Sales analytics is the technique of understanding the past and the future predicaments of the sales to manage its performance.

It is the practice of closely analyzing the sales analytics to ascertain revenue outcomes and set targets for the sales team. It helps in predicting future sales by determining the success of the previous sales.

How does sales analytics help?

It primarily focuses on the improvement and the development of a strategy for improving your sales performance in the long run and short run. A good sales analytics strategy provides focus and clarity to the team.

Why should you keep a check on sales analytics?

Examining sales analytics with the help of the right sales metrics will not only boost the performance of sales in a company but also upgrade its sales activities and accountability. Through this, a sales team can perform in a fast-paced environment and focus on a wide range of activities carried out by them.

Sales analytics reports and dashboards

Sales specific data analytics is tricky when it comes to data transparency. The sales analytics team can use tools and platforms to show actionable data on a sales dashboard which will be direct, insightful and will give a clear message.

Focus areas to track for meaningful sales analytics

To implement data analytics in an organization, taking stock of the sales metrics is an essential feature. The following examples will help improve the sales analytics metric.

Sales Increment – Sales analysis is based on the ability of an organization to raise revenue. Even a small error in the trend line will be problematic.

Target Sales – It measures the current performance of a business against the set targets. Sales can be represented in the form of revenue earned or the number of units sold.

Sales Opportunities – It is based on the chances of closing the sale and the opportunity value. There is an estimated purchase value that helps the team to accentuate and strengthen their efforts.

Sales to Date – It helps you to measure the total sales in the past year and compare the results with the previous periods and help to get a sense of historical trends.

Product Performance – It is based on revenue performance to find out which products are doing well in the market. Also, it helps to find out those products which have failed to impress the customers.

Lead conversion Rate – Conversion analysis can help teams improve performance to improve customer experience.

Sell-through rate – When selling tangible goods, it becomes important to track your sales and the total inventory. It helps in sales forecasting.

Cannibalization rate – Selling a new product may sometimes have an adverse impact on the sale of an existing product. Hence, it is important to track product cannibalization in sales analytics to improve the customer experience.

Quote-to-Close – It is a sales productivity metric that reveals the effectiveness of the team in closing a deal. It helps to assess the eminence of the sales process.

Sales per Rep – Sales per sales representative – Senior and experienced representatives usually perform better than junior representatives, so predicting this analysis is critical to future growth prospects.

Average Purchase value – To increase the sales revenue efficiently, the average purchase value of each sale needs to rise. A good strategy is to track historical trends and incorporate this metric in your analysis.

Sales by Region – Even multinational corporations have regional variations in their revenue and sales. Tracing this metric will help in finding out those markets in which your product is more profitable and competitive.