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Welcome to the Productivity Podcast.
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Today I am joined by Ben Collier, co-founder at Acaster.
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Hi Ben.
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Good to see you, Simon.
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How are things?
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Yeah, good.
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And we're looking forward to welcoming you at the Productivity Forum in Birmingham on the 10th of September, which is next month at the time recording.
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So we'll we're looking forward to welcoming you and the team there.
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Yep, it'll be our first time there, but we're very excited.
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We've heard very good things about it.
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Good.
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Good.
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So we will find out a bit more about a caster then before the event and yourself.
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So let's start with you, Ben, uh co-founder, but that's probably not what you've done since you left college uni, whatever.
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So do you want to give us a bit of a potted career history?
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Yeah, well, truth be told, we did set up a caster not very long after I'd left university.
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So we've been in business 16 years or so now.
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When we set the company up, we were an app development agency.
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And as you can imagine, back in kind of 2011, apps were the hot new thing.
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Um so we were creating apps for the likes of Virgin Media.
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We created their TV guide app before TiVo had one ready and kind of really helped um get lots of um UK brands up and running with some really cool tech.
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Um, but it was in about kind of, and back then I was uh developer at heart um and setting up a business, it was kind of uh development by day and uh project management by night, or a bit vice versa there.
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But I'd say in around about 2014 or 2015, not through any uh grand plan, but through a bit of serendipity, a few desks down at Virgin Media sat um one of the retail operations team um down from um the um our main contact there and the ops team there were looking at digitizing their operations manual, getting it off of uh an old SharePoint um website that was on one back of house PC and the uh printed A4 binder uh where they were sending out uh quarterly updates saying swap page 14 for this page and all of that stuff.
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Um so so we helped them on the project there and kind of started moving away from creating apps for consumers, but moving across into kind of bespoke projects that were um for retail and uh kind of um big enterprises behind the scenes.
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Um what I really liked about that was the stuff that we brought across was we we kept that consumer simplicity.
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So everything we do um is still as easy to use as the apps that you go and get from the app store.
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Um because uh, I mean, especially back in 2015, the tools that employees were given at work were far worse than the tools they had in their personal lives.
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So long journey.
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Um the world's changed, and Offair we were talking about and how AI's changed the world.
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So when you first started, it was around the app development, and then obviously we all kind of end up in a a situation we look back on that may have been fortunate, pre-planned, random, that kind of gives us a pivot, I suppose.
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So, was that a conscious pivot away from just developing apps, or was it just based on that conversation and it kind of then put you on a different trajectory?
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I mean, pretty much that.
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It spun us off onto a different trajectory.
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We started doing bespoke apps for enterprises, and then quite quickly realized we were building out a really good suite of tools for all of these different pain points that were coming.
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So I suppose uh, I mean, we're bootstrapped, so we're we're not kind of VC funded and scratching our heads thinking what should we build next.
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We had kind of real retail and frontline customers coming to us with real pains, and then over the years realized we had enough of those building blocks to turn it into a proper frontline operations platform and uh take it to market, which is and what we've been doing for uh the last six or so years now.
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So we're out there, we've got the platform.
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You talked about pain points.
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So, what what kind of things are clients coming to you with, customers, and how's Acasta helping them to solve those problems?
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Yeah, well, I mean, what we see is there's too much guesswork that goes on in retail, not just on the shop floor, but often back in head office as well.
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So as a frontline operations platform, we help remove that guesswork.
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So the teams on the ground actually know what they need to do in any moment, and the leaders can see what's really happening across the business.
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So, I mean, for some specific advan um some specific examples, we're looking at things like comms and task management.
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But what we've noticed from a common pane from our customers is um lots of the comms they send out are actually tasks in disguise.
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So, an example of that, we uh we work with a retailer who had just opened a new flagship store.
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The head of operations was sat in the onboarding session for the 10 new starters that were joining that day.
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And each of them were told to go and read their communications.
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And the number one comms was the poster pricing issue.
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A certain product had the wrong um pricing on all of the posters.
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But then 10 people in that room all went and read that communication.
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But the head of operations knew that the good store manager at that location had already fixed that first thing in the morning.
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So 10 people had just gone and read something that was already resolved.
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They didn't even need to know it was a problem.
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Um, so that gave us a big push kind of 24 months ago to move towards what kind of working out what comms should actually be sent as tasks and making sure once one person in that location's done it is cleared for everyone to really reduce that noise and the wastage.
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Um, another one we're seeing is around that kind of knowledge and learning side.
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So we have a knowledge base, but what we do differently from a lot of our competitors is that same knowledge base can be extended into short bits of microlearning.
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So an ops team can have their SOPs, hit a quick button.
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Of course, we use a bit of AI behind the scenes to generate some suggested questions that they can tweak and change, and then turn that into a course in the matter of um minutes as well.
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Um, and unlike having, say, your intranet and the learning management system in another place, it really helps have that one version of the truth because the learning is powered by the knowledge side of things in there.
