Tag: Machine Learning

Modern Data Warehouses and Machine Learning: A Powerful Pair

Artificial intelligence (AI) technologies like machine learning (ML) have changed how we handle and process data. However, AI adoption isn’t simple. Most companies utilize AI only for the tiniest fraction of their data because scaling AI is challenging. Typically, enterprises cannot harness the power of predictive analytics because they don’t have a fully mature data strategy.

To scale AI and ML, companies must have a robust information architecture that executes a company-wide data and predictive analytics strategy. This requires businesses to focus their data application beyond cost reduction and operations, for example. Fully embracing AI will require enterprises to make judgment calls and face challenges in assembling a modern information architecture that readies company data for predictive analytics. 

A modern data warehouse is the catalyst for AI adoption and can accelerate a company’s data maturity journey. It’s a vital component of a unified data and AI platform: it collects and analyzes data to prepare the data for later stages in the AI lifecycle. Utilizing your modern data warehouse will propel your business past conventional data management problems and enable your business to transform digitally with AI innovations.

What is a modern data warehouse?

On-premise or legacy data warehouses are not sufficient for a competitive business. Today’s market demands organizations to rely on massive amounts of data to best serve customers, optimize business operations, and increase their bottom lines. On-premise data warehouses are not designed to handle this volume, velocity, and variety of data and analytics.

If you want to remain competitive in the current landscape, your business must have a modern data warehouse built on the cloud. A modern data warehouse automates data ingestion and analysis, which closes the loop that connects data, insight, and analysis. It can run complex queries to be shared with AI technologies, supporting seamless ML and better predictive analytics. As a result, organizations can make smarter decisions because the modern data warehouse captures and makes sense of organizational data to deliver actionable insights company-wide.

How does a modern data warehouse work with machine learning?

A modern data warehouse operates at different levels to collect, organize, and analyze data to be utilized for artificial intelligence and machine learning. These are the key characteristics of a modern data warehouse:

Multi-Model Data Storage

Data is stored in the warehouse to optimize performance and integration for specific business data. 

Data Virtualization

Data that is not stored in the data warehouse is accessed and analyzed at the source, which reduces complexity, risk of error, cost, and time in data analysis. 

Mixed Workloads

This is a key feature of a modern data warehouse: mixed workloads support real-time warehousing. Modern data warehouses can concurrently and continuously ingest data and run analytic workloads.

Hybrid Cloud Deployment

Enterprises choose hybrid cloud infrastructure to move workloads seamlessly between private and public clouds for optimal compliance, security, performance, and costs. 

A modern data warehouse can collect and process the data to make the data easily shareable with other predictive analytics and ML tools. Moreover, these modern data warehouses offer built-in ML integrations, making it seamless to build, train, and deploy ML models.

What are the benefits of using machine learning in my modern data warehouse?

Modern data warehouses employ machine learning to adjust and adapt to new patterns quickly. This empowers data scientists and analysts to receive actionable insights and real-time information, so they can make data-driven decisions and improve business models throughout the company. 

Let’s look at how this applies to the age-old question, “how do I get more customers?” We’ll discuss two different approaches to answering this common business question.

The first methodology is the traditional approach: develop a marketing strategy that appeals to a specific audience segment. Your business can determine the segment to target based on your customers’ buying intentions and your company’s strength in providing value. Coming to this conclusion requires asking inductive questions about the data:

  • What is the demand curve?
  • What product does our segment prefer?
  • When do prospective customers buy our product?
  • Where should we advertise to connect with our target audience?

There is no shortage of business intelligence tools and services designed to help your company answer these questions. This includes ad hoc querying, dashboards, and reporting tools.

The second approach utilizes machine learning within your data warehouse. With ML, you can harness your existing modern data warehouse to discover the inputs that impact your KPIs most. You simply have to feed information about your existing customers into a statistical model, then the algorithms will profile the characteristics that define an ideal customer. We can ask questions around specific inputs:

  • How do we advertise to women with annual income between $100,000 and $200,000 who like to ski?
  • What are the indicators of churn in our self-service customer base?
  • What are frequently seen characteristics that will create a market segmentation?

ML builds models within your data warehouse to enable you to discover your ideal customer via your inputs. For example, you can describe your target customer to the computing model, and it will find potential customers that fall under that segment. Or, you can feed the computer data on your existing customers and have the machine learn the most important characteristics. 

Conclusion

A modern data warehouse is essential for ingesting and analyzing data in our data-heavy world.  AI and predictive analytics feed off more data to work effectively, making your modern data warehouse the ideal environment for the algorithms to run and enabling your enterprise to make intelligent decisions. Data science technologies like artificial intelligence and machine learning take it one step further and allow you to leverage the data to make smarter enterprise-wide decisions.

2nd Watch offers a Data Science Readiness Assessment to provide you with a clear vision of how data science will make the greatest impact on your business. Our assessment will get you started on your data science journey, harnessing solutions such as advanced analytics, ML, and AI. We’ll review your goals, review your current state, and design preliminary models to discover how data science will provide the most value to your enterprise.

  • Data Integration: We help you integrate data from various sources, both structured and unstructured, into your modern data warehouse. This includes data from databases, data lakes, streaming platforms, IoT devices, and external APIs. Our goal is to create a unified and comprehensive data repository for your machine learning projects.
  • Feature Engineering: We work with you to identify and engineer the most relevant features from your data that will enhance the performance of your machine learning models. This involves data preprocessing, transformation, and feature selection techniques to extract meaningful insights and improve predictive accuracy.
  • Machine Learning Model Development: Our team of data scientists and machine learning experts collaborate with you to develop and deploy machine learning models tailored to your specific business needs. We leverage industry-leading frameworks and libraries like TensorFlow, PyTorch, or scikit-learn to build robust and scalable models that can handle large-scale data processing.
  • Model Training and Optimization: We provide expertise in training and optimizing machine learning models using advanced techniques such as hyperparameter tuning, ensemble methods, and cross-validation. This ensures that your models achieve the highest levels of accuracy and generalization on unseen data.
  • Model Deployment and Monitoring: We assist in deploying your machine learning models into production environments, either on-premises or in the cloud. Additionally, we set up monitoring systems to track model performance, identify anomalies, and trigger alerts for retraining or adjustments when necessary.
  • Continuous Improvement: We support you in continuously improving your machine learning capabilities by iterating on models, incorporating feedback, and integrating new data sources. Our goal is to enable you to extract maximum value from your modern data warehouse and machine learning initiatives.

With 2nd Watch as your partner, you can leverage the power of modern data warehouses and machine learning to uncover valuable insights, make data-driven decisions, and drive innovation within your organization. Our expertise and comprehensive solutions will help you navigate the complexities of these technologies and achieve tangible business outcomes.

-Ryan Lewis | Managing Consultant at 2nd Watch

Get started with your Data Science Readiness Assessment today to see how you can stay competitive by automating processes, improving operational efficiency, and uncovering ROI-producing insights.


