Financial Analytics (And Why They’re Important)

One of the keys to a business staying profitable is being able to use every bit of information available to make informed decisions that can dictate future success. One thing that top-end companies do that others don’t is dig deeper into their own patterns to predict outcomes in the future. Or in other words, they use financial data analytics.

Financial data analytics are methods of assessing budgets, projects and past performances to predict future performance and stability. But what exactly goes into financial analytics and how can companies use them to improve their own business?

We’ll take a look at how four aspects of financial data analytics — customer profitability analytics, product profitability analytics, value-driven analytics and cashflow analytics — can be used to develop a predictable outcome.

Customer Profitability Analytics

Unfortunately, not all customers are created equally. Companies make different profit margins on different customers based on factors, such as volume and shipping costs for example. Generally, most organizations fall into the 80/20 rule, where 80 percent of their revenue comes from only 20 percent of their customers, whereas the remaining 20 percent of revenue comes from the other 80 percent of their customers. Identifying which customers generate the most revenue for your company can lead you to understanding which trends in the sales and marketing process have made these customers your larger profit drivers. Customer profitability analytics can eliminate useless processes and non-essential expenses that are not leading to any tangible growth or revenue.

Product Profitability Analytics

Different products fall into the same category as different customers; not all will generate the same type of revenue. Companies can figure out their product profitability by subtracting their costs to create the product from the revenue generated after that product is sold. Looking at the revenue per piece as opposed to the product revenue as a whole can shed light on how products are contributing to the cause. It will also highlight which products are actually costing the company money to manufacture. Companies can adjust their sales and marketing efforts towards the products that generate the highest yield and shift resources away from products that are generating little to no profit. Product profitability analytics allows companies a chance to fine-tune their pricing model in order to maximize profits.

Value-Driven Analytics

No area of analytics is more important to a company’s long-term health than value-driven analytics. Understanding the day-to-day trends in your business is important, but without a high-level strategy of where you want the business to go, it’s harder to assemble a narrative through data. With an overarching strategy assembled, search-driven financial data analytics platforms allow companies to ask questions related to that strategy and monitor progress. Because value-driven initiatives are born out of hypotheses, it’s essential to regularly check in on how reality is matching assumptions.

Cashflow Analytics

Financial liquidity is essential to day-to-day business operations. Having cash set aside to pay for employees, facilities and utilities will literally keep the doors open, but organizations need to know how much cash needs to be kept aside in order to operate at optimal levels. One exercise a company can complete to assess its cashflow is the working capital ratio, which takes the company’s current liabilities and subtracts that from the current assets. The difference is the working capital, which will show if a company is viable enough to pay off its short-term debts to stay in business, or if they’ll have trouble with creditors in the near future. Using cashflow analytics gives companies the opportunity to adjust their assets and liabilities to create an ideal cashflow balance.

There are many different types of financial data analytics that companies can use. The methods highlighted above can all produce usable data to show trends and predict future results. Regardless of industry, the market can be a tough place to navigate but it gets a little easier when using available data to your advantage.