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Sep 26 2023

How to Optimize Customer Analytics to Improve the Post-Purchase Customer Experience

In a recent Martechcube survey, only 18% of retail leaders believe that they could significantly improve the post-purchase customer experience. In contrast, a whopping 80% of consumers feel otherwise. Providing a poor post-purchase customer experience can prevent you from building customer loyalty. Customer analytics can provide valuable insights and data-driven strategies to help you get to know your customers, personalize customer experiences, and improve customer satisfaction.

Over-Reliance on Customer Segmentation

One of the biggest culprits underlying a poor post-purchase customer experience is segmentation. Analytics allows you to segment your customers into similar groups with similar characteristics such as income, gender, age, etc., or behaviors such as purchases, path-to-purchase, and promotional responses.

Marketers use segmentation to help them tailor their campaigns, promotions, and communication to each segment, hoping that these will resonate with customers in the same segment. But do they? Not always. People falling within a segment often have different needs, values, and motivations, and, even if they have the same behaviors, their reasons or motivations for that behavior can be very different.

Insufficient Personalization

By analyzing a customer’s purchase history, browsing behavior, demographics, and other customer activities, you can deliver targeted content, product recommendations, and offers that are more likely to resonate with the customers. More savvy retailers are bringing zero-party data into the personalization mix. Zero-party data is information from customers that they voluntarily and deliberately share with you. The use of zero-party data has risen in popularity after Google announced its intended phase-out of support for third-party tracking cookies in Chrome back in early 2020. Since this time, marketers have realized that zero-party data is more than a replacement strategy for cookie data and now understand that one of the best ways to know what a customer really wants is to simply ask the customer.

Predictive Analytics Can’t Always Forecast Churn

There’s no doubt that predictive analytics is a valuable tool that can help you predict customer behavior, such as their likelihood of churning or making a repeat purchase. Insights can assist you in proactively addressing issues and engaging at-risk customers.

On the downside, there are tons of factors that cause predictive analytics to fail to predict customer churn. Insufficient or poor-quality data will impact the accuracy of results for any type of modeling. Predictive models base their predictions on trends in historical data. As such, they might fail to predict that a customer has decided to churn abruptly due to a recent negative experience. This is a big shortfall for predictive accuracy because 76% of shoppers will stop doing business with a company after just one negative experience. In addition, the competitive landscape is constantly evolving, and historical data may not reveal this.

These shortcomings have several implications for users of predictive analytics. It’s important to regularly update predictive analytics models, validate results, and incorporate a variety of data sources, both internal and external. Also, predictive analytics needs to be part of a comprehensive data analytics approach that includes adaptive analytics strategies. For example, analyzing current data from customer support interactions, including call logs, chat transcripts, and email can quickly identify if a customer is experiencing an issue. And keeping track of new social media mentions and conversations can help you spot unhappy customers faster.

Let’s Make CX Easy Together

Customer analytics provide valuable insights to help you know your customers better to help you deliver a more engaging customer experience. But more is needed than traditional segmentation. You’re going to need to focus more on individual customers and engage with them directly to understand their needs. Advanced analytics such as predictive modeling are useful for understanding future customer behavior, but you’ll still need adaptive analytics to identify sudden changes in the customer experience or market dynamics.

According to a recent GigaOm TPC-H Benchmark Test, the Actian platform’s operational data warehouse is 9x faster and 16x cheaper than alternatives. The Actian Data Platform makes it easy to track, manage, and analyze customer analytics to better identify areas that need improvement and help improve business outcomes. Contact us to start your journey to improving CX.

The post How to Optimize Customer Analytics to Improve the Post-Purchase Customer Experience appeared first on Actian.

How to Build Accurate Customer Profiles Using Data Analytics

A comprehensive data analytics strategy gives financial firms a competitive edge, helping them inform decision making, drive overall financial performance, and improve business outcomes. The fact is, all types of financial firms, from banks to investment companies, are finding new uses for analytics while optimizing proven use cases.

You’re probably leveraging analytics for some use cases, but there’s more you can do. Embedding analytics processes across your organization can deliver more value—and deliver value faster. Here are 10 ways to benefit from data analytics at your financial organization:

1. Deliver personalized financial services

Tailored offerings are mandatory for success in financial services. Connecting customer and financial data for analytics gives you a better understanding of each customer’s financial goals, risk profile, and financial status. You can then deliver personalized offerings to meet customers’ unique needs. Offerings can include cash back on credit cards, or personal or business loans at a favorable interest rate. Meeting each individual’s financial needs improves customer experiences while enabling cross-selling opportunities that improve revenues.

