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Apr 25 2024

Preparing for La Niña: Adopting Predictive Maintenance Before Hurricane Season

With a La Niña watch issued for the summer, businesses operating in hurricane-prone regions face heightened concerns about the impending storm season. La Niña heavily impacts the wind shear and atmospheric conditions over the Atlantic, where most hurricanes form thanks to its warm waters. It’s rare to go a year without a hurricane hitting some part of […]

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Demystifying Data Analytics Models

In today’s global landscape, organizations worldwide are increasingly turning to data analytics to enhance their business performance. Research conducted by McKinsey Consulting revealed that data-driven companies not only experience above-market growth but also witness EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) increases of up to 25% [1]. Additionally, Forrester’s findings indicate that organizations utilizing […]

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AI-Driven Predictive Analytics: Turning the Table on Fraudsters

Fraud techniques, including phishing, vishing, deepfakes, and other scams are becoming increasingly sophisticated – making it easier than ever to perpetuate fraud at scale. This is placing businesses in danger of financial losses, and trust and reputational damage. Now, there’s an alarming trend among organized crime rings that have the potential to defraud enterprises of […]

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Synchronized Video Data Collaboration for Multi-Layered Insights

In this digital age, large amounts of data are produced and consumed every second. And particularly for experienced bloggers in the data management or IT industry, synchronized video data collaboration represents an untapped wellspring of potential. By harnessing this data revolution, businesses can gain multi-layered insights into consumer behavior and enhance organizational performance. The Undeniable […]

Introducing the Data Analytics Fabric Concept

Organizations all over the world – both profit and nonprofit – are looking at leveraging data analytics for improved business performance. Findings from a McKinsey survey indicate that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers, and 19 times more profitable [1]. Research by MIT found that digitally mature firms are 26% […]

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Author: Arun Marar and Prashanth Southekal

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ACTIAN Data Analytics predictive analytics Supply chain supply chain analytics

Aug 23 2023

Why Predictive Data Analytics Will Drive the Supply Chain of the Future

It’s hard to imagine a crucial aspect of business that’s been more disrupted, unpredictable, and volatile over the last few years than supply chains. As a result of worker shortages, geopolitical issues, manufacturing shutdowns, unstable fuel prices, and myriad other factors, supply chains across almost all industries were unable to make on-time deliveries and meet customer demand.

These problems—and the impact on consumers who were forced to wait weeks, months or longer for products—have caused organizations to rethink their supply chains. We’ve detailed how to navigate managing the complexity and volume of your supply chain in this downloadable eBook Your Supply Chain Future is Now with Predictive Analytics. Companies have realized that legacy technologies and basic forecasting methods are not sufficient to predict and meet demand, especially as many consumers now expect next day or even same day delivery.

The ability to accurately predict demand and ensure the right products are available at the right time in the right place and with a favorable price point is increasingly difficult. Even seasonal demand that used to be fairly easy to predict based on previous buying habits is breaking long-standing patterns and becoming more unpredictable. Everything has changed, and organizations that aren’t modernizing and using supply chain analytics will be at a significant disadvantage.

Supply Chains of the Future Demand Predictive Analytics

To say that supply chains of the future will require new data analytics capabilities, including predictive analytics and supply chain analytics, is certainly true. But today’s supply chains need them too. Predictive analytics and other innovative technologies enable supply chains to be automated, respond faster, become more resilient, and be more sustainable.

The need to modernize supply chain processes by integrating data for predictive analytics will define one of the priorities for Chief Supply Chain Officers (CSCOs). This role will also be tasked with implementing the right data platform to manage ever-growing data volumes, build new data pipelines easily, and deliver analytic insights at scale.

As supply chains stretch around the world, having visibility into processes and suppliers, along with managing risk, is critical, but difficult. A scalable data platform is needed that can integrate, manage, and analyze data—and perform predictive analytics—at the speed modern businesses require, while also offering strong price performance.

A Look into the Future of Supply Chains

Modern supply chains must be efficient, agile, and connected. They must also have advanced forecasting capabilities to deliver granular insights into supply and demand, ensuring there’s enough product on the shelves to meet consumer needs, but not too much or it results in costly inventory, storage, or waste.

