Growing Self-Storage with AI – Interview with Tim Slesinger, CEO of easyStorage

Tim Slesinger

CEO easyStorage – Tim has partnered with Sir Stelios and the easyGroup, as his brand partner, to offer a sensibly priced alternative to self-storage.

Previously, he had 30 years’ experience establishing businesses in Hong Kong and Eastern Europe including DX in Asia and OSG Records Management in Central & Eastern Europe, China, Turkey and Saudi Arabia.  He expanded OSG to 11 countries and 37 cities before selling it.

UL: Can you please tell us a little bit about yourself, about easyStorage, and your role there? 

I am the CEO and co-founder of easyStorage. Nigel Dawson was the other co-founder. My background is in storage and operations. Before easyStorage, I set up a document storage, or what's known as a records management company, in Central and Eastern Europe, based out of Russia. We grew it to 11 countries and 37 cities. I learned a lot about scale at that point. Nigel is very much a franchise professional. He has been in franchiseing for about 35 years and is very respected in that field. Our mission at easyStorage is to be the go-to brand for storage. It is through technology and efficiencies in operations that we can make it a cheaper option and therefore an affordable lifestyle choice for our customers. My role is to put all of that together: to make sure you've got the right funds, the right team members, the right technology, and very importantly, the right processes to make all of that happen.

UL: What were the biggest issues or bottlenecks you identified before improving your automation and CRM processes, and how did you go about solving these issues?

Data, data, data. Having access to reliable, meaningful data is one of the biggest challenges when you're a fast-growing company that is changing systems and improving systems all the time. We're still in the process of solving it ourselves. We brought on board Unwired Logic to make our systems much more fluid and much more fit for purpose for very large scale in the digital world.

We have a bespoke ERP system called Vault, which we built from the ground up. It's basically a franchise management system, a customer management system, and a customer booking system. So, it's trying to do a lot of things all at once. We decided very early on not to build a billing system or, of course, an accounting system ourselves, but to use a third-party solution. We also have a third-party CRM system. I think the biggest challenge was not knowing how to operate those internally. The setup wasn't quite as it should be. And I think we've paid the price for that ongoing.

We've got several sources of truth that conflict with each other. What Aaron and his team are doing at Unwired Logic is to make sense of that and make sure that there is one source of truth. We're quite deep into that project at the moment, which hopefully will be finished in Q1 next year.

UL: When you started making improvements, what processes did you decide to standardize first, and why did you choose those processes?

The first one was to implement Microsoft BI and to standardize the terminology. This is something that Unwired Logic was very keen to do first. Get a lexicon, define terminology, and what it means for us. And that was a lot more complicated than I thought it would be. But I think that it drew out where the problems were. For example, one of the issues we had, you'd ask one person: “How many bookings did we have today?”, and someone might say “100 ”, and then someone else would say: “No, no, it's 110”, to which then someone else would say “You’re both wrong. It's 90”

You'd end up with three different numbers from three different people. That happened because the salesperson, for example, would see 100 bookings, but in my eyes, there were 10 cancellations, so it only came to 90, but a transport person would say, “I actually did 110 bookings, even though they weren't booked in that month.” You can see with this very simple example that you can come at data from a different angle depending on the viewpoint. We've got about 300 use cases like that which are being worked through at the moment.

UL: When it comes to CRM and having good CRM discipline, what does that look like in practice? When you implement better CRM discipline, what changes day to day for your team?

I think what's very important for CRM is not overcomplicating it. We have been using HubSpot as an outsourced CRM system. I think HubSpot is a very, very good tool for longer sales cycles. We're predominantly a B2C business consumer. Our sales cycle is quite short. Most of our jobs are done within 10 days. Bookings are done within 10 days, maybe a month. One thing I wish we could have done with HubSpot was to get rid of all of the functionality and the dashboards that we weren't using, but we never were able to do that. It was always very confusing. Now Unwired Logic is implementing Zendesk. A lot of people say it is predominantly a ticketing system, really just for customer experience, but it's really a ticketing system that allows you to follow processes: This happened, this then needs to happen. If that doesn't happen, something else needs to happen within this timeline. That, I think, is very, very important for us. A fit-for-purpose system that is easily operated and, very, very importantly, easy to get data out of.

UL: You mentioned earlier a little bit that one of the biggest issues is data quality, right? Can we talk a little bit more about how to tackle those issues?

We have a product manager, Akhil Sharma, who's been with us for quite a long time, for about three years. He has been building version two of Vault and is really the custodian of data. But as I say, we've overcomplicated things, so at the moment, Unwired Logic is a temporary custodian of the truth of data. It's all about unpacking it and validating it to make sure that it's accurate and then building reports back up, because a lot of data is not single data points. They're a calculation of one or more data points that create an output, especially when you're talking about averages.

UL: What was the first automation you implemented that delivered a clear win? And what was it that made it work?

Oh, there are so many projects we've got on at the moment. We've got an AI project that's happening.

