Revealing the Power Sales Analytics - PerfectionGeeks
Revealing the Power Sales
April 28, 2022 11:00 AM
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Revealing the Power Sales Analytics - PerfectionGeeks
April 28, 2022 11:00 AM
No matter how big or small a business is, sales are always a top priority.
We believe the solution is obvious: leveraging PowerBI Sales Analytics. The majority of companies, however, do not adopt this approach in their sales departments.
We believe sales analysis is underrated. We'll share our views on the subject in this blog.
This is an overview of what we'll talk about:
Let's get started!
Sales analytics, in simple terms, is a process that provides actionable and detailed insight into the sales data of your company. These insights can be used to help you identify ways to improve the sales performance of your company.
This type of analysis focuses on the translation of historical sales data from a wide range of sources. These data can help answer questions such as "Which products or services saw the highest number of sales in the last month?" or "What was the company's gross revenue in the past quarter span.
This analysis is a more advanced version of the one before it, as its conclusions can help you to identify possible reasons for different outcomes. After analyzing your data, you may discover that the decline in sales over the past quarter could be linked to Google's algorithm changes. This is how your website rank fell in search results, further affecting the online traffic.
You can use the latest technologies such as Artificial Intelligence and Machine Learning to perform predictive sales performance. This allows you to uncover historical sales data and generate future projections. Brainvire has helped many businesses to make accurate sales predictions using data analysis.
Prescriptive analytics, which combines the results from all the sales analytics types mentioned, focuses on the delivery of a set of steps that will help you achieve desired results. Your sales rep may be able to identify a strategy that will help you close more deals in each segment by analyzing your customer's behavior patterns.
Sales analytics can help you find answers to many questions such as
These questions will help you to improve the performance and productivity of your sales department.
Use the sales analytics results to segment your customers and provide a personalized customer experience.
You can also evaluate your sales data to find out what customers need and then work to improve your customer's buying experience. You can not only take full advantage of cross- and upselling, but you also create a foundation for customer loyalty.
Sales data analytics can open up new avenues for expansion. It allows you to evaluate your potential customers and churners, as well as identify why they haven't purchased from you.
With these results, you can modify your sales process to convert non-buying clients into customers.
To get started with high-quality sales data analytics, you will need a robust solution that includes the following elements:
It doesn't necessarily mean that you have to invest a lot of money to develop sales data analytics solutions. To reduce launch time and eliminate equipment-related costs, you could start with the basic functionalities integrated into the cloud.
Once you have determined the value of your sales data analytics, and what you need to do in the future, you can start to enhance the solutions. You might add predictive sales analytics, data science, robust DWH, or data science.
You don't have to invest a lot of money to build your sales analytics solution. To reduce hardware costs and speed up deployment, you can start with basic analytics functionality that is cloud-based. Once you understand the business value of your sales analytics and have satisfied the new analytics needs, you can further enhance your solution by adding robust DWH or predictive analytics to the mix.
This will ensure that business users have access to sales analytics results whenever they are needed. Self-service software like Tableau or Power BI is a good option. To ensure high adoption, communicate the introduction of your sales analysis solution via training and solid support for end-users.
A sales analytics solution can make a huge difference in your sales process and outcomes. But, creating such a solution takes a lot of dedication - well-designed implementation strategies, the right tools, and the right data analytics methodologies. These tasks can seem overwhelming. You can always turn to a vendor for data analytics and have them assist with your sales analytics project. Book a meeting with us if you need help with your sales analytics project.
It is important that your users can conclude sales data analytics when necessary. We recommend Tableau and Power BI as tools to accomplish this.
Make sure you communicate the launch of sales analytics via training and support. This will ensure that your solution is adopted by high-ranking executives.
Innovative companies are using data analytics and artificial intelligence to increase the value of B2-B sales. They are producing remarkable results in lead generation and people management.
Companies already use historical market data to get a complete view of the area's potential sales opportunities. Companies are taking this idea further by creating lead-scoring algorithms that use granular and detailed data on each prospect. To get a 360-degree view of the customer, rich data from external sources such as news reports and social media are combined with internal data about the customer's history. These algorithms can predict which factors will influence lead conversion and help guide sales strategy. One IT services company used big-data analytics for predictive modeling to determine which leads would close. It found that established companies are more likely to be closed than start-ups. It was able to increase its overall conversion rate by 30% by focusing its attention on established businesses.
Traditional sales planning relies on account segmentation. This is often based more on historical local knowledge than current facts. This results in sales models becoming less effective over time and more inconsistent globally, while resources are not properly allocated to accounts that require different sales strategies (e.g. grow versus maintain span>).
But, sales-operations teams can quickly make resource allocation much more efficient by using basic analytics for sales planning. High-tech companies used a product-level and account-level approach in realigning their US coverage model. The sales team saw a 5-10% increase in productivity and a two-thirds reduction in planning time.
Complex product portfolios can make it difficult for companies to find solutions that meet specific customer requirements. Although salespeople can rely on simple decision-making rules, this can lead to lengthy interactions that often result in missed opportunities to sell related products.
Many B2B companies use next-product to-buy algorithms that are based on similar customer purchases. One logistics company used historical ordering patterns to find cross-sell opportunities in its customer base and built micro-campaigns based on those opportunities. The company was able to increase its revenues fivefold simply by identifying underserved customers.
This approach helps customers stay loyal. Customers at high risk of leaving the company for another one must be identified early to avoid dissatisfaction. These kinds of problems are well suited for machine-learning algorithms' pattern recognition skills. For example, the marketing-analytics department at a global chemical company wanted to decrease its SME customer turnover. A predictive model was built using more than 30 variables. It identified 10 key factors that drove customers away. The team was shocked to discover that the most important 15% of its customers were three times more likely than other customers to buy elsewhere. The key finding was that customers who have more products are less likely to leave. Cross-selling was more important than price changes and was a better driver of customer loyalty. Each regional sales manager quickly identified at-risk customers and guided how to engage them to keep them loyal. The company saw these insights and reduced churn by 25 percent.
B2B sellers used to rely heavily on their experience when making pricing decisions. However, purchasing teams became smarter and began to develop their pricing tools. This put sales teams in jeopardy.
Sales analytics can have a significant impact on not only the sales process but also its results. But, to introduce it into your business, you will need to have a well-structured execution strategy and the right tools.
These tasks can seem overwhelming so you might consider hiring a professional sales analytics company. Reach PerfectionGeeks Technologies today if you need help with this solution for your business!