‘How to increase sales?’ is a question that arises in every business, regardless of its size or industry. For me, the answer appears to be fairly self-evident–through robust sales analysis. However, many companies’ sales departments lack such. This leads me to believe that the true value of sales analytics is being overlooked, so allow me to share my thoughts on the subject.
What is the definition of sales analytics?
In a broad sense, sales data analytics is the process of extracting actionable insights from sales-related data in order to identify opportunities to improve sales performance.
We typically classify sales analytics into four categories:
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- Descriptive sales analytics is to draw conclusions from historical sales data gathered from a variety of sources. Its findings assist you in answering questions such as ‘How much revenue did the company generate last quarter?’ or ‘What products/services were the best-selling last month?’
- Diagnostic sales analytics takes it a step further by providing possible explanations for a particular outcome. Thus, after conducting a thorough diagnostic analysis, you may discover that the decline in your quarterly sales was caused by recent Google algorithm updates that impacted the ranking of your web pages in search results and, consequently, your web traffic.
- Predictive analytics utilises advanced technologies such as machine learning and artificial intelligence to mine historical data in order to generate future forecasts.
- Prescriptive sales analytics, which combines the results of the previous analytics types, aims to recommend a specific set of actions to take in order to achieve the desired outcome. For instance, a sales representative may identify an optimal strategy for closing more deals with each customer segment after analysing customer behaviour patterns.
What advantages does sales analytics provide?
Increased visibility into your sales process
With sales analytics, you can boost your sales department’s efficiency and productivity by determining which sales strategies are most effective. Which stages of your sales funnel are the most frequently abandoned? Who is underperforming on your sales team, and why? To see how it works in reality, analyze how one of our clients increased visibility into their sales process through the use of an advanced sales analysis solution.
Increased customer satisfaction
You can use the results of sales analytics to perform in-depth customer segmentation and provide personalised customer service. By analysing your sales, you can also determine which of your customers’ needs are unmet and use this information to enhance customer journeys and leverage up- and cross-selling, thereby laying the groundwork for customer loyalty.
Identified potential growth areas
By analysing your potential customers and churners, sales analytics serves as a vector for future market expansion by assisting you in identifying the reasons why they do not purchase from you. With these analytics results in hand, you’ll be able to make necessary adjustments to your products or services and sales process in order to convert non-customers to paying clients.
The fundamental components of sales analytics
To implement high-quality sales analytics, you’ll need a dedicated solution that includes the following components:
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- Data integration layer – to gather data from internal (CRM, accounting software, website) and external (social media, public data – weather, epidemiological data, survey data) sources for comprehensive sales data analysis.
- Data management layer – to ensure data quality and security.
- Data analysis layer – a synthesis of the various types of data analytics required to meet specific business requirements.
- Analytics outcome layer– to present analytics insights to decision-makers in a visually appealing format (presentations, reports and dashboards).
Below, you may see the examples of sales analytics dashboards we craft for our clients to let them answer any sales-related questions. If you want to learn more about dashboards and see them in action, schedule a demo call with us here.
Successful sales analytics: Some pointers to keep in mind
Adopt an incremental strategy
Building your sales analytics solution does not necessarily mean heavy investments from the start. You may start with basic analytics functionality implemented in the cloud to eliminate the hardware-related costs and reduce the deployment time. Once the business value of sales analytics becomes clear and you have to satisfy the newly arising analytics needs, you may further enhance your solution (adding a robust DWH, predictive analytics, data science, etc.).
Focus on delivering analytics results to business users
You ensure that your business users can get sales analytics results when they are most needed. For that, We recommend you to leverage self-service software such as Power BI or Tableau. Additionally, don’t forget to clearly communicate the introduction of your sales analytics solution through training and solid end-user support to ensure the high level of the solution’s adoption.
Grab the key to your sales growth!
With a sales analytics solution, you’ll be able to see a significant impact on your sales process and its outcomes. However, developing such a solution requires a lot of dedicated efforts – a well-designed implementation strategy, properly chosen tools and right data analytics methodologies in place. If you find these tasks overwhelming, you can always resort to a data analytics vendor and let them back up your sales analytics project. If you need assistance with your sales analytics solution, just book a date with us.
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FAQs About Sales Analytics
Q. What is Sales Analytics?
A. Sales analytics involves analyzing sales data to gain insights for improving sales performance, identifying trends, and forecasting future sales.
Q. How are Sales Analytics Categorized?
A. Sales analytics are categorized into descriptive, diagnostic, predictive, and prescriptive analytics, each offering different levels of insight and action.
Q. What is Descriptive Sales Analytics?
A. Descriptive analytics draws conclusions from historical sales data to answer questions about past sales performance.
Q. How does Diagnostic Sales Analytics Differ from Descriptive?
A. Diagnostic analytics delves deeper than descriptive, explaining reasons behind specific sales outcomes or trends.
Q. What is Predictive Sales Analytics?
A. Predictive analytics uses machine learning and AI to analyze past data and forecast future sales trends.
Q. What is the Role of Prescriptive Sales Analytics?
A. Prescriptive analytics combines insights from other analytics types to recommend specific sales actions and strategies.
Q. What are the Benefits of Implementing Sales Analytics?
A. Benefits include increased visibility into sales processes, improved customer satisfaction, and the identification of potential growth areas.
Q. How Does Sales Analytics Improve Customer Satisfaction?
A. By providing personalized service and identifying unmet customer needs, sales analytics can enhance customer journeys and loyalty.
Q. What are the Key Components of a Sales Analytics Solution?
A. Key components include a data integration layer, data management layer, data analysis layer, and an outcome layer for presenting insights.
Q. What Should Businesses Consider When Implementing Sales Analytics?
A. Businesses should adopt an incremental strategy, focus on delivering analytics to business users, and consider partnering with a data analytics vendor for specialized support.
