consider building an ecosystem that can host different technology types. These technologies can include in-memory computing for highly repetitive analytics. Companies that are measuring the best value for business are gravitating towards the use of advanced analytics. Predictive analytics is one step into the world of advanced analytics that makes use of machine learning and AI to predict future growth and success rates amongst other things.

• Use Governance and Insights to Refine the Analytics Process: Dealing with increased amounts of data and team members accounts for governance to become a significant part of the analytics process. There needs to exist a formal procedure that helps to make certain the data that is captured is consistently of high-quality. There also needs to be a common understanding of the data’s nature across the entire organization.

Relevant Read: How a Well-Implemented Data Analytics Strategy Will Directly Impact Your Bottom Line

There are many forms of intelligence that can be used by a business to derive insights from. Let’s look at how a business can improve their customer service using trend analytics in social media using the Internet of Things (IoT)

Improving Customer Service Trend Analytics in social media

Digital marketing success can be sought out by using business analytics when working with new use cases:

• The Internet of Things (IoT) opens up the possibility of intelligence to be distributed and consequently replenished in an automated fashion. This will surely change the essence of the overall supply chain and calls for companies to add new services that are relevant and of the right fit.

• The usage of chatbots on a global level has been rising in recent years, as the data that is recorded from these conversations can highly enhance future communications. Chat automation powered by past insights can help in improving overall customer service and analyse trends.

• Most companies are trying to leverage their presence and growth on social media to create a better brand image. Social media has an abundance of different types of data that can help an organization with customer service. The most important application of data from social media is analysing the public’s perception of a company’s product or services through reviews and feedback from customers. Social media data analysis also helps in determining the best time frames for company project and products to launch.

• Online commerce and digital marketing are at the forefront of business. It is important to understand different customers, and how each new tool and technology can aid in the same.

• When the marketplace is uneven and uncertain, customers inevitably end up paying more for solutions. There needs to be a certain maturity in the industry in question as the competition increases and the differentiators between businesses get more apparent.

Improve Insights from Business

Most of business cases requires business users to design, interpret, and deliver data that is produced by multiple applications to build technical and business analysis skills. The increasing complexity of the technological ecosystem, coupled with increasing number of data sources is rapidly changing what is considered cost-effective and practical to achieve.

Business intelligence and dashboards for analytics need to be created by business leaders while providing tactical requirements and constant inputs. It is difficult to find this exact combination of skills to make sure that the organization’s maturity is improved along with building competent capabilities to lead up to greater business needs. If you want to leverage the power of data and analytics for your business, you can consult our data engineering and data analytics experts now!

The post How to Leverage your Data and Analytics Resources for Innovation appeared first on Indium.

Sports as we know it today has come a long way. There were times when watching sport on television was considered a massive step forward in terms of technology.

Fast forward 60 years, watching sport on television has become the most basic thing. Today we watch sport on the go on our mobile phones or any device with a screen and internet connectivity.

Proud of how far we’ve come, aren’t we? Hopefully I can change your opinion on that by the end of this article.

What is sport all about? Sport is a bunch of people getting together to play a game with pre defined rules and a referee to ensure that these rules are adhered to during the passage of play.

I am a sport lover and play sports all time. My love for tennis and football in particular cannot be defined.

My issue when it came to technology and advanced analytics was with the game of football in particular. Football is such a beautiful game.

The strategies that the coaching staff come up with and the way it is executed on field by the players, it actually is a thing of beauty.

I was a football player myself (just an average one at that) and have been part of various teams. I know firsthand how strategies are built, how much thought goes into one single run of play.

Analytics in Football :

Most of you would’ve seen the movie Moneyball. The movie was based on the book Michael Lewis wrote in 2003.

It talks about how a jock turned luminary uses advanced statistics to gain a competitive edge over his better funded opponents.

This book brought about a revolution in sports. Fans and boards of football clubs didn’t want to settle for subpar statistics or analytics anymore.

What Moneyball did is that it took an old cliché – “sports are businesses” and made us move on to the next logical question – “how do we do things smarter?”

Analytics is also a powerful scouting tool for at least three reasons.

