104: Fireside with Simone Secci

Charlotte Ward 0:13
Hello and welcome to Episode 104 of the customer support leaders podcast. I’m Charlotte Ward. This week we have another fireside episode and this time I talk to Simone Secci.

I’d like to welcome back to the podcast today Simone Secci. Simone, it is lovely to have you back. And this week you are joining me for a fireside and while our listeners can’t see this video call that we’re recording this conversation on you are in fact, sitting by a very wonderful and welcoming but albeit virtual background over fireside so thank you for bringing a fireside to this country. The station and and also your brains the topic as well what would you like to talk about today

Simone Secci 1:07
so far that will bring up a topic that has been at the centre of my projects and and the roadmap of the support team for doodle. In the last I would say six months, which is support data. And so I wanted to give an overview of like all the different aspects of support data and how they can come in handy like to your conversations and how can they be shared and organ they have to plan out different aspects of support team and different aspects or different operational aspects of a support team.

Charlotte Ward 1:47
That’s awesome. I know we’ve talked a number of times and you’ve been on quite a journey with data Haven’t you and it can mean quite a lot of things as a as a as a topic is a broad one. We could be talking everything from setting up your dashboards and figuring out what you can measure to ultimately completely the other end of that scale, what you do with that data and who you talk to about that data. So I’m looking forward to this one. I know these are all all aspects of that journey or things that support leaders find challenging in multitudes of ways. So I’m looking forward to seeing where you take this one.

Simone Secci 2:26
Yeah, so I think not to scare anyone off but the one of the main things that you have to set yourself up for when you approach data and support and from any one of you is learning that you’re going to have along this path, like I started approaching data in a leadership role back in 2014. When I was commissioned my first KPIs report, and first what I like and then I started to bring, like, very simple KPI so let’s get solved then, you know, first response time, thanks for watching. easy to find. And I think back then I was using Zendesk insight we could data that was like my first approach took me a very long time, I gotta say, to me your eyes, with the specific language that the product used, which was for whoever remembers that, if you use that in the past, it was like a, sort of like a customised version of SQL. Which was an immediate, if you wouldn’t familiar with programming language, or, you know, you were under that kind of person per se. And then, you know, slowly, it took me to approach data, not just within, you know, and send their support, support data, but just starting to look at data from the point of view also of looking at cost Company data outside of support and see our related to support data, for example, to understand the return on investment of support and understanding the impact of support on a annual revenue or monthly revenue. So there is a number of thing to understand the connection between you know, what you’re doing there with impacted as an actor, they can be accidental discoveries, or it can be a very, very, like voluntary path where like you get somewhere

Charlotte Ward 4:36
or you say, so do you think you need to know where you’re going before you start measuring? Or is it a matter of figuring out what you can measure and finding conclusions to draw or somewhere between the two?

Simone Secci 4:48
I think, yeah, you need to you need to understand where you want to go. So why you need and why. And then I think the data science would say that the problem We’ll be to figure out what the data model is to get those results. So to the word like a technical term, begin familiar with reasonably, you have structured data and you have unstructured data. Structured Data is anything that can come in a CSV form, for example, it’s already tabular. And then your unstructured data. The simplest example is text to have conversations. And you have to understand what do I do like unstructured conversation, right? Because this is a problem. I think that that if you’re not familiar, you’re not a data scientist, you you’re just became a team lead and you have all these conversations and you know, there’s a customer sentiment or something that is happening, there’s a bar and you want to track it. So one, very common, you know, mistake you make in the beginning is just reporting the sentiment I’ve seen that there is an issue and I’ve seen more or less this many tickets, if you will, with that type of attitude towards your leadership. It doesn’t mean anything to them. You know, it’s an opinion. If you back that with like, concrete data especially, and I and I found this, you know, in this case like appearances matter, like well presented a visual and easy to digest visualisation data that makes all the difference in the world. They buy into it. And you you have the, you know, you have the attention of their own. Mm hmm. So, I think this is just

Charlotte Ward 6:29
dressing up your data in a way that makes it seem well founded and therefore more trustworthy, actually, then just a few numbers on the back of an envelope kind of thing. Right?

Simone Secci 6:40
Absolutely. But also that the fact that it’s like, you have to always think, how can I present this data so that it can be understood if somebody doesn’t have the context that I have? Right. So nomenclature that you use and support like packs, they use categories as you use. Again, you explain them visually Without having to give like a lot of context for the notes, you know, making it like, really? How can you do that?

