Tag: predictive analytics tools

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Predictive Analytics in Marketing: Key to Drive Business Profitability in 2019

What is Predictive Analytics?

Predictive analytics is an approach that helps in predicting unknown future events. There are many techniques that are used in predictive analytics such as machine learning, data mining, data modeling, and artificial intelligence to examine current data and make future predictions. Also, it helps in finding patterns in both structured and unstructured data sets, thereby assisting in the identification of future risk and opportunities. Furthermore, predictive analytics has the potential to integrate management and technology together to drive better business outcomes. By leveraging predictive analytics solutions, businesses can become dynamic and can anticipate behaviors and outcomes based on the known facts and data and not merely upon assumptions.

How can predictive analytics solutions help businesses derive successful outcome and boost ROI? Read Quantzig’s recent blog to find out.

3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

Leveraging Predictive analytics in marketing can help businesses refine their marketing strategies and provide personalized services to customers. Want to know how? Get in touch with us right now!

Predictive Analytics in Marketing Realm

How can predictive analytics in marketing drive profitability for business? Are you thinking the same? The answer to it is, any tool, process or technique that can guide marketers to identify the buying habits of consumers is nothing less than a boon to their business. This is because if the past buying habits of a customer are identified and analyzed well, it can help in projecting the future buying habits, thereby helping in future decision-making based on those projections. Predictive analytics in marketing helps to ensure that these predictions are precise and accurate.

Here are a few things that a business can do when the available data is mined and predictive analytics in the marketing realm is applied:

Analyze and predict the seasonal behavior of customers

Today most of the products and services are sold online. Application of predictive analytics in marketing especially helps in this case. It helps in highlighting the products that are on high demand and those that customers prefer to buy at any given time.

Target the most profitable product category

The second benefit that businesses gain by applying predictive analytics in marketing is that they can target the most profitable products and services.  By administering the technique of artificial intelligence and machine learning, it is easy to identify affluent customers who prefer high-end products. This is an integral part of effective and predictive marketing strategy too.

By applying predictive analytics in marketing businesses can gain insights into new profits streams, better ways to conduct the business, and ultimately lead the game. Request a free proposal to know more.

Employ the most suitable marketing strategy for winning repeat business

Predictive analytics in marketing can inform businesses about customers who are most likely to be repeat customers. Owing to the high competition, businesses need to allocate resources on targeting such customers that are likely to profit the business the most. And applying predictive analytics in marketing is the best step to achieve this.

How can predictive analytics help in forecasting consumer demand precisely? Read our latest success story here to gain better insight.

Forecasting Consumer Demand with the Help of Predictive Analytics – A Quantzig Success Story

Prioritize customers

Finally, predictive analytics in marketing helps in prioritizing customers. It helps in identifying factors that indicate that a particular customer s most likely to become a repeat customer. It guides to recognize customers who buy the highest-margin products and are most likely to initiate returns.

Use-Cases of Predictive Analytics in Marketing

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Use Case #1: Refine segmentation for better campaigns

Applying predictive analytics in marketing helps in refining customer segmentation and creating customized campaigns. It allows to mine behavioral and demographic data to push quality leads further down the sales funnel.

Use Case #2: Improves content distribution strategy

Sometimes even the good content fails to drive business and the reason behind this is an ill-defined content distribution strategy. By applying predictive analytics in developing a marketing strategy, this problem can be tackled head-on. Using predictive analytics in marketing makes it easier to analyze the types of content that resonate most with customers of certain behavioral or demographic backgrounds. Furthermore, this helps in distributing similar content to such customers sharing the same demographic or behavioral habits.

Request a FREE demo below to know how our predictive analytics solutions can help your business.

Use Case #3: Precise prediction of customer lifetime value

Artificial intelligence and machine learning can make predictive analytics in marketing more efficient. It can enable businesses to gauge the historical lifetime value of existing customers that match the backgrounds of new customers. Consequently, this can help in making a fair and precise estimate of the lifetime value of new customers.

Use Case #4: Better insight into the propensity to churn

Protecting your bottom-line becomes much easier by leveraging predictive analytics in marketing. How do you ask? By analyzing and learning from the mistakes committed in the past. By applying predictive analytics in marketing, businesses can analyze the behavioral patterns of previously-churned customers. Furthermore, this can help in identifying the warning signs from current customers. Consequently, businesses can take measures to plug such customers into a churn-prevention nurture campaign.

Use Case #5: Optimization of campaign channels and content

By leveraging predictive analytics in marketing, businesses can optimize their campaign channels as well as the content. With the entry of new customers in the business pipeline, there is an availability of their data which can be utilized for the various purpose. These purposes include identification of most suitable marketing channels, content type and even data and time to target specific and potential customers.

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3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

With the shifting key values of every industry from customer-focused to customer-centric, there is an immense growth in data and information. This has changed customer dynamics and as a result, every business is facing numerous challenges such as market uncertainties, driving efficiency, and effectiveness in their marketing productivity, immense competition, customer demands, fraud detection, and risk management.

Many businesses across all industries, in order to cater customer demands, are trying to utilize the data that their customers leave behind while interacting with the company. Harnessing this pool of data can offer several benefits to organizations. However, many companies still have not realized the importance of data mining and have not gone beyond gathering and storing their data. Although it is difficult to deal with an unstructured set of data by leveraging predictive analytics solutions businesses can fetch optimum results from such data. 

At Quantzig, we understand the impact that predictive analytics solutions can have on your business. And to help companies derive actionable insights from large and complex data sets, our team of experts has highlighted three important ways in which predictive analytics solutions can help in managing large volumes of data and setting up analytical frameworks to derive real-time insights that facilitate more informed and wise decisions.