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Um, and then of course, all of your uh classic things like um doing those store visits, um, all of those audits and those tasks and pieces that have to happen in stores every day.
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Um, but my personal favorite bit that we do is around um observations and coaching.
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So a store manager observing one of their associates, interacting with a real customer, tracking that against the ideal sales journey, and then building out an action plan and a coaching framework off the back of it.
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And then the best bit is back in head office.
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The teams get a view of where all those skill gaps are across the whole organization, where they can focus their training and their initiatives.
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Interesting.
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So how does that work?
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We're not we're not videoing things, we're not implementing new technology in the locations, it's on a tablet, I assume, or a back up back office PC.
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What's what's the process look like for a colleague if my manager was observing me?
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Yeah, so a good point.
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Everything that we do is done through an app.
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So we do work on the web as well, but obviously you're trying to use a back office PC when you're observing a real kind of interaction doesn't work.
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But we've been asked in the past, well, can we not tap into the CCTV feeds and use AI to analyse analysize what's going on?
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But quite honest with you, I think it's too easy to forget that retailers, real people kind of talking to real people in real places, and you can be sat in a boardroom or a head office thinking, wouldn't it be great just to automate all of this?
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But I think that human judgment part, especially from the coaching side, is really important.
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So a typical observation would look like the store manager observing the associate, and that would have a kind of pre-made framework from head office to track through what kind of key behaviors they want to see.
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So from simple things, so we we work with lots of specialty retail.
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So from things like are they introducing themselves by name, are they asking the customer's name?
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So they then use it back in a sentence, and all the way through to recommending the right products, meeting the needs, and then obviously closing as well.
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I think it's about um 64% of retail associates don't actually ask for the sale.
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They'll be really good at building rapport with the customer, they'll understand the products really well, they'll make great recommendations, but they won't say, Are you ready to buy or should we take this to the till?
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So loads of really great kind of opportunities through observations to spot those gaps and then close them.
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And people listening might be thinking, that's fine.
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We use mystery shopping, we use, and I've talked about it a lot, surveys, and I I personally have survey fatigue.
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I get a survey for everything and fill them in for nothing.
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What would be the advantage you've got here of that uh one leader who understands the process and what happens every day in the relationship with a colleague and the way you capture it?
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Yeah, well, obviously, mystery shopping, first of all, gets very expensive very quickly.
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And for that reason, retailers don't deploy it at a level enough to get a good overview across their estate.
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It might be that you've captured one associate in in each store every quarter, and that's typically what a good retailer would be doing if they're doing mystery shop.
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So it's it really depends on that associate, what mood they're in.
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And of course, these um mystery shoppers often stick out like a sore thumb.
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They're not your ideal customer, they're probably a student looking to make some extra money, and you can spot them a mile off when they're coming into the store as well.
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So that's already going to skew the interaction and the data you get off the back of it as well.
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So for mystery shops, what I love about observations is you're capturing real customers in real moments, and you could argue, well, maybe the associate knows they're being observed, so they're gonna do a better job.
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But if you're doing these observations on each associate, say once or once a week or even just once a month, you're still building up a bigger snapshot and helping train them in where those gaps are.
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And of course, you mentioned um the kind of survey fatigue.
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To it to get the survey, you have to have actually transacted and purchased something.
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So you're only measuring the people that went through to buy.
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But I always think the more interesting bit to me is well, why aren't people buying?
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So one of the things that our um customers do on their observations is they'll say, like, was a transaction made or not, and then you can go and look at the data saying, okay, what behaviors, when these behaviors are missed, what's the impact on that on the transactions as well?
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So I I often think, you know, if you tell me the conversion rate that you've got in store, I can know what's happened.
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But if you show me the behaviors, we actually know what we can change to to kind of affect that metric.
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Interesting.
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So yeah, I like I like the point around some of the the pieces that are out there at the moment are only capturing if there's a completed transaction.
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And at best in retail, you know, if your conversion rate's 50% or over, you're doing really well.
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So there's more than half the customers you don't know why they didn't buy.
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And yeah, that'll be for a myriad of reasons.
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But moving that dial by one, two, three, four, five percent can have a significant impact on sales and therefore bottom line, can't it?
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Yeah, and I've got an example of that actually with um Victoria's Secret, launched us across their UK stores, and um, for them, one of their key metrics is I think it's about four out of five people that are offered a bra fitting will then go on to buy a bra.
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And so one of the key behaviors that they need their teams to do is offer that bra fitting um in there.
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And when they started, I believe we're at about 86% of customers that were being offered, which is a great number, but we got that up to 96 through those observations as well.
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And obviously, that's a metric where you've got really clear um return on investment and uh of doing that time and showing that that that behavior really does move the dial.
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And of course, it's a tricky balance.
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Lots of retailers sometimes I think shy away from wanting to talk about we would do this to improve sales.
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Often you kind of want that extra step of, well, it improves the customer journey and the customer experience.
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Um but I think observations bridge that gap perfectly because you get a really good mix of um the things that improve the behaviors and sorry, the things that improve the experience.
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But those are the things that move the dial at the end of the day.
