娇色导航

Welcome to 娇色导航Leadership Live UK.

I'm Lee Rennick, Executive Director of 娇色导航Communities for CIO

and I'm very happy to welcome Jonathon Valentine, CTO of ThingCo.

Jonathon, please introduce yourself and could you tell us a little bit

about your current role? I everyone I'm Johnny Valentine.

I am the 娇色导航and co-founder.

Thingco we are a

connected car data company focusing on the motor insurance sector

use in driving data to save lives and help people get cheap car insurance.

The very interesting business model that you have

and I'm really looking forward to talking about it

and I really appreciate you joining us here today. Jonathon.

We've created this series

to support the senior technology leaders in their tech and leadership journey.

So the first question, and I ask everyone this question,

can you please tell us a little bit about your own career path

and provide some insights or tips on that road path?

Are there any lessons learned? Yes.

So my background is a bit of a weird one.

I got involved building company companies in a very young age.

I started designing websites from the age of ten, 11 commercially

from the age of 13, all because I saw some man rip off my Mum.

And when he tried to sell some of our website for a business.

Unfortunately spending your teenage years at a computer

and made me hate the industry I never wanted use it for GCSCs

I didn't use it for A-levels. I wanted nothing to do with it.

But like a boomerang, you end up just going back to what you're good at

and what you're actually passionate about.

I think I just got a bit burnt out.

I went to university like everyone does with no qualifications

purely off my portfolio, but left after a year for an opportunity

to go work for this new insurance company that was coming soon

where they needed a coming soon page for telematics motor insurance.

No one had no idea what it was.

So I went to go meet these bunch of mavericks and took a punt

and left university to go become the Head of Web development

for Insure the Box which went on to sell a million insurance policies in the UK

being bought before being bought by Amazon AD

and that was as a 19 year old

that was a crazy way to learn about IT and technology leadership.

When you're watching these four leaders of insurance build a company from scratch

and spending millions and millions building this company

from nothing to 400 people in a call center,

most offices in Gibraltar, London, Newcastle And I was extremely fortunate

to sit on the fringes of that and watch and just absorb and learn.

And that's how I really got into all this was I was just fascinated.

And you could by now and my blood is telematics and insurance and technology.

I think myself as a techie, but I'm probably more of an insurance person

who's just good at tech rather than the other way round.

That's really interesting background.

And I know when we spoke you said you started at a very young age

and you know, a lot of people get really motivated at young ages

around like computer games and software and learning and all of that stuff.

And it seems it seems like it really you know,

you brought your career together that way,

but as you said, really involved in the insurance sector.

So that really segues very well into our next question, which is around cross-sector learning.

So a lot of the CIOs I speak with talk about the opportunity to build

skills and learn skills working across sectors.

As mentioned, you started your career in tech at a very young age

and you really diversified your knowledge to working in various sectors.

So I was wondering if you could provide some insights on this

in relation to how cross-sector learning supported

you building knowledge in your technology role?

So at Insure the Box what's my role was quite unique.

And in fact I reported directly to the CEO and

and my job as Head of Innovation was purely to

look at what was going on across the business

and try to see how we could use technology to solve a problem.

And it didn't have to be AI, it didn't have be as something fancy.

It was just how could we automate something that was a problem?

An example of that was we were in the pub one night, at Insure the Box we had,

We went to the pub

If it was a good day,

things went well and if it was a bad day, we went to the pub to fix it.

So some days we went twice a day.

and so we were in there and they had a problem

with the call center and the fact that the wait time was too long.

We were we were getting very, very, very successful.

But the call center couldn't keep with the call volume, I said, Why don't we do live chat And it was

at night.

And we went in what we call the director's corner garden.

And that was it. We went to a mindset to do it. And so

at night, I went back to the office

and we implemented live chat.

We a we use live person solution.

There's no point building things from scratch

when there is a solution out there already made, you can plug in

and the next morning

the people in the operations call center got an email saying, right where

your log in details for live person, we're going to start taking live chats.

And we got it into the website.

We saw a massive drop off in call volume.

We saw agents able to handle five people instead of one phone call

and it was kind of been involved in the listing into a problem

that a department had, that I was able to solve it and just some out of the box,

not out of the box, but thinking about it from a non BAU perspective,

someone who was a teckie.

And I think that's the way I like to get involved in every department

and what they're doing because if you have worked in an industry

or done a job for 20 or 30 years,

you'll think that the only way you can do it

when actually if you're using it, is slightly different, that you can now automate Google sheets.

We can do that part of your job.

But some people get quite protective because

they do a job for 8 hours a day

and some idiot move some young and I'm not going to swear

comes along and says we can automate that part of your job.

They're going to they're going to be affronted.

No, this is my job.

But actually, we need to go now.

