Data Analytics

How to Use Financial Analytics for Customer Experience Personalization

Becky Staker

July 7, 2023

financial analytics for customer experience

Financial analytics are not just for banks and other financial institutions. They deliver value for any business by offering insights into the organization’s financial well-being. Financial analytics are typically performed on finance, accounting, transaction, and related data to predict, measure, and improve financial performance.

Integrating financial data with data from sales and marketing delivers other benefits, too. You can uncover new ways to personalize customer experiences, making CX easier through timely, relevant offers that engage and nurture your target audience. Done correctly, this builds customer loyalty, drives sales, and increases a customer’s lifetime value.

Deliver Hyper-Personalized Customer Experiences

By collecting, integrating, and analyzing customer and financial data, you can engage customers with hyper-personalized experiences. This enables a range of benefits for both you and your customers. For example, you can identify specific customers who are ideal candidates for high-value upsell and cross-sell opportunities for products, special promotions, and limited-time offers.

Understanding what customers want based on 360-degree views and previous buying habits, and complementing this understanding with financial analytics, helps you fine-tune the next best offers using an individual’s financial profile. These details help you further refine your target audience to ensure you’re making a personalized offer that’s in a customer’s price range.

Optimizing financial analytics to identify which customers are likely to buy additional products or services—and at what price—helps drive upsell strategies. These customized offers can increase customer satisfaction while improving revenues.

Benefit From Real-Time Insights

Integrating financial analytics into customer experience strategies is now a differentiator for marketing and business teams. With the right platform and strategy, integrated data provides real-time analytical insights to better understand ever-changing customer wants and needs. This includes knowing your customers’ expectations, such as what they want and when they want it, and also what they’re willing to pay.

Financial analytics delivers insights into buying patterns so you can see how much customers have spent in the past, then create offers that are in your customers’ comfort zone for spending. The right offer at the right time at the right price will enhance customer experiences and drive sales.

When you perform data analytics in real time, you gain additional benefits. For example, real-time analytics provides insights into a customer’s current account activities and spending habits. You’ll know if a customer is spending more or less than usual if their interests or preferences have changed, if their account is at risk of churn, and then take action to meet their needs.

Customize Offers

There are seemingly endless ways to segment customers for offers—demographics, location, browser history, number of purchases, and other criteria. Financial analytics offers another way to group customers—by spend. This lets you craft offers using pricing that customers are comfortable with. Or you can go one step further and offer personalized pricing, giving customers a price that’s more likely to inspire a purchase. This can be particularly effective for limited-time offers or special promotions.

Likewise, customized pricing strategies that leverage real-time financial and customer data, and also analyze current market conditions, can take you beyond dynamic pricing. You can offer personalized discounts as well as bundle products or services that appeal to specific customer segments. You can also optimize customer behavior and financial data to deliver products at the most optimal price point in real-time, such as when a customer is browsing your site.

Targeting offers to customers’ unique financial status helps with conversion rates. You can also use financial and customer data to forecast a customer’s future spending. For example, a college student’s income will change—maybe dramatically change—after graduation. Knowing this information can help you proactively nurture the customer journey, building a strong relationship now based on predicted lifetime spending.

Integrate Data and Personalize Offers

You need a modern data platform that makes it easy to collect, manage, and analyze data, and also develop predictive data models to understand and forecast customer preferences. These models must be able to recommend relevant products to the right customer segments—even a customer segment of one single person. Analytical insights and customer segmentations enable you to create hyper-personalized marketing campaigns that consider customer wants, needs, preferences, and finances.

The Actian Data Platform makes it easy to bring together all customer, financial, and other data for analysis and personalized offers. The platform lets you quickly build data pipelines to new data sources, without IT help. Everyone in your organization who needs data can easily access and use it, and they can have complete confidence in the results.

Additional Resources:

About Becky Staker

Becky Staker is Actian's Vice President of Customer Experience, focused on elevating customer outcomes across the business. Her diverse background spans marketing, sales, and CX leadership roles, including at Deloitte and EY, where she honed a customer-centric approach. Becky has led global CX projects that improved retention and satisfaction scores. She frequently speaks at industry events on CX trends and innovations. Becky's Actian blog articles cover how data can transform customer engagement and experiences. Explore her recent writings for strategies to boost loyalty and ROI.

Data Analytics

7 Key Reasons You Need Financial Analytics

Actian Corporation

July 7, 2023

Organizations across all verticals are using financial analytics to gain insights into the financial health and stability of their business. While there are many ways to define this type of analytics, Gartner sums it up nicely by stating, “Finance analytics provide insight into the financial performance of an organization.”

The data analysis is typically performed on integrated data from across finance, accounting, sales, and other relevant business areas to paint a complete picture of a company’s finances. The information can be used to inform planning, decision-making, pricing, and more to maximize profitability.

Here are seven key reasons your business uses financial analytics:

1. Get Answers to Specific Financial Questions

One advantage of analytics is getting answers to questions, even complex questions that require integrating large and disparate data sets to provide timely, accurate, trustworthy answers. Modern businesses understand the need for analytics to drive decision-making. Financial analytics offers the insights the Chief Financial Officer (CFO) and other stakeholders need to understand the organization’s financial performance. It also offers a real-time look at cash flow to understand how sales, operations, and other factors influence finance and the management of cash and cash equivalents.

2. Predict Financial Outcomes and Scenarios

The ability to accurately predict what’s going to happen in finance is one of the primary benefits of analytics. For example, financial analytics can forecast sales numbers and allow you to ask questions about the data, such as how raising or lowering a sales price will impact profitability. Predictive capabilities also let you forecast future revenue, sales performance, and other factors that impact finances. The insights allow you to make strategic changes to improve sales and performance, leading to a stronger financial outlook.

3. Uncover Trends Impacting Finance

Having early insights into trends allows you to take action to mitigate issues that could negatively impact finances or seize new opportunities to grow or add revenue streams. The trends can encourage stakeholders to slow down spending to avoid going over budgets, or change investment strategies to benefit from emerging markets, a changing economy, or shifting consumer preferences. For example, the recent trend of remote work could wipe $800 billion from the value of office buildings in major cities worldwide by 2030, according to CNN.