Organizations that don’t have a strategy for supply chain modernization will likely experience higher costs, spend more time on manually intensive processes, face ongoing inefficiencies, and fall behind their forward-looking peers.

The supply chain of the future will be more automated, with the use of technologies such as the Internet of Things (IoT), predictive analytics, blockchain, robotics, and even drones. These technologies will enable real-time tracking and monitoring of supply chain processes such as procurement, manufacturing, and distribution of goods. This eBook can help businesses like yours:

Implement predictive analytics and deliver trustworthy insights
Understand and build the supply chain of the future
Improve supply chain sustainability
Evolve the role of the CSCO to meet current and future needs
Manage diversification in your supply chain
Create a roadmap for ongoing success
Make data analytics easy to use with the right cloud data platform

Get Easy-to-Use Supply Chain Analytics You Can Trust

Supply chain success starts with the right platform. Customers trust Actian for data and analytics, including predictive analytics, for visibility, insights, and data-driven automation to better manage and optimize their supply chains. The Actian Data Platform is easy to manage, enabling you to transform your supply chain and your business by simplifying how you connect and analyze data. Forrester recently recognized Actian as one of the top 15 cloud data warehouse providers. See why and find out how Actian can help modernize your supply chain.

Get the eBook to learn more about how the Actian platform can make managing your supply chain easier.

Related resources you may find useful:

· The Power of Real-time Supply Chain Analytics

· The Top Data and Analytics Capabilities Every Modern Business Should Have

· How Your Peers Are Experiencing Their Journeys to the Cloud

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Does Using Data Analytics Improve Price Points in Retail?

How can your business stay competitive in retail? Pricing analytics is a valuable tool to help optimize pricing strategies to maximize revenue and profits. By using advanced analytics to inform pricing decisions, retailers can make more informed, data driven decisions based on customer behavior and market conditions.

Data, including internal data such as customer behavior and sales data, and external data such as market and competitor data drive pricing analytics. There are many techniques in the pricing analytics arsenal that retailers can use to analyze this data to gain insights into how customers will respond to different prices. Here’s a summary of some of the top methods.

Price Optimization

Price optimization analyzes customer and market data to find the ideal price point for a product or service. This data analytics method is based on price sensitivity, how changes in the price of products/services affect customer demand, and how much profit the retailer can earn from selling the product or service at a certain price.

Price sensitivity differs greatly across different consumers. For this reason, it’s useful to use segmentation to divide a market into distinct groups of consumers with different price sensitivities. Businesses can charge more and increase profits for some segments, and for others, businesses can offer discounts and price promotions to increase conversion.

Dynamic Pricing

Dynamic pricing automatically adjusts the price of a product or service in real-time based on customer demand, competition and other factors. By using real-time analytics to set prices that reflect current conditions, retailers can better optime their revenue or provide than with traditional pricing strategies that set prices based on fixed costs and profit margins.

Dynamic pricing is especially valuable in dynamic markets where factors such as supply and demand, seasonal patterns and competitive pricing changes quickly. This method is useful across any selling channel, but real-time analytics can be essential for competing in e-commerce. For example, Amazon dominates the e-commerce market with its ability to rapidly and frequently change prices to undercut competitors.

Price Gap Analysis

Price gap analysis compares the price of a company’s product or service to those of its competitors. Using price gap analysis shows if a product/service is priced above the market, below the market or on par with the market.

“Minding the gap” depends on the company’s price strategy for the product/service. For example, a company usually prices national brands a certain percentage higher than private labels or generics. Price gaps may vary at different retailers and a retailer may want to adjust price since some brands prefer a more consistent price because most consumers shop at multiple retailers.

Bundle Price Analysis

Bundle price analytics involves setting the optimal discounted price for a bundle of multiple products or services, taking into account the price of individual prices in the bundle, production costs, desired profit margin and expected customer demand. The goal of bundle price analytics is to encourage customers to purchase more items with a cost-effective way to buy the products together. This type of data analytics helps retailers increase their revenue and improve customer loyalty and retention.