Let's let's sort out the bit you don't like and let me

spend more time in the bit that we need you for which is your brain,

which is your intelligence, your human aspect.

And so that's all I did.

And suddenly that gives you the ability to understand operations and understand the back

end of how things work and whether it be on claimed back

and it gave me a real incentive on How will work.

But also how to approach people in terms of

if you tell someone you going to automate their job,

they respond to you saying, right, you'll get rid of me,

but now you want to automate

all that stuff as much as possible so that the humans can do the human work.

And that's why I think that

being involved in getting involved in looking at tickets, looking at all that kind of stuff

gives you that ability to understand what's going on, on that on the floor

so you can help. Right.

And so and in your case, I mean when we chatted

you've worked in various sectors so really like you've been really in tune

with what's happening with the customer journey and experience.

And and I really wanted to talk about that.

I'm just around customer centric technology.

So, you know, a lot of the I was sitting on the roundtable, I tell the story a lot.

We were talking about cloud and cloud implementation.

And one of the CIOs was there saying, you know, and, and the customer journey.

So those two together.

And one 娇色导航was sitting there saying, you know, the thing is, is that customers

now expect the Amazon experience. Which is like I order and the next day I get I need.

Right?

So you and I talked about that.

We discuss the ways you approach innovation for the customer journey

and really how you're really building customer centric

technology to ensure that the customer is the most important part of your business.

So could you talk a little bit about the ways

you do that in your technology leadership role?

I think the end user is the most

important person for us because if you think your car insurance

is something that you legally have to have and so you've been told to install

this device as part of your insurance policy

and you want to prove

that you're the best possible driver to get cheaper car insurance

and you might get frustrated because it's not representing you.

Maybe the fact that the speed limit is wrong on the road.

And so it penalized you.

And so you've had to tell the insurance company.

That's speed limits wrong. It used to be a forty, Now it's a 60.

Because you want cheaper car insurance and you want to prove you're good driver.

And and so my view is I don't want to see an issue

or we we go I go to the customer or I'll drop randomly to the customer

that did a ticket and we'll go see them we'll talk to them and you learn so much

because they're on it.

I've had people that some really unhappy customers,

whether it's for something really random maybe the app couldn't download

from the App Store for that version, of the phone, really angry.

And if someone says we don't get to see them.

You go see them and suddenly you have to have a conversation with them.

Others have their point of view on understanding.

And actually you learn a lot and they become testers of your product.

So my biggest endorsers are people that actually,

I don't know, have the experiences we spoke to and we understood.

And then suddenly they they're testing out new features for us.

They're coming up with ideas that texting to me on Saturday night at 10 p.m.

with an idea they've had and suddenly we're now in a position where

you can turn that

negativity into into a positive part of your business.

We review tickets all the time because we don't even though

they're not our customers, the insurance companies,

because I want that their brands be represented as well as possible, every insurance company.

So we look

at what people are talking about, what's there, and then we reflect on that. We're, right?

What can we do to improve that?

So it's always a cycle of learning what the customer wants, what their issue,

the fact that there is right,

there's a wide screen some about that person looking at yeah let's look at

it was a different the speed and speed limits is one that really

and resonates with me. Because it's not fair for someone

to be threatening to have their insurance canceled

because they did something naughty on the roads.

What actually wasn't their fault at all, that the data was invalid

and when we're working with third parties, you need to.

So for me to right,

hold off third parties to account, to go, look,

this customer's had a really bad experience.

They've been told by their insurance company they're going to get canceled

if they drive at the speed again on this road.

But the road allows the speed limit is incorrect.

You still have a data that this customer is going to

that's going to be financially impacting them for years.

And so I think for us, because we're, we can have such a big impact on someone's

financial life in terms of that will cost them more in the future.

Our suppliers have to realize as well that we're customer centric and it almosts

drags them into a journey as well because they need to be.

But I think when you're talking about that,

I know we didn't talk about this question, but it really

it says to me that you, as a service provider tech, not tech aspect of that

your customer, the insurance companies, is that you're really ensuring that

what they're producing, how they're approaching their business

with their customers, will be really successful.

So it must be extremely, extremely valuable.

The work that you're doing

with the insurance companies at such a granular level for them,

because I know many large companies are looking at focus groups and,

you know, user testing and all of that stuff in, in a way,

you're you're working with the clients directly on the ground

to talk about ways in which to improve that user and user experience.

So I think that's that's phenomenal.

I mean, it must be extremely rewarding.

I think so, yeah. So we start off as B2B.

And when you say B2B

customers, you're dealing with businesses and you're a service provider.

I heard the term is that I on Twitter and I really like it.

It was a it was a vertical SAS and I quite like that because

it's our device, our hardware, it's our firmware

and we do everything all the way through the white label

apps that are used by the customers are provided by us.

So it's almost like we provide an entire solution out of the box.

And I like that.