Firstly, it helps scouts and football clubs save time and money by being able to search for players and profiles of the ideal player from large and detailed databases. While databases cannot be a replacement for scouts, they can certainly complement their scouts’ eye for talent.

Secondly, the computer has all the actions that took place in a match in its memory, enabling players, scouts and team analysts revisit the moment that potentially pivoted the contest in favor of either team. From a scouts’ perspective, this is particularly valuable for scouts who can often be misled by performance of players in specific matches.

Databases also help limit pre-conceptions, providing clarity of player performances when there’s the possibility of a false-positive or a jaw-dropping moment that probably went unnoticed during the match.

Now let’s talk about advanced analytics. Advanced analytics in today’s world plays a massive role in every business sector.

Advanced analytics has been a boon for us. Moving from descriptive analytics to prescriptive analytics, we actually have come a long way.

In various businesses, where the requirement is demanding, advanced analytics are of utmost importance.

The Debate :

When we look at football, it’s a game that does not require too much machine intelligence.

Instead, it is a game that needs skills and instincts of humans playing in it.

When you bring in analytics and technology and try to reduce the human element in the sport, it simply just crushes the spirit of the game.

Relying on analytics heavily killed the Premier Leagues long ball game and brought in the pressing, continual passing tiki-taka.

Each league for that matter had its own style of play. The Premier League had the brash and brazen style of football that was termed “The way real men play football”.

There were beautiful long balls, harsh tackles but all the players just sucked it up, walked it off and it was all up to the referee on the pitch to penalize the offender or not.

There were arguments and fights, the passion from the fans was crazy, that was the football that screamed of passion, when players got in the face of other players not fearing punishment.

The Eric Cantona’s, the Ivan Genaro Gattuso’s, the Jaap Stam’s of the football world went missing soon enough and the diving and the biting began.

Then there was the tiki-taka style of football that was played in the Spanish La Liga, the silky style of play that caught everyone off guard.

The legendary Pep Guardiola and his army at Barcelona were the masters of the tiki-taka.

There was Real Madrid who were always a star studded line-up with excessive parts of their play relying on lightning quick counters which most often than not left the opponents stunned.

There was Manchester United who had their own brand of football being managed by the legendary Sir Alex Ferguson.

That United team was a team of sheer grit and character. Each of these leagues had their own beauty and the teams had their own style of play.

When you bring in excessive technology and analytics, there emerge sorry technologies like VAR (Video Assistant Referees).

There are 3 stages as to how the VAR works :

  Step 1

Incident occurs

The referee informs the VAR, or the VAR recommends to the referee that a decision/incident should be reviewed.

  Step 2

Review and advice by the VAR

The video footage is reviewed by the VAR, who advises the referee via headset what the video shows.

  Step 3

Decision or action is taken

The referee decides to review the video footage on the side of the field of play before taking the appropriate action/decision, or the referee accepts the information from the VAR and takes the appropriate action/decision.

Now the referee can consult with VAR for basically any doubts he wants clarified. What does this do?

• Removes the human element from the game.
• Takes up excess time and brings too many stoppages within the game, a game that was previously free flowing and continuous.

This makes it similar to Formula 1 racing. The analytics which brought about the fuel weight management systems and the numerous pit stops took the continuity out of the race and viewership reduced with the increase in technology.

A pretty similar trend might occur in football if this implementation becomes mandatory.

The Positive Side of Advanced Analytics in Football:

Analytics are not all that bad in football. Let’s take the case of when Simon Wilson joined Manchester City in 2006.

Simon Wilson was a consultant for an analytics startup called Prozone initially. He joined City to start a department of analytics and hired the best data analysts under him. He wanted to change the way how data was used by football teams.

He saw that, after a defeat there was no introspection as to why they had lost and what needed to be done next time.

City were a mid table club at that time.  In September 2008, when the club was acquired by the Abu Dhabi United Group for Development and Investment, a private-equity outfit owned by a member of the Abu Dhabi royal family, the team suddenly found itself with the resources necessary to mount a challenge for the Premier League.

Today, Wilson is Manchester City’s manager of strategic performance analysis.

He has five departments under him, including the team of performance analysis, which is now led by a sports scientist named Ed Sulley.