4. Understand End-to-End Financial Performance

Financial analytics offers visibility into both top and bottom-line performance, to see if revenue goals are being met, how expenses are impacting budgets, and other critical information. The insights can reveal which channels, products, and sales teams are the most profitable, and what changes could improve profit margins, cash value, and the overall value of the business. Likewise, the analytics will provide insights into accounts payable and accounts receivable, which can be used to automate some accounting and financial processes to improve efficiency.

5. Manage Assets With Confidence

Do you know the value of your assets? Are some gaining value while others are losing money? Financial analytics provides the answers. The insights allow you to maximize the value of assets while having visibility into risk. Analyzing integrated data about assets lets you identify trends and patterns to inform asset management decisions and potentially minimize operational costs. For example, manufacturers can predict when an asset is likely to shut down, allowing them to perform preventative maintenance to extend its lifecycle or determine if it makes sense to replace an asset with a new model that’s faster, more efficient, or delivers new benefits

6. Determine Risk Across the Enterprise

You need to determine what’s an acceptable level of financial risk for your organization. Analytics lets you better understand risk so you can make that determination as well as better monitor and manage risk. This allows you to assess, from an enterprise view, how changes in the business or other events could financially impact the company. Changes include launching a new product, adding a new supplier, or adjusting deliveries due to extreme weather conditions or volatile fuel prices.

7. Identify Your Most Profitable Customers

Analytics might provide surprising answers about who your most profitable customers are and who has the highest lifetime value. A detailed analysis looks at more than how much you bill a customer. It considers every interaction you have with the customer, product returns, and other factors that have a cost associated with them. This allows you to align resources with the most profitable customers.

Make Data Easy to Use for Analytics

Finances affect every area of the business, which is why having real-time, trustworthy insights into everything related to the financial health of your organization is essential. You also need the ability to easily build pipelines to new data sources, manage growing data volumes, and have confidence in your insights.

The Actian Data Platform makes it easy to connect and manage your data for financial analysis and other types of analytics, such as risk analysis, customer profitability analysis, or predictive analytics. The Actian platform also offers industry-leading price performance, which will help with your organization’s finances.

Additional Resources:

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Management

The Crisis AI Has Created in Healthcare Data Management

Actian Corporation

July 6, 2023

AI has created a crisis in healthcare data management

Through the lens of time, the study of medicine dwarfs the age of modern technology by centuries. Historically, most medical treatments require decades of research and extensive studies before they are approved and implemented into practice. Traditionally, physicians alone have been charged with the task of making treatment decisions for patients. The healthcare industry has pivoted to evidence-based care planning, where patient treatment decisions are derived from available information during systematic reviews.

Should We Trust Data Science Tools like Artificial Intelligence (AI) and Machine Learning (ML) to Make Decisions Related to Our Health?

In the first installment of this series, Algorithmic Bias: The Dark Side of Artificial Intelligence, we explored the detrimental effects of algorithmic bias and the consequences for companies that fail to practice responsible AI. Applications for Big Data processing in the healthcare and insurance industry have been found to exponentially amplify bias, which creates significant disparities related to oppressed and marginalized groups. Researchers are playing catch-up to find solutions to alleviate these disparities.

A study published by Science provided that a healthcare risk prediction algorithm, used on over 200 million people in the U.S., was found to be biased due to dependence on a faulty metric used to determine need. The algorithm was deployed to help hospitals determine risk levels for prioritizing patient care and necessary treatment plans. The study reported that African-American patients tended to receive lower risk scores. African-American patients also tended to pay for emergency visits for diabetes or hypertension complications.

Another study, conducted by Emory University’s Healthcare Innovations and Translational Informatics Lab, revealed that a deep learning model used in radiologic imaging, which was created to speed up the process of detecting bone fractures and lung issues like pneumonia, could pretty accurately predict the race of patients.

“In radiology, when we are looking at x-rays and MRIs to determine the presence or absence of disease or injury, a patient’s race is not relevant to that task. We call that being race agnostic: we don’t know and don’t need to know someone’s race to detect a cancerous tumor in a CT or a bone fracture in an x-ray,” stated Judy W. Gichoya, MD, assistant professor and director of Emory’s Lab.

Bias in healthcare data management doesn’t just stop at race. These examples scratch the surface of the potential for AI to go very wrong when used in healthcare data analysis. Before using AI to make decisions, the accuracy and relevancy of datasets, their analysis, and all possible outcomes need to be studied before subjecting the public to algorithm-based decision-making in healthcare planning and treatment.

Health Data Poverty

More concerted effort and thorough research needs to be on the agendas of health organizations working with AI. A 2021 study by Lancet Digital Health defined health data poverty as: the inability for individuals, groups, or populations to benefit from a discovery or innovation due to a scarcity of data that are adequately representative.

“Health data poverty is a threat to global health that could prevent the benefits of data-driven digital health technologies from being more widely realized and might even lead to them causing harm. The time to act is now to avoid creating a digital health divide that exacerbates existing healthcare inequalities and to ensure that no one is left behind in the digital era.”

A study by the Journal of Medical Internet Research identified the catalysts to growing data disparities in health care:

Data Absenteeism: a lack of representation from underprivileged groups.
Data Chauvinism: faith in the size of data without considerations for quality and contexts.

Responsible AI in Healthcare Data Management

Being a responsible data steward in healthcare care requires a higher level of attention to dataset quality to prevent discrimination and bias. The burden of change rests on health organizations to “go beyond the current fad” to coordinate and facilitate extensive and effective strategic efforts that realistically address data-based health disparities.

Health organizations seeking to advocate for the responsible use of AI need a multi-disciplinary approach that includes

Prioritizing addressing data poverty.
Communicating with citizens transparently.
Acknowledging and working to account for the digital divide that exists for disparaged groups.
Implementing best practices for gathering data that informs health care treatment.
Working with representative datasets that support equitable provision of treatment using digital health care.
Developing internal teams for data analytics and processing reviews and audits.

To fight bias, it takes a team effort as well as a well-researched portfolio of technical tools. Instead of seeking to replace humans with computers, it would be better to facilitate an environment where they can share responsibility. Use these resources to learn more about responsible AI in health care management.