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If we're having a better customer experience, we're gonna see those sales um kind of kick in from there.
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Yeah, and in a world, and we've talked about it endlessly on the podcast, probably in the last 12 to 18 months, where wages are going up, um turnovers are a lot higher, certainly in retail and hospitality, people aren't staying very long.
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You're probably gonna be faced with more cost challenges year on year with national living wage, when probably you are.
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It's really important that the people that are there are coached, developed, but are also trying to sell more because you're not you're not gonna keep saving your way out of where we're at.
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That race to the bottom's done and run.
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Now it's about yes, I may have a smaller pool of people, but how can they be more effective?
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And that could be effective, you know, but rethink we're interested in processing stuff, but actually more effective from a conversion and therefore selling, because the more I sell, the more people are getting my budget typically moving forward, the better the company does.
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So it it has a real big knock-on effect in that sphere, doesn't it?
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Of you can't save your way out of this, but you probably can sell your way out of it.
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That's it, yeah.
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And as you point out, it's it's a blend of the two.
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But I often think people are way too focused on how do we save money and not how do we make money.
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And the two do go hand in hand.
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Yeah, absolutely.
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Okay, so interesting journey.
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Love, love all that around the coaching and the developing sales, and certainly on the the why people didn't buy and what could you do differently?
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You can't sell to everybody, but if you can increase it by percentage points, that's a big difference.
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Where are you looking in the in the future then?
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So you mentioned AI and we talked again offline about everything that's AI now and and where's it going?
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From a business point of view, where do you see the big opportunities for a caster using using AI and and where's that going to take you?
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Yeah, I mean, we've already got it broadly across the platform, um, things like using that knowledge base I mentioned to give instant answers.
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Um, and we search across all of the comms and the other areas, so someone can say, is the till system broken today?
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And it'll give them a quick kind of Google-like AI summary at the top of the results there just to make sure that people are getting the answers.
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So I think that's a big thing we're seeing across all the industries at the moment is a shift away from um, yeah, don't give me the things to read, give me the answers that I need.
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Um, from the and obviously content generation, I've mentioned the ability to quickly create that that micro learning straight from your SOPs that you've already got.
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Um for me, a big piece around our AI strategy is to make sure that we are remembering, as I kind of hinted at earlier, that these are real people in real environments talking to real people.
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And we need to not forget that when we're building in any AI pieces in there.
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So um we recently did a webinar all around the kind of keeping human judgment in anything that's done with AI and making sure that we're not making decisions for people, we're just making sure that they're informed and prioritized as to what they should do.
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So if we stick with the coaching theme, um, we've obviously spoken about the observation side, but it's what comes after the coat the observation that actually really has an impact on to closing those gaps.
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So right now in the platform, the manager can create action plans and tasks and create a kind of uh continue and begin framework for each of their colleagues off the back of each observation.
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But what we're seeing is managers aren't necessarily the best coaches.
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They were brought into their role because they were probably had a good relationship with the team, really good operationally, but maybe lack some of that um LD um abilities that um is something you really need to be a good coach.
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So um we're just about to launch an AI coaching assistant.
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So what that does for each observation is it analyzes the ideal journey you want the customers to go on.
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It looks at the real data based on how the manager um observed that behavior and then gives some really good, succinct snapshots of recommended tasks.
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It looks at the learning that's on the platform and suggests what learning they might want to assign to that associate.
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But it's all very much to guide the manager's conversation and not replace their role in the store.
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Because I I think it's really important the manager's got their finger on the pulse of how their team are performing and behaving, and also just giving them a that associate a load of information isn't gonna solve those issues.
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But giving the manager the right questions to ask to build up a conversation, um, helping build that out into those action plans becomes a really powerful tool.
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And um, we've made sure to, I don't know if you've seen this, but a lot of the kind of um Gen Zs in the workplace really want to know the why behind whatever they're being asked.
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So across the platform, whenever we're using AI, we always make sure we put the why for anything that we're generating, whether that's uh a task that's been um flagged off the back of an observation, like why is this important?
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And it could be as simple as this helps improve conversions or this improves the customer experience, or it might be a bit more nuanced depending on the depth of it as well.
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So there's some of the kind of key areas.
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Um, and then another piece we want to look at is how do we do more of those performance reviews when we're looking across multiple observations and giving the manager the right kind of uh prompts to guide that conversation as well.
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Excellent.
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Yeah, so I think you the word you just said there at the end was prompt.
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So helping the manager, sifting away all the sorting through documents and keeping the consistency across the colleague base is all really helpful stuff, and I assume ends up with better quality and a more sustained approach and hopefully an uplift then in quality at the conversational end as we get back to the customer.
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Yeah, and it's interesting because uh I mean for me, I the thought of being observed sometimes feels a bit like overwhelming, just let me get on with it and do my job.
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But in the the times I've been in stores and seen an observation happen, the the associate and colleague often runs straight over to the manager once the customer leaves saying, How did I do?
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What could I do better?
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Um, which which did really surprise me, that kind of openness and eagerness um to be coached.
