AI Insights

Tell us a little bit about KPISoft’s story and where the company is today.

KPISoft was founded in 2008, originally as a performance management tool. That continued to grow, and we kind of stayed in the lab for a long time because we wanted to do something beyond traditional performance management. In 2016, we did our global launch as a system of intelligence, delivering hyper-personalized, contextual, time-relevant insights.

We started looking at strategies that were happening in the B2C world, whether it was something like a Facebook or a Netflix or a LinkedIn or even in an Uber type of scenario, where technology began to understand the user and began to make recommendations to the user. That’s really when we began to change the idea of what analytics was and how consumers and users wanted to be able to adopt analytics.

We did a global launch in 2016, and from that time, we’ve grown sales and operations across Asia, Middle East, North America, and our client base extends in all multiple areas or segments, whether it’s manufacturing, government, fintech, mining, health, wellness—and certainly insurance is a very large sector that we play in. We’ve had a lot of fun success in insurance.

How did you arrive at bringing the solution into the insurance market?

When we started, it was really an area where we had a client, and they were trying to engage their agents. In this particular scenario, they were a captive agent model, and they were dealing with roughly 90% of the revenues coming out of about 10% of the agents. What they were trying to understand in this very disparate model—where they had agents spread all over a very large, expansive region—was how could they engage the agents, produce information down to the agent level in a very contextual, real-time environment so they could see what kind of tendencies were going on in the marketplace, what kind of products might need to be recommended to clients. How do we increase wallet share inside the agency? How do I increase my commissions and my earnings as an agency? So that was really the beginning.

We rolled that scenario out to where we could get down to an independent agent level—what was important to that agent, how could they grow their book of business, what kind of product looks might they want to be able to bring into their particular marketplace. The way our platform works is it contextualizes at each level.

There are different personas, right? The chief revenue officer at the agency was wanting to see longer-term trends. The area managers within the agency wanted to be able to understand different performance levels across multiple agents and multiple location scenarios and how to be able to help enhance and grow each one of those agencies in their particular regions.

And then down specifically at the agent level, they wanted to know what to do about their book of business. As we contextualized those insights all the way down, what we found was you could align where the key performance factors were for the organization from a strategic level but also at the agency level. And you saw a growth in wallet share, you saw a growth in premiums, you saw a growth in market segment that began to have a very significant effect for the organization.

Tell us about how the platform takes in data and what you need at the baseline level in order for the platform to really use its brain.

A lot of organizations feel like they don’t have the right data to be able to implement a system of intelligence, of some type of artificial intelligence platform. When we really break it down for them, every organization is already doing these types of methods. There are reports that are already being generated. They’re typically some type of Excel spreadsheet. Maybe they’re in some type of performance management platform. But that data doesn’t align with data that might be sitting inside of a CRM system. So we might be collecting everything inside of a Salesforce or inside of a HubSpot or inside of maybe some locally grown CRM system that we’ve created. But getting data out of that CRM system into what kind of trends are existing, from a product tendency, is very, very challenging.

There’s a lot of real-world data that’s just sitting out on the web or other industry reports. We bring all of that data in, whether it’s structured data that might be in a database or unstructured data from reports or whatnot.

The key to all of that, though, is that we spend a lot of time with our clients understanding what is it that you’re trying to measure—what are the data points that are most significant to the organization that you’re trying to elevate, that you’re trying to grow.

As you look to the U.S. market and the commercial independent insurance brokerage community, is there an ideal customer persona that you find in the insurance sector that is really attractive to you at KPISoft?

There are really two, if you will. The first is that large insurance organization, or the large brokerage organization, that’s in high acquisition mode. They’re growing. They’re developing through acquisition, and now they’re pulling in these smaller brokerage organizations. But how do I assimilate the two organizations together? How do I grab what data is important to us and begin to push that data into that new acquisition? On the flip side, how do I look at the data they have and whether that data matches our KPI mindset?

The other organization that’s really strong inside the marketplace is that middle-market organization. They’re not really looking for deep data science. But what they do want is smart analytics. They want to be able to take the data they have, apply smart analytics to it in an easy-to-understand way, and push that out to their field. The quicker they can push that out to the field in a highly adoptive, personalized manner, now they get the benefit of analytics without the heavy, heavy investment maybe that’s needed and the deep data science that’s needed that they’re not ready to go whole hog into.

So those are the two key areas that we look for. First and foremost, it has to be an organization that recognizes data as an asset. If the organization doesn’t recognize data as an asset, it really doesn’t matter what we put into place.

Pretend I’m Broker X and I’ve talked with you several times, we’ve socialized with our executive management team and decision makers, and we want to pilot this out.

The more specific the use case, the greater the result the organization is going to get. Sales performance is always a good one to start with. It’s not the only one to start with by any stretch of the imagination, but it’s always a good one to start with. Because what we found is that in competitive environments, and in sales-driven, commission-driven environments, sales performance is always very significant. It’s always something that’s being measured. There’s a lot of data that is typically already driven around it. So you can get the very actionable insight in a short time period to be able to create the uplift you’re looking for.

But we’ve seen organizations want to do different scenarios. We’ve seen organizations want to be able to look at claim management and sales operations. Sales is that first part of it, but then sales operations is a significant part of it. And then marrying sales operations to marketing spend and being able to tailor what is going on from our marketing organization and different marketing campaigns and how that wraps back to opportunities for the sales individual to be able to drive growth. And then how sales operations is supporting that growth once an actual policy or once an actual risk is placed with a brokerage firm or with a company itself. What is the insight of our customer relation, and how well are we serving the customer on a day-in and day-out basis? There are multiple areas that we get into based on what that particular company and what the strategy of that company is inside the market that they’re trying to play.