Using Decision Augmentation for Client Retention – with Emily Bremner of Signal AI

Nicholas DeNittis

Nick DeNittis writes and edits AI industry trends and use-cases for Emerj's editorial and client content. Nick holds an MS in Management from Troy University and has earned several professional analytics certificates, including from the Wharton School.

Using Decision Augmentation for Client Retention@2x

Actionable, decision-augmenting data can be obtained internally or externally. Of course, external data is a far richer and more diverse source, as it comprises every other piece of digital information outside of the four walls of an enterprise. 

“External search” is a term used at Emerj to reference searching for and extracting data-driven insights from outside sources. That this external data is often both abundant and free of charge is appealing, but there are challenges. The main difficulty lies in building an infrastructure and workforce capable of “scraping,” cleaning, and converting raw data into actionable intelligence. 

While most enterprises now have at least passable competency in searching for and applying internal data, a fair share is deficient at gathering, refining, and acting on external data, according to a research article by McKinsey. 

Signal AI is a company that has positioned itself comfortably to address business challenges related to external search, having raised over $100 million on leveraging AI to search for actionable external data. The company offers an AI platform capable of extracting and interpreting various external data streams for its customers. 

Recently, we spoke with an executive on the front lines of Signal’s product creation and implementation. The primary use case in this episode is external search technology to develop and maintain client relationships by delivering valuable insights.

In this article, we extract three principal insights from our conversation with Emily:

  • Knowing your client’s business is invaluable in an advisory role: Keeping up with domain-specific concepts and iterating this knowledge – ideally, in the form of addressing customer needs and pain points – when appropriate builds trust and encourages repeat business.
  • “White-gloving” deliverables can be worth the effort: Delivering thorough customer onboarding and educating the client on how to use the product in an optimal way that fits their specific needs may lead to additional business opportunities.
  • Value is not always “measurable”: Business value does not necessarily need to be quantifiable in some instances, as there is potential value in knowing the client’s business, managing solution and technology expectations, and effectively communicating product capabilities. 

 

Guest: Emily Bremner, then-SVP of Product, Signal AI

Expertise: Product strategy, Organizational design, Project management

Brief Recognition: Before transitioning to a product advisory role at Signal AI, Emily was an executive at Signal AI for nearly four years, focusing on product and business strategy. Emily holds an MBA from the London Business School and an MA in International Relations and Economics from Johns Hopkins University, specializing in Quantitative Methods.

The Value of Knowing Your Client’s Business

The podcast begins with a discussion about Deloitte’s business problem and project requirements. The firm sought a platform that could help monitor and manage myriad tax regulation developments across multiple clients and jurisdictions — a “Herculean” task, Emily adds. “It was impossible to keep track of that regulatory change, and also those signals of regulatory change.”

Engineering the solution required working hand-in-hand with – and acquiring detailed feedback – experts across domains and locations. One person was designated as the “go-to” person for each domain and locale. 

She discusses the importance of knowing the client’s business throughout the discovery process and beyond: “When you’re in an advisory relationship, your ability to stay on top of issues and actually raise things with your very intelligent end clients who sometimes know almost as much or more than you do is invaluable.”

Training the AI required implementing written “briefs” from the client, which may have consisted of a set of product deliverables. From what we could gather, Emily’s team worked diligently with these domain experts to train the AI on domain-specific approaches to regulation and compliance monitoring and management.

The result was a customized solution that could scan for, translate, and drill down content from regulatory sources across 150 jurisdictions around the globe. Deloitte’s advisors could now attend to large client bases across jurisdictions while expeditiously seeing only the most relevant content. 

Regarding tangible business value, Emily states that the solution saved the client considerable research time and reduced risk. But it was more the final-end-user-defined value that “sold” Deloitte on Signal’s platform– and a long-term business partnership. “I think [our success on the Deloitte project] was because of how close we were working with Deloitte.” 

Signal also focused on more qualitative success outcomes, including reported client satisfaction and positive feedback from Deloitte’s clients. 

Elaborating further, Emily states that the solution allowed for the discovery of added insights, which provided value to Deloitte’s customer base. “Most important … were the number of times they found insight using our technology, that enabled them to broaden out and have a larger conversation [with their own clients].”

White-Gloving Deliverables

Emily also touches on something critical to all enterprises in SignalAI’s approach to developing customer relationships and resultant new business opportunities: “What drove the deepening of this relationship and the expanding into Deloitte were the insights that we were able to arm those client partners with to go to their own clients.” 

Emily elaborates on Signal AI’s continued backend support and unique value proposition for Deloitte and other customers: “We don’t try to be a vendor that automates something time-consuming. It really is about how we augment you and make you more effective in your role.” 

Regarding backend support for their customers, Emily says that Signal supports the end user with their own client engagement while them on how they can best use SignalAI’s technology.

She adds that directing your value proposition to those individuals and departments holding the purse strings is essential to earning and keeping the client’s business. “I think what really … flipped the switch much more than, as you say, the risk reduction piece, which excited a section of the organization, was whether that section of the organization is the part that has the buying power for organizations like ourselves.”

Immeasurable Value

While Emily palliates potentially quantifiable outcomes, such as risk reduction and time saved, for less measurable success metrics, such as client satisfaction and positive feedback, she adds that such an approach to measuring outcomes is only sometimes applicable. 

“It depends on what is the real value you’re delivering.” She says it is helpful to understand the solution’s linearity in your approach to outcome measurement. “If it is something that’s quite transactional, then obviously, as a business, you need to be able to speak to that in a scaled way.”

Towards the conversation’s end, Emily discusses the importance of setting and managing clear expectations and “demystifying” AI – something that we at Emerj believe to be a critical part of successful AI projects. 

“Of course, it’s your job to deliver value for them against their brief, but I think we did a lot of busting myths about what AI is, and what natural language processing is, and what it can do. The more realistic and the more we took people on the journey to explain ‘This is super powerful stuff, but there are the limitations. It’s not going to behave like a human.'”

– SVP of Product at Signal AI, Emily Bremner

 

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