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And and I think that's again where those conversations and discussions become really powerful, and that's where we don't want AI to replace that human judgment and interaction.
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Because obviously if if AI could replace that, then AI can replace retail, and you don't need the stores, but I don't think anyone um uh anyone in the kind of retail community would think that's the right thing to do.
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No, no, and an interesting example as well, which shows that that particular organisation, the culture was about learning, understanding and improving, and not kind of big brothers watching these are all the things that you did wrong, must do better.
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So, yeah, a really good example of how it can be used to drive performance.
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So, Ben, we'll we'll pause there and we will see you at the forum on the 10th of September in Birmingham.
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And if people want to come over, then you've got um stand and you'll be able to show them some of the stuff we've talked about and go through any other questions, I assume.
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Yep, we can give people an early look at the coaching assistants and really keen to get some uh some feedback from everyone at the show as to what the how they do their observations and uh help tweak and uh shape the platform.
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Amazing.
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Look forward to catching up and thanks for coming on.
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We'll speak soon.
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Anytime.
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Thanks, Simon.
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Welcome to the Productivity Podcast.
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Today I am joined by Ben Collier, co-founder at Acaster.
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Hi Ben.
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Good to see you, Simon.
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How are things?
00:00:14.509 --> 00:00:15.149
Yeah, good.
00:00:15.230 --> 00:00:22.750
And we're looking forward to welcoming you at the Productivity Forum in Birmingham on the 10th of September, which is next month at the time recording.
00:00:22.830 --> 00:00:24.990
So we'll we're looking forward to welcoming you and the team there.
00:00:25.150 --> 00:00:27.230
Yep, it'll be our first time there, but we're very excited.
00:00:27.310 --> 00:00:28.910
We've heard very good things about it.
00:00:29.310 --> 00:00:29.710
Good.
00:00:29.949 --> 00:00:30.350
Good.
00:00:30.510 --> 00:00:35.390
So we will find out a bit more about a caster then before the event and yourself.
00:00:35.469 --> 00:00:42.189
So let's start with you, Ben, uh co-founder, but that's probably not what you've done since you left college uni, whatever.
00:00:42.270 --> 00:00:44.509
So do you want to give us a bit of a potted career history?
00:00:44.750 --> 00:00:49.070
Yeah, well, truth be told, we did set up a caster not very long after I'd left university.
00:00:49.149 --> 00:00:51.789
So we've been in business 16 years or so now.
00:00:52.030 --> 00:00:55.469
When we set the company up, we were an app development agency.
00:00:55.549 --> 00:01:00.030
And as you can imagine, back in kind of 2011, apps were the hot new thing.
00:01:00.109 --> 00:01:03.469
Um so we were creating apps for the likes of Virgin Media.
00:01:03.549 --> 00:01:13.469
We created their TV guide app before TiVo had one ready and kind of really helped um get lots of um UK brands up and running with some really cool tech.
00:01:13.790 --> 00:01:25.870
Um, but it was in about kind of, and back then I was uh developer at heart um and setting up a business, it was kind of uh development by day and uh project management by night, or a bit vice versa there.
00:01:26.269 --> 00:02:01.549
But I'd say in around about 2014 or 2015, not through any uh grand plan, but through a bit of serendipity, a few desks down at Virgin Media sat um one of the retail operations team um down from um the um our main contact there and the ops team there were looking at digitizing their operations manual, getting it off of uh an old SharePoint um website that was on one back of house PC and the uh printed A4 binder uh where they were sending out uh quarterly updates saying swap page 14 for this page and all of that stuff.
00:02:01.709 --> 00:02:15.869
Um so so we helped them on the project there and kind of started moving away from creating apps for consumers, but moving across into kind of bespoke projects that were um for retail and uh kind of um big enterprises behind the scenes.
00:02:16.029 --> 00:02:21.149
Um what I really liked about that was the stuff that we brought across was we we kept that consumer simplicity.
00:02:21.309 --> 00:02:26.189
So everything we do um is still as easy to use as the apps that you go and get from the app store.
00:02:26.429 --> 00:02:33.629
Um because uh, I mean, especially back in 2015, the tools that employees were given at work were far worse than the tools they had in their personal lives.
00:02:34.750 --> 00:02:36.269
So long journey.
00:02:36.429 --> 00:02:42.269
Um the world's changed, and Offair we were talking about and how AI's changed the world.
00:02:42.509 --> 00:02:58.349
So when you first started, it was around the app development, and then obviously we all kind of end up in a a situation we look back on that may have been fortunate, pre-planned, random, that kind of gives us a pivot, I suppose.
00:02:58.509 --> 00:03:07.149
So, was that a conscious pivot away from just developing apps, or was it just based on that conversation and it kind of then put you on a different trajectory?
00:03:07.469 --> 00:03:08.269
I mean, pretty much that.
00:03:08.349 --> 00:03:10.349
It spun us off onto a different trajectory.