Why Data Science Projects Fail: Key Takeaways for Success

87% of data science projects never make it beyond the initial vision into any stage of production. Even some that pass-through discovery, deployment, implementation, and general adoption fail to yield the intended outcomes. After investing all that time and money into a data science project, it’s not uncommon to feel a little crushed when you realize the windfall results you expected are not coming.

Yet even though there are hurdles to implementing data science projects, the ROI is unparalleled – when it’s done right.

Looking to get started with ML, AI, or other data science initiatives? Learn how to get started with our Data Science Readiness Assessment.

Opportunities

You can enhance your targeted marketing.

Coca-Cola has used data from social media to identify its products or competitors’ products in images, increasing the depth of consumer demographics and hyper-targeting them with well-timed ads.

You can accelerate your production timelines.

GE has used artificial intelligence to cut product design times in half. Data scientists have trained algorithms to evaluate millions of design variations, narrowing down potential options within 15 minutes.

With all of that potential, don’t let your first failed attempt turn you off to the entire practice of data science. We’ve put together a list of primary reasons why data science projects fail – and a few strategies for forging success in the future – to help you avoid similar mistakes.

Hurdles

You lack analytical maturity.

Many organizations are antsy to predict events or decipher buyer motivations without having first developed the proper structure, data quality, and data-driven culture. And that overzealousness is a recipe for disaster. While a successful data science project will take some time, a well-thought-out data science strategy can ensure you will see value along the way to your end goal.

Effective analytics only happens through analytical maturity. That’s why we recommend organizations conduct a thorough current state analysis before they embark on any data science project. In addition to evaluating the state of their data ecosystem, they can determine where their analytics falls along the following spectrum:

Descriptive Analytics: This type of analytics is concerned with what happened in the past. It mainly depends on reporting and is often limited to a single or narrow source of data. It’s the ground floor of potential analysis.

Diagnostic Analytics: Organizations at this stage are able to determine why something happened. This level of analytics delves into the early phases of data science but lacks the insight to make predictions or offer actionable insight.

Predictive Analytics: At this level, organizations are finally able to determine what could happen in the future. By using statistical models and forecasting techniques, they can begin to look beyond the present into the future. Data science projects can get you into this territory.

Prescriptive Analytics: This is the ultimate goal of data science. When organizations reach this stage, they can determine what they should do based on historical data, forecasts, and the projections of simulation algorithms.

Your project doesn’t align with your goals.

Data science, removed from your business objectives, always falls short of expectations. Yet in spite of that reality, many organizations attempt to harness machine learning, predictive analytics, or any other data science capability without a clear goal in mind. In our experience, this happens for one of two reasons:

1. Stakeholders want the promised results of data science but don’t understand how to customize the technologies to their goals. This leads them to pursue a data-driven framework that’s prevailed for other organizations while ignoring their own unique context.

2. Internal data scientists geek out over theoretical potential and explore capabilities that are stunning but fail to offer practical value to the organization.

Outside of research institutes or skunkworks programs, exploratory or extravagant data science projects have a limited immediate ROI for your organization. In fact, the odds are very low that they’ll pay off. It’s only through a clear vision and practical use cases that these projects are able to garner actionable insights into products, services, consumers, or larger market conditions.

Every data science project needs to start with an evaluation of your primary goals. What opportunities are there to improve your core competency? Are there any specific questions you have about your products, services, customers, or operations? And is there a small and easy proof of concept you can launch to gain traction and master the technology?

The above use case from GE is a prime example of having a clear goal in mind. The multinational company was in the middle of restructuring, reemphasizing its focus on aero engines and power equipment. With the goal of reducing their six- to 12-month design process, they decided to pursue a machine learning project capable of increasing the efficiency of product design within their core verticals. As a result, this project promises to decrease design time and budget allocated for R&D.

Organizations that embody GE’s strategy will face fewer false starts with their data science projects. For those that are still unsure about how to adapt data-driven thinking to their business, an outsourced partner can simplify the selection process and optimize your outcomes.

Your solution isn’t user-friendly.

The user experience is often an overlooked aspect of viable data science projects. Organizations do all the right things to create an analytics powerhouse customized to solve a key business problem, but if the end users can’t figure out how to use the tool, the ROI will always be weak. Frustrated users will either continue to rely upon other platforms that provided them with limited but comprehensible reporting capabilities, or they will stumble through the tool without unlocking its full potential.

Your organization can avoid this outcome by involving a range of end users in the early stages of project development. This means interviewing both average users and extreme users. What are their day-to-day needs? What data are they already using? What insight do they want but currently can’t obtain?

An equally important task is to determine your target user’s data literacy. The average user doesn’t have the ability to derive complete insights from the represented data. They need visualizations that present a clear-cut course of action. If the data scientists are only thinking about how to analyze complex webs of disparate data sources and not whether end users will be able to decipher the final results, the project is bound to struggle.

You don’t have data scientists who know your industry.

Even if your organization has taken all of the above considerations into mind, there’s still a chance you’ll be dissatisfied with the end results. Most often, it’s because you aren’t working with data science consulting firms that comprehend the challenges, trends, and primary objectives of your industry.

Take healthcare, for example. Data scientists who only grasp the fundamentals of machine learning, predictive analytics, or automated decision-making can only provide your business with general results. The right partner will have a full grasp of healthcare regulations, prevalent data sources, common industry use cases, and what target end users will need. They can address your pain points and already know how to extract full value for your organization.

And here’s another example from one of our own clients. A Chicago-based retailer wanted to use their data to improve customer lifetime value, but they were struggling with a decentralized and unreliable data ecosystem. With the extensive experience of our retail and marketing team, we were able to outline their current state and efficiently implement a machine-learning solution that empowered our client. As a result, our client was better able to identify sales predictors and customize their marketing tactics within their newly optimized consumer demographics. Our knowledge of their business and industry helped them to get the full results now and in the future.

In conclusion, implementing successful data science projects can be challenging, but the potential return on investment is unparalleled when done right. By addressing common hurdles such as analytical maturity, goal alignment, user-friendliness, and industry expertise, you can increase your chances of achieving meaningful results. Don’t let a failed attempt discourage you from harnessing the power of data science. Take the next step towards success by partnering with 2nd Watch.

Schedule a whiteboard session with our experienced team to explore how we can help you navigate the complexities of data science, align your projects with your business goals, and deliver tangible outcomes. Don’t miss out on the opportunity to unlock valuable insights and drive innovation in your organization. Contact us today and let’s embark on a data-driven journey together.


Strategies for Data Science ROI: Business Preparation Guide

Enhanced predictions. Dynamic forecasting. Increased profitability. Improved efficiency. Data science is the master key to unlock an entire world of benefits. But is your business even ready for data science solutions? Or more importantly, is your business ready to get the full ROI from data science?

