Stories from the Trenches: Part 1 of 3
We recently finished an engagement with a Fortune 500 firm where we had some freedom to decide where to initiate a Data Science project, based on highest value to the business. Naturally, improving the success rate of a profit center — e.g. conversion rates between each stage in the sales pipeline — is the fastest way to see obvious revenue growth from a new project. The internal analytics group pointed us to a particular stage that was clearly struggling, so we dove in.
In this industry, every solution is highly customized, so when a company sends a prospect a quote, they’ve already invested significant time and money into the deal. Many requirements need to be uncovered and many product configurations need to be considered to meet those requirements. You wouldn’t expect a company to incur the substantial effort and expense to generate a custom quote unless there was a good chance to win the contract. In contrast, this company was seeing 17% conversion from the Quote stage. So, after all that time and effort gathering requirements and architecting a solution, only 1 in 6 contracts quoted proceed to the next stage in the sales pipeline. That’s a problem.
Always distrust first impressions (though they may be justified in the end)
There are a lot of factors that could contribute to this low conversion rate: poor lead qualification, long quote turnaround times, underperforming sales personnel, poor pricing models, etc. Or maybe incurring some expense is part of a sales tactic to induce reciprocity by the prospect. A good scientist suspends judgment until there is a clear body of evidence to support a hypothesis. So we kept digging into historical data, and we found 2 critical insights: 1) 20% of deals that did convert from quote to the next stage reverted back to get re-quoted due to missed requirements, 2) a disproportionate number of exit surveys from former customers (i.e. churned customers) pointed to misalignment between requirements and solutions.
In other words, not only were 5 out of 6 quoted prospects not interested in the quoted solutions — a surprising number who were initially interested were ultimately dissatisfied with the solution. With insights in-hand, we set off to find a solution architect who could help us understand the current quoting process, in all its gory detail.
In the immortal words of Sir Arthur Conan Doyle…
Down the rabbit hole
Our first meeting with the lead solution architect revealed a very complex set of product configuration rules guiding the solutioning process, which he admitted was challenging for new solution engineering personnel to learn. They had built tools in the past (in Excel) to assist in computing certain mathematical relationships using manually-defined rule systems. However, those tools soon became outdated as products changed, and they were painful to maintain. Ultimately, they had to rely primarily on experience to guide their solutioning practices. Naturally, the newer personnel made a lot of mistakes. Some embarrassingly obvious. And due to time constraints, the senior staff aren’t able to check every solution thoroughly before it goes out the door. Clearly, the quoting process requires an intervention. But what intervention will most reliably and economically deliver the sought-after performance improvement?
Coming up with roses
It is reasonable at this point in a Discovery to step back and look at the big picture of the organization. Where is the company headed? Are major product changes or infrastructure overhauls planned within the next 5 years? How does the company want to position itself to its customers/prospects? Is it a technology leader, building next-gen services and support tech? Is it a technology follower, adopting only well-proven technologies as part of the Late Majority?
Talking with executives, we learned the major competitors were eating our client’s lunch, in part because of more effective technology infrastructure supporting more valuable service delivery to customers. This company was hungry to lay a foundation for internal technological innovation — which was why we were there in the first place. So we were encouraged to begin laying that foundation with our proposed solution. There were 3 clear options to select from:
- The most obvious solution to human performance gaps is better training for the new guys. And that would likely be a valid solution here, especially if coupled with a customized Learning Management System that effectively organized the curricular content, labs, and assessments to better scale the senior personnel’s available time. The upfront investment by senior resources would be substantial, and there would be ongoing maintenance to keep the content up-to-date. Not the most innovative choice.
- Another obvious solution is better quality control for quotes sent to the customer. This would also rely heavily on senior resources, who would be checking the more junior resources’ work, but it’s possible the senior resources’ actions could be monitored and used to train an AI to detect common mistakes. The primary innovation here would be a collaborative, versioned configuration management system that “listens in” on changes made by senior resources and learns by observation.
- Finally, we can revisit the concept of creating tools to actually recommend solution components based on requirements — i.e. requirements go in one side, and solution recommendations come out the other. This dramatically reduces time and complexity of the quoting operation, as the human operator faces a much smaller total body of decisions. This solution would rely the least on senior personnel. It would rely instead on historical requirements documentation and their corresponding solutions, which together would serve as the data and labels for a supervised learning-based recommendation engine. This option has a distinct advantage in that it dramatically reduces the time and cost to quote a solution by minimizing human decision-making to only those critical edge-conditions. It also makes best use of a largely untapped asset: historical data.
The 3rd solution represents a step-change in the company’s business: Faster, more reliable, more economical quotes through a technological innovation. It’s an inspiring vision, which aligns well with the company’s eagerness to prove itself as a technology leader. We went for it. First step was to assess the quality of the existing requirements documentation — which we will dive into in Part 2: Natural Language Processing for Enterprise Data Science.
About the Author: Eric Nelson
Eric Nelson is a Senior Software Engineer with MS3, Inc., living in Minneapolis, Minnesota with his wife Alisa, 7-year-old daughter Freyda, and 5-year-old son Arthur. Eric received his Bachelor of Science in Electrical Engineering from University of Minnesota and worked in photovoltaics and thin films. In 2015, he founded his own cloud software consulting firm where he trained dozens of student interns in software design and development skills. He is now focused on building large-scale, secure, futureproof production AI applications and software systems for smart brands as a member of the MS3 family.