00:03:10.429 --> 00:03:19.789
We started doing bespoke apps for enterprises, and then quite quickly realized we were building out a really good suite of tools for all of these different pain points that were coming.
00:03:19.949 --> 00:03:25.789
So I suppose uh, I mean, we're bootstrapped, so we're we're not kind of VC funded and scratching our heads thinking what should we build next.
00:03:25.949 --> 00:03:41.549
We had kind of real retail and frontline customers coming to us with real pains, and then over the years realized we had enough of those building blocks to turn it into a proper frontline operations platform and uh take it to market, which is and what we've been doing for uh the last six or so years now.
00:03:41.949 --> 00:03:46.269
So we're out there, we've got the platform.
00:03:46.509 --> 00:03:47.869
You talked about pain points.
00:03:48.029 --> 00:03:55.069
So, what what kind of things are clients coming to you with, customers, and how's Acasta helping them to solve those problems?
00:03:55.309 --> 00:04:02.429
Yeah, well, I mean, what we see is there's too much guesswork that goes on in retail, not just on the shop floor, but often back in head office as well.
00:04:02.669 --> 00:04:06.429
So as a frontline operations platform, we help remove that guesswork.
00:04:06.509 --> 00:04:12.509
So the teams on the ground actually know what they need to do in any moment, and the leaders can see what's really happening across the business.
00:04:12.669 --> 00:04:19.550
So, I mean, for some specific advan um some specific examples, we're looking at things like comms and task management.
00:04:19.709 --> 00:04:26.829
But what we've noticed from a common pane from our customers is um lots of the comms they send out are actually tasks in disguise.
00:04:26.990 --> 00:04:32.110
So, an example of that, we uh we work with a retailer who had just opened a new flagship store.
00:04:32.269 --> 00:04:38.269
The head of operations was sat in the onboarding session for the 10 new starters that were joining that day.
00:04:38.349 --> 00:04:40.909
And each of them were told to go and read their communications.
00:04:40.990 --> 00:04:44.029
And the number one comms was the poster pricing issue.
00:04:44.110 --> 00:04:47.709
A certain product had the wrong um pricing on all of the posters.
00:04:47.789 --> 00:04:50.829
But then 10 people in that room all went and read that communication.
00:04:50.990 --> 00:04:56.189
But the head of operations knew that the good store manager at that location had already fixed that first thing in the morning.
00:04:56.349 --> 00:04:59.310
So 10 people had just gone and read something that was already resolved.
00:04:59.389 --> 00:05:01.069
They didn't even need to know it was a problem.
00:05:01.229 --> 00:05:15.310
Um, so that gave us a big push kind of 24 months ago to move towards what kind of working out what comms should actually be sent as tasks and making sure once one person in that location's done it is cleared for everyone to really reduce that noise and the wastage.
00:05:15.709 --> 00:05:20.430
Um, another one we're seeing is around that kind of knowledge and learning side.
00:05:20.589 --> 00:05:28.589
So we have a knowledge base, but what we do differently from a lot of our competitors is that same knowledge base can be extended into short bits of microlearning.
00:05:28.750 --> 00:05:32.269
So an ops team can have their SOPs, hit a quick button.
00:05:32.430 --> 00:05:42.029
Of course, we use a bit of AI behind the scenes to generate some suggested questions that they can tweak and change, and then turn that into a course in the matter of um minutes as well.
00:05:42.189 --> 00:05:52.430
Um, and unlike having, say, your intranet and the learning management system in another place, it really helps have that one version of the truth because the learning is powered by the knowledge side of things in there.
00:05:52.509 --> 00:06:02.750
Um, and then of course, all of your uh classic things like um doing those store visits, um, all of those audits and those tasks and pieces that have to happen in stores every day.
00:06:02.909 --> 00:06:08.110
Um, but my personal favorite bit that we do is around um observations and coaching.
00:06:08.349 --> 00:06:20.430
So a store manager observing one of their associates, interacting with a real customer, tracking that against the ideal sales journey, and then building out an action plan and a coaching framework off the back of it.
00:06:20.669 --> 00:06:22.829
And then the best bit is back in head office.
00:06:23.069 --> 00:06:29.389
The teams get a view of where all those skill gaps are across the whole organization, where they can focus their training and their initiatives.
00:06:29.789 --> 00:06:30.430
Interesting.
00:06:30.589 --> 00:06:32.430
So how does that work?
00:06:32.589 --> 00:06:41.310
We're not we're not videoing things, we're not implementing new technology in the locations, it's on a tablet, I assume, or a back up back office PC.
00:06:41.389 --> 00:06:47.069
What's what's the process look like for a colleague if my manager was observing me?
00:06:47.389 --> 00:06:48.829
Yeah, so a good point.
00:06:48.909 --> 00:06:51.389
Everything that we do is done through an app.
00:06:51.550 --> 00:06:58.189
So we do work on the web as well, but obviously you're trying to use a back office PC when you're observing a real kind of interaction doesn't work.
00:06:58.349 --> 00:07:04.990
But we've been asked in the past, well, can we not tap into the CCTV feeds and use AI to analyse analysize what's going on?
