Business

How To Turn Financial Data Into Insights – 6 Analytics Everyone Should Know

This column is by Leading Business and Data Expert, Bernard Marr

In today’s data-filled world, analytics is an essential part of staying competitive. Financial analytics help businesses understand current and past performance, predict future performance and make smarter decisions. Let’s look at some of the key financial analytics that any business, regardless of size, should be using.

Predictive sales analytics

Sales revenue is the lifeblood of any business so knowing how much you can expect to receive has important tactical and strategic implications. Predictive sales analytics involves figuring out how successful your sales forecast is and improving your sales predictions in the future. There are many ways to predict sales, such as looking for trends in past data or using predictive techniques like correlation analysis.

Predictive sales analytics is an extremely useful tool for planning and peace of mind, helping you manage the peaks and troughs of your business. For example, many businesses experience more and less sales at certain times of the year. If you know that year on year you make fewer sales in July then you can encourage staff to take holiday then and stay calm when sales drop in that period.

Tip: Predicting future sales is always helped by detailed and thorough sales data from the past so keep accurate records.

Customer profitability analytics

It’s important to differentiate between the customers that make you money and the customers that lose you money. Customer profitability usually falls within the 80/20 rule, whereby 20% of your customers account for 80% of your profit, and 20% of your customers account for 80% of your customer-related costs. Knowing which is important.

By understanding the profitability of certain groups of customers you can also analyse each group and extract useful insights. For example, you may discover that your very best customers made their first purchase from a particular advertisement in a particular magazine. That knowledge can help direct your future marketing efforts

Tip: The biggest danger with customer profitability analytics is when you don’t analyse a customer’s full lifetime value. It’s important to focus on a customer’s cumulative value to your business.

Product profitability analytics

In order to stay competitive, businesses need to know where money is being made and lost. Product profitability analytics is a way of discovering profitability by individual product, rather than looking at the business as a whole. To do this you need to assess each product and its costs individually. Admittedly, this can be tricky because your products may well share production processes or cost bases. Therefore, you need to find a reliable and fair way to apportion costs to your various products.

Product profitability analytics helps businesses uncover profitability insights across the product range so better decisions are made and profit is protected and grown over time. For example, if you discover that one product makes more profit than all the others then you may want to promote that product more heavily.

Tip: Watch out for loss leaders. Some products may lose you money but their purchase leads on to more profitable purchases (think of a razor and the expensive replacement blades).

Cash flow analytics

The day-to-day running of a business requires a certain amount of cash to keep the lights on, wages paid, etc. Knowing how money is moving in and out of your business is essential for gauging the health of your business. Cash flow analytics involves using retrospective or real-time indicators such as the Cash Conversion Cycle and Working Capital Ratio. You can also use tools like regression analysis to predict future cash flow.

On top of helping you manage cash flow and making sure you have enough cash to keep the cogs turning, cash flow analytics can also support a variety of corporate functions. For example, analytic software can help accounts receivable personnel to increase cash flow by prioritising which customers are contacted by collection staff and when.

Tip: When trying to predict future cash flow based on past data, it’s important to ensure you are making the right assumptions. Scenario analysis can help with this.

Value driver analytics

Most businesses have a sense of where they are heading and what they are trying to achieve. Often these goals are formalized on a strategy map that identifies the value drivers in the business. These value drivers are the key levers that the business needs to pull in order to meet its strategic objectives. Value driver analytics is the assessment of these levers to ensure they actually deliver the expected outcome.

Value drivers are often based on assumptions which need to be tested to check they are correct. For example, you may use price as one of your value drivers and assume that price influences sales and revenue, but you need to test that hypothesis so you can establish if you are right or not.

Tip: You must properly identify your value drivers and be really clear about what it is you are trying to achieve strategically.

Shareholder value analytics

The results and interpretation of the results by investors, analysts and the media will determine how successful your business is on the stock market. Shareholder value analytics is a calculation of the value of a company made by looking at the returns the business provides to its shareholders. It effectively measures the financial consequences of strategy and assesses how much value the business’s strategy is actually delivering to the shareholders.

Shareholder value analytics should be used frequently alongside profit and revenue analytics. To measure shareholder value analytics, you can use a metric called Economic Value Added (EVA). This calculates the profit of a business when the cost of equity finance has been removed.

Tip: This metric needs to be tempered with additional customer-based analysis to ensure that the shareholder value is not occurring at the expense of customer value.

As always, let me know your thoughts on the topic, please share them in the comments below.

Disclaimer: This is a curated post. The statements, opinions and data contained in this column are solely those of the individual authors and contributors and not of iamwire or its editor(s). This article in its original form was published here.


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