Is your team's email performance hurting customer satisfaction?

Slow, inconsistent email replies quietly erode customer satisfaction. See how to connect email performance to churn, and build an early-warning system.

What you’ll learn

• How email response patterns predict customer churn
• The metrics that tie email performance to satisfaction
• How to build an early-warning system from your inbox data

Transcript

Laurence Edwards: And what we do is build customized analytics dashboards based on your use case. So, unlike a CRM or a ticketing system, which is gonna make you reroute the way that you work, it's awkward, you know, it involves training the team members on what to do, unlike that, what we really want to do is let you work the way that's most comfortable for you, and we are just a silent layer on top. Building a platform separately, and allowing you to get the data that you need without having to inconvenience you, basically. Now, the first question I had that I wanted you to type in the chat whilst we get going is, if you have come with a specific need or issue, just to write that in the chat, and then I can make sure that we cover it in today's session. Yeah. But as I said, today's session will be primarily focused on making sure that your team's performance isn't hurting customer satisfaction. Now, an example of some of the work that we've done with clients in the past, whilst you type your questions in the chat. is, for example, with our client Fujifilm, you might know, the camera maker. Now, they came to us, and they had a few issues, but the main issue was that they were worried about workload distribution. they had team members complaining about the fact that they were overworked, and they knew that, obviously, this wasn't good for team morale, and it's not, you know, not particularly nice as an employee. But also, in turn, you know, this means that some people are overworked, and they can't respond to clients. in a goodly time, and most likely that's affecting churn, which I know is something they were concerned about. So what we did for them is we built them a dashboard that, first, addressed the issues with workload distribution, that easily showed them, you know, who was busy, who wasn't. And then also a dashboard that showed them response time metrics, so they could easily see how that was affecting their responses to clients, and do something about that. So, they've been with us several years, but we wrote, we wrote a customer story with them, which you can find on our website, which one of our team members will post in the chat, and it basically outlines what the experience was like working with us for the first few months. And how they were able to change their response times, which I believe they reduced by 11 or 12% or something in the first few months of working with us. Yeah, so this is an example of the kinds of people that we work with, how we work, but today we can dig specifically a bit more into, yeah, how email performance might be hurting your CSAT scores. And nice to see that we've got such a broad array of people joining, and someone from my home country of England, which is very nice. So, let me just share my screen, and we can hop into some use cases. So… The first thing that I wanted to talk about, which, I mean, relates really directly to CCAT, is an example that we see really often. So, for an example. Let's say, you've got, obviously, a large team managing many accounts, and one of the clients that you're managing, their tone is shifting ever so slightly over several weeks. You know, individually, each of these emails seems fine, you know, you can't see anything wrong. However, if you're looking closely, you can see the sentiment is becoming, you know, more and more neutral, or from neutral to bad. You know, 3 months later, that client leaves, and although there was no glaring signal, the data was there to tell you that something was changing with this client, but you didn't have it at hand, and so that client's left. what we do typically to handle a use case like this at Email Meter is we would train an AI model, to handle sentiment. So we would train it based on examples of your email data to say, okay, this is what a positive email looks like. This is what a neutral email looks like, and this is what a negative email looks like. And then, in this situation, we can really easily see how those emails are changing over time. So, if I go to the top here, and I select a specific client, then I can look at them here. once I've selected them, I can see what the trend of those emails look like over time, and I can easily see, you know, if their tone is decreasing, you know, if there's something to worry about there. Now, at a much broader scale. It's really easy to… to have a view of all the clients that you work with, the tool like Email Meter. you know, let's say I'm handling 100 accounts, you know, obviously I might flag emails that are particularly problematic, but having a view of all of them as a scoring system to know who do I need to be concerned about, you know, just to affect churn in that way without having to manually look through emails and, you know, and consider the responses myself. A list like this is really, really useful. So I can easily go to the bottom. find the clients that I should be most worried about. for example, selecting this client that I can see has got a really concerning sentiment score. On the right here, it tells me what the questions that the client has been asking me have been about, you know, what our conversations have been about. And then going down, I've got a list of all of the client… sorry, all of the emails that the client sent me in the date range that we're looking at, and I can see what the client has been talking to me about. So, really easy to pinpoint, who do I need to be concerned about leaving? You know, and what kind of questions are they asking me? And then if I deselect this client and just look at the whole team. we have a list here of all of the questions that I've been asked by customers. So, another, you know, another really common use case here at Email Meter is someone coming to us and saying, okay, I have a customer success team, I know that we're not handling things in the most efficient way, but I don't really know the volume of traffic that I'm getting on each question. You know, if I see, for example, that most of my queries come about sales and orders. but we don't have, you know, materials to support our agents for these types of questions, and I can see that they're drowning, then understanding, you know, what we are busy with, but also what we are not so good at handling, can help us, you know, build Workflows around those questions, and provide our team with the materials they need to succeed. Yeah, so a simple view, but it solves a question that is, you know, is not so simple, if you don't have access to this kind of data. Another thing to mention is that with these pages, you can also integrate the data from your CRM. So let's say, for example, my source of truth is HubSpot. I have all of my client information in HubSpot, including the worth of my clients, and I want to have a page just like this For highest-paying clients, you know, obviously every client is important, but if somebody who makes up 15% of our revenue leaves, then it's obviously going to hurt a lot more. On a page like this, I mean, either I could have a page dedicated to my highest revenue clients, or… I could cross-reference that information and have it as a metric here. Highest