About Actian Corporation

AI & ML

Algorithmic Bias: The Dark Side of Artificial Intelligence

Actian Corporation

July 6, 2023

AI ethics and the dark side of artificial intelligence

The growth of social media and the advancement of mobile technology has created exponentially more ways to create and share information. Advanced data tools, such as AI and data science are being employed more often as a solution for processing and analyzing this data. Artificial Intelligence (AI), combines computer science with robust datasets and models to facilitate automated problem-solving. Machine Learning (ML) models, a subfield of AI that uses statistical techniques that enable computers to learn without explicit programming, use data inputs to train actions and responses for users. This data is being leveraged to make critical decisions surrounding governmental strategy, public assistance eligibility, medical care, employment, insurance, and credit scoring.

As one of the largest technology companies in the world, Amazon Web Services (AWS) relies heavily on AI and ML as the solution they need for storing, processing, and analyzing data. But, in 2015, even with their size and technical sophistication, they discovered bias in their hiring algorithm. It was biased to favor men because the data set it referenced was based on past applicants over the previous 10 years, which contained a much larger sample of men than women.

Bias was found in an algorithm COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), which is used by US court systems to predict offender recidivism. The data used, the chosen model, and the algorithm employed overall, showed that it produced false positives for almost half (45%) of African American offenders in comparison to Caucasian-American offenders (23%).

Without protocols and regulations to enforce checks and balances for the responsible use of AI and ML, society will be on a slippery slope of issues related to bias based on socioeconomic class, gender, race, and even access to technology. Without clean data, algorithms can intrinsically create bias, simply due to the use of inaccurate, incomplete, or poorly structured data sets. To avoid bias, it starts with accurately assessing the quality of the dataset, which should be:

Accurate.
Clean and consistent.
Representative of a balanced data sample.
Clearly structured and defined by fair governance rules and enforcement.

Defining AI Data Bias

The problem that exists with applying Artificial Intelligence to make major decisions is the presence and opportunity for bias to cause significant disparities in vulnerable groups and underserved communities. A part of the problem is volume and processing methods of Big Data, but there is also the potential for data to be used intentionally to perpetuate discrimination, bias, and unfair outcomes

“What starts as a human bias turns into an algorithmic bias,” states Gartner. In 2019, Algorithmic bias was defined by Harvard researchers as the application of an algorithm that compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability, or sexual orientation and amplifies inequities in health systems. Gartner also explained four types of algorithmic bias:

Amplified Bias: systemic or unintentional bias in processing data used in training machine learning algorithms.
Algorithm Opacity: end-user data black boxes, whether intrinsic or intentional, cause concern about levels of integrity during decision-making.
Dehumanized Processes: views on replacing human intelligence with ML and AI are highly polarized, especially when used to make critical, life-changing decisions.
Decision Accountability: there exists a lack of sufficient reporting and accountability from organizations using Data Science to develop strategies to mitigate bias and discrimination.

A study by Pew Research found that “at a broad level,” 58% of Americans feel that computer programs will always reflect some level of human bias – although 40% think these programs can be designed in a way that is bias-free. This may be true when you’re looking at data about shipments in a supply chain or inventory predicting when your car needs an oil change, but human demographic, behaviors, and preferences can be fluid and subject to change based on data points that may not be reflected in the data sets being analyzed.

Chief data and analytics officers and decision-makers must challenge themselves by ingraining bias prevention throughout their data processing algorithms. This can be easier said than done, considering the volume of data that many organizations process to achieve business goals.

The Big Cost of Bias

The discovery of data disparities and algorithmic manipulation to favor certain groups and reject others has severe consequences. Due to the severity of the impact of bias in Big Data, more organizations are prioritizing bias mitigation in their operations. InformationWeek conducted a survey on the impact of AI bias on companies using bad algorithms. It revealed that bias was found to be related to gender, age, race, sexual orientation, and religion. In terms of damages to the businesses themselves, they included:

Lost Revenue (62%).
Lost Customers (61%).
Lost Employees (43%).
Paying legal fees due to lawsuits and legal actions against them (35%).
Damage to their brand reputation and media backlash (6%).

Solving Bias in Big Data

Regulation of bias and other issues created by using AI, or having poor-quality data are in different stages of development, depending on where you are in the world. For example, in the EU, an Artificial Intelligence Act is in the works that will identify, analyze, and regulate AI bias.

However, the true change starts with business leaders who are willing to do the leg work of ensuring diversity and responsible usage and governance remain at the forefront of their data usage and policies “Data and analytics leaders must understand responsible AI and the measurable elements of that hierarchy — bias detection and mitigation, explainability, and interpretability,” Gartner states. Attention to these elements supports a well-rounded approach to finding, solving, and preventing issues surrounding bias in data analytics.

Lack of attention to building public trust and confidence can be highly detrimental to data-dependent organizations. Implement these strategies across your organization as a foundation for the responsible use of Data Science tools:

Educate stakeholders, employees, and customers on the ethical use of data including limitations, opportunities, and responsible AI.
Establish a process of continuous bias auditing using interdisciplinary review teams that discover potential biases and ethical issues with the algorithmic model.
Mandate human interventions along the decision-making path in processing critical data.
Encourage collaboration with governmental, private, and public entities, thought leaders and associations related to current and future regulatory compliance and planning and furthering education around areas where bias is frequently present.

Minimizing bias in big data requires taking a step back to discover how it happens and preventive measures and strategies that are effective and scalable. The solution may need to be as big as big data to successfully surmount the shortcomings present today and certainly increasing in the future. These strategies are an effective way to stay informed, measure success, and connect with the right resources to align with current and future algorithmic and analytics-based bias mitigation.

Additional Resources:

About Actian Corporation

Data Management

5 Common Factors That Reduce Data Quality—And How to Fix Them

Actian Corporation

June 29, 2023

gears depicting the web and forms of data gathering and data quality

As any successful company knows, data is the lifeblood of the business. But there’s a stipulation. The data must be complete, accurate, current, trusted, and easily accessible to everyone who needs it. That means the data must be integrated, managed, and governed by a user-friendly platform. Sound easy? Not necessarily.

