‘How to increase sales?’ is the question that appears on the agenda in every company regardless of its size and the industry it operates in. For me, the answer seems to be rather obvious – with robust sales analysis. But still, sales departments of many companies don’t have such. This makes me think that the real value of sales analytics is underestimated, so let me share my thoughts about the issue.
In the general sense, sales data analytics is a process of generating actionable insights out of sales-related data to find ways to boost sales performance.
At Pine Analytical, we usually define 4 types of sales analytics:
- Descriptive sales analysis aims at interpreting historical sales data collected from a variety of sources to draw conclusions. Its results help you answer such questions as ‘What were the company’s total sales last quarter?’ or ‘What products/services were best-selling last month?’
- Diagnostic sales analysis makes one step further as its results offer you possible reasons for a certain outcome. Thus, after conducting some thorough diagnostic analysis, you may find out that the decrease in your quarterly sales was connected with the recent updates in a Google algorithm, which affected your web pages ranking in search results and, consequently, your web traffic.
- Predictive analytics serves to mine historical data to produce forecasts for the future, and it can be conducted with such advanced technologies as machine learning and artificial intelligence. To get an example of this sales analytics type, explore one of Pine Analytical’s projects, in which our experts helped a dairy manufacturer receive an accurate sales forecast with data science.
- Prescriptive sales analytics, combining the results of all of the above analytics types, aims at recommending a particular set of actions to take to gain a desirable outcome. For example, after analyzing customer behavior patterns, a sales rep sees an optimal strategy to close more deals with each customer segment.
With sales analytics, you can increase the efficiency and productivity of your sales department by finding answers to such questions as: What sales strategies are working best? What stages of your sales funnel are abandoned most often? Who out of your sales team is underperforming, and why? To see how it works in real life, have a look at how one of ScienceSoft’s clients gained visibility into their sales process with a solution for advanced sales analysis.
You can use sales analytics results to conduct profound customer segmentation and deliver personalized customer service. By analyzing your sales, you can also identify what of the customers’ needs are unmet and apply this knowledge to improve customers’ journeys and leverage up-selling and cross-selling, thus laying the groundwork for building customer loyalty.
Sales analytics serves as a vector of your future market expansion by analyzing your potential customers and churners, which helps you identify the reasons why they don’t buy from you. With such analytics results on board, you’ll be able to adjust your products or services and the sales process accordingly so that to convert non-customers into paying clients.
To start with quality sales analytics, you need a dedicated solution with the following components:
- Data integration layer – to collect data from internal (CRM, accounting software, website) and external data sources (social media, public data – weather, epidemiological data, survey data) for all-rounded sales data analysis.
- Data management layer – to ensure high data quality and data security.
- Data analysis layer – the combination of the required data analytics types to suit particular business needs.
- Analytics outcomes layer – to deliver analytics insights to decision-makers in a suitable visual 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 dig deeper and see dashboarding in action, feel free to watch our BI demo.
Follow an incremental approach
Building your sales analytics solution does not necessarily mean heavy investments form 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 have to ensure that your business users can obtain sales analytics results when they are most needed. For that, I 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 drop us an email at Info@pineanalytical.com.