Power BI sales dashboard: A 6-step guide to better sales insights
Nathan Piccini
| February 13, 2023

Are you geared to create a sales dashboard on Power BI and track key performance indicators to drive sales success? This step-by-step guide will show you through connecting to the data source, build the dashboard, and add interactivity and filters.

Creating a sales dashboard in Power BI is a straightforward process that can help your sales team to track key performance indicators (KPIs) and make data-driven decisions. Here’s a step-by-step guide on how to create a sales dashboard using the above-mentioned KPIs in Power BI: 

sales dashboard on Power BI 
Creating a sales dashboard on Power BI – Data Science Dojo

Step 1: Connect to your data source 

The first step is to connect to your data source in Power BI. This can be done by clicking on the “Get Data” button in the Home ribbon, and then selecting the appropriate connection type (e.g., Excel, SQL Server, etc.). Once you have connected to your data source, you can import the data into Power BI for analysis. 

Step 2: Create a new report 

Once you have connected to your data source, you can create a new report by clicking on the “File” menu and selecting “New” -> “Report.” This will open a new report canvas where you can begin to build your dashboard. 

Step 3: Build the dashboard 

To build the dashboard, you will need to add visualizations to the report canvas. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen, and then selecting the appropriate visualization type (e.g., bar chart, line chart, etc.). Once you have added a visualization to the report canvas, you can use the “Fields” pane on the right-hand side to add data to the visualization. 

Read more about maximizing sales success with dashboards by clicking on this link.

Step 4: Add the KPIs to the dashboard 

To add the KPIs to the dashboard, you will need to create a new card visualization for each KPI. Then, use the “Fields” pane on the right-hand side of the screen to add the appropriate data to each card. 

Sales Revenue:

To add this KPI, you’ll need to create a card visualization and add the “Total Sales Revenue” column from your data source. 

Sales Quota Attainment:

To add this KPI, you’ll need to create a card visualization and add the “Sales Quota Attainment” column from your data source. 

Lead Conversion Rate:

To add this KPI, you’ll need to create a card visualization and add the “Lead Conversion Rate” column from your data source. 

Customer Retention Rate:

To add this KPI, you’ll need to create a card visualization and add the “Customer Retention Rate” column from your data source. 

Average Order Value:

To add this KPI, you’ll need to create a card visualization and add the “Average Order Value” column from your data source. 

Step 5: Add filters and interactivity 

Once you have added all the KPIs to the dashboard, you can add filters and interactivity to the visualizations. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen and selecting the appropriate filter or interactivity option. For example, you can add a time filter to your chart to show sales data over a specific period, or you can add a hover interaction to your diagram to show more data when the user moves their mouse over a specific point.

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Step 6: Publish and share the dashboard 

Once you’ve completed your dashboard, you can publish it to the web or share it with specific users. To do this, click on the “File” menu and select “Publish” -> “Publish to Web” (or “Share” -> “Share with specific users” if you are sharing the dashboard with specific users). This will generate a link that can be shared with your team, or you can also publish the dashboard to the Power BI service where it can be accessed by your sales team from anywhere, at any time. You can also set up automated refresh schedules so that the dashboard is updated with the latest data from your data source.

Ready to transform your sales strategy with a custom dashboard in Power BI?

By creating a sales dashboard in Power BI, you can bring all your sales data together in one place, making it easier for your team to track key performance indicators and make informed decisions. The process is simple and straightforward, and the end result is a custom dashboard that can be customized to fit the specific needs of your sales team.

Whether you are looking to track sales revenue, sales quota attainment, lead conversion rate, customer retention rate, or average order value, Power BI has you covered. So why wait? Get started today and see how Power BI can help you drive growth and success for your sales team! 

Maximizing sales success with dashboards: Understanding its importance
Nathan Piccini
| January 28, 2023

Dashboarding has become an increasingly popular tool for sales teams, and good reason. A well-designed dashboard can help sales teams to track key performance indicators (KPIs) in real-time, which can provide valuable insights into sales performance and help teams to make data-driven decisions.

In this blog post, we’ll explore the importance of dashboarding for sales teams, and highlight five KPIs that every sales team should track. 


