Sales analytics is the practice of using data to make decisions for your business, using it both long-term strategies and daily operations.
Data cleansing requires gathering, integrating, and cleansing data from various sources and applications in order to produce reliable analytic outcomes – this may take more time and resources from your analyst than anticipated.
1. Identifying the Right Metrics
When monitoring current sales, conducting sales analysis, or making decisions for your business, using the appropriate metrics is of utmost importance. Doing so allows you to maximize your data’s use while gaining insight that help achieve short- and long-term goals more easily.
If your revenue is increasing but new customers aren’t, one approach might be promoting your product to more of the target audience through advertising or social media campaigns, while another would involve revising pricing strategy or features to attract more leads while keeping existing ones satisfied.
Repeat customer rate is another useful metric to keep an eye on, which measures the percentage of existing customers who continue purchasing from your company over time. It serves as an indication of customer loyalty and can help identify which territories bring in the most revenue so that you can target those areas with your marketing and promotional efforts.
An important metric to monitor is the average deal size, which measures how much each sales rep generates in total value. This metric can provide insight into each member of your sales team as they perform and allow you to identify any improvements necessary to increase overall performance.
2. Analyzing Your Data
Sales data analytics provide a useful way of gaining a clearer picture of your customer base, whether that means assessing pricing strategies or discovering which products are most popular among them. But before proceeding with sales data analytics, ensure your data is accurate and complete as poor quality can lead to errors that skew results of analysis.
Analytics tools can also help identify key factors that contribute to high churn rates or slow revenue growth, like inefficient email communication or generic messages that sender rely on. One chemical company realized it was losing 25% of business due to this combination, prompting its team to devise more effective customer communication tactics.
Sales analysis goes beyond measuring current sales to provide insight into planning for the future. By evaluating customer data, sales analysis allows you to spot opportunities to upsell or cross-sell products to increase lifetime client value; and quickly identify signs of customer churn so you can develop strategies for engaging them again and potentially turning them into repeat buyers.
Utilizing sales data analytics is straightforward if you employ a consolidated dashboard that offers dynamic visualization and allows for comparison over time. Depending on the scope of your analysis, it may be worthwhile tracking overarching metrics such as total sales or deal size on a monthly basis or more specific measures like MQL-to-SQL ratio or lead-to-close ratio more frequently (weekly or quarterly).
3. Creating Dashboards
Your sales analytics tools must be user-friendly in order to help save you time, increase productivity and make sounder decisions. Dashboards should be designed with simplicity in mind, providing only essential data profiles and content relevant to those accessing them – this works best with high-level big picture data at the top half and day-to-day insights and granular detail at the bottom. Furthermore, consistency must be maintained across your sales dashboards in terms of fonts titles etc being maintained between dashboards.
To provide an example, when reviewing your sales funnel it can be useful to have a visualization that displays where most of your revenue is coming from (by customer type, region etc). This can help identify areas for growth and focus efforts accordingly. Conversely, conducting diagnostic analysis on areas in which sales may lag could enable you to discover causes more effectively predict future performance and make informed predictions for success.
This dashboard allows salespeople to easily see and drill into account data that has been pre-sorted by industry or region for any period they choose, providing a high-level overview of their performance against their quota.
4. Creating Reports
Sales analytics is an essential tool for businesses. It can identify and address weak points within your operation while simultaneously expanding revenue and profits.
However, to extract maximum benefit from sales analysis it is crucial that you understand how to effectively use and interpret your data so as to create meaningful reports which will aid decision making processes.
Establish your audience and the purpose of the report before proceeding with its creation. This will allow you to select data most pertinent to them while eliminating anything irrelevant or distracting – for instance comparing sales pipeline conversion rates across time periods.
Decide the timeframe in which you want to analyze your data, as this will affect its accuracy and depth of reports. For instance, when looking at rep performance it may make sense to conduct year-over-year comparisons; when tracking sales velocity monthly or quarterly comparisons may prove more useful.
Once you’ve selected your metrics, collected and analyzed your data, and interpreted your analysis results, it is time to create your sales reports. A comprehensive sales report should cover each element listed herein:
