What's next? | Mohammed Alqaq | TDSU Ep. 56

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This is a JP analogy I'm about to use here. It's like a Power Ranger operating a Megazord. All right, you guys ready? Let's do it.

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What's up, Lifers, and welcome to the Daily Standup with Lifetime Value, where we're giving you fresh new customer success ideas every single day. I got my man Rob with us. Rob, do you want to say hi? Ahlan wa sahlan, Lifers.

I messed up that pronunciation. Why don't you try again? Ahlan wa sahlan, Lifers. Thank you.

Bringing culture to this show. Doing what I can. Doing what I can.

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Got my man JP here. JP, do you want to say hi? What's up? What's up? What's up? And we've got Mo here. Mo, do you want to say hi? Ahlan wa sahlan.

Hi. Marhaba. He's so much better at it than I am.

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I wonder why. And I'm your host. My name is Dillon Young.

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Mo, thank you so much for being here. Can you please introduce yourself? Thank you, Dillon. Thank you for having me.

My name is Mohammed Alqaq. I'm from Jordan. I'm a customer success manager in a SaaS software company.

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I've been in the customer-facing role for the last 18, 19 years, and I'm the founder of Customer Success Middle East, trying to empower the customer success practice and professionals in the Middle East region. Awesome. Awesome.

I love everything that you do. I love every time I see an update, I'm cheering you on from afar. But Mo, you know what we're doing here on this show? We are asking one simple question of every one of our guests, and that is, what is on your mind when it comes to customer success? So can you please tell us what that is? What's next? That's what I keep thinking about.

What's next? I love it. I love it. I love it.

What's next? So I also think that, but mine is couched in anxiety. And I don't think that's what you mean. It sounds like you've got this ray of optimism about all of it.

So tell me what it means to you to think about what's next. Is it specific to the Middle East? Is it about the profession in general? Is it positive, negative, this giant evolution for the profession? Just tell me what you're thinking. It's a little bit of everything, to be honest, but I'm very generic.

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It's not related to any specific region or specific practice. You know, being in customer success, and as you know, customer success is very dynamic. It's proactive.

It's changing every day. And now with the advancement of the AI and machine learning, I keep thinking about, you know, with all the amount of data we collect, what are we going to do now having the AI and machine learning? What is the future for that data that we are collecting today? Because, you know, I'm sure the majority of people, they are collecting data, but they are not using it. And, you know, data, if it is raw data, it means nothing.

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Unless you put it in a context, then it will start to mean something for you. And now with the advancement of AI and machine learning, I think we will reach a level where, you know, we can start to understand the customer usage behavior, and we can start to build trends, and we start to predict and drive some analytics that will give us more information so we can better build our decisions. So I think with the advancement of the new technology and AI and machine learning, I think customer success will be more integrated.

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They will have the holistic overview of the customer from all perspectives. And they will be the drive within any company that they can drive sales, marketing, support, product, whatever. They will be the one who's giving back and driving internally what is the next step for the company and for the product and for the growth and so on.

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But everything is built on top of that, you know, being able to collect the data, the right data, and build the model that will start to give you a trendings, or it will give you a behaviors. And that behaviors, when you start to correlate between different behaviors, you can build some base that, you know, it will start to predict the future. So if the user is at this stage, we predict this, because based on the history that we collect from hundreds of customers, the signs will tell us that the next step for this customer might be option A or B. So we be prepared and we can be more, even more proactive and predict things before it happens.

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There's a phrase in American history about Abe Lincoln of, give me, you know, five, six hours to chop down a tree. I'll spend the first four sharpening the ax. And what I take from what you said, Mo, is we're at this inflection point where AI is evolving at light speed, but it's really only going to be most able to be leveraged by those companies that have the, almost the coal to put into the engine, which is the data.

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And making sure that it's clean. And tell me if that is in line with what you're thinking of. We better be prepared.

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We better be stockpiling and preparing for that moment and that opportunity by having as much data as possible, the right data, clean data, organized data. Is that sort of what you're, the way you're thinking about it? Absolutely. Absolutely.

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You know, we start to be, or we need to start to be very careful with what data we collect. And we sort this data in a very clean and clear way so that, you know, we can inject it in the future and learn our system that this is the data we're collecting. Now we need to correlate this data together.

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We need to build some analysis that we can see something before it happens, or we can understand why things happen even. You know, sometimes if you start to look at the trending of the usage behavior, and then you can find that, okay, the seasonality of the usage drop, it's because of one, two, three, because, you know, you can relate data to each other and you can understand the drop or the spikes happens for a reason. Yeah.

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What's up, guys? It's Dillon here. And you know why I'm here. Hat in hand.

