Summary The sales team at commercial laundry and linen rental organization could open any record in Dynamics 365 and still could not answer how much of the open book sat in healthcare. Sector was never a field on a deal, it was four boolean flags on the product, three joins away from the quote a manager wanted to group. We replaced the standard sales dashboards with Custom Sales Dashboard that reads the Dataverse Web API directly, rolls sector up through the line items, and puts leads, opportunities, quotes and signed contracts under a single set of filters. Every tile, cell and chart segment opens the records behind it and exports to Excel with real numbers and real dates. Table of Contents Summary→ Business Challenges→ Solution Overview→ Technical Approach→ Impact→ Conclusion→ Get in touch→ Business Challenges Commercial laundry and linen rental organization runs multiple laundries, called plants in the CRM, and quotes work by the weight a customer sends each week. A deal is a weekly tonnage and a weekly dollar figure before it is anything else, which is why the standard sales charts had so little to say about it. The reporting asks were ordinary. The data model underneath them was not, and every ask broke on a different part of it. Sector is not stored on the lead, opportunity, quote or order. It is independent flags on the product record: healthcare, motel, garments, etc. A chart grouped on the quote has no column to group by A single product can carry more than one flag, so one quote can legitimately belong to two sectors. Grouping without a rule double counts the same dollars Total Amount reads 0.00 on every quote and every order in the org. All money sits in annualrevenue and weeklyrevenue, which the standard sales charts and rollups do not read Contracts are salesorder records. The out of the box contract table is empty, so anything pointed at it returns nothing at all The plant lookup laundry is populated on roughly six quotes in ten. The rest only know their laundry through the originating opportunity Orders copied from an accepted quote frequently carry no line items of their own, so a product level report built on order lines quietly loses them Managers wanted one choice of sector, sales person or plant to move leads, opportunities, quotes and contracts together. Standard dashboards filter each chart against its own entity Nobody could see the forward book: which signed contracts start in the next twenty weeks, and what weekly tonnage each start week brings ⚠The real blocker Every one of these needs a value computed from grandchild rows, from the quote down to its lines down to the product flags. The chart designer groups on fields that exist on the record being charted, and this one does not exist anywhere. Solution Overview We built one page that lives inside the CRM as a web resource. It opens like any other dashboard, signs the user in with the session they already have, and reads live records rather than a nightly copy. The page carries six tabs. Overview holds a pipeline matrix and the stage, sector, sales person and plant mix. Pipeline Activity plots leads and quotes created per week for the last fifteen weeks. Contracts Won shows the forward book by contract start week. Win and Loss compares closed won against closed lost by value and by count. Approvals and Activities measures how long contract, logistics and freight rate decisions take. Reports lists contracted and quoted stock at product line level. Three filters at the top, sector, sales person and plant, drive every chart, tile and table on every tab at once A fourth filter, customer, appears only on the Reports tab because it only narrows those two tables Clicking any chart segment, matrix cell or tile opens a drawer listing the records behind that exact number Each row in the drawer links to the real CRM form, and the whole selection exports to Excel with numbers as numbers and dates as dates ⓘWhy the drill through mattered more than the charts A sales manager who cannot see the eleven contracts behind a bar will not act on the bar. Registering the source records for every visual was the difference between a picture and a working tool. Technical Approach Where the page runs The dashboard is a single HTML web resource, customDashboard, shipped in the main solution. Running inside the model driven app means it inherits the user’s authentication and their record level security, so a sales person sees their own scope without us writing a line of security code. It resolves the org URL from parent.Xrm.Utility.getGlobalContext() and falls back to window.location.origin when it is opened outside a form, which is what makes local debugging possible. SourceDataverse Web API v9.2Live reads with the signed in user’s context Load14 parallel queriesOne Promise.all on page open IndexLookup maps and sector indexProduct flags resolved once, then cached RenderMatrix, tiles and Chart.jsRedrawn in memory on every filter change ActDrawer and XLSX exportRecords behind the number, typed for Excel Everything loads once. Accounts, factories, depots, leads, opportunities, quotes, orders, the three line item tables, products, freight rates, delivery point risk assessments and activities all come down in a single Promise.all, and every later interaction is a filter over arrays already in memory. Changing a filter costs nothing on the network. Rolling sector up from the product The rollup walks the line items of a quote, order or opportunity, reads the flags off each product, and weights each sector by line weight times quantity. The heaviest sector becomes the primary, which is what the breakdown charts group on so the totals stay additive. The full list is kept separately and is what the sector filter tests against, so a quote that touches healthcare and motel appears under both filters and is counted once in the donut. cf_customDashboard.html, sector rollupjavascript function rollupSectors(lines, productIdField) { const weight = {}; lines.forEach(l => { const sectors = PRODUCT_SECTORS[String(l[productIdField] || ”).toLowerCase()] … Continue reading How an Australia-Based Commercial Laundry Company Built a Custom Dynamics 365 Sales Dashboard Beyond Standard Reporting →