Sales revenue:

This is the most basic KPI for a sales team, and it simply represents the total amount of money generated from sales. Tracking sales revenue can help teams to identify trends in sales performance and can be used to set and track sales goals. It’s also important to track sales revenue by individual product, category, or sales rep to understand the performance of different areas of the business. 

Sales quota attainment:

Sales quota attainment measures how well a sales team performs against its goals. It is typically expressed as a percentage and is calculated by dividing the total sales by the sales quota. Tracking this KPI can help sales teams to understand how they are performing against their goals and can identify areas that need improvement. 


Read more about: Data science to boost eCommerce sakes


Lead conversion rate:

The lead conversion rate is a measure of how effectively a sales team is converting leads into paying customers. It is calculated by dividing the number of leads that are converted into sales by the total number of leads generated. Tracking this KPI can help sales teams to understand how well their lead generation efforts are working and can identify areas where improvements can be made. 


Customer retention rate:

The customer retention rate is a measure of how well a company is retaining its customers over time. It is calculated by dividing the number of customers at the end of a given period by the number of customers at the beginning of that period, multiplied by 100. By tracking customer retention rate over time, sales teams can identify patterns in customer behavior, and use that data to develop strategies for improving retention.  


Average order value:

Average order value (AOV) is a measure of the amount of money a customer spends on each purchase. It is calculated by dividing the total revenue by the total number of orders. AOV can be used to identify trends in customer buying behavior and can help sales teams to identify which products or services are most popular among customers. 

All these KPIs are important for a sales team as they allow them to measure their performance and how they are doing against the set goals.

Sales revenue is important to understand the total money generated from sales, sales quota attainment gives a measure of how well the team is doing against their set targets, lead conversion rate helps understand the effectiveness of lead generation, the customer retention rate is important to understand the patterns of customer behavior and the average order value helps understand which products are most popular among the customers. 


Read about: Big data problem, its impact, and a solution for it


All of these KPIs can provide valuable insights into sales performance and can help sales teams to make data-driven decisions. By tracking these KPIs, sales teams can identify areas that need improvement, and develop strategies for increasing sales, improving lead conversion, and retaining customers.

A dashboard can be a great way to visualize this data, providing an easy-to-use interface for tracking and analyzing KPIs. By integrating these KPIs into a sales dashboard, teams can see a clear picture of performance in real time and make more informed decisions. 


Take data-driven decisions today with creative dashboards!

In conclusion, dashboarding is an essential tool for sales teams as it allows them to track key performance indicators and provides a clear picture of their performance in real time. It can help them identify areas of improvement and make data-driven decisions. Sales revenue, sales quota attainment, lead conversion rate, customer retention rate, 

A Data Science approach to boost eCommerce sales
Tim Robinson
| December 28, 2022

Every eCommerce business depends on information to improve its sales. Data science can source, organize and visualize information. It also helps draw insights about customers, marketing channels, and competitors.


Every piece of information can serve different purposes. You can use data science to improve sales, customer service, user experience, marketing campaigns, purchase journeys, and more.


How to use Data Science to boost eCommerce sales

Sales in eCommerce depend on a variety of factors. You can use data to optimize each step in a customer’s journey to gain conversions and enhance revenue from each conversion.

Analyze Consumer Behavior

Data science can help you learn a lot about the consumer. Understanding consumer behavior is crucial for eCommerce businesses as it dictates the majority of their decisions.


Consumer behavior analysis is all about understanding the relationship between things you can do and customers’ reactions to them. This analysis requires data science as well as psychology. The end goal is not just understanding consumer behavior, but predicting it.


For example, if you have an eCommerce store for antique jewelry, you will want to understand what type of people buy antique jewelry, where they search for it, how they buy it, what information they seek before purchasing, what occasions they buy it for, and so on.



buyer journey
Buyer journey using different platforms – Source


You can extract data on consumer behavior on your website, social media, search engines, and even other eCommerce websites. This data will help you understand customers and predict their behavior. This is crucial for audience segmentation.


Data science can help segment audiences based on demographics, characteristics, preferences, shopping patterns, spending habits, and more. You create different strategies to convert audiences of different segments.