And I wouldn't do it any other way because we tried to in B2B at first

without talking to the customer.

And the insurers do a really similar the

the job wasn't done properly, the data wasn't used properly,

which means that it's a bad experience for insurance

companies, a bad experience for the end user.

Who is the person who's got this device in the car.

They've got the app

and they're the ones

who are going to get cheaper, or more expensive car insurance.

So that's where we took the decision

look, let's bring it in-house, let's do everything.

The insurer gets a better experience

and I can be more confortable at night knowing that data is being used properly because if the

data not being used properly, then it's just a very expensive paperweight. Yeah, I know.

That's amazing.

It must be very, like I said, very rewarding. All right.

So now we're

you are talking about data, and I wanted to talk a little bit more about innovation.

So, I mean, obviously, since last November,

the world has transformed a little bit technologically with Gen

AI and large language models, you know, so prevalent in discussions

right now around innovation and data and cloud and everything.

So could you share your views on Gen AI and LLMs and perhaps

some of the ways you're looking to deploy or what you're seeing in market?

Oh, it's good to see.

It's going to change everything at some level. We'll insurance for a second and

we'll focus on, I think, and obviously that's a whole talk about ChatGPT got dumber, all the things

you've got done there and all these going on like that. Overall is amazing.

And I bought the API access soon for the business, as soon as we could

because we could start.

We've got a lot of data using an insurance policy normally

just like one row an Excel spreadsheet, we capture 9000 bits a day for a second.

The average person does 21 minute trips three times a day.

That adds up to a lot of data.

We've got and obviously trying to turn that into value.

There's a there's a lot of value that can be generated out of data

without the need for machine learning models.

And I think sometimes you can jump to using AI,

when actually there's a lot more value that can be got out of something

with some intelligent thinking and some common sense.

But it does have its place.

And where now the plug ins, for example,

that you can plug in to TripAdvisor or you can last minute

and you can actually have it plan a trip for you and is actually incredible.

Insurance is a difficult one, though, because it's a regulated market.

And if you're saying, right Lee your your insurance

premium is ?1,000 and mine is ?2,000.

We should have the right to understand why why that premium is up.

And no one wants to hear that because the black box said that

this is some algorithm that we can't understand

because it is said that yours is a thousand and mines 2000,

because that's not fair on individuals, because so insurance

has got a difficult thing to play.

It can be really helpful for, let's say, counter fraud

or claims or things where you're trying to find patterns.

But I think one area that does is that we need to make sure that we don't use this to

penalize individuals because what will happen

if you think, people who pay for their car insurance monthly

are deemed to be a higher rate than people who pay for the car insurance annually.

But people who pay for the car insurance monthly tend to be less well off

and people pay annually,you know. Like it's

the same as people are going to do food shopping five times

a week are higher risk than people who go food shopping once a week.

Well, suddenly a model is going to look at that

and it's going to penalize those people.

But suddenly all we're doing is penalizing the less well-off.

And we've created as a

regulator, regulations going to be really interesting in this sector.

I know there's quite a few people looking at looking at it.

Insurance in the UK have got quite a few members in that space, but

I'm really excited to see where it goes, But that just needs to be

some I think regulation around the financial aspects of it.

Where it's impacting people's premiums and their financial activity.

Personally like we love it, I think, and then we've got, for example,

we're now scanning news articles across the entire web.

We're going to work out right have there been any car accidents,

what type of vehicles are involved, where did that occur,

and look at our database, if we had any people nearby at that point in time.

So there are quite a few bits that I can't mention that we do.

but it's going to change the way we work.

But I don't think it's going to get rid of engineers.

I don't think it's going to get rid of developers or anything like that.

It's just going to make our life easier.

You look at GitHub, Copilot.

A prime example of that is just helping developers do their job more efficiently.

Like I spoke about earlier, automating the process so humans can do the human stuff.

And you know, that's you're saying this and this is our last question.

But you know, I just think that when you talked about

Thingco of being a vertical SaaS, I'm sure, you know, when you're primarily focused on insurance.

Right.

But I think of Gen AI and LLMs will probably maybe transform,

which, you know, companies you work with in which sectors you work in. Right.

As a business.

Yeah, I think it's going to have a massive impact on what we can do to date

and start looking at the data.

So now we're absorbing data from third party sources.

We would never absorb data from before,

but from models we want to use before it's make it.

If it's going to help us become more intelligent in what we do.

Because humans can only go so far.

Go so far, models can only get you so far.

But now these new models will allow us to do a lot more.

But it's very early days. We are not experts.

We are pure, just admirers and users

and we're only just scratching the surface.

But I'm excited to see what more is given to us. That's awesome.

Well, that's a great way to end this interview.

Thank you so much very much, Jonathon, for joining us today.

I really appreciate it now.

Thanks for having me. Really enjoyed it.