In today’s fast-paced business landscape, alignment between sales teams and overall business objectives is crucial for success. However, many organizations struggle with fragmented communication, misaligned goals, and inefficient decision-making. This is where sales reporting plays a transformative role. By leveraging accurate and real-time data, businesses can ensure that every department—from sales to marketing to finance—is working towards a unified vision. The Importance of Sales Reporting in Business Alignment Sales reporting is more than just tracking revenue—it’s a strategic tool that helps businesses: How Sales Reporting Aligns Teams 1. Data-Driven Goal Setting Sales reporting provides clear benchmarks for teams to measure performance. By using historical data, businesses can set realistic sales targets that align with revenue goals, ensuring that every department contributes to overall growth. 2. Transparency and Accountability When all departments have access to sales performance metrics, it promotes accountability. For example, if a sales team struggles with conversions, marketing can adjust its lead generation strategies accordingly. This ensures that teams are not working in silos but rather as a cohesive unit. 3. Optimizing Sales Strategies Regular sales reports highlight which products or services are performing well and which need improvement. Sales managers can use these insights to refine sales pitches, adjust pricing strategies, or reallocate resources to high-performing areas. 4. Customer Insights for Better Engagement Sales reports provide valuable data on customer behavior, preferences, and buying patterns. This enables teams to personalize their approach, leading to higher customer satisfaction and increased retention rates. For example: A mid-sized SaaS company struggling with declining sales implemented real-time sales dashboards to track performance across multiple teams. By analyzing the data, they: Example 1: CRM Dashboard for Sales Performance Analysis A CRM Dashboard, like the one shown below, helps businesses track critical sales metrics: By leveraging such dashboards, companies can make data-driven decisions, enhance collaboration, and ultimately align sales efforts with overarching business goals. Example 2: Sales and Brand Performance Dashboard Another example of effective sales reporting is a Sales and Brand Performance Dashboard, which provides: This level of visibility ensures that sales, marketing, and finance teams are working towards common business objectives, optimizing resources, and increasing profitability. To Conclude, sales reporting is not just about numbers—it’s about aligning teams with business goals to drive success. If your business is looking to improve sales performance, start by implementing data-driven reporting tools to enhance collaboration, optimize strategies, and achieve long-term growth. Want to learn more about how sales reporting can transform your business? Get in touch with us today for consultation! We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

In today’s fast-paced business landscape, data-driven decision-making is essential for project success. Organizations must navigate vast amounts of data and determine which reports provide the most valuable insights. Effective project oversight relies on selecting the right reports that align with business objectives, operational efficiency, and strategic growth. The Importance of Data-Driven Oversight Data-driven project oversight ensures that organizations make informed decisions based on real-time and historical data. It enhances accountability, improves resource allocation, and mitigates risks before they become significant issues. The key to success lies in choosing reports that offer relevant, actionable insights rather than being overwhelmed by excessive, unnecessary data. Identifying the Right Reports for Your Business 1. Define Your Business Objectives Before selecting reports, clarify your project goals. Are you monitoring financial performance, tracking project timelines, evaluating team productivity, or assessing risk factors? Each objective requires different metrics and key performance indicators (KPIs). 2. Categorize Reports Based on Project Needs Reports can be categorized into various types based on their function: 3. Leverage Real-Time and Historical Data A balanced mix of real-time dashboards and historical trend analysis ensures a comprehensive understanding of project performance. Real-time reports help in immediate decision-making, while historical data provides context and trends for long-term strategy. 4. Customize Reports to Stakeholder Needs Different stakeholders require different levels of detail. Executives may prefer high-level summaries, while project managers need granular insights. Tailoring reports ensures that each stakeholder receives relevant and actionable information. 5. Automate and Visualize Reports for Better Insights Leveraging automation tools can streamline report generation and reduce human error. Data visualization tools such as Power BI, Tableau, or built-in reporting features in project management software can enhance comprehension and decision-making. Real-World Examples of Data-Driven Reports To illustrate the importance of selecting the right reports, here are two examples: 1. Return Management Dashboard This dashboard provides an overview of product returns, highlighting trends in return reasons, active cases, and return processing efficiency. By analyzing such reports, businesses can identify common product issues, improve quality control, and streamline return processes. 2. Billable Allocation Report This report tracks resource allocation in a project, helping businesses monitor utilization rates, availability, and forecasting staffing needs. By using such reports, companies can optimize workforce planning and reduce underutilization or overallocation of resources. To conclude, selecting the right reports for project oversight is crucial for achieving business success. By aligning reports with business objectives, categorizing them effectively, leveraging both real-time and historical data, and customizing insights for stakeholders, organizations can enhance efficiency and drive strategic growth. A well-structured reporting framework ensures that project oversight remains proactive, insightful, and results driven. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

Introduction: Azure Machine Learning Studio is an amazing tool that lets us create efficient ML experiments with simple drag and drop features. We can predict anything from Flight Predictions to Churn Analysis. But what if we want to represent this predicted data a more visually appealing format? Well it is possible to do this by representing your predictions on Power BI! Pre-Requisites: Basic Understanding of Azure Machine Learning Studio. Basic Understanding of Power BI. A Blob Container created on Azure Storage.   Steps: Create your Azure Machine Learning Experiment on Azure Machine Learning Studio. Convert your Training Experiment to a Predictive Experiment and Deploy it as a Web Service. We will create a Console application in Visual Studio and copy paste the code inside Batch Execution. For automation we can create automated data pipelines but for now we will just use a simple Console application. Remove the existing code from the Console Application and copy paste the Batch Execution code. Install the necessary Nuget Packages and also update the following parameters. – BaseURL will be the same. – Storage Account Name, Storage Account Key and Storage Container Name will be parameters that can be found in your Azure Blob Storage which was created. – Api Key can be found in the Web Experiment Page in Azure Machine Learning Studio. – The input path is the path where you have saved your input csvfile for Batch Execution. Your Input csv file should have all the features which you have used to train your experiment After you run your Console application a new output1results.csv file should get generated in your Blob Container. The output results should include the labels which your experiment generates in it’s output. It should include the Scored Labels and Scored Probabilities labels as well. Now you can get your data using Azure Blob Storage as your source in Power BI and use the columns in the output1result.csv file to generate your ML Predicted Reports. The Report can look something like this. I hope this blog helps you to combine Azure Machine Learning Studio and Power BI to create a powerful predictive solution.