06 Jun DigIn 2018 Demo Narrative
In Case You Missed the Real Thing
Thanks again to those of you who joined us for our DigIn 2018 demo showcase. If you weren’t able to attend the conference, or if you missed our demo, we’ve put together a narrative version of CEO, Justin Ullman’s presentation just for you!
To kick things off, we’ll talk about the insurance industry demographic. Most of these facts probably aren’t a surprise for those of you familiar with the industry:
- The numbers say 25% of the Insurance workforce is eligible for retirement within the next 5 years.
- Millennials will make up 40% of the workforce in five years, and they think insurance is an ‘old person’s industry.’
- Millennials don’t even really like the concept of insurance AND they are ALL going to work for Apple or Google to change the world.
Luckily I like insurance. My favorite thing about Insurance is it’s complexity. The CEO of Axis Capital said it best when he stated, “Insurance won’t be Revolution, it will be evolution.” So we have a complex industry, aging workforce and Millennial who probably aren’t going to come work for us. So what are we going to do? The answer is the RhinoDox Intelligent Content Platform.
We all have legacy ECM platforms. Maybe you call it document management. Either way, you probably have a lot of content stored in these systems. Now, I want you to start thinking about that content differently. Content is audio, video, images, documents, forms, text. And there is Revenue, Risk and Valuable Information stored within all of that. There is a lot of manual interaction involved with content. Adjusters, claims agents, customer service representatives, underwriters, auditors…everyone is interacting with content, applying their tribal knowledge by looking/listening and making decisions. When they do that, what are they hopefully doing? Something to drive premium OR reduce loss. We need to think about automating and augmenting decision making across the industry in nearly every department.
The RhinoDox Approach
The first step is just raw search horsepower. We want a ‘Consumer Grade Search Experience.’ Now, what does that mean exactly? Think about how you find information on Google, Amazon or LinkedIn. Type what you’re looking for, find it in milliseconds.
Search is important. In fact, for every 500 employees at $65K per year, McKinsey says you currently spend 1.8 hours per day searching for stuff. That’s $7.3M per year per 500 employees of wasted time searching. Faster search across content and data silos saves time. Better search lowers average response time in customer service, speeds up claim processing, speeds up underwriting and creates a better customer experience.
Now if we turn our attention to the demo, you’ll see that Travis searched for ‘Boat’ and the platform brought back searches for both ‘Boat’ and ‘Watercraft’. You will also notice that the platform is not just looking at Meta-Data BUT also inside the documents. We’ve built an Intelligent Thesaurus around terms, concepts and even people. So, now you don’t have to worry about making sure everyone is using the same words to search OR storing things in a specific place within the platform.
Cool, right? But most people don’t think they have a search problem and we weren’t satisfied with just that functionality alone, anyways. While search can drive process efficiency, plugging into workflow engines to create content and move it through the organization takes driving efficiency to a whole new level. Application Program Interface (API) frameworks allow you to consume everything you will see here from pretty much any platform.
Search, workflow, intelligent thesaurus. All this reducing the cost of claims processing and speeding up the claims processing itself is valuable. But let’s talk wallet share and revenue opportunities – this is what we are really interested in.
Revenue Touch Points
I want you to imagine all the customer touch points and think about the potential revenue opportunities in each of them. When do those revenue opportunities happen?
- Broker/Insured conversations
- Customer service
- Claims process
Now, let’s turn our attention to the demo again. We took in John’s Renters Insurance Policy. When we do that, we start pulling out information from the document. No one manually entered the data on the side bar in, the platform is just using machine learning to pick apart interesting pieces of information. We call this entity extraction. People, places, things, companies, dates, amounts for simplification.
Notice the ‘related documents.’ What the platform is doing is building a ‘graph’ of John. Quick lesson on graph technology – you use it everyday, even if you don’t know what it is. Some of you are on your phones probably using it right now. Here is an example: think about how Linkedin connects billions of pieces of tiny information and figures out relationships. Another way to think about it is the 6 degrees of Kevin Bacon, or the theory that you’ll find a connection to Kevin Bacon within 6 degrees (or less) of every person. For example, Lebron James → Trainwreck with Marisa Tomeii → Loverboy → Kevin Bacon.
So let’s take a closer look at John and the documents/content related to him. The platform has identified that John is a person, and has pulled a number of documents related to him. But, what’s wrong with this picture? More importantly, what questions should be asked here, based on these facts?:
- John has a homeowners policy in the suburbs.
- John has a rental policy in the city.
- John has an auto policy registered to his house in the suburbs.
There are probably 1 of 3 likely scenarios here:
- John’s wife has thrown him out of the house, and he’s renting in the city.
- John and his wife have an apartment in the city, in addition to their house in the suburbs.
- John and his wife moved to the city, their house in the suburbs is empty and the car is really in the city.
By building this insight, we now have revenue opportunities. If the car is really in the city, we need to charge more. If the house is really empty, we need to cancel his policy OR charge him more because it’s an empty house.
Now what do we do? We definitely can’t train a CSR, claims rep or trigger a notification to kick off an agent workflow. We need to be thinking about content differently and leveraging the unstructured data that exists.
Where are we going? Surfacing insight to revenue and risk in unstructured data creating revenue touch points and automation. Everyone is talking about Big Data – we’re talking about Big Content.
Justin Ullman is the Founder & CEO of RhinoDox. He has been in the Technology and Content Management industry, helping companies create differentiation and workforce efficiency, for over two decades. When Justin’s not a busy Rhino, he enjoys playing shows with his band, and spending time with his family and friends.