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Sales Data Analysis – The Revenue Booster Pack Every Organization Needs

Sales data analysis may sound like a very complex and time-consuming process; however, in reality it isn’t. Organizations must invest in advanced analytical capabilities that facilitate data collection and analysis; thereby, offering actionable insights that the sales representatives can use to ameliorate salesFree demo and the overall revenue. Prior to big data and advanced analytics, marketers and sales leaders based their business decisions on intuition and trial and error methods. But today, sales heads have realized the importance of making decisions based on insights gained from predictive analytics tools and other advanced data analytics solutions.

Past Imperfect – The Trial and Error Method of Decision Making

Traditionally, organizations made use of intuitive analytics to develop strategies with uncertain outcomes, usually known as the trial and error method. If these strategies worked, they applied it everywhere. If not, they would tweak it and make changes until it delivered favorable outcomes. With the intuitive analytics method, the salesforce was unable to monitor their customer’s activities and identify the growth drivers. The sales teams are often baffled with the mammoth amounts of data at their disposal and often struggle to build effective strategies that drive sales and revenue. The trial and error method for strategy development to boost sales and revenue might prove beneficial for the short term but may have adverse effects on the organizations revenue in the long run.

Sales Data Analysis Boosts Revenue – How?

Sales teams accumulate an overwhelming volume of data from various sources; thereby, making it essential for organizations to adopt sales analytics and predictive analytics tools to gain actionable insights. Technology and data analytics allow teams to track their market performance, measure the outcomes, monitor customer activities and behavior, identify growth drivers, and pinpoint successful sales efforts. Sales data analysis is all about, data mining and collection, organizing the data, and extracting meaningful insights that facilitate strategic decision making and boost sales revenue. How can we leverage sales data analysis to boost sales revenue, you ask? Here’s how:

  • First step in conducting a sales data analysis is to narrow your product and service offerings. Usually, organizations must gain an in-depth understanding of their consumer’s needs and expectations from the business, analyze past transaction information, and use the insights to build custom deals that are aligned to customers’ needs and cater to the target market
  • Next, put in place an efficient pipeline management system to segment your potential customer base, prioritize them based on their level of profitability, and identify the product that will cater to their needs. An efficient pipeline management reduces wastage of time and resources on incorrect leads and creates a stringent lead qualification and ranking process that drive sales quickly
  • Finally, sales heads must develop a robust and dynamic sales incentive plan for their team by using the sales data analysis and developing yardsticks or milestones to measure their performance

 

To know more about sales data analysis and its impact on the organization’s revenue

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Claims Analytics Helps a Leading Insurance Company Save Millions

Quantzig’s recent claims analytics engagement for a leading insurance company guarantees 85% increase in correct classification of high-risk claims compared to heuristic ways.

Claim handling and risk management

Many insurance companies are utilizing key data analytics, performance indicators, and an advocacy-based model to successfully manage claims. Sifting through documents manually and priority handling of high-risk claims is very challenging for service providers. The use of the effective analytical solution to identify both high and low-risk claims will help companies to reduce processing costs, identify fraudulent insurance claims, and process claim requests quicker. The claims analytics team at Quantzig offers predictive analytics solutions to the insurance companies to identify potential high-risk claims and subsequently reduce future costs involved with the claims. This engagement has combined decision analysis with logistic regression technique to classify high and low-risk claims. By leveraging these services, insurance companies develop individual logistics regression models for different disabilities and categorize them according to the risks associated with it.

By using logistic regression model, companies are developing effective solutions to identify and manage risks levels of different claims. These models are specifically designed to analyze data sets such as injury dates, nature of the injury, the tenure of employment, payment details, and monthly payments to identify the risk probability threshold value for each model and assess the expected future cost savings at that cutoff value.

Use of predictive analytics tools

Predictive analytics is a statistical and analytical technique that assess the past events to anticipate the future. In the insurance sector, it analyzes historical data such as nature of the injury, treatment, insured data, liability, characteristics of the claimant, attorneys involved, and venue to formulate settlement values for the losses incurred. This tool helps in ranking the priority of the claim process and amount of compensation that is associated with the claim. It removes manual intervention and results in faster claim settlement.

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Improving consumer experience

The smart implementation of innovative technology and other analytical tools will improve consumer satisfaction. Big data helps insurance companies improve claim management, customer retention, and channel productivity. Moreover, these technologies can be leveraged to identify the key trends and device appropriate business strategies to expand to larger markets. By increasing the speed and accuracy of settlements, companies can promote consumer confidence and build a stronger reputation in the market.

Outcomes and solutions offered

With hands-on expertise in claims analytics, Quantzig helped a leading insurance company by providing the various variable selection techniques such as PCA, variable clustering along with the strength of prediction tests to identify the top predictors developed individual logistic regression models. Some of the solutions offered are as follows:

  • Improved claim examiner efficiency based on highly accurate predictive model and business rule based on solution
  • Showed results that among the top three costliest disabilities, 68% higher probability of incurring a high-risk claim was observed in women when compared to men
  • Developed an alert tracker tool to flag the high-risk claims based on the predicted probability from the regression model
  • Automated the claims classification process to help the client reduce the claims processing time significantly
  • Analyzed the extremely high-risk claims from usual and non-risky disabilities to identify the fraudulent claims and flagged the respective characteristics for future enablement

The complete case study on claims analytics helped a leading insurance company identify potential high-risk claims is now available.

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For any queries, reach us at – hello@quantzig.com