Sales data analysis may sound like a very complex and time-consuming process; however, in reality it isn’t. Organizations must invest in advanced analytical capabilities that facilitate data collection and analysis; thereby, offering actionable insights that the sales representatives can use to ameliorate sales and the overall revenue. Prior to big data and advanced analytics, marketers and sales leaders based their business decisions on intuition and trial and error methods. But today, sales heads have realized the importance of making decisions based on insights gained from predictive analytics tools and other advanced data analytics solutions.
Past Imperfect – The Trial and Error Method of Decision Making
Traditionally, organizations made use of intuitive analytics to develop strategies with uncertain outcomes, usually known as the trial and error method. If these strategies worked, they applied it everywhere. If not, they would tweak it and make changes until it delivered favorable outcomes. With the intuitive analytics method, the salesforce was unable to monitor their customer’s activities and identify the growth drivers. The sales teams are often baffled with the mammoth amounts of data at their disposal and often struggle to build effective strategies that drive sales and revenue. The trial and error method for strategy development to boost sales and revenue might prove beneficial for the short term but may have adverse effects on the organizations revenue in the long run.
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Sales Data Analysis Boosts Revenue – How?
Sales teams accumulate an overwhelming volume of data from various sources; thereby, making it essential for organizations to adopt sales analytics and predictive analytics tools to gain actionable insights. Technology and data analytics allow teams to track their market performance, measure the outcomes, monitor customer activities and behavior, identify growth drivers, and pinpoint successful sales efforts. Sales data analysis is all about, data mining and collection, organizing the data, and extracting meaningful insights that facilitate strategic decision making and boost sales revenue. How can we leverage sales data analysis to boost sales revenue, you ask? Here’s how:
- The first step in conducting a sales data analysis is to narrow your product and service offerings. Usually, organizations must gain an in-depth understanding of their consumer’s needs and expectations from the business, analyze past transaction information, and use the insights to build custom deals that are aligned to customers’ needs and cater to the target market
- Next, put in place an efficient pipeline management system to segment your potential customer base, prioritize them based on their level of profitability, and identify the product that will cater to their needs. Efficient pipeline management reduces wastage of time and resources on incorrect leads and creates a stringent lead qualification and ranking process that drive sales quickly
- Finally, sales heads must develop a robust and dynamic sales incentive plan for their team by using the sales data analysis and developing yardsticks or milestones to measure their performance.