How payment analytics enhances business agility in real-time markets

Payment analytics is one of the most powerful tools for surviving in today’s fast-paced and highly competitive market. This is because a business must be agile and able to respond quickly and efficiently to opportunities and threats found in its internal and external environments.

Payment analytics provides real-time visibility into financial performance and customer spending patterns.

This allows you to make data-driven decisions that optimize cash flow and strengthen your competitive advantage.

In the following sections, you’ll discover the ways payment analytics can improve the agility of your business. You’ll also learn how to implement this technology to transform raw data into actionable business intelligence. Let’s dive in.

6 ways payment analytics improves business agility in real-time markets

Traditional data that was generated hourly or daily was good enough in the past. However, today’s digital economy demands real-time insights to stay competitive and meet evolving customer expectations.

This is why big data, like payment analytics, is essential. It ensures businesses can meet these expectations by delivering actionable insights at the speed of modern commerce.

Below are six ways this technology is revolutionizing business agility.

1. Real-time business decision-making

Things happen fast in real-time markets, so you need to be able to respond quickly. Whether it’s a sudden increase in demand or an unexpected supply chain disruption, you must be adaptable.

Payment analytics makes this possible. It processes and visualizes transaction data instantly, enabling you to make informed decisions at a moment’s notice.

For instance, assuming payment metrics shows increased cart abandonment with specific payment methods. Then you can quickly offer alternative payment options to maximize conversions. A payment processor also shows you the status of each transaction. This means you can pinpoint exactly which payment method is failing and resolve the issue before it impacts more sales.

This level of responsiveness would be impossible with traditional reporting methods.

2. Improved cash flow management

Poor cash flow management is the leading cause of business failure. This is especially true for small and medium-sized businesses. According to research, 60% of small businesses struggle with cash flow management. This is why you need to get better at it. Payment analytics can help you do that.

This tool can reveal patterns in payment cycles, which allows you to better anticipate and plan for cash inflows and outflows.

It gives you insights into individual customers or group payment habits. This way, you can tailor payment terms and conditions to satisfy customer preferences. For example, your payment data shows some customers often pay their invoices late, you can offer an early payment discount for customers who pay within 10 days.

Payment analytics optimizes the invoicing and collection processes. It can reveal how to charge recurring invoices better for greater payment success. For example, your payment analytics shows that payments often fail when billed on the 1st of the month. Many customers may have low funds after paying rent and other expenses. To fix this, you can offer a bi-weekly billing option, helping customers manage smaller payments instead of one large charge at the start of the month.

Additionally, payment analytics can help you create more accurate cash flow forecasts based on historical payment data. As a result, you can prepare for potential cash flow issues and take proactive measures.

3. Better customer experience

Customers today have high expectations from businesses regarding experience. They want to feel valued and will only purchase from companies that make them feel that way.

According to research, 74% of consumers are likely to buy based on experience alone. This won’t change anytime soon. Instead, the demand will be higher. As such, you need to be able to meet that demand.

You can do so by using payment analytics. This tool can help you understand your customers. Through analytics data like purchase frequency, chargeback rate, and preferred payment method, you can gain insights into customer behavior.

For example, when you discover higher chargeback rates for certain products, this could indicate that you need to communicate your policies better. It could also mean that you need to improve your product quality, unboxing experience or post-purchase customer experience.

So, let’s say you need to improve the post-purchase experience for your customers. You can refine your marketing strategies to ensure efficient follow-up communication to reduce buyer’s remorse.

Another solution could be integrating payment analytics with your virtual contact center solutions. This gives your customer service team access to real-time payment data during interactions, leading to faster and more personalized support.

Recognizing these issues and resolving them quickly not only increases customer satisfaction but also boosts loyalty.

4. Fraud detection and risk mitigation

Payment fraud is an issue all businesses face. But there has been positive news in the fight against this menace. A recent survey revealed that revenue lost to payment fraud globally fell from 3.6% in 2022 to 2.9% in 2023.

Despite this, it’s still important to be proactive to avoid being a victim. After all, criminals are always devising new ways to attack.

Payment analytics can help you detect fraud by identifying unusual patterns to reveal anomalies in transaction data. For instance, a sudden spike in transaction volumes or multiple failed attempts could indicate suspicious activities.

If you’re a Helcim merchant, Helcim’s fraud detection tool analyzes seven key transaction factors, including IP geolocation and the bank's location, to calculate a risk score. The higher the score, the higher the chance of fraud. This helps you spot and stop fraudulent transactions before they impact your business.

Using payment analytics to identify and prevent fraud ensures you don’t lose money to dodgy transactions. It also helps maintain your customers’ trust due to a secure payment environment.

5. Enhanced operational efficiency

Payment analytics delivers information on several aspects of operational efficiency to facilitate business processes and reduce payment processing costs.

By analyzing transaction data, you can identify inefficiencies within payment processing, such as high payment processing fees or higher chargebacks. Once you’ve detected these inadequacies, you can find the right solutions.

Say your high chargeback rate is due to unsatisfactory service. You could resolve this issue by equipping customer relationship management (CRM) call center agents with real-time payment analytics. This reduces resolution times and improves first-call resolution rates.

Furthermore, payment analytics supports automated reconciliation processes, reducing manual effort and human error. This automation saves time and allows staff to focus on more strategic activities that add value to the business.

6. Supports expansion strategies

Scaling a business is an exciting endeavor. However, before you do so, ensure you have comprehensive data from all aspects of your operation, including payment data, which is integral to informed decision-making. Failure to do so can result in significant setbacks.