Let’s look at the overall market for some answers. Most organizations have increased their ability to use their data to their advantage in recent years. BCG surveys have shown that the average organization has moved beyond the “developing” phase of data maturity into a “mainstream” phase. This means more organizations are improving their analytics capabilities, data governance, data ecosystems, and data science use cases. However, there’s still a long way to go until they are maximizing the value of their data.

Looking to get started with ML, AI, or other data science initiatives? Learn how with our Data Science Readiness Assessment.

So, yes, there is a level of functional data science that many organizations are exploring and capable of reaching. Yet if you want to leverage data science to deliver faster and more complete insights (and ROI), your business needs to ensure that the proper data infrastructure and the appropriate internal culture exist.

The following eight tips will help your machine learning projects, predictive analytics, and other data science initiatives operate with greater efficiency and speed. Each of these tips will require an upfront investment of time and money, but they are fundamental in making sure your data science produces the ROI you want.

Laying the Right Foundation with Accurate, Consistent, and Complete Data

Tip 1: Before diving into data science, get your data in order.
Raw data, left alone, is mostly an unruly mess. It’s collected by numerous systems and end users with incongruous attention to detail. After it’s gathered, the data is often subject to migrations, source system changes, or unpredictable system errors that alter the quality even further. While you can conduct data science projects without first focusing on proper data governance, what ends up on your plate will vary greatly – and comes with a fair amount of risk.

Consider this hypothetical example of predictive analytics in manufacturing. A medium-sized manufacturer wants to use predictive maintenance to help lower the risk and cost of an avoidable machine breakdown (which can easily amount to $22,000 per minute). But first, they need to train a machine learning algorithm to predict impending breakdowns using their existing data. If the data’s bad, then the resulting detection capabilities might result in premature replacements or expensive disruptions.

Tip 2: Aim to create a single source of truth with your data.
Unifying data from assorted sources into a modern data warehouse or data mart simplifies the entire analytical process. Organizations should always start by implementing data ingestion best practices to extract and import high-quality data into the destination source. From there, it’s critical to build a robust data pipeline that maintains the flow of quality data into your warehouse.

Tip 3: Properly cleanse and standardize your data.
Each department in your organization has its own data sources, formats, and definitions. Before your data can be data science-ready and generate accurate predictions, it must be cleansed, standardized, and devoid of duplicates before it ever reaches your analytics platform or data science tool. Only through effective data cleansing and data governance strategy can you reach that level.

Tip 4: Don’t lean on your data scientist to clean up the data.
Sure, data scientists are capable of cleaning up and preparing your data for data science, but pulling them into avoidable data manipulation tasks slows down your analytical progress and impacts your data science initiatives. Leaning on your data scientist to complete these tasks can also lead to frustrated data scientists and increase turnover.

It’s not that data scientists shouldn’t do some data cleansing and manipulation from time to time; it’s that they should only be doing it when it’s necessary.

Tip 5: Create a data-driven culture.
Your data scientist or data science consulting partner can’t be the only ones with data on the mind. Your entire team needs to embrace data-driven habits and practices, or your organization will struggle to obtain meaningful insights from your data.

Frankly, most businesses have plenty of room to grow in this regard. For those looking to implement a data-driven culture before they forge deep into the territory of data science, you need to preach from the top down – grassroots data implementations will never take hold. Your primary stakeholders need to believe not only in the possibility of data science but in the cultivation of practices that fortify robust insights.

A member of your leadership team, whether a chief data officer or another senior executive, needs to ensure that your employees adopt data science tools, observe habits that foster data quality, and connect business objectives to this in-depth analysis.

Tip 6: Train your whole team on data science.
Data science is no longer just for data scientists. A variety of self-service tools and platforms have allowed ordinary end users to leverage machine learning algorithms, predictive analytics, and similar disciplines in unprecedented ways.

With the right platform, your team should be able to conduct sophisticated predictions, forecasts, and reporting to unlock rich insight from their data. What that takes is the proper training to acclimate your people to their newfound capabilities and show the practical ways data science can shape their short- and long-term goals.

Tip 7: Keep your data science goals aligned with your business goals.
Speaking of goals, it’s just as important for data-driven organizations to inspect the ways in which their advanced analytical platforms connect with their business objectives. Far too often, there’s disconnection and data science projects either prioritize lesser goals or pursue abstract and impractical intelligence. If you determine which KPIs you want to improve with your analytical capabilities, you have a much better shot at eliciting the maximum results for your organization.

Tip 8: Consider external support to lay the foundation.
Though these step-by-step processes are not mandatory, focusing on creating a heartier and cleaner data architecture as well as a culture that embraces data best practices will set you in the right direction. Yet it’s not always easy to navigate on your own.

With the help of data science consulting partners, you can make the transition in ways that are more efficient and gratifying in the long run.

Conclusion

In conclusion, data science holds immense potential for businesses to gain enhanced predictions, dynamic forecasting, increased profitability, and improved efficiency. However, realizing the full ROI of data science requires careful preparation and implementation. It is crucial for organizations to ensure they have the proper data infrastructure, a data-driven culture, and a solid foundation of accurate and standardized data.

By following the eight tips outlined in this article, businesses can optimize their machine learning projects, predictive analytics, and other data science initiatives. These tips emphasize the importance of data governance, data cleansing, creating a data-driven culture, training the entire team on data science, aligning data science goals with business objectives, and considering external support when needed.

2nd Watch, with its team of experienced data management, analytics, and data science consultants, offers comprehensive support and expertise to guide businesses through their data science journey. From building the business case to data preparation and model building, our customized solutions are designed to deliver tangible results and maximize the value of your data.

Partner with 2nd Watch and harness the power of data science to drive enhanced predictions, dynamic forecasting, increased profitability, and improved efficiency for your business. Schedule your data science readiness whiteboard session now and take the first step towards unlocking the full potential of your data.

Schedule a data science readiness whiteboard session with our team and we’ll determine where you’re at and your full potential with the right game plan.


How Insurance Fraud Analytics Protect Businesses from Fraudulent Claims

With your experience in the insurance industry, you understand more than most about how the actions of a smattering of people can cause disproportionate damage. The $80 billion in fraudulent claims paid out across all lines of insurance each year, whether soft or hard fraud, is perpetrated by lone individuals, sketchy auto mechanic shops, or the occasional organized crime group. The challenge for most insurers is that detecting, investigating, and mitigating these deceitful claims is a time-consuming and expensive process.

Rather than accepting loss to fraud as part of the cost of doing business, some organizations are enhancing their detection capabilities with insurance analytics solutions. Here is how your organization can use insurance fraud analytics to enhance fraud detection, uncover emerging criminal strategies, and still remain compliant with data privacy regulations.

