Most, if not all, organizations need to understand their financial performance to improve business processes, identify the risks they face, and focus on the right goals. Harvard Business School provides an excellent list of important financial key performance indicators (KPIs) that companies should track, measure, and analyze across a wide range of categories, including profitability, liquidity, solvency, efficiency, and valuation. However, the tools and techniques for analyzing KPIs vary greatly and reveal different insights.
Traditional spreadsheets and reporting tools provide value by showing what has happened in the past. But, they fall short when it comes to explaining why something has happened and predicting future outcomes. This is where advanced financial analytics closes the gap. This type of analysis applies sophisticated techniques such as statistics, machine learning, and data mining to fill these voids in understanding data.
Detecting Patterns, Trends, Correlations, and Relationships
Advanced financial analytics can detect hidden patterns, trends, correlations, and relationships in data that explain changes in performance. Here are just a few examples of how to use advanced financial analytics to grow revenue and profits:
If you raise the price of a product or service, will you increase revenue? Not if a drop in customer demand because of the increase causes revenue to fall. Price elasticity will help you make the right pricing decision by measuring the sensitivity of customer demand to changes in price. Developing an optimal pricing strategy is key to maximizing revenue.
The revenue of numerous products varies with seasons. For example, back-to-school seasons see an increase in the sale of school supplies and differences in seasonal weather influence the purchases of coats, swimsuits, and other items. Retailers understand the seasonality of many types of goods and services and maximize revenue through seasonal promotions. However, there’s also hidden seasonality when trends are not immediately obvious or easily detectable in the data. Advanced financial analytics can help identify these subtle or less obvious recurring patterns. With this knowledge, retailers can add items with sales uplifts at a particular time of the year to their promotional strategies.
Cost of Goods Sold (COGS)
The Cost of Goods Sold (COGS) is the direct costs incurred in the production or acquisition of goods that a company sells. Using advanced financial analytics is a valuable way to reduce COGS by uncovering hidden opportunities to optimize the supply chain and reduce energy consumption and fraud.
- Supply chain costs: Advanced financial analytics can identify patterns and correlations to help businesses make better decisions when selecting suppliers, negotiating contracts, and managing supply chain data.
- Energy costs: Companies can use advanced analytics to analyze energy usage patterns to reduce utility costs, which in turn lowers COGS.
- Fraud costs: Detecting anomalies and patterns that indicate fraud in procurement, billing, and inventory management avoids financial losses that drive up COGS. In addition, advanced analytics can spot fraudulent accounting schemes such as asset misappropriation, financial statement fraud, and corruption schemes, including bribes and kickbacks.
Accurate Demand Forecasting
Accurate demand forecasts powered by advanced financial analytics help businesses make their operational and product processes more efficient. By aligning procurement, production levels, staffing, and other resources with expected revenue, businesses can improve operational efficiency, avoid overstocking or understocking, and minimize waste.
Do You Need More Than Traditional Financial Analytics?
Business intelligence and other financial reporting tools play a huge and important part in a company’s analytics portfolio. Advanced financial analytics helps to provide a deeper understanding of data, discover hidden patterns, and reveal what’s next on the horizon. Organizations that don’t have a strategy for advanced financial analytics will likely experience lower revenue and higher costs, face ongoing inefficiencies, and fall behind their forward-looking peers.
The Actian Data Platform supports the needs of traditional and advanced financial analytics. You can easily move data into the Actian Platform and leverage any analytical application to support users who have a wide range of needs and technical skills. Financial analysts, business analysts, data experts, and business users can access and query data without having to choose between performance and savings.