After each match, the team’s performance data would be examined. The list is extensive. Line breaks (a rugby term), ball possession, pass success rates, ball win/loss time ratio were what used to be analyzed.

“Instead of looking at a list of 50 variables we want to find five, say, that really matter for our style of play,” says Pedro Marques, a match analyst at Manchester City.

“With the right data-feeds, the algorithms will output the statistics that have a strong relationship with winning and losing.”

Wilson recalls one particular period when Manchester City hadn’t scored from corners in over 22 games, so his team decided to analyze over 400 goals that were scored from corners.

It was noticed that about 75 percent resulted from in-swinging corners, the type where the ball curves towards the goal.

The next 12 games of the next season saw City score nine goals from corner.

In Today’s Context :

Teams are investing heavily in analytics today and it is working in their favor. Look at where Manchester City are today, sitting atop the Premier League table and not being threatened at all.

Look at Manchester United this season, their game has been such where their possession percentages are low but their goal conversions are high.

The Manchester Derby on 7^th April 2018 saw United have only 35% of the possession but they managed to trump City 3-2. Each team has their set of analysts who provide inputs as per the strength of the team.

Conclusion

Advanced analytics is like the coin Two Face in Batman has, “Heads you die, Tails you survive!”

It can reap crazy rewards from a team’s point of view but at the same time can disrupt the lovely game by bringing in unnecessary stoppages, replays and by taking the human element out of it.

The numerous replays and the different angles, show the fans if the referee has made an error or not.

Let the error happen, after all to err is human. Refereeing in football is not an exact science and it’s all real time.

Let there be arguments about a decision, let the passion in the argument come through.

Do you want to watch a football match like the El Classico or the Manchester Derby and sit with your bunch of friends and say “it was a very clean game, the best team won!” Hell NO! Don’t drive the passion out of football with technology and analytics. Let football be football and let technology stay away!

The post Analytics in Football – A Double Edged Sword appeared first on Indium.

How are healthcare organizations leveraging predictive analytics to derive actionable insights from their ever-growing datasets? We find out here.

Staying ahead of Patient Health Deterioration

It is the most essential application of predictive analytics in healthcare.

It helps caregivers react quickly to any change in a patient’s vitals and gather foresight into possible deterioration before symptoms are evident.

A 2017 study demonstrates this: at the University of Pennsylvania, a predictive analytics tool using machine learning and EHR data helped identify patients vulnerable to severe sepsis or septic shock a full 12 hours before the onset of the illness.

Predictive insights are particularly valuable in the intensive care unit (ICU), where timely intervention can help save someone’s life and prevent patient health deterioration.

The increased adoption of wearable biosensors offers manifold benefits, too, for care providers. They enable remote health monitoring and help detect early symptoms of health deterioration.

Preventing Patient self-harm

Early identification of individuals likely to self-harm will help provide the essential mental healthcare to avoid potentially serious or fatal events.

According to the World Health Organization, almost 800,000 people die of suicide each year, which is one person every 40 seconds.

Studies have showed that predictive analytics, using electronic health record (EHR) data and depression questionnaire, helps identify individuals at higher risk of committing suicides or other forms of self-harm.

In a study led by Kaiser Permanente (a leading American healthcare provider) and conducted together with Mental Health Research Network, EHR data combined with a depression questionnaire helped accurately detect those with a higher risk of attempting suicide.

Another study, featured on the American Journal of Psychiatry, aimed to build and validate predictive models with the help of electronic health records to predict suicide attempts and suicide deaths after an outpatient visit.

Based on predictors such as prior suicide attempts, mental health substance diagnoses, mental health and more, it was found that within 90 days of a mental health visit, suicide attempts and suicide deaths among individuals in the upper one percent of predicted risk were 200 times more common than those in the bottom half of the predicted risk scale.

Predicting patterns in patient utilization

Predictive analytics helps healthcare organizations ensure adequate staffing levels for busier clinic hours, minimize wait times and improve patient satisfaction.