Recognizing Patterns Faster

When you look at exceptional claim’s adjusters or special investigation units, one of the major traits they all share is an uncanny ability to recognize fraudulent patterns. Their experience allows them to notice the telltale signs of fraud, whether it’s frequent suspicious estimates from a body shop or complex billing codes intended to hide frivolous medical tests. Though you trust adjusters, many rely on heuristic judgments (e.g., trial and error, intuition, etc.) rather than hard rational analysis. When they do have statistical findings to back them up, they struggle to keep up with the sheer volume of claims.

This is where machine learning techniques can help to accelerate pattern recognition and optimize the productivity of adjusters and special investigation units. An organization starts by feeding a machine learning model a large data set that includes verified legitimate and fraudulent claims. Under supervision, the machine learning algorithm reviews and evaluates the patterns across all claims in the data set until it has mastered the ability to spot fraud indicators.

Let’s say this model was given a training set of legitimate and fraudulent auto insurance claims. While reviewing the data for fraud, the algorithm might spot links in deceptive claims between extensive damage in a claim and a lack of towing charges from the scene of the accident. Or it might notice instances where claims involve rental cars rented the day of the accident that are all brought to the same body repair shop. Once the algorithm begins to piece together these common threads, your organization can test the model’s unsupervised ability to create a criteria for detecting deception and spot all instances of fraud.

What’s important in this process is finding a balance between fraud identification and instances of false positives. If your program is overzealous, it might create more work for your agents, forcing them to prove that legitimate claims received an incorrect label. Yet when the machine learning model is optimized, it can review a multitude of dimensions to identify the likelihood of fraudulent claims. That way, if an insurance claim is called into question, adjusters can comb through the data to determine if the claim should truly be rejected or if the red flags have a valid explanation.

Detecting New Strategies

The ability of analytics tools to detect known instances of fraud is only the beginning of their full potential. As with any type of crime, insurance fraud evolves with technology, regulations, and innovation. With that transformation comes new strategies to outwit or deceive insurance companies.

One recent example has emerged through automation. When insurance organizations began to implement straight through processing (STP) in their claim approvals, the goal was to issue remittances more quickly, easily, and cheaply than manual processes. For a time, this approach provided a net positive, but once organized fraudsters caught wind of this practice, they pounced on a new opportunity to deceive insurers.

Criminals learned to game the system, identifying amounts that were below the threshold for investigation and flying their fraudulent claims under the radar. In many cases, instances of fraud could potentially double without the proper tools to detect these new deception strategies. Though most organizations plan to enhance their anti-fraud technology, there’s still the potential for them to lose millions in errant claims – if their insurance fraud analytics are not programmed to detect new patterns.

In addition to spotting red flags for common fraud occurrences, analytics programs need to be attuned to any abnormal similarities or unlikely statistical trends. Using cluster analysis, an organization can detect statistical outliers and meaningful patterns that reveal potential instances of fraud (such as suspiciously identical fraud claims).

Even beyond the above automation example, your organization can use data discovery to find hidden indicators of fraud and predict future incidents. Splitting claims data into various groups through a few parameters (such as region, physician, billing code, etc., in healthcare) can help in detecting unexpected correlations or warning signs for your automation process or even human adjusters to flag as fraud.

Safeguarding Personally Identifiable Information

As you work to improve your fraud detection, there’s one challenge all insurers face: protecting the personally identifiable information (PII) of policyholders while you analyze your data. The fines related to HIPAA violations can amount to $50,000 per violation, and other data privacy regulations can result in similarly steep fines. The good news is that insurance organizations can balance their fraud prediction and data discovery with security protocols if their data ecosystem is appropriately designed.

Maintaining data privacy compliance and effective insurance fraud analytics requires some maneuvering. Organizations that derive meaningful and accurate insight from their data must first bring all of their disparate data into a single source of truth. Yet, unless they also implement access control through a compliance-focused data governance strategy, there’s a risk of regulatory violations while conducting fraud analysis.

One way to limit your exposure is to create a data access layer that tokenizes the data, replacing any sensitive PII with unique identification symbols to keep data separate. Paired with clear data visualization capabilities, your adjusters and special investigation units can see clear-cut trends and evolving strategies without revealing individual claimants. From there, they can take their newfound insights into any red flag situation, saving your organization millions while reducing the threat of noncompliance.

Are you ready to take your data science initiatives to the next level? Partner with 2nd Watch, the industry leader in data management, analytics, and data science consulting. Our team of experts will guide you through the entire process, from building the business case to data preparation and model building. Schedule a data science readiness whiteboard session with us today and unlock the full potential of data science for your business. Don’t miss out on the opportunity to enhance fraud detection, uncover emerging criminal strategies, and remain compliant with data privacy regulations. Get started now and experience the transformative power of insurance fraud analytics with 2nd Watch by your side.

Check out our insurance analytics solutions page for use cases that are transforming your industry.


Unleashing the Benefits of Machine Learning in Retail

You already know that data is a gateway for retailers to improve customer experiences and increase sales. Through traditional analysis, we’ve been able to combine a customer’s purchase history with their browser behavior and email open rates to help pinpoint their current preferences and meet their precise future needs. Yet the new wave of buzzwords such as “machine learning” and “AI” promise greater accuracy and personalization in your forecasts and the marketing actions they inform.

What distinguishes the latest predictive analytics technology from the traditional analytics approach? Here are three of the numerous examples of this technology’s impact on addressing retail challenges and achieving substantial ROI.

Want better dashboards? Our data and analytics experts are here to help. Learn more about our data visualization starter pack.

Machine learning has revolutionized various industries, and the retail sector is no exception. With the abundance of data generated by retailers, machine learning algorithms can extract valuable insights, improve decision-making processes, and enhance overall operational efficiency.

Benefits of Machine Learning for Retail

Here are some key benefits of machine learning for the retail industry:

Personalized Customer Experience: Machine learning algorithms can analyze customer data, including purchase history, browsing behavior, and demographics, to create personalized recommendations. By understanding individual preferences and patterns, retailers can offer tailored product suggestions, personalized marketing campaigns, and targeted promotions, leading to improved customer satisfaction and increased sales.

Demand Forecasting: Accurate demand forecasting is crucial for effective inventory management and ensuring product availability. Machine learning models can analyze historical sales data, market trends, seasonal patterns, and external factors to predict future demand with higher accuracy. This enables retailers to optimize inventory levels, reduce out-of-stock situations, minimize excess inventory, and improve overall supply chain efficiency.

Pricing Optimization: Machine learning algorithms can analyze market dynamics, competitor pricing, customer behavior, and other relevant data to optimize pricing strategies. Retailers can dynamically adjust prices based on factors such as demand, inventory levels, and competitive landscape. This helps maximize revenue, increase profit margins, and respond quickly to market changes.

Fraud Detection and Prevention: Retailers often face the challenge of fraud, including online payment fraud, identity theft, and counterfeit products. Machine learning algorithms can analyze vast amounts of transactional data in real-time to detect fraudulent patterns, anomalies, and suspicious activities. This proactive approach enables retailers to mitigate fraud risks, protect customer data, and maintain a secure and trusted shopping environment.