One problem that organizations continue to face is poor data quality, which can negatively impact business processes ranging from analytics to automation to compliance. According to Gartner, every year, poor data quality costs organizations an average of $12.9 million. Gartner notes that poor data quality also increases the complexity of data ecosystems and leads to poor decision-making.

The right approach to enterprise data management helps ensure data quality. Likewise, recognizing and addressing the factors that reduce data quality mitigates problems while enabling benefits across data-driven processes.

Organizations experiencing any of these five issues have poor data quality. Here’s how to identify and fix the problems:

1. Data is Siloed for a Specific User Group

When individual employees or departments make copies of data for their use or collect data that are only available to a small user group—and is isolated from the rest of the company—data silos occur. The data is often incomplete or focused on a single department, like marketing. This common problem restricts data sharing and collaboration, offers limited insights based on partial data rather than holistic views into the business, increases costs by maintaining multiple versions of the same data, and several other problems. The solution is to break down silos for a single version of the truth and make integrated data available to all users.

2. A Single Customer Has Multiple Records

Data duplication is when more than one record exists for a single customer. Duplicated data can end up in different formats, get stored in various systems, and lead to inaccurate reporting. This problem occurs when data about the same customer or entity is stored multiple times, or when existing customers provide different versions of their information, such as Bob and Robert for a name or a new address. In these cases, additional records are created instead of a single record being updated. This can negatively impact customer experiences by bombarding individuals with the same offers multiple times, or marketing being unable to create a full 360-degree profile for targeted offers. Performing data cleansing with the right tools and integrating records can remove duplicate data and potentially create more robust customer profiles.

3. Lacking a Current, Comprehensive Data Management Strategy

Organizations need a strategy that manages how data is collected, organized, stored, and governed for business use. The strategy establishes the right level of data quality for specific use cases, such as executive-level decision-making, and if executed correctly, prevents data silos and other data quality problems. The right strategy can help with everything from data governance to data security to data quality. Strategically managing and governing data becomes increasingly important as data volumes grow, new sources are added, and more users and processes rely on the data.

4. Data is Incomplete

For data to be optimized and trusted, it must be complete. Missing information adds a barrier to generating accurate insights and creating comprehensive business or customer views. By contrast, complete data has all the information the business needs for analytics or other uses, without gaps or missing details that can lead to errors, inaccurate conclusions, and other problems. Organizations can take steps to make sure data is complete by determining which information or fields are needed to reach objectives, then making those fields mandatory when customers fill out information, using data profiling techniques to help with data quality assurance, and integrating data sets.

5. Shadow IT Introduces Ungoverned Data

The practice of using one-off IT systems, devices, apps, or other resources rather than leveraging centralized IT department processes and systems can compromise data quality. That’s because the data may not be governed, cleansed, or secured. These IT workarounds can spread into or across the cloud, leading to data silos, with little to no oversight and resulting in data that does not follow the organization’s compliance requirements. Offering staff easy and instant access to quality data on a single platform that meets their needs discourages the practice of Shadow IT.

Ensuring Data Quality Drives Enterprise-Wide Benefits

Having enterprise data management systems in place to ensure data quality can be a competitive advantage, helping with everything from better data analytics to accelerated innovation. Users throughout the organization also have more confidence in their results when they trust the data quality—and are more likely to follow established protocols for using it.

Achieving and maintaining data quality requires the right technology. Legacy platforms that can’t scale to meet growing data volumes will not support data quality strategies. Likewise, platforms that require ongoing IT intervention to ingest, integrate, and access data are deterrents to data quality because they encourage silos or IT workarounds.

Data quality issues are not limited to on-premises environments. Organizations may find that out the hard way when they migrate their data warehouses to the cloud—any data quality issues on-premises also migrate to the cloud.

One way to avoid data quality issues is to use a modern platform. For example, the Actian Data Platform simplifies how people connect, manage, and analyze their data. The easy-to-use platform provides a unified experience for ingesting, transforming, analyzing, and storing data while enabling best practices for data quality.

Additional Resources:

About Actian Corporation

Data Intelligence

13 Churn Prevention Strategies to Improve CX

Becky Staker

June 27, 2023

a person providing a good customer experience

Happy customers can be advocates for your brand, make repeat purchases, and influence others to buy from your business. This type of success is the result of using data to holistically understand each customer and develop a customer-centric business strategy that engages and rewards each individual when they interact with your company.

Elevating the customer experience (CX) is a proven way to connect with customers and prevent churn. It also helps with profitability, as it’s much more expensive to acquire new customers than to keep existing ones.

How to Retain Customers and Enhance CX

1. Simplify Customer Onboarding

Ensuring a fast and painless onboarding experience is essential since it’s often your first opportunity to make an impression on the customer—and first impressions shape CX. An intuitive and positive first experience sets the tone for the customer journey. Whether onboarding entails filling out an online form, activating a new product, or hands-on training on a product, delivering an engaging experience gives customers the confidence that they’re making a good decision by working with your business.

2. Deliver Timely, Truly Meaningful CX

One of the best ways to prevent churn is to continually provide relevant and authentic customer experiences. These experiences nurture customers by delivering the next best action at the most opportune time. With the right cloud data platform and analytics, you can accurately predict what customers want and when they want it, then delight them with the right offer at the right time and at the right price point. This is where a comprehensive CX strategy that optimizes customer data delivers ongoing value.

3. Personalize All Interactions

Personalized CX is now table stakes for companies. According to McKinsey & Company, 71% of consumers expect organizations to deliver personalized interactions, and 76% are frustrated when this doesn’t happen. Personalization improves customer outcomes—and drives more revenue. Product and service offers must be customized, too. Once you’ve built 360-degree profiles, you can segment customers for special offers. You can even personalize offers to a single customer for a truly customized experience.

4. Engage Customers at All Touchpoints

CX is an ongoing journey that requires support and nurturing at every step. Customers typically have several interactions with a company before making a purchase. Understanding each touchpoint and ensuring a positive experience is essential—or the customer could abruptly end the journey. These touchpoints, such as website visits, downloading an app, or social media views, shape the way customers view your brand, company, and offerings. This is why each touchpoint is an opportunity to impress customers and guide their journey.