Getting Started

Businesses need to make informed decisions about pricing to be successful in retail using data analytics. Pricing analytics can help transform pricing through better data science if its built on the right platform where data can be collected, cleansed, managed, and analyzed in a centralized location. See how the Actian Data Platform makes price analytics easy.

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Leveraging AI and Automation to Streamline Clinical Trial Data Management

Clinical trials are critical in developing and approving new medical treatments and technologies. These trials generate massive data that needs to be managed efficiently and accurately to ensure patient safety and successful research outcomes. The good news is that advances in AI and automation technology, such as AI-based data extraction, virtual clinical trials, and predictive analytics, […]

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6 Steps to Leveraging Supply Chain Data to Inform Predictive Analytics

Predictive analytics is a powerful tool to help use supply chain data to make more informed decisions about the future. This might involve analyzing data about inventory, order fulfillment, delivery times, manufacturing equipment and processes, suppliers, customers, and other factors that impact your supply chain. Predictive analytics can help you deal with some of your supply chain challenges more effectively, including demand volatility, supply shortages, manufacturing downtime, and high warehouse labor costs.

Six Steps to Inform Predictive Analytics

Knowing what’s going to happen in the future can help you transform your supply chain, but you’ll need to first understand how to leverage your supply chain data to inform predictive analytics. Here are some foundational steps to help you get started:

Collect Data

Predictive analytics relies on historical data to predict future events. How much data you’ll need depends on the type of problem you’re trying to solve, model complexity, data accuracy, and many other things. The types of data required depend on what you are trying to forecast. For instance, to forecast demand, you would need to gather data on past sales, customer orders, market research, planned promotions, and more.

Clean and Pre-Process Data

Data quality is key for predictive analytics to make accurate forecasts. Your data collection process needs to ensure that data is accurate, complete, unique, valid, consistent, and from the right time period.

Select a Predictive Analytics Technique

Machine learning uses algorithms and statistical models to identify patterns in data and make predictions. You need to select the appropriate machine-learning technique based on your data and the nature of your use case. Here are the major ones to choose from:

Regression Analysis: Finds a relationship between one or more independent variables and a dependent variable.
Decision Tree: Type of machine learning used to make predictions based on how a previous set of questions were answered.
Neural Networks: Simulates the functioning of the human brain to analyze complex data sets. It creates an adaptive system that computers use to learn from their mistakes and improve continuously.
Time-Series Analysis: Analyzes time-based data to predict future values.
Classification: Prediction technique that uses machine learning to calculate the probability that an item belongs to a particular category.
Clustering: Uses machine learning to group objects into categories based on their similarities, thereby splitting a large dataset into smaller subsets.

Train the Model

Training a machine learning model is a process in which a machine learning algorithm is fed with data from which it can learn.

Validate the Model

After training, you need to validate the model to ensure that it can accurately predict the future. This involves comparing the model’s predictions with actual data from a test period.

Use the Model to Forecast the Future

Once you have validated your model, you are ready to start using it to forecast data for future periods.

You’ll also need the right machine learning platform to execute these six predictive analytics steps successfully. Our blog “What Makes a Great Machine Learning Platform” helps you to discover how to evaluate a solution and learn about the Actian Data Platform’s capabilities.

Try our Actian Data Platform Free Trial to see for yourself how it can help you simplify predictive analytics deployment.

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7 Steps to Leveraging Segment Analysis and Predictive Analytics to Improve CX

Today’s customers expect a timely, relevant, and personalized experience across every interaction. They have high expectations for when and how companies engage with them—meaning customers want communications on their terms, through their preferred channels, and with personalized, relevant offers. With the right data and analytics capabilities, organizations can deliver an engaging and tailored customer experience (CX) along each point on the customer journey to meet, if not exceed, expectations.

Those capabilities include segment analysis, which analyzes groups of customers who have common characteristics, and predictive analytics, which utilizes data to predict future events, like what action a customer is likely to take. Organizations can improve CX using segment analysis and predictive analytics with the following steps.