Supply Chain Optimization: Machine learning can optimize various aspects of the supply chain, including demand forecasting, inventory management, logistics, and delivery routes. By analyzing data from multiple sources, including suppliers, warehouses, and transportation systems, machine learning algorithms can identify bottlenecks, streamline operations, reduce costs, and enhance overall supply chain efficiency.

Customer Sentiment Analysis: Machine learning techniques can analyze customer feedback, reviews, and social media data to understand customer sentiment towards products, brands, or the overall shopping experience. Retailers can gain valuable insights into customer preferences, identify areas for improvement, and take proactive measures to enhance customer satisfaction and loyalty.

Increase customer lifetime value: Repeat customers contribute to 40% of a brand’s revenue. But how do you know where to invest your marketing dollars to increase your customer return rate? All of this comes down to predicting which customers are most likely to return and factors that influence the highest customer lifetime value (CLV) for these customers, which are both great use cases for machine learning.

Consider this example: Your customer is purchasing a 4K HD TV and you want to predict future purchases. Will this customer want HD accessories, gaming systems, or an upgraded TV in the near future? If they are forecasted to buy more, which approach will work to increase their chances of making the purchase through you? Predictive analytics can provide the answer.

One of the primary opportunities is to create more personalized sales process without mind-boggling manual effort. The sophistication of machine learning algorithms allows you to quickly review large inputs on purchase histories, internet and social media behavior, customer feedback, production costs, product specifications, market research, and other data sources with accuracy.

Historically, data science teams had to run one machine-learning algorithm at a time. Now, modern solutions from providers like DataRobot allows a user to run hundreds of algorithms at once and even identify the most applicable ones. This vastly increases the time-to-market and focuses your expensive data science team’s hours on interpreting results rather than just laying groundwork for the real work to begin.

Attract new customers: Retailers cannot depend on customer loyalty alone. HubSpot finds that consumer loyalty is eroding, with 55% of customers no longer trusting the companies they buy from. With long-running customers more susceptible to your competitors, it’s important to always expand your base. However, as new and established businesses vie for the same customer base, it also appears that customer acquisition costs have risen 50% in five years.

Machine learning tools like programmatic advertising offer a significant advantage. For those unfamiliar with the term, programmatic advertising is the automated buying and selling of digital ad space using intricate analytics. For example, if your business is attempting to target new customers, the algorithms within this tool can analyze data from your current customer segments, page context, and optimal viewing time to push a targeted ad to a prospect at the right moment.

Additionally, businesses are testing out propensity modeling to target consumers with the highest likelihood of customer conversion. Machine learning tools can score consumers in real time using data from CRMs, social media, e-commerce platforms, and other sources to identify the most promising customers. From there, your business can personalize their experience to better shepherd them through the sales funnel – even going as far as reducing cart abandon rates.

Automate touch points: Often, machine learning is depicted as a way to eliminate a human workforce. But that’s a mischaracterization. Its greatest potential lies in augmenting your top performers, helping them automate routine processes to free up their time for creative projects or in-depth problem-solving.

For example, you can predict customer churn based on irregularities in buying behavior. Let’s say that a customer who regularly makes purchases every six weeks lapses from their routine for 12 weeks. A machine learning model can identify if their behavior is indicative of churn and flag customers likely not to return. Retailers can then layer these predictions with automated touch points such as sending a reminder about the customer’s favorite product – maybe even with a coupon – straight to their email to incentivize them to return.

How to Get Started

In summary, machine learning offers a range of benefits for the retail industry, including personalized customer experiences, accurate demand forecasting, pricing optimization, fraud detection, supply chain optimization, and customer sentiment analysis. By leveraging the power of machine learning, retailers can gain a competitive edge, drive growth, and deliver exceptional shopping experiences to their customers.

Though implementing machine learning can transform your business in many ways, your data needs to be in the right state before you can take action. That involves identifying a single customer across platforms, cleaning up the quality of your data, and identifying specific use cases for machine learning. With the right partner, you can not only make those preparations but rapidly reap the rewards of powering predictive analytics with machine learning.

Want to learn how the 2nd Watch team can apply machine learning to your business? Contact us now.


How Machine Learning Can Save You Millions on Server Capacity

TL;DR

  • While most servers spend the majority of their time well below peak usage, companies often pay for max usage 24/7.
  • Cloud providers enable the ability to scale usage up and down, but determining the right schedule is highly prone to human error.
  • Machine learning models can be used to predict server usage throughout the day and scale the servers to that predicted usage.
  • Depending on the number of servers, savings can be in the millions of dollars.

How big of a server do you need? Do you know? Enough to handle peak load, plus a little more headroom? How often is your server going to run at peak utilization? For two hours per day? Ten hours? If your server is only running at two hours per day at peak load, then you are paying for 22 hours of peak performance that you aren’t using. Multiply that inefficiency across many servers, and that’s a lot of money spent on compute power sitting idle.

Cloud Providers Make Scaling Up and Down Possible (with a Caveat)

If you’ve moved off-premise and are using a cloud provider such as AWS or Azure, it’s easy to reconfigure server sizes if you find that you need a bigger server or if you’re not fully utilizing the compute, as in the example above. You can also schedule these servers to resize if there are certain times where the workload is heavier. For example, scheduling a server to scale up during nightly batch processes or during the day to handle customer transactions.

The ability to schedule is powerful, but it can be difficult to manage the specific needs of each server, especially when your enterprise uses many servers for a wide variety of purposes. The demands of a server can also change, perhaps without their knowledge, requiring close monitoring of the system. Managing the schedules of servers becomes yet another task to pile on top of all of IT’s other responsibilities. If only there was a solution that could recognize the needs of a server and create dynamic schedules accordingly, and do so without any intervention from IT. This type of problem is a great example for the application of machine learning.

How Machine Learning Can Dynamically Scale Your Server Capacity (without the Guesswork)

Machine learning excels at taking data and creating rules. In this case, you could use a model to predict server utilization, and then use that information to dynamically create schedules for each database.

Server Optimization In Action

We’ve previously done such an application for a client in the banking industry, leading to a 68% increase in efficiency and a cost savings of $10,000 per year for a single server. When applied to the client’s other 2,000 servers, this method could lead to savings of $20 million per year!

While the actual savings will depend on the number of servers employed and the efficiency at which they currently run, the cost benefits will be significant once the machine learning server optimization model is applied.

If you’re interested in learning more about using machine learning to save money on your server usage, click here to contact us about our risk-free server optimization whiteboard session.


Why the Healthcare Industry Needs to Modernize Analytics

It’s difficult to achieve your objectives when the goalposts are always in motion. Yet that’s often the reality for the healthcare industry. Ongoing changes in competition, innovation, regulation, and care standards demand real-time insight. Otherwise, it’s all too easy to miss watershed moments to change, evolve, and thrive.