paying clients, you know, $100,000 monthly, $100,000 monthly, and really easily filter for that on the dashboard. This is a use case that we've worked with for many clients of ours, using different CRMs as well, and even ERP systems, like internal systems that they use that aren't particularly common. Anything that we can connect to via API, we can display on this dashboard. So yeah, just a case, because we are a productized consultancy and very flexible. In that nature. Just a case of coming to us and explaining what you use and how you use it, and then, yeah, we can basically tell you if we can solve it. No. The next page that I want to show you is a page that handles a bit more, a bit more around response time. metrics. Now… For example, if I have got a key account, and I know that I have an agreement with that key account that says I should be responding to them within 12 hours, but, you know, over the last 3 months I've been failing that, even if it's not been by much, you know, the client obviously feels that on the other end, especially if they're, you know, waiting for your response, and you're dealing with many people. And again, it's something that, although it seems pretty easy to get access to, is something that most of our clients struggle with, especially when it comes to having an easy, consumable view. So, a page like this handles that really well. You can customize the response time goals at the top here, so I can easily see, you know, who within my team is achieving the goals that we have, and who isn't. And it also gives you the ability to see, basically, how many emails were missing. So, if I see here, our old friend Dwight is, you know, is missing a few emails. Four emails last week, we can easily select this bucket, go down, and see what those emails were. So in a situation like this, it's really easy to see who is the person within my team that needs training, and who are the clients that are being affected, and how badly. Yeah, we have a list of all of the raw data here, and we select that bucket, so I can see the questions that they're asking. You know, the… the overall response time? and, you know, where the questions are coming from. So, yeah, you get the overall view, but also the granular data that you need. And it's really another common thing that, the clients of ours might have different response time metrics based on, you know, different things. Maybe the client, you know, the client group, or as I mentioned earlier. the value of the client, which might be information that comes from your CRM. And this, again, is something that we can cross-reference and use inside of Email Meter. So I could say, okay, clients of X value have a response time goal of 4 hours. We have a page for this. Clients of middle value have a response time goal of 6 hours. All of this we can set up really easily, and you can have this applying to different managers, too. You know, let's say a manager who relates to these clients, has their own view, the other manager with their own view. Yeah, the roles and permissions of access are really flexible. So, yeah, for large organizations where you've got different people, different objectives, all of this can live inside of the same dashboard, but With certain views allowed to certain managers. Yeah. And another thing to mention with all of this data, all of this data can be automated, too. You know, really what we want to do is make your life easier with this data. We don't want to have you, you know, having to dig around for things. So if you know you need a report about this team and this group of clients for your meeting on a Monday at 9am, then we can automate that for you, so you don't have to go in. Even down to automating really simple reports for your top management, because we understand that, you know, top management might need something really concise, an overview. Middle management really want to dig into the data and find out specific things. Yeah, so we can customize all of those views and have it in a format that's easy to consume for you. Yeah, now, another thing that I wanted to… to run through was… just one of our more basic views, but I think in terms of workload distribution, like I mentioned with, you know, with Fujifilm at the beginning. It's pretty common, especially with people working from home, you know, it's pretty common that maybe with, you know, with the way people are using their email, that maybe workload distribution is not the fairest, you know, especially with traffic growing, and maybe people being assigned different things, it's hard to know who's doing what, basically. Now, for individual inboxes, we can give you a really simple view like this, so I can understand, you know, within my team, how many emails does each person get? How many do they reply to? So, if there are outliers, and you know that one person's overworking while another is underworking, then, you know, it's really easy to see that. And as I said, with Fujifilm, it's a case of, you know, some people were overworked, and it meant that the clients that were assigned to them were really suffering. Yeah, so this is another thing that can really affect churn. But a view that's a little bit more complicated to get, for teams, and a really common issue. is, you know, let's say I've got 3 or 4 support inboxes. Generic inboxes, where it's kind of a first-come, first-served situation. Some people aren't pulling their weight, but it's really hard to know, you know, who that is. A view like this can be really useful. And what this is, is a shared inbox view, where we are reading all of the agent data, you know, the comings and goings within that shared inbox. or delegated inbox within Google. And I can hop in and see who's doing what inside of it. So, let's look at this sales inbox. I can easily see how many emails each person's sending, what percentage of the workload each person's handling, and how long they're taking to do it. So, it's very simple metrics, but, you know, if I've got 4 people in the inbox and only 1 person. is handling the stuff inside of it. You know, in the end, the client's the one that's suffering that. So, yeah, it's a really important metric to have, especially in these success inboxes where, you know, urgency. Is, is what we should really be looking for. And I think for the majority. that's the majority of what I wanted to mention. Just one other thing, obviously, all of the views that we had the, you know, up until now, are customizable. So, you know, we've got these brackets where we're looking at sent emails or replied emails. These are just kind of metrics that people commonly want. you know, there might be other things that are important to you. You know, let's say, for example, I want to find out who is the most effective inside of this inbox, you know, like, by looking at thread length and how long a conversation typically is. this is another metric that we can really easily put in. You know, we are monitoring all of the comings and goings of these inboxes, and it's kind of our job to meet you and then visualize whatever it is that you need. So yeah, it's just a case of us having a conversation. Nothing is too, you know, too much of a reach. We just really need to chat and see what you need. Yeah. But for the pages I wanted to show you, those, that's it for today. We have, you know, we have many more views, but I don't want to bore you with too much more. So, yeah, we can… we'll finish the presentation there, and we can move on to any questions that anyone had in the chat.

