Surprisingly, a mere 47% of businesses are presently leveraging data analytics to optimize their sales. As a result, more than 50% of businesses are still trying to avail the benefits offered by sales data analysis.
Takeaway: Merely capturing data across multiple sources will not yield the desired results.
So how do we change this?
Maintaining an edge over competitors requires companies to make decisions backed by in-depth data analysis. In fact, there is a famous quote that many leaders resonate with, ‘The money lies hidden in data.’ With great tech, this decade, the relevance of this statement has grown stronger.
While there are numerous types of data that companies regularly capture, only a few of them are as important as sales data. After all, companies that make data-driven decisions have a better chance of attaining their revenue goals than others.
The key lies in continuous data monitoring and analysis; companies need to understand the intricacies of data collection and analysis. Latest reports indicate that businesses that leverage data for their sales decisions witness up to 20% growth in their ROI.
What is Sales Data Analysis?
Sales data is the quantifiable information gathered during a sales process. Company leaders use this data as a reference point for making data-driven policy decisions to guide every salesperson. These sales decisions can range from sales automation to prospecting and strategizing. Consequently, companies can align their sales targets and improve the sales cycle.
After collecting sales data, companies perform in-depth sales data analytics. Here, company leaders use the defined metrics to optimize internal processes, set sales goals, and forecast sales with greater accuracy. The main goal of sales data analytics is to present the data in an easy-to-understand manner. This data enables the sales managers to analyze the performance of the team, sales opportunities, and future trends.
Moreover, sales data analytics offers key insights into customer behaviour and helps management understand the reasons behind the behaviour. With sales data analytics, companies can determine trends in lead conversions and drop-offs to further improve the processes to yield better results.
Increasing Revenue with Sales Data Analytics
The margin of error is becoming narrower for companies, with an increasing number of businesses focusing on making data-driven sales decisions. Any sort of guesswork can lead to data-driven competitors racing ahead. With comprehensive sales data analytics, companies can make informed decisions. This means improved profit margins and revenue growth.
Better Retention Rates
A recent report highlights that improving customer retention rates by 5% can deliver up to 95% higher profits. Hence, when go-to-market teams are targeting new customers to boost revenues, they should also assign equal attention to retaining existing customers. This strategy assumes greater importance because upselling and cross-selling to existing customers is far more successful than acquiring new ones.
Sales data analytics enables GTM teams to better serve the existing customers by understanding the specific customer requirements. With this report, success teams can effectively identify the best customers and create a plan to deliver better customer experiences and higher retention rates.
Research highlights terrible experiences as the reason behind churn for 67% of customers, but less than 4% of customers report this. This is where sales data analytics reports can help the sales teams by helping with the identification of factors behind higher churn rates. Issues like spam mail, irrelevant content, and wrong call time are the most significant reasons behind higher churn rates. Leaders can then create a plan to address these issues and improve customer retention rates.
Accurate Forecasts
Forecasting is a crucial function under data analytics, where sales leaders leverage the available data to predict future sales. Companies can use analytics to determine sales opportunities, challenges, and future risks and be better prepared for them. After taking care of the guesswork in decisions, a company is in a better position to drive future revenue growth. The key here lies in the integrity of the data used for the forecasting process; hence it is essential to use only factual and verifiable data.
Sales leaders must also pay adequate attention to the quality of their data, as the quality is directly proportional to the accuracy of forecasts. One of the most important aspects of sales data analytics is the technique used for forecasting, as it can directly impact future revenue. The three important techniques are: -
- Time Series: This technique focuses on sales patterns to predict future outcomes.
- Qualitative: This leverages historical sales data to predict expected future outcomes.
- Casual: This involves making predictions based on variables that can impact the market's future direction.
The model selection depends on the available sales data and the stage of the sales process requiring forecasts. Many companies also use these approaches simultaneously to improve the accuracy of sales data analytics.
Robust Pipeline Management
When the list of potential leads is growing steadily, it is crucial for companies to focus on pipeline management. No business would want to have leads of poor quality that cannot be converted to sales, as it would directly impede revenue growth. To drive future revenues steadily, companies must create a pipeline wherein it is easy to identify profitable potential customers.
But as the leads grow, maintaining a healthy pipeline becomes a challenge. 48% of salespersons do not make even a single follow-up attempt. This issue can be effectively handled through sales data analytics - the reports can help identify customers with better conversion prospects. Any attempts to sell to unsuitable customers would result in higher churn rates and slower revenue growth.
Analytics can also help segment prospective customers based on engagement levels and profitability. Sales managers can accurately monitor the movement of leads in the sales pipeline and address any bottlenecks that can impede the sales process.
Precise Value Proposition
Despite having excellent value propositions, many companies struggle to achieve them. This is where sales data analytics comes to the fore. Detailed reports created through this process help the sales leaders better understand customer requirements. This understanding drives revenue growth by ensuring that the value proposition is designed to target prospective customers efficiently.
Many companies struggle with creating value propositions that can convince different customer segments to purchase the products or services offered. The biggest mistake that several businesses make here is taking a one-size-fits-all approach which is less likely to yield desired results in hyper-competitive business domains.
Companies that analyze sales data in depth can experiment with various value propositions across multiple customer segments and then ascertain the best options. By collecting data from multiple touchpoints, companies can create personalized value propositions to cater to the needs of different customer segments.
Targeted Marketing
Targeted marketing is considered one of the best strategies any business can pursue. The focus here is on a particular customer segment so that sales teams can operate on limited budgets. Targeted marketing focuses on branding that appeals to a specific group, which further leads to higher conversions and consistent revenue growth. It is important to remember that 60% of customers say no four times before agreeing to the offering.
Analytics helps marketers better understand the target customers, enabling the creation of target groups quickly. Companies can boost revenue growth by leveraging data-based marketing strategies to target specific customer segments.
Companies can undertake customized direct marketing strategies to reduce the sales process's length, thereby developing a competitive edge. Businesses can use the reports created through sales data analytics to target more leads, reduce costs, drive revenue growth, and maximize profits.
Analyzing sales data is so important that it helps company leadership with informed decisions and sound sales strategies. Through a proper understanding of the processes, companies can promptly notice sales patterns to empower the sales teams. These empowered sales teams are highly motivated to quicken the pace of revenue growth through better lead conversions.