Advanced or modernized analytics are often presented as the answer to reveal these hidden patterns, trends, or predictive insights. Yet when spoken about in an abstract or technical way, it’s hard to imagine the tangible impact that unspecified data can have on your organization. Here are some of the real-world use cases of big data analytics in healthcare, showing the valuable and actionable intelligence within your reach.

Improve Preventative Care

It’s been reported that six in ten Americans suffer from chronic diseases that impact their quality of life – many of which are preventable. Early identification and mediation reduce risk of long-term health problems, but only if organizations can accurately identify vulnerable patients or members. The success of risk scoring depends on a tightrope walk exploring populace overviews and individual specifics – a feat that depends on a holistic view of each patient or member.

A wide range of data contributes to risk scoring (e.g., patient/member records, social health determinants, etc.) and implementation (e.g., service utilization, outreach results, etc.). With data contained in an accessible, centralized infrastructure, organizations can pinpoint at-risk individuals and determine how best to motivate their participation in their preventive care. This can reduce instances of diabetes, heart disease, and other preventable ailments.

Encouraging healthy choices and self-care is just one potential example. Big data analytics has also proven to be an effective solution for preventing expensive 30-day hospital readmissions. Researchers at the University of Washington Tacoma used a predictive analytics model on clinical data and demographics metrics to predict the return of congestive heart failure patients with accurate results.

From there, other organizations have repurposed the same algorithmic framework to identify other preventable health issues and reduce readmission-related costs. One Chicago-based health system implemented a data-driven nutrition risk assessment that identified those patients at risk for readmissions. With that insight, they employed programs that combated patient malnutrition, cut readmissions, and saved $4.8 million. Those are huge results from one data set.

Boost Operational Efficiency

It’s well known that healthcare administrative costs in the United States are excessive. But it’s hard to keep your jaw from hitting the floor when you learn Canadian practices spend 27% of what U.S. organization do for the same claims processing. That’s a clear sign of operational waste, yet one that doesn’t automatically illuminate the worst offenders. Organizations can shine a light on wastage with proper healthcare analytics and data visualizations.

For instance, the right analytics and BI platform is capable of accelerating improvements. It can cross-reference patient intake data, record-keeping habits, billing- and insurance-related costs, supply chain expenses, employee schedules, and other data points to extract hidden insight. With BI visualization tools, you can obtain actionable insight and make adjustments in a range of different functions and practices.

Additionally, predictive analytics solutions can help to improve the forecasting of both provider organizations. For healthcare providers, a predictive model can help anticipate fluctuations in patient flow, enabling an appropriate workforce response to patient volume. Superior forecasting at this level manages to reduce two types of waste: labor dollars from overscheduling and diminished productivity from under-scheduling.

Enhance Insurance Plan Designs

There is a distinct analytics opportunity for payers, third-party administrators, and brokers: enhancing their insurance plan designs. Whether you want to retain or acquire customers, your organization’s ability to provide a more competitive and customized plan than the competition will be a game-changer.

All of the complicated factors that contribute to the design of an effective insurance plan can be streamlined. Though most organizations have lots of data, it can be difficult to discern the big picture. But machine learning programs have the ability to take integrated data sources such as demographics, existing benefit plans, medical and prescription claims, risk scoring, and other attributes to build an ideal individualized program. The result? Organizations are better at catering to members and controlling costs.

Plenty of Other Use Cases Exist

And these are just a sample of what’s possible. Though there are still new and exciting ways you can analyze your data, there are also plenty of pre-existing roadmaps to elicit incredible results for your business. To get the greatest ROI, your organization needs guidance through the full potential of these groundbreaking capabilities.

Want to explore the possibilities of data analytics in healthcare situations? Learn more about our healthcare data analytics services and schedule a no-cost strategy session.


Analytics and Insights for Marketers

Analytics & Insights for Marketers is the third in a series of our Marketers’ Guide to Data Management and Analytics. In this series, we cover major terms, acronyms, and technologies you might encounter as you seek to take control of your data, improve your analytics, and get more value from your MarTech investments.

In case you missed them, you can access part one here and part two here.

In this post, we’ll explore:

  • Business intelligence (BI)
  • Real-time analytics
  • Embedded analytics
  • Artificial intelligence (AI)
  • Machine learning

Business Intelligence

Business intelligence refers to the process in which data is prepared and analyzed to provide actionable insights and help users make informed decisions. It often encompasses various forms of visualizations in dashboards and reports that answer key business questions.

Why It Matters for Marketers:

With an increasing number of marketing channels comes an increasing amount of marketing data. Marketers who put BI tools to use gain essential insights faster, more accurately define key demographics, and make marketing dollars last.

Marketers without access to a BI tool spend a disproportionate amount of time preparing, rather than analyzing, their data. With the right dashboards in place, you can visualize observations about customer and demographic behaviors in the form of KPIs, graphs, and trend charts that inform meaningful and strategic campaigns.

Real-World Examples:

Your BI dashboards can help answer common questions about more routine marketing metrics without spending hours preparing the data. In a way, they take the pulse of your marketing initiatives. Which channels bring in the most sales? Which campaigns generate the most leads? How do your retention rate and ROI compare over time? Access to these metrics and other reports can shape the big picture of your campaigns. They help you make a measurable impact on your customer lifetime value, marketing RPI, and other capabilities.

Real-Time Analytics

Real-time analytics utilizes a live data stream and frequent data refreshes to enable immediate analysis as soon as data becomes available.

Why It Matters for Marketers:

Real-time analytics enhances your powers of perception by providing up-to-the-minute understanding of buyers’ motivations. A real-time analytics solution allows you to track clicks, web traffic, order confirmations, social media posts, and other events as they happen, enabling you to deliver seamless responses.

Real-World Examples:

Real-time analytics can be used to reduce cart abandonment online. Data shows that customers abandon 69.57% of online transactions before they are completed. Implementing a real-time analytics solution can enable your marketing team to capture these lost sales.

By automatically evaluating a combination of live data (e.g., abandonment rates, real-time web interactions, basket analysis, etc.) and historical data (e.g., customer preferences, demographic groups, customer click maps, etc.), you can match specific customers to targeted messaging, right after they leave your site.

Embedded Analytics

Embedded analytics is the inclusion of a business intelligence functionality (think graphs, charts, and other visualizations) within a larger application (like your CRM, POS, etc.)

Why It Matters for Marketers:

The beauty of embedded analytics is that you do not need to open up a different interface to visualize data or run reports. Integrated BI functionality enables you to review customer data, sales history, or conversion rates along with relevant reports that enhance your decision-making. This enables you to reduce time-to-insight and empower your team to make data-driven decisions without leaving the applications they use daily.