5. Respond Promptly to Complaints or Concerns

Customer journeys are not always smooth or linear. Shipping delays, product glitches, and user errors all impact CX. Unhappy customers have a higher likelihood of churn, which brings the challenges of identifying these customers and addressing their concerns. This is especially important when it’s a high-value customer. Sometimes feedback is direct, such as a call or email to a customer service desk or sales rep. Other times, you need to identify negative sentiment indirectly, like through social media. And sometimes customers won’t proactively share at all, which is where surveys and post-sales follow-up provide value. Simply connecting with a customer is sometimes enough to make a difference and make them feel valued.

6. Reward Loyalty

Loyalty programs are a great way to know and recognize your best customers. You can use the programs to gather information about customers, then reward loyalty with special offers, like free merchandise, a discount, or a chance to buy a product before it goes on sale to the public. While these programs improve CX, they also encourage customers to engage with the brand more often to accumulate points. Another benefit is loyalty programs, which can turn customers into authentic advocates for your brand. In addition, studies have shown that consumers are more likely to spend—and spend more—with companies offering a loyalty program. Gartner predicts that one in three businesses without a loyalty program today will establish one by 2027.

7. Build Excitement

When you can build customer excitement, then you know your CX strategy is excelling. This excitement can organically inspire conversations, posts, and comments on social media about your brand. Effective ways to build this excitement include giving loyal customers a sneak peek at upcoming product releases, offering “behind the scenes” content, and creating customer contests on social media that award prizes.

8. Foster Trust

People want to do business with companies they trust and that share their values. Meeting or exceeding customer expectations and resolving problems before they occur builds trust. So does making an emotional connection with customers through your content. Other ways to foster trust include demonstrating that you protect their data, showing concern for the environment through sustainable business practices, and delivering products and services when and how the customer expects.

9. Listen to Customers

Your customers have a lot to say, even if they don’t tell you directly. They might be sharing their thoughts on social media or through their browsing history. Integrating customer data from all relevant sources allows you to understand each customer. You can then listen based on their behaviors and feedback before, during, and after a sale. This can help you determine which features and price points are most effective. Also, addressing any changes in behaviors and responding to complaints quickly can help mitigate churn.

10. Find Out Why Customers Are Leaving

Understanding why customers are ending subscriptions, switching to a competitor, or no longer purchasing from your company allows you to identify churn patterns. This can help you evaluate if you’re experiencing a level of churn the business is comfortable with—some amount of churn is to be expected—or if there’s a sudden spike or ongoing problem. Churn analysis offers insights into why customers are leaving, such as products that don’t meet expectations, prices that are higher than competitors, poor customer service, or other reasons.

11. Be Proactive

It’s important to identify customers at risk of churning, then engage them before they leave. Measuring customer sentiment helps to determine areas needing improvement and creates a consistent channel for feedback. Proactively addressing customers’ concerns before they spiral into full-blown problems can encourage them to stay. Being proactive requires a robust customer retention strategy and the ability to perform granular customer analytics for insights into the early stages of churn.

12. Know What Your Competitors are Doing

Knowing your business and your customers is not enough. You must also know what your competitors are doing. This allows you to better understand the competitive landscape and have insights into potential market changes—or major market disruptions. A competitive analysis can help you understand key differences between your products and competitors’ offerings. This can help you update your product design and marketing strategy, and even be an opportunity to poach customers.

13. Stay Relevant

Growing the business and staying relevant are ongoing challenges. It requires continually delivering innovative products and services, regularly connecting with customers, staying ahead of changing customer preferences, and updating the brand as needed. You also need to evaluate if you have gaps in your product or service offerings, and if so, plan how to address them. As customer wants and needs change, your brand also needs to change in ways that are relevant to your customers.

Let’s Get Started

Your ability to tackle customer attrition while enhancing customer experiences starts with data. You need the right data, and you need the ability to integrate it using a single, scalable platform for analytics. The Actian Data Platform can help you transform your churn and CX strategies by bringing together all of the customer data you need on an easy-to-use platform. Our advanced capabilities for data integration, data management, and analytics give you the insights and confidence needed to retain and engage customers.

Additional Resources:

About Becky Staker

Data Analytics

Using Customer Analytics to Create Lifetime Value

Becky Staker

June 22, 2023

customer analytics in the palm of a business man's hands

Moving clients from being one-time customers to doing business with them for life is the ultimate goal for organizations. It’s what ensures ongoing success and profitability. Your products, services, and business priorities can change over time, but one constant is the need for loyal customers.

Customer Lifetime Value (CLV) is a measure of how valuable a customer is to your business throughout your entire relationship. CLV provides an expectation of what a customer is predicted to spend on your brand if you deliver the right experiences.

The process of successfully building lifetime customers starts with analyzing data at every step along the customer journey. You’re probably already using a data-driven approach to engage, reward, and retain customers. Here are ways to build on what you’re already doing to support a customer-for-life strategy:

Know Everything About Your Customer

All customer strategies—reducing churn, using targeted selling, creating customers for life—require you to know everything you can about the customer. This entails creating a single view of your customer by integrating all relevant customer data from all available sources for a complete 360-degree profile. From there, you can uncover deep insights into customer behaviors and buying patterns, then predict what customers want next so you can meet their emerging needs.

The ability to accurately identify customer wants and needs is essential to creating customers for life. It represents a significant shift in traditional customer-centric strategic visions. That’s because it takes a forward-looking view to understand what customers want before they tell you, instead of a rearview mirror approach that explains what has already happened. While past behaviors are important and help predict future actions, performing analytics across all relevant customer data is needed to forecast how customer preferences are changing.

Staying ahead of customer wants, needs, and challenges will inspire customers to trust your brand. But don’t expect to “find” customers for life. It’s up to you to nurture and reward current customers, and then cultivate successful relationships that ensure loyalty. In other words, you have to “create” customers for life.

Engage and Delight Customers at Every Touchpoint

Creating customers for life is an ongoing process that requires consistently gathering and analyzing data to ensure current insights. Customer behaviors and needs can change incredibly fast and with little warning, which makes real-time data essential.