00:07:05.229 --> 00:07:16.750
But quite honest with you, I think it's too easy to forget that retailers, real people kind of talking to real people in real places, and you can be sat in a boardroom or a head office thinking, wouldn't it be great just to automate all of this?
00:07:16.909 --> 00:07:20.829
But I think that human judgment part, especially from the coaching side, is really important.
00:07:21.069 --> 00:07:34.189
So a typical observation would look like the store manager observing the associate, and that would have a kind of pre-made framework from head office to track through what kind of key behaviors they want to see.
00:07:34.349 --> 00:07:37.949
So from simple things, so we we work with lots of specialty retail.
00:07:38.029 --> 00:07:42.269
So from things like are they introducing themselves by name, are they asking the customer's name?
00:07:42.430 --> 00:07:50.430
So they then use it back in a sentence, and all the way through to recommending the right products, meeting the needs, and then obviously closing as well.
00:07:50.589 --> 00:07:55.709
I think it's about um 64% of retail associates don't actually ask for the sale.
00:07:55.870 --> 00:08:03.870
They'll be really good at building rapport with the customer, they'll understand the products really well, they'll make great recommendations, but they won't say, Are you ready to buy or should we take this to the till?
00:08:04.189 --> 00:08:09.629
So loads of really great kind of opportunities through observations to spot those gaps and then close them.
00:08:10.029 --> 00:08:13.789
And people listening might be thinking, that's fine.
00:08:13.949 --> 00:08:20.829
We use mystery shopping, we use, and I've talked about it a lot, surveys, and I I personally have survey fatigue.
00:08:20.909 --> 00:08:23.469
I get a survey for everything and fill them in for nothing.
00:08:23.789 --> 00:08:35.870
What would be the advantage you've got here of that uh one leader who understands the process and what happens every day in the relationship with a colleague and the way you capture it?
00:08:36.190 --> 00:08:40.909
Yeah, well, obviously, mystery shopping, first of all, gets very expensive very quickly.
00:08:40.990 --> 00:08:47.470
And for that reason, retailers don't deploy it at a level enough to get a good overview across their estate.
00:08:47.549 --> 00:08:55.789
It might be that you've captured one associate in in each store every quarter, and that's typically what a good retailer would be doing if they're doing mystery shop.
00:08:55.870 --> 00:08:59.309
So it's it really depends on that associate, what mood they're in.
00:08:59.549 --> 00:09:03.870
And of course, these um mystery shoppers often stick out like a sore thumb.
00:09:03.949 --> 00:09:12.189
They're not your ideal customer, they're probably a student looking to make some extra money, and you can spot them a mile off when they're coming into the store as well.
00:09:12.269 --> 00:09:17.309
So that's already going to skew the interaction and the data you get off the back of it as well.
00:09:17.549 --> 00:09:28.509
So for mystery shops, what I love about observations is you're capturing real customers in real moments, and you could argue, well, maybe the associate knows they're being observed, so they're gonna do a better job.
00:09:28.669 --> 00:09:38.750
But if you're doing these observations on each associate, say once or once a week or even just once a month, you're still building up a bigger snapshot and helping train them in where those gaps are.
00:09:38.990 --> 00:09:42.669
And of course, you mentioned um the kind of survey fatigue.
00:09:42.909 --> 00:09:46.590
To it to get the survey, you have to have actually transacted and purchased something.
00:09:46.669 --> 00:09:50.110
So you're only measuring the people that went through to buy.
00:09:50.189 --> 00:09:53.389
But I always think the more interesting bit to me is well, why aren't people buying?
00:09:53.549 --> 00:10:08.909
So one of the things that our um customers do on their observations is they'll say, like, was a transaction made or not, and then you can go and look at the data saying, okay, what behaviors, when these behaviors are missed, what's the impact on that on the transactions as well?
00:10:09.149 --> 00:10:16.110
So I I often think, you know, if you tell me the conversion rate that you've got in store, I can know what's happened.
00:10:16.189 --> 00:10:21.870
But if you show me the behaviors, we actually know what we can change to to kind of affect that metric.
00:10:22.830 --> 00:10:23.470
Interesting.
00:10:23.629 --> 00:10:31.870
So yeah, I like I like the point around some of the the pieces that are out there at the moment are only capturing if there's a completed transaction.
00:10:32.029 --> 00:10:37.149
And at best in retail, you know, if your conversion rate's 50% or over, you're doing really well.
00:10:37.230 --> 00:10:39.629
So there's more than half the customers you don't know why they didn't buy.
00:10:39.870 --> 00:10:42.669
And yeah, that'll be for a myriad of reasons.
00:10:42.830 --> 00:10:51.789
But moving that dial by one, two, three, four, five percent can have a significant impact on sales and therefore bottom line, can't it?
00:10:52.029 --> 00:11:06.669
Yeah, and I've got an example of that actually with um Victoria's Secret, launched us across their UK stores, and um, for them, one of their key metrics is I think it's about four out of five people that are offered a bra fitting will then go on to buy a bra.