Real-World Examples:

A member of your marketing team is reviewing individual customers in your CRM to analyze their customer lifetime value. Rather than exporting the data into a different analytics platform, you can run reports directly in your CRM – and even incorporate data from external sources.

In doing so, you can identify different insights that improve campaign effectiveness such as which type of content best engages your customers, how to re-engage detractors, or when customers expect personalized content.

Artificial Intelligence

AI is the ability for computer programs or machines to learn, analyze data, and make autonomous decisions without any major contributions from humans.

Why It Matters for Marketers:

Implementing AI can provide a better understanding of your business as it detects forward-looking data patterns that employees would struggle to find – and in a fraction of the time. Additionally, marketers can improve customer service through a data-driven understanding of customer behavior and with new AI-enabled services like chatbots.

Real-World Examples:

Customizing email messaging used to be a laborious process. You’d need to manually create a number of campaigns. Even then, you could only tailor your messages to segments, not to a specific customer. Online lingerie brand Adore Me pursued AI to mine existing customer information and histories to create personalized messages across omnichannel communications. As a result, monthly revenue increased by 15% and the average order amount increased by 22%.

AI chatbots are also making waves, and Sephora is a great example. The beauty brand launched a messaging bot through Kik as a way of engaging with their teenage customers preparing for prom. The bot provided them with tailored makeup tutorials, style guides, and other related video content. During the campaign, Sephora had more than 600,000 interactions and received 1,500 questions that they answered on Facebook Live.

Machine Learning

Machine learning is a method of data analysis in which statistical models are built and updated in an automated process.

Why It Matters for Marketers:

Marketers have access to a growing volume and variety of complex data that doesn’t always provide intuitive insight at first glance. Machine learning algorithms not only accelerate your ability to analyze data and find patterns, but they can identify unforeseeable connections that a human user might have missed. Through machine learning, you can enhance the accuracy of your analyses and dig deeper into customer behavior.

Real-World Examples:

One Chicago retailer used a centralized data platform and machine learning to identify patterns and resolve questions about customer lifetime value. In an increasingly competitive landscape, their conventional reporting solution wasn’t cutting it.

By combining data from various sources and then performing deeper, automated analysis, they were able to anticipate customer behavior in unprecedented ways. Machine learning enabled them to identify which types of customers would lead to the highest lifetime value, which customers had the lowest probability of churn, and which were the cheapest to acquire. This led to more accurate targeting of profitable customers in the market.

That’s only the beginning: a robust machine learning algorithm could even help predict spending habits or gather a customer sentiment analysis based on social media activity. Machine learning processes data much faster than humans and is able to catch nuances and patterns that are undetectable to the naked eye.

We hope you gained a deeper understanding into the various ways to analyze your data to receive business insights. Feel free to contact us with any questions or to learn more about what analytics solution would work best for your organizational needs.


Marketing Dashboard Examples for Data-Driven Marketers

In recent years, marketers have seen a significant emphasis on data-driven decision-making. Additionally, there is an increased need to understand customer behavior and key metrics such as ROI or average order value (AOV). With an endless number of data sources (social media, email marketing, ERP systems, etc.) and a rapidly growing amount of data, both executives and analysts struggle to make sense of all the available information and respond in a timely manner.

A well-developed marketing dashboard helps companies overcome these challenges by organizing data into digestible metrics and reports that update automatically. Dashboards provide intuitive visuals that unlock the business value within your data quickly and without the manual effort of getting pieces of information from multiple places. Below, we have outlined four ways that dashboards benefit the entire marketing team, from the executives to the analysts.

Download Now: Sales and Marketing Dashboard Lookbook

1. Dashboards centralize all of your key information in one place.

Dashboards combine all of the essential information that would typically be found across various reports from disparate systems such as your CRM, ERP, or even third-party reports. Questions such as “How does our average order value compare to this time last year?”, “Which marketing channels are driving the most new customers?”, or “What is our marketing ROI?” can be quickly, consistently, and reliably answered without waiting on IT to put the information together or spending your department’s working day going to each software to download a new report and then marry them together. Instead, you can simply refer to a dashboard that highlights key statistics.

2. Dashboards automate manual processes and ensure reliable and consistent reports.

Marketing analysts often spend more time wrangling, cleaning, and validating data for a report than they do gleaning insights from it. Even after these reports are created, it’s difficult to ensure the metrics will remain consistent each time they are delivered to executives. Dashboards are typically built using one source of data that has predefined metrics and inputs, to ensure the reports remain consistent. They can automatically refresh data on a set schedule or surface it as it’s collected. Not only does this create more time for marketing analysts to focus on creating campaigns and incentives based on these insights, but it also allows executives to make decisions using accurate and consistent data points.

This dashboard highlights the trend in a selected metric, including its predicted future value, allowing marketers to quickly pivot when current campaign ROI is trending downward. It also allows the marketing department to demonstrate ROI to the company as a whole, which often leads to an increase in marketing budget.

This dashboard highlights the order activity associated with experimental products to allow the merchandising team to pivot quickly when a new product isn’t working, or it can allow the marketing team to increase advertising dollars if a new product isn’t getting enough attention.

3. Quick and reliable understanding of customer behavior paves the way to a stronger customer relationship.

Dashboards consolidate and visualize the story your data is telling. This often reflects the reality of how customers interact with your brand and clearly points out new trends.

For example, creating a picture that combines key pieces of information across systems, such as how many orders a customer placed and the value of those orders from your ERP with click analytics for that customer from your email marketing platform, allows you to identify like-customers who may respond to similar incentives. These customer profiles can then grow and change over time as you gather more data, leading to insights that allow you to more efficiently target the audiences who are more likely to convert based on that incentive. It also cuts down on the number of incentives or touchpoints you put in front of a customer who isn’t interested in that particular part of your business. Less spam for your customers and more conversions for you.

This dashboard quickly highlighted which customer segment was more engaged when the brand pushed social/email/web content, giving the marketing department the perfect focus group on which they could test new ideas.

This dashboard provides an executive-level overview of marketing performance.

This dashboard shows the comparison in purchase behavior across loyalty and non-loyalty customers. Our clients use dashboards like these to inform when they should incentivize customers to participate in a loyalty program or jump to the next tier and when the loyalty program is actually costing them more money than it’s yielding.

This dashboard highlights the time between orders across your customer base. Say, for example, you have a set of customers who place an order every six weeks, but they haven’t returned for 10. Our clients automate the discovery of these customers and send that email list to their email service provider (ESP) to automatically re-engage that customer base.

4. Dashboards are a gateway to advanced customer analytics.

With your analysts no longer consumed by manually building out reports, you’re on your way to identifying strong use cases for machine learning (ML) and predictive analytics. This form of advanced customer analytics covers a wide variety of use cases.