Charlotte Ward 7:08
Yeah, yeah, it’s more than just the colours of the graphs you use, it’s actually, like, sometimes the difference between the type of charts and that kind of thing can make quite a difference in, in providing that context that you’re talking about, because we might understand quite naturally, you know, a Aline kind of trend or something over time, because we have the whole context that’s built into we have the whole backstory of say, what a handle time means, and how and how the different types of work that our team do affect that handle time and all the other interconnecting pieces that that kind of bubbled up around that handle time in terms of other data points or other activities. But if that’s all you present, if you just give that line shot to someone in the organisation, it could be

Simone Secci 7:56
pretty meaningless, right? Yeah, it could be not enough. No, there’s always you have to consider whether the follow up questions and you know, obviously, for everyone, you start with some incomplete data, and you hear some follow up questions and then you learn like, okay, whenever this entered the data that way that was missing and I didn’t really reach the goal that I wanted to reach and like I didn’t really communicate it was clear in my mind and then you you go by trial and error and you perfect that going forward. So, to quote some, you know, concrete examples, I would say, of what are the uses that you can have in data, we can break it down, in, in sport in a few aspects, like we can say that some what we’ll really care about is performance data. Then, we have self service data, about like all our self service initiatives in the help centre. If you have to use an AI for example, like understanding the impact of that on on reducing costs on on on this team the stress that you put on the team and you know, then you also have like quantitative data in terms of like forecasting for example. So, you know, in the in that sense, you just start with everything I would say pretty much from tagging from categorization. categorization is like the first thing that you have to to try to nail which is you know, understanding Okay, what categories so what what principles guide that they gotta go to decision that you want to make? So I would say three main things, understanding the impact on reducing the cost for the organisation, understand understanding the impact of a change in the randomization product change for example, like you have a feature request or you have feedback negative positive marketing campaign, once again back to positive. And you know, so this this this part, I would say

is very important but as well understanding your your performance. So these three things, one is internal or external, if you will start over they gotta go to the right categorization. What are you up for successful gathering of data?

Charlotte Ward 10:44
Yeah, I’m trying to understand, like, practically, how I translate that into my categories that I might set up So, are you saying I mean, obviously, it’s gonna vary significantly from organisation to organisation, but So, but, but, but are you saying that in your experience, you need a very limited number of categories, because you really the model you use is to just try and capture in the categories the influence of that ticket on one of those three areas

Simone Secci 11:17
right. So, those are the guiding principle right they all come from understanding the customer experience. So, you have you know, understanding customer experience to do to improve the product understanding some experience to reduce the cost and and the customer experience to improve the quality and consistency of support interactions. From there you generate your large buckets of data, right for categorization, like what type of issues you deal with. For example, they can be very general things like and they can be common to all support organisation no matter what you do, it can be ecommerce, it can be. Social media can be a SaaS company, you will always have bugs, you will always feature requests or feedback or more in a more general, you know, if you have social media you have those categories, it can be social media, your big bucket like Twitter, Facebook, LinkedIn, whatever it is, or it can be broken down by specific social media, it doesn’t matter. Like, you know, this message will come in most companies have this, this external channels, right, and they you will categorise them like that. You have mobile apps. So there will be another category. It’ll be mobile apps or it can be broken down by type of mobile, Android and iOS. So this type, for example, like I have these five buckets, no matter what team you will go, you will have this category, then you will go more granular, and start using tags to characterise for example, a theme or all events, like an outage. You want to be able to track it or you want to be able to track a specific type of critical issue over time segera trendline. So if an issue is and could happen in some organisation that maybe there’s an issue that’s overlooked a little bit, and you want to prove that there is an impact or that issue, you want to make sure that you have a specific tag, you track that over time. And then you could, for example, go and measure that against trial conversion. So let’s say that this issue affects user you would see how many users on a trial if you are a test company, for example, are affected by this this issue and how many then churn? So churn metrics that you can get from you know, your data scientists or you can get yourself towards like, I don’t know, chat mobile or what have you, you know, game sites, culture of customer success or other thoughts. Your support data in your help centre and sorry nerves in your desk. You can put those like side by side, or compute those together in an external tool can be as easy as like Google spreadsheet and figure out what the relationship is gonna give you very interesting data that you can present to your, you know, to your leaders and say I can you can clearly see this and the impacts and the correspondence between these two. Huh.