With the help of big data visualization tools and analytics strategies to model patient flow patterns, healthcare centers can ensure the inpatient department has adequate beds available for patient admission, that the outpatient and physician offices have enough resources to reduce patient wait times and manage workflow and scheduling adjustments accordingly.

Scheduling changes help nurses and doctors cope with the increased patient flow while reducing the burden on them, thus ensuring they provide timely care and improve patient satisfaction.

Data Security

Predictive analytics and artificial intelligence (AI) play a key role in boosting cybersecurity, with the sophistication of cyberattacks (involving malware, phishing and more) rapidly on the rise.

Confidential patient information worth big money, a vast network of connected medical devices, outdated technology, among other factors, make the healthcare industry a constant target of cyberattacks.

Predictive analytics tools and machine learning help calculate real-time risk scores for different transactions and requests, making the system respond differently based on how the event is scored.

David McNeely from the Institute for Critical Infrastructure Technology says: “Once the risk score has been determined in real-time, the system can use this during a login event to either grant the access for a low-risk event or to challenge for Multi Factor Authentication [MFA] or possibly block the access for high-risk events.”

Create risk scores for chronic diseases

Early identification of individuals with a higher risk of developing chronic illnesses is essential for two reasons. It gives care providers and patients the best chance of preventing long-term health issues. It also helps mitigate the potential cost and complexities of the treatment.

By creating a risk score—from examining patients with identical characteristics, gathering lifestyle and clinical data and using algorithms to understand how various factors effect patient outcomes—healthcare providers gain insight into the type of therapy and wellness activities which can benefit their patients.  

Summary

As far as health management is concerned, prediction is the foundation for prevention and treatment. Predictive analytics helps healthcare providers in different ways. In addition to those mentioned above, the technology helps identify individuals likely to miss a clinical appointment and send timely reminders, manage supply chain to enhance efficiency and cut down on unnecessary costs, develop effective therapies and new medication, improve patient engagement and more.

Given its manifold benefits, it’s no wonder that, according to a 2017 study by the society of actuaries, 89 percent of healthcare providers were then either already using predictive analytics in their organizations or planned to in the next five years.

The post Top 5 use cases of Predictive Analytics in Healthcare appeared first on Indium.

Being a marketer, one would recognize the immense power of data. Never before have we had access to data like we do today.

For many organizations difficulties arise in collecting, integrating and storing the data. However, making use of this data to drive better business decisions gives organizations a competitive advantage.

And I sure am not talking about reporting here.

Of course it’s intriguing to know what happened in the past and those monthly excel sheets might even get read once, but the organizations that use this historical data to focus on the future and predict future outcomes are the organizations that are surging ahead by leaps and bounds and are discovering enormous value.

When you look at the world of data science today, there is a lot of sophisticated work happening in the field that may be beyond your scope of understanding.

But, Predictive Analytics is something that is within reach for just about anyone and is waiting for it’s advantages to be exploited.

To put it simply, predictive analytics is making use of historical data to predict the likelihood of future outcomes.

The major case in point is increasing your measure of success because you can optimize anything that can be measured or defined.

Predictive models are very different from descriptive models – which can tell you what happened in the past, and diagnostic models – models that can explain or provide rationale as to why something happened.

Now that you know what Predictive Analytics is about, you should be intrigued about it’s applications.

We’re going to see 5 applications that will get you thinking about how you are going to make use of data to boost performance across various verticals in your organization.

Conversions – Yes, we’re all chasing conversions. At the same time it is critical to know who is converting and this is exactly where understanding and targeting the right prospects comes in to play. With the wealth of customer data already in your possession, predictive analytics can help you with quite a few things.

• Customer Loyalty : Predictive models will help you understand what segments and behaviors point towards the tendency to keep on consuming your products and services. Predictive models also help you understand the behaviors and attributes that are likely to cause a switch to another brand.
• Lifetime Value : As you’re scouting for new prospects and evaluating the existing customer base, you can make use of your data to forecast the net profit that will be accredited to the entire future relationship. How this helps is you can target your outreach, marketing campaigns, bonus/loyalty programs etc. more accordingly.
• Churn : Losing customers is never good for business. However, predicting the risk of a customer abandoning your brand can help you drive more targeted and personalized retention programs.
• Market Basket : The checkout basket can be turned into an advantage with the use of predictive analytics. You can understand which products are purchased together and which are likely to be purchased one after the other. This helps you identify your buyer’s purchasing behaviors.

