What is financial analytics?
A complete guide

Learn how businesses use financial analytics to improve decision-making and lower risks, and then apply it to your own business.

Financial data is at the backbone of every business seeking to make a profit and grow. Unsurprisingly then, financial data analysis is crucial for driving informed, effective business decision-making.

That’s why interpreting financial data effectively using financial analytics is a critical component of business success. Financial analytics helps organizations understand the state of their current financial position, identify and understand their financial risks, and make intelligent decisions about how to achieve future financial goals.


What is financial analytics?

Financial analytics is the process of investigating a business’s financial data through analytics in order to assess its financial health and optimize its financial performance.

Financial analysis and analytics starts by collecting financial data about a business. This data can take a variety of forms, but common examples include information like revenue reports, budgets, and financial statements about existing assets.

Businesses then evaluate this data in both a systematic and an ad hoc way. They sometimes have particular questions that they seek to answer when performing financial data analysis, but the analytic process can also yield insights into trends or challenges that the business hadn’t even considered previously — such as revenue streams that are growing more slowly than a budget anticipated, or unforeseen opportunities to increase ROI on a product.

The financial analytics process hinges on two types of resources. First, financial analysts need data literacy, which allows them to interpret the data yielded by analytics. Second, they need the necessary domain expertise to understand how financial data fits within the broader business context.


Why are financial analytics important?

Financial analytics is critical to a business, as it informs and shapes the vast majority of business functions.

Financial data can be complex due to the variety of sources involved and the volume of data from those sources. It can be challenging to interpret a single budget or financial statement, let alone assess a large volume of different types of financial data sources to understand the overall financial health of a business. By systematizing this process, however, financial analytics helps to ensure that businesses glean all of the insights possible from the financial data they generate. In turn, they can improve business processes, make better decisions, plan for the future, and manage risks.

Complex business process

Business processes are complex. They typically involve multiple stakeholders, and they require a mix of different types of expertise. For example, procurement operations require business users or units who need to procure a resource to interface with specialists who have expertise in acquiring the resource. Likewise, an advertising process necessitates collaboration between sales and marketing departments to determine how best to position a product and introduce it to consumers.

Financial analytics helps ensure that the various stakeholders within processes like these make the most financially informed decisions during the processes. In a procurement process, for instance, financial analytics insights can help procurers identify the most cost-effective suppliers for a resource that the business needs. In advertising, financial analytics based on the impact of previous marketing campaigns may reveal which strategies drove the highest conversion rates in the past, providing guidance on how to navigate a new advertising process.

Future goals

It’s common for businesses to set financial goals, such as reaching a certain revenue figure within a fixed period of time, achieving a set sales goal within a quarter, or growing a certain product within a target market segment.

Financial analytics plays a key role in helping businesses to set goals in the first place. Analytics data helps businesses form sound decisions for which financial goals to prioritize and pursue.

From there, financial analytics helps ensure that goals like these are realistic. Using insights from past financial trends, a business might adjust its revenue goals for the upcoming quarter, for example.

At the same time, financial analytics helps track progress toward goals. It’s much easier to know whether you’re likely to achieve a stated financial goal when you can track your company’s financial position in real time.

Key business decisions

Businesses are constantly making decisions about questions like which products to develop, which features to add to existing products, whom to hire, which companies to acquire and so on.

With financial analytics, these decisions can be based on hard financial data — as opposed to intuition or subjective human analysis, which might otherwise form the basis for decision-making in a company. Financial analytics help businesses determine which decisions proved the most cost-effective in the past so that they can make similar decisions in the future.

Risk assessment

Staying ahead of financial risks is essential to long-term business success. Problems like decreasing ROI on an investment or a steadily increasing debt load can critically threaten a business’s financial health if left unaddressed.

Financial analytics helps organizations identify problems like these early-on, so they can take steps to remediate them before they turn critical. Just as important, financial analytics allows businesses to assess how effective a risk reduction policy is once it has been implemented. In turn, they enable companies to adjust their strategies if a current risk-management strategy is proving less effective than anticipated.


Types of financial analytics

There are a variety of ways to pursue financial analytics. The type of financial analytics process that a business chooses depends on what its financial goals are.

There are too many types of financial analytics to describe each one here, but the following is an overview of some of the most common financial analytics categories.

TYPE ONE

Working capital management

Running low on cash or other forms of capital is one of the most serious financial risks that businesses can face. Working capital management is a form of financial analytics that provides insight into how much capital they have on hand so that they can get ahead of potential cash flow issues. Just as important, working capital management helps businesses know when they have excess capital, so they can invest it rather than let it sit idle.


TYPE TWO

Product profitability

Calculating the total profitability of a given product is often challenging, due especially to the fact that there are many product-related costs — such as research and development and customer support — that can be easy to overlook when determining a product’s ROI. By leveraging product profitability financial analytics, however, businesses can gain a holistic understanding of how much profit each product generates, based on their total direct and indirect product costs.


TYPE THREE

Client profitability

It can be difficult for businesses to assess the customer lifetime value, or the total profitability or value of the client to the business, both over the lifetime of an account, and over a fixed period (such as a year). You have to factor in not just how much revenue each account generates, but also items like how much time your marketing and sales teams spent acquiring a client and how many contract renewals you receive from the client. Client profitability financial analytics evaluate considerations like these to provide comprehensive insight into client profitability, allowing businesses to compare each account in terms of how much it contributes to the bottom line.

Depending on the type of business you operate, you may choose to analyze product profitability, client profitability, or both. Businesses that mainly sell products will benefit most from product profitability, for example, while services businesses will prioritize understanding of client profitability.


TYPE FOUR

Predictive sales

Determining how sales for different products or services may change between quarters or seasons can be challenging. So can assessing how developments like fluctuations in the stock market or the introduction of new product features impact sales. Businesses can answer these questions with predictive sales, a form of financial analytics that answers questions like these. In turn, it helps businesses plan for the future.


Use case for financial analytics

To understand what financial analytics looks like in practice, consider the example of a bank that currently specializes in retail banking services, but wants to expand by offering investment products as well. Making this change will require the company to develop not just new types of financial services, but also new marketing, sales and support services to drive those products.

In a situation like this, financial analytics can provide crucial decision-making guidance. The company could use predictive sales financial analytics to predict how many new investment accounts it can expect to open in the first year after launch, for instance. It could also use client profitability analytics to understand how the total profitability of its new offerings compares to that of its retail banking services. And it might leverage working capital management to ensure that it maintains a healthy cash flow even as it invests in the new line of services.


Revolutionize your financial analytics

When it comes to implementing financial analytics, you can do it the hard, tedious way — which means collecting and analyzing financial data manually, sleuthing over reports and statements to try to pick out relevant trends.

But you could also do it the easy way. Using a platform like ThoughtSpot, anyone in your organization can analyze data through simple, powerful searches, while AI brings insights to users they didn’t even know they needed. You’ll not only save enormous amounts of time on data collection. You’ll also enjoy the confidence of identifying all relevant insights within that data – including patterns and anomalies that human reviewers might miss. .

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