Reading the tea leaves: call analytics and legal risk

Nothing brings a chill to the boardroom like the threat of an impending lawsuit.

It’s with good reason that companies in virtually every industry – B2B and B2C, from manufacturing to financial services to retail to technology – have sophisticated legal departments at the ready in case of legal action. In the United States (the most litigious country in the world, and home to 80% of the global attorney population), 2.2% of annual GDP is spent on tort litigation. And plaintiffs win, on average, 55% of US tort cases.

This explains why enterprises care so much about managing the threat of legal action. But here’s the thing: when it comes to legal risk, an ounce of prevention is worth a pound of cure. It’s much easier to proactively remediate issues before they get to the stage of litigation.

Of course, that’s easier said than done. How could any company possibly predict what issues are likely to arise in the future?

If this sounds like reading tea leaves, it turns out that most companies are already sitting on whole chests of tea. Every customer interaction – voice, chat, support desk tickets, product reviews, emails – is a source of potential intelligence on what risks a company is facing. Add in the lines of communication maintained with suppliers, distributors, and partners, and the picture of a company’s impending legal landscape comes even more sharply into focus.

Consider, for example, what a company could learn from the following customer interactions – and how it could proactively respond to minimize legal risk:

By and large, customer interactions remain an under-utilized source of intelligence on legal and compliance risk. This is largely because the most valuable data – rich conversational interactions – are unstructured, making them difficult to store, classify, and review at scale. Additionally, it’s relatively uncommon that a company’s major interaction channels “talk” to each other seamlessly. The result is that key insights remain scattered across multiple systems rather than being surfaced to decision-makers with the executive authority to manage legal risk.

But companies that have invested in the tools to mine insights from their unstructured data have seen phenomenal gains in cost savings from a proactive approach to legal risk. For example, a retailer that implemented AI-powered call analytics uncovered a sharp spike in incoming call volume shortly while running a nationwide price promotion. Using data mining, the retailer rapidly identified the incoming call wave as being driven by complaints – specifically because advertised promotional pricing wasn’t being honored in certain retail locations.

Internal research concluded that this was driven by a defect in the retailer’s point of sale (POS) technology. The retailer immediately reached out to identifiable customers affected by the defect to apologize and honor the promotion. It also made upgrades to its POS technology to avoid similar situations in the future.

The retailer could have joined the ranks of Dell, Costco, Zara, and other retailers that have famously been hit by multi-million dollar “bait and switch” class action lawsuits in recent years. But instead they averted litigation and transformed a risk into an opportunity to restore customer trust and improve their internal technology.

Like this retailer, most companies already have the tea leaves, the rich insights buried in mounds of conversational data: the challenge is just reading them.

About Bonobo

Bonobo is the first conversational intelligence platform for organizations seeking to know their customers and grow customer relationships at scale. The platform centralizes fragmented customer interaction data from across channels and leverages AI to power action-ready insights from throughout the customer journey – driving an immediate lift in customer conversion, upsell, and retention.