Your organization must have the ability to integrate, manage, and analyze all required data, including data from new and emerging sources. This helps you spot early indicators of changing trends or behaviors, allowing you to shift your customer experience strategy, serve up timely offers that meet customers’ current needs, and build long-lasting customer relationships.

Once someone makes a purchase from your company, you have an opportunity to entice that customer with the next best action—whether it’s a limited-time discount, exclusive access to a product or content, or another special offer—to drive a second sale. A repeat purchase puts them on the path to being a customer for life.

Have Full Confidence in Your Customer Data

Data needs to be trustworthy and easy to use to deliver the insights needed to understand your customers and guide their purchasing decisions. This includes customer data such as transactional details of when, where, and what products a customer has already purchased from your business. You also need the ability to integrate other relevant data, such as demographic information to help with segmentation, and behavior data, which offers insights into how customers responded to previous marketing campaigns and their past buying behaviors.

Analyzing the data can reveal which customers have the highest potential lifetime value so you can focus on ensuring they remain customers—you do not want to let these customers switch to a competitor. The analytics process must start by bringing together data for a single, current, and accurate view of each customer, including their purchase history across all channels—in-person, online, and via third-party resellers—to understand their habits and preferences. These insights are key to providing a personalized, nurturing experience with targeted offerings that lead to life-long customers.

The Actian Data Platform can offer the data and analytics capabilities needed to create customers for life. It integrates seamlessly, performs reliably, and delivers at industry-leading speeds to drive your customer strategy and maximize customers’ lifetime value.

Additional Resources:

About Becky Staker

Data Intelligence

What is Edge Analytics?

Actian Corporation

June 20, 2023

Edge Analytics enables data-driven companies to go straight to analyzing their data after it has been collected by IoT devices. It helps eliminate data processing bottlenecks.

Learn more about Edge Analytics, its benefits, and concrete use cases to better understand this new data trend.

Speed up data processing and analysis, and reduce the number of steps between collecting and using your data assets: That’s the promise of Edge Analytics. This method of data processing is all about proximity to the data source. It avoids all the steps involved in sending data to a data processing center.

How Edge Analytics Works

Edge Analytics responds to a very different logic than traditional data analysis, with which data is generally transferred to a remote processing center, such as a server or cloud, and the analysis is performed. In the case of Edge Analytics, connected devices or sensors located at the edge of the network collect data in real-time from various sources such as industrial machines, vehicles, surveillance equipment, IoT sensors, etc.

The raw data collected is pre-processed locally – it is then filtered and prepared for immediate analysis. The purpose of this local pre-processing is to clean, sample, normalize and compress the data, in order to reduce the quantity of data to be transferred and guarantee its quality, prior to analysis. Once this preliminary phase has been completed, data analysis is also carried out on-site, at the edge of the network, using algorithms and models previously deployed on local devices or servers.

With Edge Analytics, you can fine-tune your data analysis strategy by transferring only essential data or exceptional events to a remote processing center. The objective? Reduce network bandwidth requirements and save storage resources.

What are the Benefits of Edge Analytics

If the proximity between the source of the data and the means of processing and analyzing it appears to be the main advantage of Edge Analytics, you’ll be able to reap five main benefits:

Accelerate Real-Time Decision-Making

Less distance between the place where data is collected and the place where it is processed and analyzed means the prospect of time savings on two levels. As Edge Analytics processes data at the network edge, where the data is generated, this enables real-time analysis, eliminating the latency associated with sending data to a remote location. Another advantage of this real-time dimension is that it enables autonomous data analysis.

Reduce Latency Between Data Collection and Analysis

Edge Analytics is a promise of real-time exploitation of your data assets because data processing is done locally. In the case of applications requiring rapid responses, such as the Internet of Things (IoT) or industrial control systems (production or predictive maintenance, for example), proximity data processing drastically reduces latency and optimizes processing times.

Limit Network Bandwidth Requirements

Traditional data analysis almost always relies on the transfer of large quantities of data to a remote data processing center. The result: intensive use of network bandwidth. This is particularly true when your business generates large volumes of data at high speed. Edge Analytics has the advantage of reducing the amount of data that needs to be transferred, as part of the analysis is carried out locally. Only essential information or relevant analysis results are transmitted, reducing the load on the network.

Optimize Data Security and Confidentiality

As you know, not all data have the same level of criticality. Some sensitive data cannot be transferred outside the local network for security or confidentiality reasons. Edge Analytics enables this data to be processed locally, which can enhance security and confidentiality by avoiding transfers of sensitive data to external locations.

Embark on the Road to Scalability

Because Edge Analytics enables part of the data analysis to be carried out locally, it enables a significant reduction in network load. In so doing, Edge Analytics facilitates scalability by avoiding bandwidth bottlenecks and paves the way for the multiplication of IoT devices without the risk of network overload.

Data analysis can be distributed across several processing nodes, facilitating horizontal scalability. Adding new devices or servers at the edge of the network increases overall processing capacity and enables you to cope with growing demand without having to reconfigure the centralized processing architecture.

What are the Main Use Cases for Edge Analytics

While the Edge Analytics phenomenon is relatively recent, it’s already being used massively in many business sectors.

Manufacturing

Edge is already widely used in manufacturing and industrial automation. In particular, it helps to monitor production tools in real-time, in order to detect breakdowns, optimize production, plan maintenance, or even improve the overall efficiency of equipment and processes.

Healthcare

In the healthcare and telemedicine sector, Edge Analytics is used in connected medical devices to monitor patients’ vital signs, detect anomalies, and alert healthcare professionals in real-time.

Smart Cities and Mobility

Edge Analytics is also well suited to the urban mobility and smart cities sector. In the development of autonomous urban transport, for example, real-time analytics can detect obstacles, interpret the road environment, and make autonomous driving decisions.

Security and Surveillance

The surveillance and security sector has also seized on Edge Analytics, enabling real-time analysis of video streams to detect movement or facial recognition.