00:11:07.230 --> 00:11:12.350
And so one of the key behaviors that they need their teams to do is offer that bra fitting um in there.
00:11:12.509 --> 00:11:22.110
And when they started, I believe we're at about 86% of customers that were being offered, which is a great number, but we got that up to 96 through those observations as well.
00:11:22.269 --> 00:11:32.029
And obviously, that's a metric where you've got really clear um return on investment and uh of doing that time and showing that that that behavior really does move the dial.
00:11:32.269 --> 00:11:34.189
And of course, it's a tricky balance.
00:11:34.350 --> 00:11:39.549
Lots of retailers sometimes I think shy away from wanting to talk about we would do this to improve sales.
00:11:39.710 --> 00:11:45.149
Often you kind of want that extra step of, well, it improves the customer journey and the customer experience.
00:11:45.309 --> 00:11:55.230
Um but I think observations bridge that gap perfectly because you get a really good mix of um the things that improve the behaviors and sorry, the things that improve the experience.
00:11:55.470 --> 00:11:57.629
But those are the things that move the dial at the end of the day.
00:11:57.710 --> 00:12:02.269
If we're having a better customer experience, we're gonna see those sales um kind of kick in from there.
00:12:02.590 --> 00:12:16.029
Yeah, and in a world, and we've talked about it endlessly on the podcast, probably in the last 12 to 18 months, where wages are going up, um turnovers are a lot higher, certainly in retail and hospitality, people aren't staying very long.
00:12:16.509 --> 00:12:22.669
You're probably gonna be faced with more cost challenges year on year with national living wage, when probably you are.
00:12:22.990 --> 00:12:33.470
It's really important that the people that are there are coached, developed, but are also trying to sell more because you're not you're not gonna keep saving your way out of where we're at.
00:12:33.629 --> 00:12:35.629
That race to the bottom's done and run.
00:12:35.949 --> 00:12:41.149
Now it's about yes, I may have a smaller pool of people, but how can they be more effective?
00:12:41.230 --> 00:12:54.350
And that could be effective, you know, but rethink we're interested in processing stuff, but actually more effective from a conversion and therefore selling, because the more I sell, the more people are getting my budget typically moving forward, the better the company does.
00:12:54.429 --> 00:12:58.669
So it it has a real big knock-on effect in that sphere, doesn't it?
00:12:58.830 --> 00:13:02.909
Of you can't save your way out of this, but you probably can sell your way out of it.
00:13:03.230 --> 00:13:03.870
That's it, yeah.
00:13:03.949 --> 00:13:05.789
And as you point out, it's it's a blend of the two.
00:13:05.870 --> 00:13:10.189
But I often think people are way too focused on how do we save money and not how do we make money.
00:13:10.269 --> 00:13:11.710
And the two do go hand in hand.
00:13:11.949 --> 00:13:12.909
Yeah, absolutely.
00:13:13.149 --> 00:13:15.389
Okay, so interesting journey.
00:13:15.549 --> 00:13:22.029
Love, love all that around the coaching and the developing sales, and certainly on the the why people didn't buy and what could you do differently?
00:13:22.110 --> 00:13:27.789
You can't sell to everybody, but if you can increase it by percentage points, that's a big difference.
00:13:28.110 --> 00:13:30.509
Where are you looking in the in the future then?
00:13:30.669 --> 00:13:36.830
So you mentioned AI and we talked again offline about everything that's AI now and and where's it going?
00:13:36.990 --> 00:13:44.269
From a business point of view, where do you see the big opportunities for a caster using using AI and and where's that going to take you?
00:13:44.590 --> 00:13:51.629
Yeah, I mean, we've already got it broadly across the platform, um, things like using that knowledge base I mentioned to give instant answers.
00:13:51.710 --> 00:13:56.669
Um, and we search across all of the comms and the other areas, so someone can say, is the till system broken today?
00:13:56.750 --> 00:14:02.990
And it'll give them a quick kind of Google-like AI summary at the top of the results there just to make sure that people are getting the answers.
00:14:03.069 --> 00:14:12.429
So I think that's a big thing we're seeing across all the industries at the moment is a shift away from um, yeah, don't give me the things to read, give me the answers that I need.
00:14:12.590 --> 00:14:20.029
Um, from the and obviously content generation, I've mentioned the ability to quickly create that that micro learning straight from your SOPs that you've already got.
00:14:20.269 --> 00:14:31.549
Um for me, a big piece around our AI strategy is to make sure that we are remembering, as I kind of hinted at earlier, that these are real people in real environments talking to real people.
00:14:31.710 --> 00:14:36.669
And we need to not forget that when we're building in any AI pieces in there.
00:14:36.830 --> 00:14:51.710
So um we recently did a webinar all around the kind of keeping human judgment in anything that's done with AI and making sure that we're not making decisions for people, we're just making sure that they're informed and prioritized as to what they should do.
00:14:51.870 --> 00:15:03.309
So if we stick with the coaching theme, um, we've obviously spoken about the observation side, but it's what comes after the coat the observation that actually really has an impact on to closing those gaps.