Charlotte Ward 14:30
Yeah, I like that, actually, that if you’re using those guiding principles for your categories, and then really using tags to identify particular issues or activities or like in the in terms of say, like the reducing cost guiding principle, for instance, maybe impacts on cost or or impact or efficiency in the team.

That it’s very easy to draw those lines isn’t Because I think I think one thing that is pretty common in my experience is when people set up their new Zendesk or any help desk, they think, right, what are the things we do? Let’s just categorise and tag the things we do. So we’ll tag the types of work and we might tag some bit or will categorise the types of work. So they might categorise categorise by feature, or they might categorise by, you know, a particular activity that their team does, like, you know, support a customer through a particular, you know, particular type issue type or something, but, but doesn’t necessarily but we, we throw all of that in there, all of those categories and tanks because we think that and also there’s, it’s often not clear what the distinction is between those two levels as well between categories and tags. So quite often you end up with a mishmash of activities and categories and then a mishmash of you know what happens really are bigger bigger types of data in tax right?

Simone Secci 16:05
Now what you know what works is like what the old most effective system for filing you know, as in we can learn from our old like office habits. So what’s a successful filing is in a tree structure. And this is exactly the same thing your guiding principle your categories, your tags, everything it’s like, you know, in this tree structure right? If you’re a female tags and you’re in your car and your large categories aren’t the same thing, it gets very confusing like you have large categories that have four tickets a month, that’s not really useful, you know, like you want to understand the big picture and then you want to understand individual events. So I could mention, very simple things thing you can do. Very interesting piece of data is Understanding your cset by lunch issues. So what in what issue are what is the customer sentiment for each category that you are? So you have large categories, you say you have different features or different on different products. Which one is creating the most negative sentiment when understand where to act and for example, if you’re tracking from a project management perspective box that you’re filing in JIRA, you could see how many tickets per bug how many tickets there. Sorry, how many bugs, if category that is used in your project management is JIRA, for example, for engineering, so those categories are most likely different from the ones you use in support. Right. So understanding that data, and then that gives you you know, a new feature They’re like it’s external from the data that you add, but you can compare the two. And then you see a correspondence within we file this many bugs. People are very upset because this is broken. You know, and you’re right. There you are the correspondence like of the impact doesn’t, you know, you’re not, if you want to push engineering, to fix a or product team to push the engineering team to fix something, you can back it up with this customer sentiment. You have to be able to quantify it. Yeah.

Charlotte Ward 18:31
Yeah, absolutely. So you mentioned at the start of this, this data journey, let’s assume we’re in Nirvana, and we have our categories and our tags sorted. We’ve got those nailed, never need to touch them. Again. We’re building data from that we’re built, you know, we’re figuring out what, what data points we can extract from that. And I guess the next question I want to ask you is you’ve touched on some of them there about the relations Between issues in certain buckets and how they relate to bugs, for instance, I would like to I would like to ask you to expand on like some other interesting data points that you might have particular experience with or a love for. And but but then let’s, let’s go on the rest of that journey that we talked about at the start of this conversation, which is like then how you have conversations about our data.

Simone Secci 19:24
Yeah. So, one something that is particularly interest interesting for me is because we particularly put a lot of attention on our knowledge base, we have a lot of case manager that you know, as a litigator role right staffing us, that’s our internal knowledge base for for the team and for the external teams and in the in the company. One thing that I was interested in understanding the default the goal the deflection statistics, So, you have in most you know tested a US you have some basic like XML data, visualisations sometimes you have votes on articles. But it’s really hard to have a clear picture of customer sentiment from those, you know, those survey system that you put in place, because it’s hard to understand like the path of those users like you example, if you have complex like business models like freemium for example, you have a mix of like si users and paying users different tears like it gets all mixed up. It’s hard to sort their way. So one thing that I did was bring in all this data together on a dashboard on a spreadsheet, and having okay this is my this are my tickets. number of tickets they have where users ask product information. Then what I did is I could output a little bit of JavaScript, I was able to calculate the path to the context for so how many times a single events users will go to the contact form? Find this an automatic suggestion for an article, click on that and not submit a ticket. And I classify those as deflection events. So, number of deflection events. Then when we get when it came to artificial intelligence, we have very basic answer boat, nothing fancy I use more elaborated like AI systems in the past. It can work for some people didn’t work my case, but it doesn’t matter. You have your deflection through your AI, quantify there, and then you have for example, data like all many article suggestions, your age Give him tickets. That’s a very interesting piece of data. And then you can calculate what’s the percentage of those suggestion on the overall volume and then gives you the percentage of ability to expand the flexion on productive permission tickets specific. And you have a very complete picture at that point of self service, one that is much larger than what we started with, with facilitation article votes, and you know, how many times that article was, was visualised and things like that? Yeah, yeah. That’s,