Elevating Customer Experiences Starts with Seven Key Steps

Use a Scalable Data Platform

Bringing together the large volumes of data needed to create customer 360-degree profiles and truly understand customers requires a modern and scalable data platform. The platform should easily unify, transform, and orchestrate data pipelines to ensure the organization has all the data needed for accurate and comprehensive analytics—and make the data readily available to the teams that need it. In addition, the platform must be able to perform advanced analytics to deliver the insights necessary to identify and meet customer needs, leading to improved CX.

Integrate the Required Data

Unifying customer data across purchasing history, social media, demographic information, website visits, and other interactions enables the granular analytic insights needed to nurture and influence customer journeys. The insights give businesses and marketers an accurate, real-time view of customers to understand their shopping preferences, purchasing behaviors, product usage, and more to know the customer better. Unified data is essential for a complete and consistent customer experience. A customer data management solution can acquire, store, organize, and analyze customer data for CX and other uses.

Segment Customers into Groups

Customer segmentation allows organizations to optimize market strategies by delivering tailored offers to groups of customers that have specific criteria in common. Criteria can include similar demographics, number of purchases, buying behaviors, product preferences, or other commonalities. For example, a telco can make a custom offer to a customer segment based on the group’s mobile usage habits. Organizations identify the criteria for segmentation, assign customers into groups, give each group a persona, then leverage segment analysis to better understand each group. The analysis helps determine which products and services best match each persona’s needs, which then informs the most appropriate offers and messaging. A modern platform can create personalized offers to a customer segment of just one single person—or any other number of customers.

Predict what each Segment Wants

Elevating CX requires the ability to understand what customers want or need. With predictive analytics, organizations can oftentimes know what a customer wants before the customer does. As a McKinsey article noted, “Designing great customer experiences is getting easier with the rise of predictive analytics.” Companies that know their customers in granular detail can nurture their journeys by predicting their actions, and then proactively delivering timely and relevant next best offers. Predictive analytics can entail artificial intelligence and machine learning to forecast the customer journey and predict a customer’s lifetime value. This helps better understand customer pain points, prioritize high-value customer needs, and identify the interactions that are most rewarding for customers. These details can be leveraged to enhance CX.

Craft the Right Offer

One goal of segment analysis and predictive analytics is to determine the right offer at the right time through the right channel to the right customers. The offer can be recommending a product customers want, a limited time discount on an item they’re likely to buy, giving an exclusive deal on a new product, or providing incentives to sign up for loyalty programs. It’s important to understand each customer’s appetite for offers. Too much and it’s a turn off. Too little and it may result in missed opportunities. Data analytics can help determine the optimal timing and content of offers.

Perform Customer Analytics at Scale

Once customers are segmented into groups and organizations are optimizing data and analytics to create personalized experiences, the next step is to scale analytics across the entire marketing organization. Expanding analytics can lead to hyper-personalization, which uses real-time data and advanced analytics to serve relevant offers to small groups of customers—or even individual customers. Analytics at scale can lead to tailored messaging and offers that improve CX. The analytics also helps organizations identify early indicators of customers at risk of churn so the business can take proactive actions to reengage them.

Continue Analysis for Ongoing CX Improvements

Customer needs, behaviors, and preferences can change over time, which is why continual analysis is needed. Ongoing analysis can identify customer likes and dislikes, uncover drivers of customer satisfaction, and nurture customers along their journeys. Organizations can use data analytics to continually improve CX while strengthening customer loyalty.

Make Data Easily Accessible

To improve CX with data and analytics, organizations need a platform that makes data easy to use and access for everyone. For example, the Avalanche Cloud Data Platform offers enterprise-proven data integration, data management, and analytics in a trusted, flexible, and easy-to-use solution.

The platform unifies all relevant data to create a single, accurate, real-time view of customers. It makes the customer data available to everyone across marketing and the business who needs it to engage customers and improve each customer experience.

Related resources:

6 Predictive Analytics Steps to Reduce Customer Churn

7 Ways Market Basket Analysis Can Make You More Money

How Application Analytics Can Optimize Your Customer Experience Strategy

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