Automating high-stakes business analysis for decision-making

A global consultancy helps Fortune 100 companies gain insight into their businesses' financial risks and offers recommendations for how to respond. This analysis helps C-Suite executives from various industries make mission-critical business decisions.

Unfortunately, the consultancy’s existing processes were highly manual. Studies took 10 to 12 weeks to complete, meaning the findings could be outdated as soon as they were created, and client companies might act based on outdated assumptions.

Leaders at the consultancy had a hunch that finding a way to automate some of their analysis could lead to faster results – and, therefore, more timely analysis. They also suspected that automation would allow them to spin up a more cost-effective offering, making their services more accessible to customers and available to a larger swathe of businesses.

They also knew that the best way to determine whether and how automation could lead them to a less manual way of operating was a product innovation approach. So they turned to TXI for help.

We launched our partnership with the ambitious goal of reducing the time to conduct a study by 50 percent. Perhaps even more daunting, we soon realized, would be building a tool that analysts would use instead of their standbys, Excel and PowerPoint.

As our work got underway, we identified three major challenges we’d have to overcome to achieve the above:

1. No consistent process for studies. Analysts we spoke to all had their own way of conducting studies. Rather than figuring out how to add a technology layer on top of an existing process, we first had to get alignment from our stakeholders on the set and sequence of process steps.

2. Building trust in the tool. To make the consultancy’s services more accessible, analysts had to actually use the tool we built – which means they had to trust it. The key to success would be our ability to build users’ trust in the tool.

3. Math and engineering. Analyses are powered by a lot of complicated math. To show clients what risks they’re exposed to, the likelihood of various scenarios, and how each would affect financial performance, the tool would have to run tens of thousands of simulations customized to each client engagement.

To build a tool that worked for our client, we had to both create something capable of doing complex analysis and convince analysts to use it. To achieve both, we relied on the product innovation principle of building iteratively, seeking input from end users at every step.

This principle was perhaps best illustrated by our use of iterative feedback and co-creation throughout the project. We met with teams of users and stakeholders from the consultancy at least three times per week to gather information and test prototypes. Because of the technical sophistication of the project, we relied on internal experts (including PhDs in econometrics) to explain the domain to us, then synthesized their information into the designs and prototypes we presented to them for feedback.

This process of co-creation and making tangible artifacts based on what we were hearing enabled us to get in alignment with consultancy users and stakeholders and make product strategy decisions. The close partnership also helped build buy-in and mutual trust.

On a product level, our close collaboration led to several key decisions about the product:

• Ingestion of multiple data sources: To deliver all the necessary functionality in one tool, we had to figure out how to best ingest, parse, structure, and visualize vast amounts of data from different sources.

• Transparency of calculations and assumptions: To win users’ trust, we had to find ways to show elements of the work going on “in the background of the tool” so users could follow along as they can in Excel.

• Data-powered storytelling: Throughout the project, users were adamant about being able to download certain visualizations to use in PowerPoint. We saw this request as an opportunity to learn more about their process––specifically, how they organized a narrative when presenting to clients. Using their answers as a jumping-off point, we developed a dashboard that let them explore in real-time, thanks to technology powered by automated background calculations.

• Predictive analytics: Our solution combined company-specific information with simulated economic trend modeling to show how a given scenario would affect a company’s financial performance. It also facilitated testing the impact of different responses a company might take.

We proved out the leaders’ hunch that a tech solution could indeed deliver meaningful analysis for clients more efficiently than – and just as accurately as – the manual processes they used to rely on.

In one early application, a client heavily invested in renewable energy asked the consultancy to predict what would happen under various hypothetical scenarios (removal of government subsidies, significant increase in rainfall levels, sharp drop in the price of crude oil) and recommend strategic responses for each.

Our solution enabled analysts to provide insight into what might happen and, perhaps more importantly, facilitated their ability to design, test, and compare different ways of responding.

Interested in finding more efficient ways to operate your business? Get in touch.