Mélanie Lelait - Email Meter: Yeah, thank you so much, Lawrence. So yeah, we have put it as well in the chat, but just a quick reminder, you can ask questions either in the chat or, and as well, if you are on Zoom, you've got this Q&A feature and the raise your hand feature. So, first question that came in, so, I use a CRM for my client information. Can we use this data in email meter?

Laurence Edwards: Yeah, absolutely. Yeah, good question. So, this is what I was talking about earlier. Yes, basically, and there's a load of different ways that you can use that. So, I mean, a really common way is, let's say, for example, you are a sales team. and you have leads coming in constantly, and you want to basically have a way to filter for people that have already contacted you, or that haven't, you know, this is something that we could be pulling automatically from your CRM, so you don't need to manually filter for things, because, yeah, I mean, it's applicable to everyone, but especially in sales team, where it's a lot of traffic, people coming and going, and you want to have, you know, current leads. current people are in the hot stage, you know, whatever it is, whatever definitions you have, yeah, we can import that data, and have it just regularly updating via API. So, yeah, just, just a case of us investigating the tool that you use. Yeah. So, yeah, basically, yes, we can do that for you. And I see, sorry, there's one more, in the chat. So do we have an OpenAI API where I can integrate this with my tool and build it in Claude? So what we can offer is basically a direct connection to our data warehouse, which is BigQuery, yeah, the Google data warehouse. We have some documentation about that, if our team should have access to it, and if not, we can always forward it on to you afterwards. But yeah, this is something that we do for loads of clients. You know, often we offer it as a support package to the main dashboard, because, you know, it's sometimes… can be a bit, you know, a bit of a bit of a slog building nice views, for a dashboard like this, but it's something that we can offer on its own as well. So yeah, we can, yeah, we can find that link and post it, and if not, then we will follow up after the call, with it for you. So yeah, long, long story short, yes, basically. Okay, and I see there's another question. The feature I'm most excited about is getting metrics from our shared inbox. Is there a limit to users from your free version to the paid version? So, in terms of the free version, unfortunately, we don't offer shared or delegated inbox metrics to that plan. We offer that in our enterprise plan. So this is the plan that we've been looking through today. Yeah, and the… basically, the pricing structure on that is on a project-by-project basis, so… because we're, you know, a productized consultancy, it's just a case of talking with us, and us explaining, you know, what it is you… understanding what it is you want to monitor, and then we can, yeah, we can offer you a package based on that. So what I can do is basically put, our Calendly, like, my Calendly link, in, in the chat, and then just book a meeting with me, and we can make it super quick, 10-15 minutes, and then, yeah, we can offer you a proposal based on that. Yeah, so if anyone from the team could post that in the chat, that would be much appreciated. Oh, and as well to mention, sorry, there is no limit to the amount of… because I know this is something that we didn't mention, there's no limit to the amount of mailboxes we can monitor, or the amount of agents within a shared inbox we can monitor. You know, all of that is really open-ended. So yeah, that's not a limitation for us at all. Great, so any more questions? Thank you. I see my colleague Meneli has posted, yeah, my meeting link in the chat, so… Yeah. If anyone wants to follow up with me, you know, book a quick 15-minute call. It says 30 minutes, but we can keep it to 15, I'm sure, to keep your valuable time to you. Yeah, feel free to go through and book a time with me.

Mélanie Lelait - Email Meter: Lawrence, you've got maybe one last question. What is the building time for a dashboard like this?

Laurence Edwards: Yeah, good question. So, it really depends on what you're looking to build. So dashboards based around, you know, just sort of metadata, as opposed to the body processing module. Those we typically say about a week to build, one to two weeks. The body processing modules, you know, AI body processing, take a little bit longer, just because we need to train, you know, the module to be, you know, to be accurate, basically, based on your data, and we would train it, you know, using examples of your data, you know, email examples. So, yeah, we say about a month for that, to have you… the dashboard built, have you onboarded, and you would then be assigned an account manager, to be, you know, your point of contact at the company. And their job is to really understand everything about what you're looking to do. Yeah, be your person there. If your team needs training. To make recommendations of how to use the data, recommendations of how to customize the dashboard, because they're obviously working with loads of different clients and seeing different use cases day to day, so they're your expert at the company, basically, to help you out with anything you need. And I see… sorry, I do see one more question in the chat. How do teams typically approach implementing this tool? So that's a good question. It really varies, honestly. So, you… I mean, I would say the beauty of EmileMeter is it's not something that you need team members to do anything for. Like, for example, if you want to monitor Person A, they do not need to log in and make an account. Everything with EmileMeter Enterprise is done from the admin level, so… your IT admin installs an application, grants access to the mailboxes to monitor, we build it for you, and then the people that get to log in and view the data are completely separate. So, really, the ball is in your court there, and I would say that we have clients basically doing it all kinds of different ways, you know, some that involve their team directly and let the members they're monitoring view their own data, just their own data. This is a level of access that you have on the dashboard. And then other people that kind of would like to keep it just for management and assess how, you know, how the team's working. So, it's really in your court, but you're not, you're not kind of forced to go in either direction with email meter. We let you decide.

Mélanie Lelait - Email Meter: Awesome. Thank you so much, Lawrence. And yeah, we will, shortly start the fireside chat, so… Thank you all for joining, and yeah, basically what we, what Lawrence explained, so it's really how email data can surface what's really happening. In your team's performance, and now we really wanted to bring some, so four leaders who, have lived those, those challenges in, in their everyday, operation, everyday life. and from different angles, from different industry, and from different countries. So, what they all have in common is that they realize that the signal predicting the customer satisfaction and the churn are almost always here, but it's just sometimes teams are not looking for it. So, we've got, four speakers. We've got Jeff Cam. Hamak Kohayb Said? Topi Jarvinin and Alpe Patel. I don't know if you want to, introduce yourself, maybe, Jeff?

Jeff Cann: Sure, yeah, thanks for… thanks for having me on. Great, great presentation and overview. I'm a… I'm a lifelong, CS leader. over 10, 15 years in B2B SaaS, specifically scale-ups and startups, and always in CS, so the conversation around retention and customer turn-in engagement's near and dear to my heart.

Mélanie Lelait - Email Meter: Perfect, thanks a lot for the presentation, Jeff. Topi? Your mic is, is on mute, Zoe.

Topi Järvinen: Yeah, let's start again. Hey, Topi Arvin, great to be here, thanks for the invite. So, I lead the customer success at Inparallel, and we enable companies and people to have one shared reality, people, and agents, and, And I've been working quite a bit on, sort of, looking at how AI and customer success work together, and how that changes everything.

Mélanie Lelait - Email Meter: Perfect. We'd love to explore it a bit, a bit deeper if we've got the… if we've got the time. Amar and Alpesh, I don't know if you are here with us? Yeah, Alpesh?

Alpesh Patel: Yeah, thank you for the opportunity. So, Alpish Patel, been in this space around client success, professional services, pretty much the post-sales lifecycle, for most of my career, so a lot of, The topics you raise, the key points, are near and dear to my heart around customer experience and being proactive about it. Appreciate it.

Mélanie Lelait - Email Meter: Perfect. And finally, Amar?

Ahmar Zohaib Syed: Thank you for having me. My name is Emer. I spent the last decade building CX Function, a function that is being trusted by the CFOs specifically. Currently leading customer experience and the AI transformation at Fleak, which is a B2B marketplace for vintage clothing.

Mélanie Lelait - Email Meter: Perfect. So, we've been, chatting a bit on LinkedIn all together, and, yeah. Let's go a bit into the conversation. So, let's start with Jeff. So, you said that the worst outcome in CS is losing a customer to a feature that already exists. So, how can… can you tell us a bit more? How does that happen? And how does it tell us about the gap between what the team track and what actually customers need?

Jeff Cann: Yeah, yeah, thanks, thanks for the question. So, I mean, always painful losing a customer. I think the point here is that… is that when you lose a customer that's… to something that's theoretically avoidable, it's even more painful. And I like to think of all churn in two camps. There's avoidable and unavoidable. So you gotta control what you can control, and… and… you spend a lot of money, attracting customers, closing customers, managing customers. To lose someone to a feature that… or a problem that you can solve, pretty, pretty painful. So for me, you know, why does this happen? Happens… in part because drift happens with accounts over time, and so new stakeholders come in and out, perhaps a lack of understanding of what the original pain points were, and how you're dovetailing into feeding ROI and business impact. So that's really important, and I think the goal really is just being as relevant as possible, really understanding what the customer pain points are, the direction that their company is headed, and if you can continuously package up your correspondence and your engagement. To ladder up to those pains. you're gonna have a greater chance of, you know, in your world, open rates, engagement levels, you reduce the noise a little bit more, and you become more relevant. So, I tune people out that aren't relevant, it's your job's team to be as relevant as possible, and, you know, part of doing that is understanding what success looks like for the customer.

Laurence Edwards: I was actually going to jump in there, Jeff, just to… yeah, I was reading through, you know, reading through some of the stuff on your LinkedIn earlier, and it was very interesting. What I was going to ask is. you know, if there's one thing that you use typically as a metric to predict customer health, what is the thing that you look to? Like, what, you know, what are all the warning signs there about customer health?

Jeff Cann: Yeah, it's a good question. I think there's a couple ways to look at it. One is in a scenario where you have one-to-one relationships. your coverage model is such that, you know, a CSM has a book of business and they can generate relationships. In my view. that human insight and that human lens can't be replaced by an algorithm. Why would you? You know, your ability to get off a phone call and understand where a customer is at. And, and… analyze all those intangibles is really important. That's not always possible. Lots of businesses have a long tail. The engagement model doesn't always support that. In that case. sort of a pecking order. I think survey data is always powerful, but the response rate, as we all know, isn't great. So, I really take a look at product usage cues, you know, what's the breadth and depth of the usage data showing you? Do you have a one-legged stool? Is there a single user that's perhaps using your product quite lightly, or is there a diverse user base that's kind of getting into all of the bells and whistles that your product can offer? I think you generally see a pretty good correlation between retention and that, if that's, you know, the only place that you had to focus.

Laurence Edwards: I know that our head of customer success, listening to this webinar, he's probably screaming and pumping his fist in the air now, listening to that. I know he agrees. Thanks, Jeff.