Payment analytics can help you with your expansion plans in many ways. First, it can help you understand the competitive landscape, regulatory environment, and payment infrastructure. For example, your payment processor can generate reports on your best-selling or most profitable product categories. You can use this data alongside logistics and supply-demand insights to determine if it’s the right time to expand the inventory and promote a product further.

It can also help you identify growth opportunities by revealing patterns and trends in customer behavior, preferences, and spending habits. This, in turn, enables you to identify markets with high demand for your products and untapped customer segments. For instance, if you notice high order volumes from a specific location, you might consider opening a small physical store or warehouse there. This can reduce shipping times and improve customer satisfaction.

Payment data can help tailor your offerings by uncovering insights into customers' unique preferences and needs. Additionally, this technology can also help you assess risks and challenges. It helps you understand foreign exchange fluctuations, cultural differences in payment behavior, and payment fraud.

Having these insights when formulating your expansion plans leads to smarter decisions and increases your chance of success.

Now, you know how payment analytics can make your business agile. But how can you integrate this technology into your existing operations? We answer this question below.

How do you perform payment analytics

Adopting payment analytics in your business involves three steps: data collection and integration, data analysis and visualization, and real-time monitoring and alerting.

Step 1: Transaction data collection and integration

The first thing to do is identify relevant data sources that will provide valuable insights. This typically includes payment gateways, point-of-sale (POS) systems, bank accounts, CRM systems, and web analytics.

Once identified, extract and standardize the data. Your payment processor can automatically collect sales data from multiple touchpoints, such as online payments, in-person POS systems, and credit card machines, saving you time and effort.

If you need to combine data from different sources to address a specific issue, you can request or purchase an API. Using APIs, your team can extract data from various sources or employ ETL (Extract, Transform, Load) processes to extract, clean, and transform data into a standardized format.

Afterward, integrate the data you collected from various sources into a unified repository. You can achieve this through data warehousing solutions or data lakes.

Real-time data streaming platforms like Kafka and Kinesis can also help. These tools can facilitate seamless data integration and payment processing in real time.

Step 2: Data analysis and visualization

Now that you’ve collected, integrated, and cleaned the data, the next phase is analysis and visualization to extract meaningful insights.

In terms of analysis, there are four approaches. You can run a descriptive, diagnostic, predictive, or prescriptive analysis. The one you perform will depend on your business needs and objectives.

• Descriptive Analysis (What happened?): This method summarizes past transactions to identify payment trends and patterns. For example, you can analyze historical sales data to determine which payment methods customers prefer, peak transaction times, or regions with the highest revenue.
• Diagnostic Analysis (Why did it happen?): If you notice an issue—such as a spike in failed transactions or increased chargebacks—diagnostic analysis helps uncover the root cause. For instance, by breaking down payment failures by card type or location, you might discover that a particular payment gateway is experiencing technical issues.
• Predictive Analysis (What might happen in the future?): The predictive analysis helps you forecast trends. For example, if your data suggests that sales typically drop in certain months, you can adjust inventory ahead of time to avoid overstocking.
• Prescriptive Analysis (What should you do next?): This method provides actionable recommendations based on past data and predictive models. If predictive analysis forecasts an increase in credit card fraud attempts, you need to be more conscious and avoid high-risk transactions.

After analysis, you need to present the information you found in a way all stakeholders can understand. This is called data visualization. Here, you’ll use tools like Power BI and Tableau to create dashboards, graphs, and maps. For example:

• A sales heat map can highlight which geographic regions generate the most revenue.
• A trend chart can show the rise or fall of certain product categories over time.
• A real-time transaction dashboard can alert your team to sudden changes in payment success rates or fraud risks.

Next, analyze and visualize the data.

Step 3: Real-time monitoring and alerting

Payment analytics isn’t a one-time task. It’s a process that involves continuous review of the data and changing strategies based on insights gained.

So, regularly monitor key metrics like transaction volume, average transaction value, and chargeback rates. Track the health of payment systems and infrastructure, including server uptime, database performance, and network latency.

Keep an eye out for unusual patterns in transaction data that may indicate fraudulent activity. You should also monitor customer experience metrics, like average wait time and customer satisfaction.

Then set up alerts for when these metrics exceed predefined thresholds — for instance, high fraud rate or low system uptime. Artificial intelligence (AI) tools for communication can streamline this process. These tools make it easy to notify relevant team members instantly when issues arise.

Through real-time monitoring and alerts, you can address issues proactively, improve customer experience, and reduce fraud.

Get real-time insights with Helcim payment analytics

Payment analytics gives you valuable insights to make informed decisions and remain agile in a dynamic market. It does this by helping you manage your cash flow, improving customer experiences, detecting fraud, and so on. If you haven’t started implementing this technology, you miss out on opportunities to strengthen your competitive position and drive sustainable growth.

Here's how Helcim payment analytics help you get real-time insights:

• Your payment data automatically syncs across all sales channels—POS, online, retail, and invoicing.
• Identify top-performing products with detailed reports and receive automatic inventory alerts to prevent stockouts.
• Access complete customer history, including invoices and orders, to build stronger relationships.

Ready to unlock the power of your payment data? Sign up with Helcim today.

FAQs

What is payment data analytics?

Payment data analytics is the process of collecting, analyzing, and visualizing transaction data. It helps your business track sales performance, detect fraud, manage cash flow, and enhance customer experiences.

What is the best payment analytics tool?

If you need real-time insights, a payment processor’s analytics tool can track payments, sales, and transactions instantly. However, if you prefer to export and analyze data in-depth, tools like Power BI and Tableau allow for advanced data visualization and custom reporting.