About Actian Corporation

Data Analytics

How to Build a Growth-Focused Data Analytics Tech Stack

Teresa Wingfield

June 19, 2023

chart showing price index and earnings with growth-focused data analytics

Building a growth-focused data analytics tech stack is all about cloud deployment flexibility and cloud-native support. According to Gartner, more than 85% of organizations will embrace a cloud-first principle by 2025, but they will not be able to fully execute their digital strategies unless they use cloud-native architectures and technologies. Cloud-native technologies empower organizations to build and run scalable data analytics in modern, dynamic environments such as public, private, and hybrid clouds.

Cloud Deployment Models

Your data analytics solution should support multi-cloud and hybrid cloud deployment models for greater flexibility, efficiency, and data protection. Here’s a brief overview of each model and its benefits:

Multi-Cloud simply means that a business is using several different public clouds such as AWS, Microsoft Azure, and Google Cloud, instead of just one. Why multi-cloud? Below are some of the compelling reasons:

Being able to choose the best-fit technology for a cloud project.
Getting the best value by choosing providers with the lowest cost and having leverage during price negotiations.
Obtaining different geographic choices for cloud data center locations.

A hybrid cloud model uses a combination of public clouds, on-premises computing, and private clouds in your data center with orchestration among these platforms. Hybrid cloud deployment is useful for companies who can’t or do not want to make the shift to cloud-only architectures. For example, companies in highly regulated industries such as finance and healthcare may want to store sensitive data on-premises but still leverage elastic clouds for their advanced analytics. Other businesses may have applications that would require too much expensive movement of data to and from the cloud, making on-premises a more attractive option.

Cloud-Native Technologies

Beware; even though most analytics databases today run in the cloud, there are huge and significant differences between cloud-ready and cloud-native. Let’s explore what cloud-native means and its benefits.

The Cloud Native Computing Foundation defines cloud native as:

“Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. Containers, service meshes, microservices, immutable infrastructure, and declarative APIs exemplify this approach.”

“These techniques enable loosely coupled systems that are resilient, manageable, and observable. Combined with robust automation, they allow engineers to make high-impact changes frequently and predictably with minimal toil.”

Below are some of the key benefits of a cloud-native analytics database versus a cloud-ready analytics database.

Scalability

On-demand elastic scaling offers near-limitless scaling of computing, storage, and other resources.

Resiliency

A cloud-native approach makes it possible for the cloud-native database to survive a system failure without losing data.

Accessibility

Cloud-native uses distributed database technology to make the database easily accessible.

Avoid Vendor Lock-In

Standards-based cloud-native services support portability across clouds.

Business Agility

Small-footprint cloud-native applications are easier to develop, deploy, and iterate.

Automation

Cloud-native databases support DevOps processes to enable automation and collaboration.

Reduced Cost

A cloud native database allows you to pay-as-you-go and pay for only resources that you need.

Get Started With the Actian Data Platform

The Actian Data Platform provides data integration, data management, and data analytics services in a trusted and flexible platform. The Actian platform makes it easy to support multi-cloud and hybrid-cloud deployment and is designed to offer customers the full benefits of cloud-native technologies. It can quickly shrink or grow CPU capacity, memory, and storage resources as workload demands change. As user load increases, containerized servers are provisioned to match demand. Storage is provisioned independently from compute resources to support compute or storage-centric analytic workloads. Integration services can be scaled in line with the number of data sources and data volumes.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

Data Analytics

Data-Driven Analytics Use Cases Powered by the Actian Data Platform

Teresa Wingfield

June 15, 2023

data analytics being pointed to on a large screen

Use Cases for Data-Driven Analytics

Our new eBook “Data-Driven Analytics Use Cases Powered by the Actian Data Platform” is designed for users and application builders looking to address a wide range of data analytics, integration, and edge use cases. We have included the following examples from real-world customer experiences and deployments to serve as a guide to help you understand what is possible with the Actian Data Platform (formerly Avalanche).

Customer 360

With the Actian Data Platform powering Customer 360, organizations can rapidly personalize the customer experience through micro-segmentation, next-best-action, and market basket analysis while improving customer acquisition and retention through campaign optimization, and churn analysis to increase customer loyalty.

Healthcare Analytics

The Actian Data Platform helps healthcare payer and provider organizations leverage analytics to protect their businesses against fraud, and increase care delivery, provider efficiency, and accuracy while accelerating the transformation to an outcome-centric model.

IoT-Powered Edge-to-Cloud Analytics

Edge applications and devices rely on complex data processing and analytics to improve automation and end-user decision support. The underlying cloud and edge data management solutions must leverage a variety of hardware architectures, operating systems, communications interfaces, and languages. The platform and its Zen Edge data management option provide broad, high-performing, and cost-effective capabilities for this demanding set of requirements.

ITOps Health and Security Analytics

With the explosion of ITOps, DevOps, AIOps, and SecOps data streaming from multiple clouds, applications, and on-premises platforms, many vendors are working to provide data visibility in their domains. However, they fall short of creating a holistic view to predictively identify trouble spots, security risks, and bottlenecks. How can businesses gain real-time actionable insights with a holistic IT analytics approach? The platform makes it easy to combine data from thousands of data sources into a unified hybrid-cloud data platform capable of real-time analysis of applications, infrastructure, and security posture.

Supply Chain Analytics

Manufacturing is a far more complex process, compared with just a few decades ago, with subcomponents required to assemble a single final product sourced from several places around the globe. Along with this complexity is a massive amount of data that needs to be analyzed to optimize supply chains, manage procurement, address distribution challenges, and predict needs. The Actian Data Platform helps companies easily aggregate and analyze massive amounts of supply chain data to gain data-driven insights for optimizing supply chain efficiency, reducing disruptions, and increasing operating margins.

Machine Learning and Data Science

The Actian Data Platform enables data science teams to collaborate across the full data lifecycle with immediate access to data pipelines, scalable compute resources, and preferred tools. In addition, the platform streamlines the process of getting analytic workloads into production and intelligently managing machine learning use cases from the edge to the cloud. With built-in data integration and data preparation for any streaming, edge, or enterprise data source, aggregation of model data has never been easier. Combined with direct support for model training systems and tools and the ability to execute models directly within the data platform alongside the data, companies can capitalize on dynamic cloud scaling of analytics, compute, and storage resources.