Mélanie Lelait - Email Meter: Thank you, Jess. Amar, you… you… I know that you… your start as well on track… tracking other… are the metrics. Can you… Tell… tell us a bit more. I know that we were talking on LinkedIn, so that your stat tracking how, like, customer came back after 90 days after a ticket, but yeah, can… can you give us a bit more about all the metrics that you are tracking, and, the change that it made in your… in your day-to-day?

Ahmar Zohaib Syed: Sure, I think, We all have those hygiene KPIs, always resting with us, which are usually your resolution times, your AHTs, your CSAT scores, which are tracked day in, day out, quarterly, monthly, and weekly basis. That's how we're doing, in terms of the service levels that we are clocking. I think it just came out as a moment of suspicion. Our CSAT were looking very healthy. Agents were, like, hitting the target like anything. Leadership was pretty happy about it. But there was something different that was being told on, which was around the part that something is missing. So we dug around some data points, and we saw that people, or the customers who reached out to us. and they rated our experience as very satisfied, we're really not coming back to our platform and buying again, which means something that we were tracking, or took it in a very different way, where We used to celebrate those as success metrics for us, but in terms of retention, those weren't doing great for us. So, we went through different cricket categories, we see… we saw, like, what type of customers are falling out, so… For example, a customer who had a product quality issue came to us, raised a complaint, your service well, resolution was done within the next 48 hours, refund was done, pickup was cleared. He was very satisfied with the service, but was pretty disappointed with the way the platform acted for him, and eventually he churned. So, relating CSAT, or celebrating CSAT alone with a drop in retention wasn't a great metric for us. So what he started to do is start monitoring every time a interaction is being made by a customer, is he being written on the platform? Yes or no? And that's where, for the last 12-odd months at Fleek, we have been successfully increasing our quarterly retention over every quarter, and we started pretty low at that time. We're right now in, like, mid-50s to 60s right now, and that is somehow also helping us in having those discussions with our top-tier CFOs and CEOs that, why do we need to increase our contact rate? Why a healthy contact rate is good for the business, and why does it not always end up as a cost for the center?

Laurence Edwards: Something I wanted to ask Amar, I know you said that, obviously, some types of data, like the CSAT reports, are maybe not the most useful for you. Like, at what point does that data, you know, start becoming noise for you, and how do you decide what you use and what you don't use, like, to make sure that you haven't got too much visibility getting in the way of, you know, what's important?

Ahmar Zohaib Syed: So I think, as long as you're servicing your customer base very well, and your CSAT is continuously going, like, high from mid-70s to mid-80s to high 80s, but your retention of new buyers or slash repeat buyers is falling down, I think that tells a lot about not how you're servicing your customer today, but how do they perceive about the business in totality. And that's where we thought, you know, something is doing Great at customer support level. On a customer experience level, things are being dropped off, and that needs to be catered right away.

Laurence Edwards: Yeah, interesting. Yeah, I'm sure as well, yeah, as people that work in the sales and the account side, and not so much in the engineering side, yep, we can, yeah, this is something that we've had to think about a lot as well, and at what point is it us dropping the ball, or someone else dropping the ball, and yeah, looking a bit deeper. Interesting, alright, thank you.

Mélanie Lelait - Email Meter: Yeah, super interesting. Thank you so much, Amar. I know that, in the… to stay in the same, You must say, in the same idea, Alpez, you… you've lost as well as everyone, accounts, where every signal were… were… were green. So, in your experience, what really predicts you? And why does it keep, getting unnoticed? What can we do about it to prevent?

Alpesh Patel: Yeah, not a… there's no perfect single answer, right?

Mélanie Lelait - Email Meter: Damn.

