Monday, November 5, 2018

Case Study: Empowering Leadership with Better Sales Reporting

Key Challenges

   Create a cloud-based unified business intelligence engine to consolidate sales data from disparate sources.
   Create a reporting system that tracks sales performance across multiple sales programs.
   Create a report that shows a unified overview of sales performance with key metrics tailored to the leadership team.

A Comprehensive View for Leadership

For growing companies, maintaining alignment between sales teams and leadership is a challenge. As growth increases, divisions tend to focus on individual products or specialized markets, and teams become segmented. Sales information becomes increasingly disparate, and leadership often struggles to keep track of sales metrics.

Our client, the internal sales team of a large software provider, approached us with an aging sales metric tracking system. The sales team consisted of specialists trained to sell an array of commercial software products. The company tracked sales metrics using multiple Excel worksheets, and due to customizations, the worksheet data was often inconsistent. Entering the data was cumbersome. With the legacy system, the leadership team could not track metrics to manager and seller levels. Worse yet, there was no unified view of operational performance, impairing the leadership team’s ability to accurately gauge sales performance.

Our Process

The sales team initially contacted us to build a single Power BI report. The team was so pleased with the report that when they decided to consolidate their vendor projects, they turned to us to build a cloud-based unified business intelligence engine. The result was a suite of eight reports driven by an array of Azure services. We created one report for each of the seven programs the sales team managed. The final report provided a unified overview of the programs, allowing the client’s leadership team to evaluate sales performance at a glance.

For us, the first step of any implementation is conducting close brainstorming sessions with the client to determine their specific needs. This case was no different. We met with the client’s leadership team to discuss industry-standard tracking metrics and KPIs. The client surveyed their user base to identify the top ten metrics and KPIs to track for each sales program.

To build the metrics, we contacted other teams within MAQ Software that had previously worked with the sales datasets. We also worked directly with the data platform teams to better understand the relationships within the datasets. We then hosted several whiteboarding sessions, created visual mock-ups, and reviewed the mock-ups to determine the best way to present the data.

Our goal was to learn about the client’s needs so that we could create a powerful implementation that was intuitive enough to ensure widespread adoption. The project lead observed that there is an art to finding the balance between user interface (UI) complexity and target audience adoption. “I learned a lot about user experience and how to tune the UI complexity to the audience. Complex visualizations offer many insights, but clean, straightforward visuals are more intuitive and help drive adoption.”

Pairing the right visualizations with the right KPIs was not an easy task. To ensure we’re always aligned with the client’s expectations, we practice continuous delivery. The instant a deliverable is finished—even if the implementation itself is not finished—we touch base with our client to ensure the deliverable meets their needs. Aligning expectations saves time for our teams and for our clients. Instead of taking months to deliver a finished business solution that might not fulfill the client’s needs, we submit our deliverables to immediate scrutiny.

In this case, the sales team informed us that our initial metrics and visualizations were difficult to understand. Due to our commitment to Agile practices, our team immediately identified the problem and rectified it by working with members of the sales team. We simplified the data visualizations but still allowed advanced users the ability to drill down to more complex views.

Another challenge arose when implementing the cloud-based business intelligence engine. Azure Analysis Services initially seemed far too expensive, but an optimization analysis of the existing tabular model design allowed our team to implement multiple efficiencies. These efficiencies led to lower space and processing needs, bringing the cost to acceptable levels.

Azure Data Factory presented a final technical hurdle. Azure Data Factory cannot directly connect to cube data sources, but the business intelligence engine needed to connect to multiple upstream data platforms with on-premise cubes. To resolve the issue, we set up a solution that created small virtual machines (VMs) used to pull data from the upstream cubes and process it onto Azure Data Factory. Our solution allowed the cube data to be utilized along with data from other sources.


The completed business intelligence engine and report suite were met with effusive praise from the sales team members. The team was thrilled that we had succeeded in creating a platform that consolidated data and drove business insights. The platform enabled a comprehensive view of sales performance and provided KPIs that were crucial to the leadership team’s needs. We consolidated our tabular model to integrate over 200 data points and reduced monthly Azure infrastructure costs by over $5,000. Most importantly, our work proved to be of the utmost significance to our client; one of the reports from the suite was ranked in the top ten for usage amidst a collection of over 250,000 reports across the company.