A common marketing use case for ML is predicting customer lifetime value (CLV/LTV) prior to investing marketing dollars on acquisition by matching a potential customer to the profile of existing customers that either yield high net profit or end up costing your business money. Another great marketing use case for data science is predicting the probability of conversion for a specific campaign or promotion based on a customer or customer segment’s previous behavior with like-campaigns. Your branding and merchandising teams may want to focus on identifying products that would yield a higher profit or increase in orders as a bundle instead of being sold individually.

Regardless of the use case, your dashboards will put you in a strong position to have a more targeted and therefore effective data science use case.

This is an example of a marketing dashboard that helps better understand customer demographics.

This dashboard gives marketers a place to test theories on customer demographics that would yield the highest LTV for a specific campaign.

Implementing strategic, goal-oriented dashboards significantly improves your marketing efforts at all levels. They provide analysts with the ability to spend their time acting on information rather than searching for and cleaning up data. More importantly, they enable executives to make informed decisions that ultimately increase ROI and ensure marketing budget is spent on impactful efforts.

2nd Watch has a vast array of experience helping marketers create dashboards that unlock valuable insights. Contact us or check out our Marketing Analytics Starter Pack to quickly gain the benefits listed above with a marketing dashboard specialized for your company.


How Machine Learning Can Benefit the Insurance Industry

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. High premium volumes show no signs of slowing down and make the American insurance industry one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence (AI) technology like machine learning (ML), insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen. 

Insurance data is vast and complex. This data is comprised of many individuals with many instances and many factors used in determining the claims. Moreover, the type of insurance increases the complexity of data ingestion and processing. Life insurance is different than automobile insurance, health insurance is different than property insurance, and so forth. While some of the processes are similar, the data and multitude of flows can vary greatly.

As a result, insurance enterprises must prioritize digital initiatives to handle huge volumes of data and support vital business objectives. In the insurance industry, advanced technologies are critical for improving operational efficiency, providing excellent customer service, and, ultimately, increasing the bottom line.

ML can handle the size and complexity of insurance data. It can be implemented in multiple aspects of the insurance practice, and facilitates improvements in customer experiences, claims processing, risk management, and other general operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud, which plagues the entire industry. It is a big development in fraud detection and insurance organizations must add it to their fraud prevention toolkit. 

In this article, we lay out how insurance companies are using ML to improve their insurance processes and flag insurance fraud before it affects their bottom lines. Read on to see how ML can fit within your insurance organization. 

What is machine learning?

ML is a technology under the AI umbrella. ML is designed to analyze data so computers can make predictions and decisions based on the identification of patterns and historical data. All of this is without being explicitly programmed and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and are constantly learning. Ultimately, AI/ML will handle menial tasks and free human agents to perform more complex requests and analyses.

What are the benefits of ML in the insurance industry?

There are several use cases for ML within an insurance organization regardless of insurance type. Below are some top areas for ML application in the insurance industry:

Lead Management

For insurers and salespeople, ML can identify leads using valuable insights from data. ML can even personalize recommendations according to the buyer’s previous actions and history, which enables salespeople to have more effective conversations with buyers. 

Customer Service and Retention

For a majority of customers, insurance can seem daunting, complex, and unclear. It’s important for insurance companies to assist their customers at every stage of the process in order to increase customer acquisition and retention. ML via chatbots on messaging apps can be very helpful in guiding users through claims processing and answering basic frequently asked questions. These chatbots use neural networks, which can be developed to comprehend and answer most customer inquiries via chat, email, or even phone calls. Additionally, ML can take data and determine the risk of customers. This information can be used to recommend the best offer that has the highest likelihood of retaining a customer. 

Risk Management

ML utilizes data and algorithms to instantly detect potentially abnormal or unexpected activity, making ML a crucial tool in loss prediction and risk management. This is vital for usage-based insurance devices, which determine auto insurance rates based on specific driving behaviors and patterns. 

Fraud Detection

Unfortunately, fraud is rampant in the insurance industry. Property and casualty (P&C) insurance alone loses about $30 billion to fraud every year, and fraud occurs in nearly 10% of all P&C losses. Overall, insurance fraud steals at least $80 billion every year from American consumers. ML can mitigate this issue by identifying potential claim situations early in the claims process. Flagging early allows insurers to investigate and correctly identify a fraudulent claim. 

Claims Processing

Claims processing is notoriously arduous and time-consuming. ML technology is the perfect tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker/adjuster.

Why is ML so important for fraud detection in the insurance industry?

Fraud is the biggest problem for the insurance industry, so let’s return to the fraud detection stage in the insurance lifecycle and detail the benefits of ML for this common issue. Considering the insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums each year, there are huge opportunities and incentives for insurance fraud to occur.  

Insurance fraud is an issue that has worsened since the COVID-19 pandemic began. Some industry professionals believe that the number of claims with some element of fraud has almost doubled since the pandemic. 

Below are the various stages in which insurance fraud can occur during the insurance lifecycle:

  • Application Fraud: This fraud occurs when false information is intentionally provided in an insurance application. It is the most common form of insurance fraud.
  • False Claims Fraud: This fraud occurs when insurance claims are filed under false pretenses (i.e., faking death in order to collect life insurance benefits).
  • Forgery and Identity Theft Fraud: This fraud occurs when an individual tries to file a claim under someone else’s insurance.
  • Inflation Fraud: This fraud occurs when an additional amount is tacked onto the total bill when the insurance claim is filed. 

Based on the amount of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection toolkits. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. The ML approaches and algorithms that facilitate fraud detection are the following:

  • Deep Anomaly Detection: During claims processing, this approach will analyze real claims and identify false ones. 
  • Supervised Learning: Using predictive data analysis, this ML algorithm is the most commonly used for fraud detection. The algorithm will label all input information as “good” or “bad.”
  • Semi-supervised Learning: This algorithm is used for cases where labeling information is impossible or highly complex. It stores data about critical category parameters even when the group membership of the unlabeled data is unknown.
  • Unsupervised Learning: This model can flag unusual actions with transactions and learns specific patterns in data to continuously update its model. 
  • Reinforcement Learning: Collecting information about the environment, this algorithm automatically verifies and contextualizes behaviors in order to find ways to reduce risk.
  • Predictive Analytics: This algorithm accounts for historical data and existing external data to detect patterns and behaviors.

ML is instrumental in fraud prevention and detection. It allows companies to identify claims suspected of fraud quickly and accurately, process data efficiently, and avoid wasting valuable human resources.

Conclusion

Implementing digital technologies, like ML, is vital for insurance businesses to handle their data and analytics. It allows insurance companies to increase operational efficiency and mitigate the top-of-mind risk of insurance fraud.

Working with a data consulting firm can help onboard these hugely beneficial technologies. By partnering with 2nd Watch for data analytics solutions, insurance organizations have experienced improved customer acquisition, underwriting, risk management, claims analysis, and other vital parts of their operations.

2nd Watch is here to work collaboratively with you and your team to design your future-state data and analytics environment. Request a complimentary insurance data strategy session today!