Alpesh Patel: I've been in renewal conversations where… the scores have been green, CSAT strong, QBRs have been completed, you know, all those kind of things, and you're still… you still lost your count, right? And the uncomfortable part is nobody saw it coming because everything that was measured said we were fine. So, so… to me, the core insight, green metrics aren't necessarily accurate, right? Most metrics we report measure how the customers feel at that time. They don't measure whether the customer's getting the value in whatever they've, bought, or the solution, right? And these are different things. So, a customer can like you, they enjoy the relationship, rate you highly, and still not renew, because the product doesn't move their business forward, right? Sentiment isn't retention. So, some of the things, to predict churn, right? Aren't… measurable in my… some of the things. And one of those are expel… executive sponsor goes quiet. That's… that's a big, big indicator, right? That's your strongest when the sponsor stops showing up, now the renewal clock has already started. Right? You gotta be thinking, okay, now it's started. And whether anybody's tracking that or not. And then the other thing is stakeholder depth, right? Is it just based on one contact, or several? Single-threaded accounts are fragile from a customer perspective, right? So, those are things that, to me, would be predicting churn. There's obviously others, you know, time to value and all that, but these are… these are two that are… I believe, are very strong, because you can't just simply put a measure or put it in a… in a nice Whatever, health card, scorecard. And, why does, why does it get a, you know, a notice? Well, I think there's two reasons. First, your organization's measured, you know, we measure what's easy to report, not… and not what's hard to ignore, right? Health score colors are easy. You know, the economic buyer stopped, replying 3 weeks ago, so it's harder to put that on the dashboard. Right? Second, the real signals show up upstream in onboarding, in the sales handoff, and whether the outcome was ever clearly, defined. And so by the time it reaches renewal. it's a lagging indicator. So you're really… so you're reading the autopsy, not the diagnosis, right? So that's sort of, some of the things that I've… sort of… experienced in trying to work around. One of the things that I've done in prior roles is, is, really state what the customer… why the customer bought, or is using our solution, or is working with us, and that becomes, like, the North Star. And that's posted for sales. Success, support, anybody that touches a customer that's posted. That, that, that, that, that, that… reason why the customer is engaged with us, so nobody forgets it. So any interaction, doesn't have to be with the executives, any interaction with anybody at the customer side, they have, they understand why these customers with us, right? So you don't lose that. North Star from the beginning. That helps as well.

Laurence Edwards: Hmm, that's interesting. Yeah, that partially leads back to what you were saying, Jeff, and I know this ability to have that constant connection with the people that really are the decision makers, they might not have been the people that come to you, but the people that are the ones actually driving this project, really. Like, yeah, when that goes, I know that, yeah, it's the… I mean, we, yeah, we're all aware here that that's the… the clock ticking. So yeah, yeah, it's interesting. Yeah, very true.

Mélanie Lelait - Email Meter: And I don't know, Topi, if you want to mention a bit more about the signal, the customer churn to keep into, into this, this idea of, or if you prefer to move on with what you implement in your organization.

Topi Järvinen: Well, it's, it's, it's very much, sort of related to, to all kinds of things, like, light journey, but also, you know, just, just the whole onboarding, or, or just keep, keeping customers happy along the way. But, so, so what we've been building on top of the parallel system, from the customer success point of view, is that, We try to keep, understand all the data that is coming in from the customers, be they emails, or, you know, face-to-face discussions, or, or product signals from the product usage, and then combine all those things in order to understand where we are. And I think one of the key things is that all those signals need to be structured. So, so a lot of times, you know, we find that, you know, we, at least some of the companies, are working from anecdotes. So, so, you know, what, what is the, what did the customer say in the last meeting? And then we act upon it. But at the same time, I think, you know, what has been really good for us is that we track everything, you know. Based on the customer, what are the things that you're saying along the customer journey, but also. In time, so that we know that what is fresh, you know, what was said yesterday and what was said 3 months ago, maybe that's not relevant anymore, but also across the different customers, so that Is there something that everyone is saying? Or is something that only one customer is saying? And then, to the point that, maybe that customer is the flagship customer, that even though it's just one customer, we need to act upon it. And so, so, with all this. And I really liked Jeff's point about, you know, it's about sort of the human relationships, with all of these things. I think what we've achieved is that, having, this structured data and AI helping us to understand Sort of the things behind all of that, and making sure that we understand what is important. it has given me more time to be with the customers, and having those human relationships, and spend more time in those discussions, rather than just looking at Excel spreadsheets and rows of data.

Laurence Edwards: it's… what you were saying, it's interesting for us, I mean, especially IMA… well, a lot of companies work this way, and, you know, the way in which you're dealing with feedback about the product, but especially for us, it's kind of like a productized consultancy, and pushing certain features, and, you know, following what people are interested in, I know this is very important for us, and something that, you know, we take real care of. And having, you know, that idea of what people want and what people are doing is, yeah, something that we really focus on. So it's interesting to hear you say that.