I think a lot of these happen concurrently, so I can't say just one. The AI project helped us get more calls. We had quite a high percentage of calls that we were missing on the first call. I think it was about 43%. We've implemented a voice AI agent, which has brought that down to closer to 6% or 7%. That means that a lot of the issues that people want to deal with were dealt with by the AI agent. There were things like signposting, handing off links so that they can do self-serve, but also some of it was just informational. That was one very big win.

We've also implemented an automatic sign-on process where we've got digital signatures, ID verification, and credit checks. Since that has been implemented, it enables people to book into our drive-up self-storage seamlessly. That's been a very good project.

We also have, as I say, the data project that's still ongoing, but it's iterative. So, we're getting value for that as we go along.

Also, the sales processes. Simplifying sales processes, both physical and digital, with sequencing: for example, physical phone calls to follow up by SMS or by email has been very successful.

Those are four of probably about 12 projects that are underway.

UL: How was the reception among your customers, and did it perform as well as you thought it would?

I think it's the classic case of three steps forward, one step back. Yes, it's performing well, depending on what we're looking at. It goes backwards sometimes, but the general direction of travel is definitely forward.

Some customers have asked: “Am I speaking to an AI?” They weren't sure. That's got to be a positive thing. I think the general feedback has been: “Very helpful”. Eve, that is the name of our AI, has been very helpful. We definitely get a few cases where Eve hallucinates a little bit. But with the help of Unwired Logic and our training manager, Akmeela Khan, who is reviewing all of the transcripts on a daily basis, we are retraining Eve. There's a great quote: “AI will never be as bad as it is today”. And I'm not saying it's bad, but it's going to improve so much.

UL: To tie into this, based on your experience, what makes a facility truly AI-ready?

What will make our it truly AI-ready is that customers enter sites that are completely unmanned, by design. When a customer arrives, they should be able to choose how they get help, whether it is “How do I do this?” or “Where do I find that?” I don't think people necessarily need to speak to a human, but they can speak to an AI avatar. It does not have to be voice only, either, but can be a full visual experience with an on-screen avatar that feels like a real assistant. When the AI realises that a customer needs to be handed off to a human, it needs to be able to do that. You always have to have the ability of an AI to hand off to a human. That's on one side.

We have AI security in our facilities. It has been in place for a few years now. The AI system can sense if there's unusual behavior. If someone comes in with a balaclava over their head and they've got a crowbar or a blowtorch, it will alert a human, then a human will conform, whether it needs to be investigated further. There's a lot more on the AI side. For example, if someone's climbing onto one of the facilities rather than going through the door, all of this kind of stuff. We also have an unmanned gate entry system. I wouldn't say that's AI, it's automation. But I guess AI is a form of automation. All of our storage units have Bluetooth locks, and there are many access levels. For instance, our customers are allowed to give further access to people, even if it's for one hour or a few days, or ongoing. We have time limits based on whether they have standard access or extended 24-hour access. If the account is in arrears, it doesn't allow access and all that. I think there's a mix there of what is automation and what is AI. I think the two become blended.

UL: For an operator who is now starting this journey, what advice would you give them to fix before they touch AI tools?

They need to have a very clear knowledge base because the AI tools will use that as a source of reference. You need to have a very clear understanding of what you want them to be able to do and not do. When does the handoff come? They should not expect AI to solve every problem straight away. It's not going to happen. Start off with the simplest things and make sure that those are working well.

UL: How did you get your team comfortable with the new systems and the new automations? Can you share some change management tips?

It's an ongoing process. I think the main way of implementing change is to show results. I think you need to explain to people why the change is happening, what results are expected. Specifically, what results in their world, in their life, they can expect as an enhancement or as a benefit to them. Then, when the results do come through, to measure those against the expectations, and hopefully it will be a positive, because it did do what you thought it would do. Those are the main things. I think, in our case, since we're scaling very, very quickly, everyone can feel growing pains. To be able to show that we're using technology effectively not only improves their lives but to enables us to scale quickly is generally well accepted. 

UL: Last question. What's next for you in automation or AI experimentation? What are you excited about?

Other things that we're working on, other than the customer service, because of everything we've talked about that already, are price management and also financial analysis. With the price management, we're looking at very much AI-driven yield management to make sure that we're getting the best prices possible within a spectrum. We are a value proposition. We're part of the easy family of brands like easyJet and all of those. We all want to give the best price-value possible, but that does have a lot of seasonal and demand-driven levers that are attached to it. In the next six months, we'll be implementing a very sophisticated price and yield management AI tool. That's the first thing. The second thing is to understand trends within our customer base and within our gross and net margins, and to make sure that we can really use it for financial analysis. So those are the next two steps for AI for us.

UL: Thank you very much. Any closing remarks, things that we haven't touched upon that you want to get off your chest regarding this topic?

I think the other bit of advice is to find someone like Unwired Logic who've got the skill set, who have got the abilities, they know the best tools, and who can methodically implement these systems while we get on and do the business. I think that's really my closing remark.