Why Actian?

Customers trust Actian because we provide more than just a platform. We help organizations make confident, data-driven decisions to reduce costs and enhance performance. Using our Actian Data Platform, companies can easily connect, manage, and analyze their data for a wide range of use cases. You can trust that your teams are making the best decisions that address today’s challenges and anticipate future needs.

Read the eBook to learn more.

About Teresa Wingfield

Data Analytics

Leveraging Supply Chain Data to Inform Predictive Analytics

Teresa Wingfield

June 13, 2023

boats representing supply chain data and 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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

About Teresa Wingfield

Data Management

7 Ways to Stop Data Quality Issues in Their Tracks

Traci Curran

June 8, 2023

footprint showing data quality issues being stopped in their tracks

Data quality is one of the most important aspects of any successful data strategy, and it’s essential to ensure that the data you collect and store is accurate and reliable. Poor data quality can lead to costly mistakes in decision-making, inaccurate predictions, and ineffective strategies. Data quality is essential in any organization, and there are a few key strategies you can use to instantly improve your data quality. Here are seven strategies to improve data quality:

1. Automation of Data Entry

Automating data entry is one of the most effective strategies for improving data quality. Automation helps ensure that data is entered accurately and quickly, reducing the risk of human error. Automation also allows you to quickly identify any errors or inconsistencies in the data, which allows you to trust the data you use to make decisions. Automation can help reduce the time spent manually entering data, freeing up more time for other tasks.

2. Data Standardization

Data standardization is another key strategy for improving data quality. Data standardization helps to ensure that data is consistent and reliable, and that data is entered in the same format across the organization. This helps to ensure that data is comparable and easy to analyze. Standardizing data also helps to reduce the risk of errors due to different formats and versions.

3. Data Verification

Data verification is another essential strategy for improving data quality. Data verification helps to ensure that the data is accurate, and it helps to detect any discrepancies or errors in the data. Data verification can also help you identify any patterns or anomalies that could indicate a problem with the data or your data pipelines. This allows staff to diagnose and resolve issues faster.

4. Use Data Integration Tools

Data integration tools are a great way to improve data quality. Data integration solutions, like Actian Data Platform, allow you to quickly and easily combine data from multiple sources, which helps to ensure that the data is accurate and up-to-date. Data integration tools can also help you automate the process of combining data and transformation, which can help to reduce the amount of time spent manually entering data.

5. Encourage Self-Service Data Quality

Encouraging self-service data quality is another excellent strategy. Self-service data quality empowers users to take ownership of the data they enter. By providing users with easy-to-use tools, training, and support, you can help ensure that data is entered correctly and quickly.

6. Implement Data Profiling

Data profiling helps to identify any patterns or anomalies in the data, which can help you identify any potential issues with the data. Implement tools or processes that can easily identify and segregate data that doesn’t adhere to your organization’s data standards.

7. Integrate Data Quality into your Pipelines

Create profiling and quality rules that can be integrated into your pipelines. Data management tools vary wildly in capabilities, so look for products that can provide a quick “at-a-glance” view of data quality based on the rules you’ve established. This can make it easier for staff to determine if there are expected results in data quality anomalies or something that could single a more significant problem at an earlier stage in the pipeline.

Benefits of Improving Data Quality

Improving data quality can have a number of benefits for any organization. Here are a few of the key benefits of improving data quality:

Improved Decision-Making: When data is accurate and reliable, it can help improve decision-making by ensuring that decisions are based on accurate and up-to-date data.
Enhanced Efficiency: Improved data quality can also help to improve efficiency, as it reduces the amount of time spent manually entering and verifying data, freeing up more time for other tasks.
Better Customer Service: Improved data quality can also help to improve customer service, as it helps to ensure that customer data is accurate and up-to-date.
Cost Savings: Improved data quality can also help save costs, as it reduces the time and resources spent manually entering and verifying data.

Get Started!

Automation of data entry, data standardization, data verification, data integration tools, and data quality processes are great strategies for improving data quality. Data governance is also essential for ensuring data accuracy and reliability. By following these strategies, you can ensure that your data is accurate and reliable, which can help to improve decision-making, enhance efficiency, and improve customer service. It can also help save costs, as it reduces the time and resources spent manually entering and verifying data. Actian’s Data Platform can support you in implementing these strategies to get the most out of your data.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

How to Use Financial Analytics for Customer Experience Personalization

Deliver Hyper-Personalized Customer Experiences

Benefit From Real-Time Insights

Customize Offers

Integrate Data and Personalize Offers

About Becky Staker

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7 Key Reasons You Need Financial Analytics

1. Get Answers to Specific Financial Questions

2. Predict Financial Outcomes and Scenarios

3. Uncover Trends Impacting Finance

4. Understand End-to-End Financial Performance

5. Manage Assets With Confidence

6. Determine Risk Across the Enterprise

7. Identify Your Most Profitable Customers

Make Data Easy to Use for Analytics

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The Crisis AI Has Created in Healthcare Data Management

Health Data Poverty

Responsible AI in Healthcare Data Management

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Algorithmic Bias: The Dark Side of Artificial Intelligence

Defining AI Data Bias

The Big Cost of Bias

Solving Bias in Big Data

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5 Common Factors That Reduce Data Quality—And How to Fix Them

1. Data is Siloed for a Specific User Group

2. A Single Customer Has Multiple Records

3. Lacking a Current, Comprehensive Data Management Strategy

4. Data is Incomplete

5. Shadow IT Introduces Ungoverned Data

Ensuring Data Quality Drives Enterprise-Wide Benefits

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13 Churn Prevention Strategies to Improve CX

How to Retain Customers and Enhance CX

1. Simplify Customer Onboarding

2. Deliver Timely, Truly Meaningful CX

3. Personalize All Interactions

4. Engage Customers at All Touchpoints

5. Respond Promptly to Complaints or Concerns

6. Reward Loyalty

7. Build Excitement

8. Foster Trust

9. Listen to Customers

10. Find Out Why Customers Are Leaving

11. Be Proactive

12. Know What Your Competitors are Doing

13. Stay Relevant

Let’s Get Started

About Becky Staker

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