00:15:03.549 --> 00:15:14.269
So right now in the platform, the manager can create action plans and tasks and create a kind of uh continue and begin framework for each of their colleagues off the back of each observation.
00:15:14.509 --> 00:15:19.230
But what we're seeing is managers aren't necessarily the best coaches.
00:15:19.389 --> 00:15:32.509
They were brought into their role because they were probably had a good relationship with the team, really good operationally, but maybe lack some of that um LD um abilities that um is something you really need to be a good coach.
00:15:32.750 --> 00:15:36.509
So um we're just about to launch an AI coaching assistant.
00:15:36.669 --> 00:15:42.189
So what that does for each observation is it analyzes the ideal journey you want the customers to go on.
00:15:42.269 --> 00:15:52.669
It looks at the real data based on how the manager um observed that behavior and then gives some really good, succinct snapshots of recommended tasks.
00:15:52.909 --> 00:15:57.549
It looks at the learning that's on the platform and suggests what learning they might want to assign to that associate.
00:15:57.710 --> 00:16:03.789
But it's all very much to guide the manager's conversation and not replace their role in the store.
00:16:04.029 --> 00:16:16.029
Because I I think it's really important the manager's got their finger on the pulse of how their team are performing and behaving, and also just giving them a that associate a load of information isn't gonna solve those issues.
00:16:16.110 --> 00:16:23.949
But giving the manager the right questions to ask to build up a conversation, um, helping build that out into those action plans becomes a really powerful tool.
00:16:24.110 --> 00:16:33.949
And um, we've made sure to, I don't know if you've seen this, but a lot of the kind of um Gen Zs in the workplace really want to know the why behind whatever they're being asked.
00:16:34.110 --> 00:16:45.549
So across the platform, whenever we're using AI, we always make sure we put the why for anything that we're generating, whether that's uh a task that's been um flagged off the back of an observation, like why is this important?
00:16:45.789 --> 00:16:53.309
And it could be as simple as this helps improve conversions or this improves the customer experience, or it might be a bit more nuanced depending on the depth of it as well.
00:16:53.470 --> 00:16:55.789
So there's some of the kind of key areas.
00:16:55.870 --> 00:17:07.150
Um, and then another piece we want to look at is how do we do more of those performance reviews when we're looking across multiple observations and giving the manager the right kind of uh prompts to guide that conversation as well.
00:17:07.470 --> 00:17:07.870
Excellent.
00:17:08.029 --> 00:17:11.470
Yeah, so I think you the word you just said there at the end was prompt.
00:17:11.710 --> 00:17:34.590
So helping the manager, sifting away all the sorting through documents and keeping the consistency across the colleague base is all really helpful stuff, and I assume ends up with better quality and a more sustained approach and hopefully an uplift then in quality at the conversational end as we get back to the customer.
00:17:34.910 --> 00:17:43.070
Yeah, and it's interesting because uh I mean for me, I the thought of being observed sometimes feels a bit like overwhelming, just let me get on with it and do my job.
00:17:43.230 --> 00:17:52.509
But in the the times I've been in stores and seen an observation happen, the the associate and colleague often runs straight over to the manager once the customer leaves saying, How did I do?
00:17:52.590 --> 00:17:53.550
What could I do better?
00:17:53.630 --> 00:17:59.550
Um, which which did really surprise me, that kind of openness and eagerness um to be coached.
00:17:59.630 --> 00:18:06.750
And and I think that's again where those conversations and discussions become really powerful, and that's where we don't want AI to replace that human judgment and interaction.
00:18:06.910 --> 00:18:17.950
Because obviously if if AI could replace that, then AI can replace retail, and you don't need the stores, but I don't think anyone um uh anyone in the kind of retail community would think that's the right thing to do.
00:18:18.270 --> 00:18:32.990
No, no, and an interesting example as well, which shows that that particular organisation, the culture was about learning, understanding and improving, and not kind of big brothers watching these are all the things that you did wrong, must do better.
00:18:33.150 --> 00:18:38.030
So, yeah, a really good example of how it can be used to drive performance.
00:18:38.350 --> 00:18:45.790
So, Ben, we'll we'll pause there and we will see you at the forum on the 10th of September in Birmingham.
00:18:45.870 --> 00:18:55.950
And if people want to come over, then you've got um stand and you'll be able to show them some of the stuff we've talked about and go through any other questions, I assume.
00:18:56.270 --> 00:19:07.790
Yep, we can give people an early look at the coaching assistants and really keen to get some uh some feedback from everyone at the show as to what the how they do their observations and uh help tweak and uh shape the platform.
00:19:08.110 --> 00:19:08.750
Amazing.
00:19:08.910 --> 00:19:10.910
Look forward to catching up and thanks for coming on.
00:19:10.990 --> 00:19:11.630
We'll speak soon.
00:19:11.870 --> 00:19:12.350
Anytime.
00:19:12.430 --> 00:19:13.150
Thanks, Simon.