Audience segments play a crucial role in designing purchase journeys, starting from awareness campaigns all the way to purchase and beyond.


Optimize digital marketing for better conversion

You need insights from data analytics to make important marketing decisions. Customer acquisition information can tell you where the majority of your audience comes from. You can also identify which sources give you maximum conversions.


You can then use data to improve the performance of your weak sources and reinforce the marketing efforts of high-performing sources. Either way, you can ensure that your marketing efforts are helping your bottom line.


Once you have locked down your channels of marketing, data science can help you improve results from marketing campaigns. You can learn what type of content or ads perform the best for your eCommerce website.


Data science will also tell you when the majority of your audience is online on the channel and how they interact with your content. Most marketers try to fight the algorithms to win. But with data science, you can uncover the secrets of social media algorithms to maximize your conversions.


Suggest products for upselling & cross-selling

Upselling & Cross-selling are some of the most common sales techniques employed by ecommerce platforms. Data science can help make them more effective. With Market Basket or Affinity Analysis, data scientists can identify relationships between different products. 


By analyzing such information of past purchases and shopping patterns you can derive criteria for upselling and cross-selling. The average amount they spend on a particular type of product tells you how high you can upsell. If the data says that customers are more likely to purchase a particular brand, design, or color; you can upsell accordingly. 



Related products recommendations
Related products recommendations – Source


Similarly, you can offer relevant cross-selling suggestions based on customers’ data. Each product opens numerous cross-selling options.


Instead of offering general options, you can use data from various sources to offer targeted suggestions. You can give suggestions based on individual customers’ preferences. For instance, A customer is more likely to click on a suggestion saying “A Red Sweater to go with your Blue Jeans’ ‘ if their previous purchase shows an inclination for the color red.


This way data science can help increase probability of upsold & cross-sold purchases so that eCommerce businesses get more revenue from their customers.

Analyze consumer feedback

Consumers provide feedback in a variety of ways, some of which can only be understood by learning data science. It is not just about reviews and ratings. Customers speak about their experience through social media posts, social shares, and comments as well.

Feedback data can be extracted from several places and usually comes in large volumes. Data scientists use techniques like text analytics, computational linguistics, and natural language processing to analyze this data.

data visualization dashboard
Data visualization dashboard – Source


For instance, you can compare the percentage of positive words and negative words used in reviews to get a general idea about customer satisfaction.


But feedback analysis does not stop with language. Consumer feedback is also hidden in metrics like time spent on page, CTR, cart abandonment, clicks on page, heat maps and so on. Data on such sublime behaviors can tell you more about the customer’s experience with your eCommerce website than reviews, ratings and feedback forms.


This information helps you identify problem areas that cause your customers to turn away from a purchase.

Personalize customer experience

To create a personalized experience, you need information about the customer’s behavior, previous purchases, and social activity. This information is scattered across the web, and you need lessons in data science to bring it to one place. But, more importantly, data science helps you draw insights from information.


With this insight you can create different journeys for different customer segments. You utilize data points to map a sequence of options that would lead a customer to conversion. 80% customers are more likely to purchase if the eCommerce website offers a personalized experience.


For example: Your data analytics say that a particular customer has checked out hiking boots but has abandoned most purchases at the cart. Now you can focus on personalizing this customer’s experience by focusing on cart abandonment issues such as additional charges, postage shipping cost, payment options etc.


Several eCommerce websites use data to train their chatbots to serve as personal shopping assistants for their customers. These bots use different data points to give relevant shopping ideas.


You can also draw insights from data science to personalize offers, discounts, landing pages, product gallery, upselling suggestions, cross-selling ideas and more. 

Use data science for decision making & automation

The information provided by data science serves as the foundation for decision-making for eCommerce businesses. In a competitive market, a key piece of information can help you outshine your competitors, gain more customers and provide a better customer experience.

Using data science for business decisions will also help you improve the performance of the company. An informed decision is always better than an educated guess.

Related Topics

Machine Learning
Generative AI
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Development and Operations
Data Visualization
Data Security
Data Science
Data Engineering
Data Analytics
Computer Vision
Artificial Intelligence

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