2. Gain real-time insights

Real-time insights position your firm to seize opportunities or enable you to take action if you spot a potential problem. For example, you can deliver special terms on a loan or make a limited time debit card offer while someone is browsing your site, or take immediate action if you suspect fraud on an account. For example, credit card companies use real-time analytics to approve transactions exceptionally fast and also analyze purchases for fraud. Likewise, in stock trading, every millisecond can make a difference when buying or selling at market prices, making real-time insights invaluable.

3. Improve operational efficiency

Analytics let you automate processes to improve operations. Manual and repetitive tasks can be automated to minimize human intervention and errors while speeding up processes. For instance, onboarding customers, approving loans, and processing transactions are common tasks ripe for automation. Data analytics can also play a key role in digital transformations, enabling digital processes and workflows that make operations more efficient. For example, Academy Bank transformed operations in a hybrid environment, saving more than four hours of manual data entry per day and developing new online services to improve the customer experience.

4. Manage risk across the enterprise

The financial industry is constantly exposed to risk—market risk, credit risk, operational risk, and more. Data analytics offers early insights into risk, giving you time to proactively mitigate issues before they become full-blown problems. Applying analytics lets you better understand and manage risk to protect your organization against potential losses while supporting financial stability. For example, analyzing customer data, historical data, credit scores, and other information predicts the likelihood of a person defaulting on a loan.

5. Inform financial investment decisions

In an industry as complex as financial services, you need the ability to analyze vast amounts of data quickly to understand trends, market changes, and performance goals to guide investment strategies. Sophisticated data models and analytic techniques offer granular insights and answers to what/if scenarios to inform investments. In addition, financial analytics can help you strategically build a diversified investment portfolio based on your risk tolerance and objectives.

6. Ensure accurate regulatory reporting

In your heavily regulated industry, timely, trustworthy reporting is critical. Compliance with myriad rules that are constantly changing requires analytics for visibility into adherence and to create accurate compliance and regulatory reports. Data analytics also helps monitor compliance to identify potential issues, helping you avoid penalties by ensuring operations follow legal protocols. Plus, analytics processes offer an audit trail in reporting, giving your stakeholders and auditors visibility into how the reports were created.

7. Enhance fraud detection and prevention capabilities

Fraud is ever-present in the financial sector—and fraudulent tactics are becoming increasingly sophisticated and harder to detect. Your business must be able to identify fraud before financial losses occur. Analytics, including advanced fraud detection models that use machine learning capabilities, help identify patterns and anomalies that could indicate fraud. Analytics must also prevent false positives. For example, analysis must be able to distinguish between a customer’s legitimate purchases and fraud to avoid suspending a valid customer’s account.

8. Create accurate financial forecasts

Forecasts directly impact profitability, so they must be trustworthy. Data analytics can deliver accurate forecasts to help with budgeting and investments. The forecasts predict revenue, expenses, and organization-wide financial performance. Having a detailed understanding of finances enables you to make informed decisions that increase profitability. Data-driven predictions also inform scenario analysis, which lets you evaluate potential business outcomes and risks based on assumptions you make about the future.

9. Determine customers’ credit scores

Credit scoring is essential in finance, allowing banks and other lenders to evaluate a customer’s creditworthiness based on their credit history, income, and other factors. Analytics can determine if the person is a good credit risk, meaning the customer will repay the loan on time and manage their credit responsibly. Analytics can be used for any sort of financing, from offering a loan to raising credit card limits.

10. Understand customer sentiment

Like other industries, financial services firms want to understand the perception customers and the public have about their business. That’s where sentiment analysis helps. It interprets the emotions, attitudes, and opinions behind social media posts, reviews, survey responses, and other customer feedback. This lets you better understand customer feelings about your brand and services. You can determine if your customer and business strategies are working, and make improvements accordingly. Customer sentiment also serves as an economic indicator, giving you insights into how optimistic customers are about their personal finances and the overall economy.

Unify Data for Financial Services Use Cases

Data analytics has become an essential part of decision making, automated processes, and forecasting for financial services. The insights help firms like yours stay competitive and proactively adjust to changing market conditions and customer needs. New analytics use cases are constantly emerging. One way to capitalize on these use cases is to have all data unified on a single, easy-to-use cloud data platform that makes data readily available for analysts and anyone else who needs it. The Actian Data Platform does this and more. It connects all your data so you can drive financial services use cases with confidence and enable enterprise data management for financial services.

Related resources you may find useful:

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Boost E-Commerce with Customer Verification

Data breaches have been rampant these days, and many companies realize they need to enhance their customer verification process. Proper customer verifications help prevent young children from accessing products and services that are age sensitive. It also prevents fraudulent transactions from happening online. Businesses have different options to address this issue to reduce the risk, […]

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