Sales Analytics: Revealing the Power of Data in Sales
What is sales analytics?
Sales data analytics, in its broadest meaning, is the process of extracting actionable insights from sales-related data in order to improve sales performance.
We commonly identify four categories of sales analytics:
- Descriptive sales analysis seeks to extract insights from historical sales data gathered from a range of sources. Its findings can assist you answer questions like 'How much did the company make in total sales last quarter?' or 'What were the best-selling products/services last month?'
- Diagnostic sales analysis takes it a step further by revealing possible causes for a certain outcome. Thus, after doing a thorough diagnostic study, you may discover that the drop in your quarterly sales was caused by recent changes in a Google algorithm, which influenced the ranking of your web pages in search results and, as a result, your online traffic.
- Predictive analytics uses advanced technologies like machine learning and artificial intelligence to mine previous data and provide future forecasts. To get an example of this type of sales analytics, in which our specialists used data science to help a dairy manufacturer acquire an accurate sales prediction and data analytics services.
- Prescriptive sales analytics seeks to prescribe a specific set of activities to follow in order to achieve a desired outcome by incorporating the results of all of the aforementioned analytics kinds. A sales rep, for example, may discover an effective technique for closing more deals with each customer category after evaluating consumer behavior patterns.
Gaining a better understanding of your sales process
You can improve the efficiency and productivity of your sales department by using sales analytics to answer questions like: What sales methods are the most effective? Which steps of your sales funnel are the most frequently abandoned? Who is underperforming in your sales team, and why? Take a look at data analytics solutions gained visibility into their sales process with a solution for advanced sales analysis to see how it works in practice.
Increased client satisfaction
You can use the results of sales analytics to do deep client segmentation and provide individualized customer support. By evaluating your sales, you may figure out which of your customers' demands aren't being satisfied and use that information to optimize customer journeys and leverage up-selling and cross-selling, creating the framework for customer loyalty.
Opportunities for growth have been identified
By studying your potential consumers and churners, sales analytics serves as a vector for future market expansion, allowing you to determine the reasons why they don't buy from you. You'll be able to change your products or services, as well as the sales process, based on the analytics data, in order to convert non-customers into paying clients.
The fundamentals of sales analytics
You'll need a specific solution with the following components to get started with great sales analytics:
- Data integration layer – collects data from internal (CRM, accounting software, website) and external (social media, public data – weather, epidemiological data, survey data) sources for a comprehensive sales analysis.
- To maintain excellent data quality and data security, there is a data management layer.
- Data analysis layer - a collection of needed advanced data analytics solutions kinds tailored to specific business requirements.
- Analytics outcomes layer — to communicate analytics findings to decision-makers in a clear and understandable manner (presentations, reports and dashboards). Examples of sales analytics dashboards that we create for our clients to help them answer any sales-related inquiries may be found below. If you want to learn more about dashboarding and see it in action, check out our BI demo.
Success in sales analytics: Some ideas to keep in mind
Take a step-by-step strategy
Investing heavily in your sales analytics system does not have to be the case from the outset. To remove hardware-related costs and shorten implementation time, start with basic analytics capability implemented in the cloud. When the commercial benefit of sales analytics becomes obvious and you need to meet new analytics requirements, you can improve your solution (adding a robust DWH, predictive analytics, data science, etc.).
Focus on delivering analytics results to business users
You must guarantee that your sales analytics results are available when they are most needed by your business users. I propose using self-service software such as Power BI or Tableau for this. Also, remember to effectively describe the introduction of your sales analytics solution through training and strong end-user support to achieve a high level of acceptance.
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