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I got a favor to ask of you. If you like what we're doing, give us a like on whatever platform that you find us on. And if you want to know when we're dropping new stuff, give us a follow.

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Thanks so much, guys. I'll let you get back to the show. Rob, what do you think about everything we've said? I think, like you said, Dillon, it worries a lot of people.

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I actually don't think it needs to worry people. Because I think when done well, this is a JP analogy I'm about to use here. It's like a Power Ranger operating a Megazord.

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It's like, Mohammed, you have kids, right? So you follow. It's like we can amplify our efforts substantially, right? Turn ourselves into superhuman CSMs. I think the question is, what is the nature of that identity that we then have? I think the example that Mo was just sharing is, to me, that sounds a lot like a product style entity.

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And I've seen in other conversations, CS starts to look more like a sales style entity. And I think it's a great question that companies are going to have to reconcile themselves with, which is the charter that we have for customer success. One that does deep user journey mapping and studies the customer experience and customer value outcomes and does trend analysis on that data that drives those outcomes efficiently.

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And make sure that we catch the risk factors early. There's like that style of a CS org. There's other styles of orgs that are going to just put all of their efforts into the dollars and cents that can make CSMs efficient selling machines.

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So I forecast that there probably will be a substantial divide between those two different types of CS entities in the future. But I'm not sure. And I don't think it's not different from the challenges we run into today.

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The forks we have in the road today. But it is just amplified, is how I see it. Yes.

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But the way I look at it, I look at it from a very positive perspective. Because if we implement this correctly, I think that will create an evolution in the customer success world. And we'll start to see customer success data analysts or customer success data scientists.

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We will start to see customer success technologists. And we will see customer health or customer success health manager and product success manager and user success manager. All those changes, all those new titles, it's because the amount of data, the amount of information we are capturing and the amount of results we are getting, one person cannot do everything.

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So we need to split those things. And this will lead to a further step where I believe companies will start to have different levels of customer success practice or engagement. Well, I would say there will be a tier which is 100% automated and the mid tier will be a pooled customer success and only a high touch who will be having a dedicated customer success.

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The rest, based on the data, based on the trendings that we are understanding, based on the prediction, we can build our systems in a way that it will deliver before you ask. And you will start to have all those tips and tricks and know-how built in within the software that user don't need to call anybody to ask any question. Because if you click on one command or one feature that he doesn't know how it works, the software will be smart enough to pop up a screen telling him, okay, here is the steps, go one, two, three, four, done.

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So then he's done within a second or within a minute in front of his screen without even reaching to anyone. The future is there. I love it.

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JP, what do you have to add? Yeah, I think there's going to be that data collection is a key component for sure. I think there's going to be, you know, even if we just take a simple example, like a chat GPT, right? It does a really great job at typing things up and doing that, but like still needs to be checked. It can still be inaccurate.

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And so the data, I think we began talking about that, how important that was. That Mo, you mentioned the raw data, but it needs to be contextualized, right? I think that is what's going to be interesting to see. We can collect data, but we still know that when we deal with humans, there's a certain level of nuance and gradient to the way that we interact with one another.

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You could have two customers who could have the same data, but there could be two different things. There could be things that we can sort of pick up on. And I say this in the support of the optimism that you have Mo, which is that we're still going to be part of the equation, going to quote paradigm Rob, the Megazord and the Power Ranger thing.

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But I think that we will still, humans will still need to be involved with this to a high degree. And I think it's about, there's a saying, right? Automate what you can, so we can clearly make some of our tasks easier as CSMs, which will lead us to be more efficient. And then of course, there's that other end as well, right? Once the customer data that we see, but we also know data is like the new gold.

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And sometimes you face bureaucratic things in the way, the politics of data, privacy, ethics. These things can also complicate just how far you can go with how well things integrate. So it'll be interesting for sure.

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I'm definitely on the side of, I think it's going to be exciting to see where things go. Yeah, it's a long journey, but I would say, seeing what is going around now, I believe this is going to happen in a year, two, three, five. But I can see this happening because the more technologies we have every day, we have a new platform that it's very specific for customer success.

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And we see a lot of AI tools and the machine learning. I believe when those technologies start to integrate, I think we're going to see a great tool that it will help customer success to do their job even better than today. I agree.

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That's our time, Mo. Thank you so much for bringing this to our attention. We'd love to have you back for a round two, Mo.

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But for now, we've got to say goodbye. Thank you so much. I really appreciate it.

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Thank you for having me. Thank you all. Thank you, Jean.

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Find us on YouTube at LifetimeValue and find us on the socials at LifetimeValueMedia. Until next time.