Managers need to perform sales analysis as part of running a successful business. Analytics in sales help companies to determine which products they will focus on, which markets to target, and the best customer-winning strategies.
A variety of sales analysis tools are available in the market. They provide actionable insights into the performance of sales reps which help them improve their strategies and decision-making. Companies receive immense benefits from sales analytics.
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What is sales data?
Sales data includes any type of data generated from sales-oriented activities. The data must be measurable and provide accurate information about the revenue generated from sales, the cost versus ROI, and the customer journey. The sales analytics software by Revenue Grid provides predictive sales analytics to help boost sales. It uses sales data to identify patterns in customers’ and leads’ behavior to forecast future sales performance. Sales data contains key features such as:
- Current customers: Provides a view of the total cost spent to win each customer, volumes or types of purchases, and their current behavior online. Reps get information about revenue per sale and the average customer lifetime value.
- Key pipeline trends: Gives a 3600 view of sales opportunities and insights on leads and activities in each sales funnel phase.
- Sales team performance data: Includes total deals closed, units sold, campaigns achieved, and cost.
Sales data is generated from a variety of platforms like CRM, social media, blogs, warehouses, and shipping. Data analytics in sales help sales reps plan better and make decisions that propel them into success.
Why sales analysis is important for business?
Analytics in sales help organizations understand their customers needs. Sales teams understand the areas of their strengths and weaknesses. They take the right actions to improve their failures and strengthen their success. Without analytics, sales reps and their employers cannot get a clear view of whether they are succeeding or failing.
Analytics in sales encompasses a wide area of activities. It includes leads, deals, costs, trends, and revenue. Teams get a view of which products are performing better. They create strategies to boost the performance of products that are lagging. The teams develop new approaches to improve brand awareness and sustain an upward growth trend.
The sales funnel is an important part of the success of sales teams. It provides them with a platform to guide customers through the conversion phase. The funnel contains promising leads at different phases in the journey. Analytics in sales helps the team get a view of leaks in the funnel and a forecast of total leads that might convert. They get an idea of where to focus.
How to analyze sales data
Organizations need to regularly perform sales data analysis to get real-time insights into their performance. It is crucial in maintaining consistent improvement and growth. There are different ways for sales data analysis, but organizations can adopt them to get insights.
Choose an analysis strategy and define company objectives
The first step is to determine what a company wants to achieve with data analysis. Some of the goals can be customer retention, an increase in sales, and posting more campaigns. After defining objectives, the company needs to choose the best analysis strategy. Some of the strategies are tracking total units sold, tracking improved solutions to current challenges, and increasing customer satisfaction rate.
Collect sales data
Companies must gather data from various sources and consolidate it into one central point. The data can be from the sales pipeline, CRM, marketing platforms, and internal data. The data needs to be cleaned to obtain accuracy in sales analytics.
Analyze data
Perform analysis and identify the key items that need attention. It may include the identification of bottlenecks, success of sales activities, lead generation, and CRM functionality. The data should provide a clear view of all sales activities. Sales reps need to have a view of what needs improvement and what has succeeded.
Use data to improve customer retention
From sales analytics reports, it is possible to identify top customers and develop strategies to improve their retention. It is easier to retain customers than to find new ones. Identifying factors that cause high churn rates, such as generic email marketing or poorly timed emails, could help with developing a more engaging email strategy. Paying attention to customers’ habits and patterns could help with upselling and cross-selling products. It could lead to making more targeted product recommendations.
Forecast sales more accurately
Forecasting is the use of sales data to predict future outcomes. It is vital to use accurate factual data when forecasting. Companies can use various techniques for forecasting. They can analyze and use past sales data to predict future outcomes. Another way is to focus on sales patterns and changes in them. They can also consider different variables such as population, GDP and general economic conditions that could affect the future direction of the market. Using these techniques collaboratively could lead to a more accurate sales forecast.
Forecasting can help a company to prepare for future challenges and developments. Projecting potential sales allows for forecasting of revenue. This, in turn, allows for more accurate resource allocation to sales teams.
Fully understand what customers expect
Collecting and analyzing sales data can help companies to understand exactly what customers expect. They can develop a more accurate value proposition. Their value proposition should promise what they will deliver, what customers will gain by choosing them, and why they should choose them over their competitors. Having an accurate value proposition can help them to increase customer loyalty and improve conversion rates.
Offer personalization
Today customers expect personalization. They want to know that they are more than just a source of revenue for a company. Insights into customer behavior from data can lead to more personalization of offers for them. For example, running a predictive model on e-commerce transaction data could help a company to recommend certain products before customers check out to increase sales.
Develop a healthy customer pipeline
It should be easy for companies to track customers through the five stages of the customer journey. Trying to sell products or services to the wrong customers can hurt the company’s bottom line. Analyzing sales data helps with segmenting leads in the pipeline according to the level of engagement and profitability. It is easier to identify any weaknesses in the pipeline and address them. Identifying various details, including demographic details such as location and age, can make it easier for a sales team to know what strategies to use.
Minimize financial losses
Using data analytics can help to guide business decisions and minimize potential loss. Predictive analytics suggest what could happen to a business in response to changes. Prescriptive analytics indicate how a business should respond to changes. For instance, a business could model changes to prices and see how this would affect sales. They could A/B test changes to product offerings and use data analytics tools to check the success of the changes. This could help decision-makers to decide whether it would be worth rolling out the products or not.
Reduce risks
Data analytics can help a company to understand risks and take measures to address them. There are statistical models that can automatically make recommendations on how to deal with various problems. If a company overestimates demand for a product, there are analytics tools they can use to decide on an optimal price for a clearance sale. A company can determine what the risks are of product theft, identity theft etc. and enhance security measures accordingly.
Summary
Analytics in sales provides sales teams with an accurate view of their performance. The teams get insights into leads’ successes and leaks. They understand their strengths and weaknesses, which helps them to plan for future success. It makes it easier to track progress measures against the company’s strategic goals.