Marketing budgets are better allocated when predictive analytics is used. The newest tools in the market, the best techniques when combined with the bundle of data being generated via every click and impression is a huge opportunity to make sure every marketing dollar is well spent.

• Marketing/Media Mix : There are lots of channels, up and down the funnel where you are likely to spend money. Being able to credit each touchpoint with value in the purchase path and predicting the budget allocation can help you attain more performance out of less spend.
• Audience Targeting : The “spray and pray” targeting tactic has become old school as today, we are gaining more and more data about who may become a customer and where we can find them. Predicting the probability of someone in the audience converting to a customer and the value that they bring can help the targeting become more precise and lessen the marketing dollars being spent.
• Purchase Intent : Usage of customer data/behavioral data to predict the intent of purchase for any lead/prospect can be immensely valuable to an organization. This can also be modeled to predict digital’s role in driving offline sales.

If you are investing in digital assets like websites and mobile apps, it only make sense that you’ll want to make sure that you’re getting the most from them.

Predictive Analytics can help you understand what factors will result in the best content, what areas can be customized to particular users and which areas of the digital experience are ideal for optimization.

• Content optimization : Time and resources are spent on creation, development and maintenance of content and it we have a lot of data about how the content is performing. From this data, pulling out factors that have been successful will help guide your content strategy in a way where you will produce pages and experiences with a high likelihood of achieving the set goals.
• Personalization : The combination of digital experiences and customer data results in you starting to segment and predict which group of users is likely or not likely to respond to your messages, offers etc. Today, the personalization tools give you the power to achieve user level customizations to give people what you know they are likely to want.
• Testing Strategy : A/B and multivariate testing is not a new phenomenon but the difficult part of testing is figuring out what to test. Predictive analytics can help you understand which grey areas of the experience need maximum improvement and it also helps define a hypothesis. Apart from providing a better experience for the users, the results can also feed the model for improved accuracy.

Risk is a very broad category. In reality though, all organizations try to mitigate risk with every action of theirs.

Data is used to pin point the factors that tend to create risk and then predict unwanted scenarios that are likely to occur in order for you to come to terms with the unknown and mitigate consequences.

• Fraud : This one is for the e commerce space where a lot of work has gone in. Organizations can use their own data in order to evaluate factors that are likely to be associated with fraudulent activities and in addition they can address these issues by improving security by adding more steps for checkout, selective payment options etc.
• Collection & Recovery : The accounts receivable has a direct impact on your cash flows and making sure you have a handle on accounts receivable is imperative as it also affects the organization’s ability to operate. Predictive analytics can help identify at risk accounts and will help formulate strategies that mitigate collections risk and have high success rates.
• Pricing : Pushing a product out in the market is influenced by price. With a price too high, there is the risk of acceptance and volumes ; with the price too low, profitability becomes an issue. Prediction of price elasticity, pricing gaps, thresholds and profitability targets can be done with the help of existing products and competitive data. This will help you arrive at an optimal price point.

Marketing and customers are extremely important, yes. However, at the end of the day the products and services have to be delivered with maximum operational efficiency.

Demand prediction to Supply chain management – Predictive analytics can prove to be an integral part of the planning and execution stages of operations.

• Forecasting : Be it planning of production cycles, demand predicition for new products and services or estimating financial performance, historical data can be used to model plausible scenarios or outcomes. Those models can be manipulated to understand what should be done now to impact the results you are most likely to see in the future.
• Network Optimization : Networks can mean many things, this may include supply chains, processes and just about anything that has inputs, outputs and dependencies. Using the data to work around the factors that influence the efficiency of each node within the process will help find the optimal paths through them.

These are just a few areas in which organizations can leverage the power of predictive analytics to make informed decisions about future states.

The tools and technology available today make these analyses accessible to almost every organization.

What’s left to do? Identify a business challenge, evaluate the data you have to work with and finally come up with a modeling solution that will help you see the future and make decisions driven by insight.

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