Mélanie Lelait - Email Meter: And maybe just one… one last question to all of you, if… There's something that, for you, what is the hardest part of getting your organization to act on the signal earlier? Is it the data? Is it the process? Is it the people? Like, can you give us a bit more detail about this This… this part where we need to… to go into, and we don't know where… in which direction, and yeah, so that, people can… can start, implementing it. I don't know.

Jeff Cann: I, I, yeah, I can… I can jump in on that. I think… As leaders, you have to demonstrate a linkage Between what you're asking them to do. And the reason why you're asking them to do something, and what the expected outcome is there. there's a lot of… speaking just from CS teams, we can get pulled in a lot of directions, there's a lot of asks from a lot of organizations, and so… Being really clear and prescriptive with, hey, here's the context of the ask. here's why we're asking you to do it, and here's what the expected outcomes will be, which, you know, if it's good for the company, it's going to be good for the CSM, and get some buy-in that way, so… you know, without data, all you have is an opinion, and it's… you don't want to go to your team with lists of different opinions, you want to go to them with, here are the linkages, and here's the why. And be prepared to deprioritize things where that linkage doesn't exist. You know, it's a zero-sum game. There's only so many hours in the day, so if you're going to ask them to do something with their precious time, that's their most valuable resource, you have to be pretty sure that the expected outcomes are going to be positive.

Alpesh Patel: Yeah, to what Jeff just said, right, is, obviously you want data, sometimes data doesn't exist, or it's not clean and whatnot, but I think you just touched on it, the keyword context, right? Giving them context, if you give the team the context and understand Sort of how everything is linked or touched, and if something happens upstream. what are the ramifications downstream, right? You may not have the underpinning data, but if you can explain that operating model, then that's how I've tried to work with the teams to sort of get buy-in and whatnot to explain the operating model, to say, if we… if sales is doing something XYZ upstream, what are the ramifications downstream, right? If we're not selling to our ideal customer profile, then you don't have references, you're gonna have your NNR and GRR, all those other metrics, lagging metrics are getting impacted, but explaining how all those are touching and intertwined, and how what we as a team, can do to help upstream. So…

Topi Järvinen: Yeah, and also, I think it's, I totally agree with both of you, and I also think that it's… I tend to think of it as a loop, so that, We, we, we, of course, we… we bring in the understanding, what we hear from the customers and, and what's happening there, and, and then. Having all… having the data, having… having all the relevant information about that, but also, you know, what… when they build… when the product builds something. what is the result of that? You know, how did we… what… did we succeed with that, or was there learning that we could improve upon that? And that's a sort of a continuous cycle that we have to go through And, and the more you do that, the more the product will, will, appreciate, sort of that relationship. But also. having The customers to see that, you know, we are working on things that are really relevant for them, and that we are actively listening, that will make the customers happy, which, of course, in the end, is what we're doing.

Ahmar Zohaib Syed: I think, based on my, prior experiences, I've seen, like, two phases of this coin. First would be… These signals are usually not on any given dashboard for any particular department. These are either lost in some pipeline for our product, they're not on the CX dashboard, they're not on, like, finance dashboard, so these signals are there. It's pretty obvious if you look from a holistic bird's-eye view, but since no one is owning it, it seems to get dropped. The other is, I think, by default, there are a lot of organization. They are wired to reward firefighters and not preventers. So, the person who is actually, quietly working, in preventing a leak, putting up a patch together, is usually not recognized compared to someone who has stopped that leak at a disastrous moment. So, usually, those signals are then quite noticeably… prevalent there in front of everyone, but since that chaos has not been made, the fire's not out there, they tend to lose the management's eye until something erupts at some point, and everyone gets to it. So I think that's two-faced to the coin that I've seen over past experiences.

Mélanie Lelait - Email Meter: Perfect. Thank you guys so much for all the… the insight that you shared across, like, for all… all industry, all company. I think it would be super helpful for every CS team, and yeah, thank you for… for your time, for… for being with us. And, yeah, just, real quick, I just wanted to share, the upcoming webinar that we have. So, it will be on, on July, it will be on AI, so we will speak about sentiment. Recurrent topics, emerging issue. all that are sitting in the inbox, so I will share the link directly in the chat, and we'll be happy to meet you there again. And yeah, thank you, thank you all so much for joining us, and have an amazing time.

Laurence Edwards: Yeah, thanks, everyone. Lovely to, lovely to speak with you all. It's been really interesting.

Alpesh Patel: Thank you, appreciate it. Thank you so much.