Charlotte Ward 22:40
I actually that’s really fascinating because I hadn’t really thought before about the number of times agents refer interest the text of a response to a document as being a potentially a deflected ticket, if I guess, if only there was another way to surface that document earlier in that customer journey, right? But But that’s what you’re talking about is like how you start to draw, extract some of those insights and make use of them. Right? So let’s talk about a final part of this journey them, which is with all of this data, what conversations do you have?

Simone Secci 23:16
Right, so then it comes to the VAT, it comes to the part where you have your data together, you have your performance data, so that your internal that it’s okay. But you want to bring out messages, you want the data to be accessible. So then there are some technical issues there. You talk with your data demons, like how do we exported in what format the week or what it communicates with, you have an internal data layer, so understanding you want to preserve all this work, they did all this customization is filtering. And then at the same time, we want to display to be accessible. So there’s a number of things like you want, for example, ticket IDs and user IDs to be accessible to your customer success team so they can match it with their own tools. So exporting their raw data that it’s about and understanding out of the way with your, with your, with your data science team. There are a few approaches that you can have even you know, if you have the possibility of an engineer’s to write scripts for them and do it in you know, with like more structured format CSV or or just general like XML export or JSON files like that, you know, depending on how much they can work with it. And then there is some, some like more product related data that can be useful. So you have all the customer feedback and you have all the you can break down their feedback by features by different products and depending on the company, and centralise that data with You know, the other department, so then it becomes very powerful. Because if your findings are confirmed by other departments, then your voice is much louder,

Charlotte Ward 25:11
sanctified. And actually what you’re talking about then is not really it’s much more sophisticated, isn’t it than just putting your graphs on a slide deck once a month for for a town hall or whatever, it’s, it’s about keeping that data accessible, but live effectively or semi life. Right?

Simone Secci 25:32
Exactly. So understand you have your source. And you know, the certain features that were very common or supporting, or there’s a certain negative sentiment about the change. You bring that in, for example, through what I know, to be a connection into an endpoint. That is you want a tool that is shared by most most teams, so whatever the tool might Be no Confluence and ocean. You know, notion is very difficult to customise but, you know, spreadsheet or air table, whatever that is where you have your product team, your marketing team, your sales team somewhere, we, you know, it has to be a common tool to add more visibility. And then you you sort of like narrow it down by, let’s say, what do you think are the priorities, the 10 most important things, the 20 what’s important things, too, because a lot of data, it’s very difficult to, to digest. You know, let’s say you have a large volume of tickets, if you bring down 3000 conversations, or you try to break them down. It’s that unstructured data without upon the beginning, very difficult to figure out what’s going on there. That’s, once again, categorization very important there. But once you let’s say we got you got that down. Then, if you can match the customer sentiment and the feature request with what it says by other teams, or you can bring a different point of view. Either way, it’s a different type of conversation than being an isolated voice.

Charlotte Ward 27:19
Yeah, yeah, absolutely. So the this kind of dashboard building relies on you having the right tooling, even if it’s fairly, fairly rudimentary, and I don’t, you know, I mean, it can sort of be a bunch of Google Sheets and a bit of automation content. Absolutely. If you if you’ve got the budget, you can go for a more sophisticated tool, dare I say something like snow plough, which is my where I happen to

have staked my claim for as a support lead at the moment. So you can get pretty sophisticated about this and you can draw data from almost as many data sources as there are available that you can hook hook into With with any of those tools that that you know, you have access to you have budget for you have capability to use, right?

And then once you have that data, it becomes a real and you have that tooling becomes a really collaborative exercise. I suppose this this is not a dashboard that you as a support lead are just going to build.

Simone Secci 28:26
Yeah, exactly. It becomes like a collaborative effort. And then that’s where you really give a voice to your team and to your customer. So, because in that case, we have feature requests. So something that might be overlooked, like you’re making sure that their voice is served or that that feature is a feature that was seen. Hmm, yeah.

Charlotte Ward 28:47
Yeah. How much once you have that kind of visibility? To everyone in the organisation? How much conversation Do you still need to have around that data? Or can people do you really Lie on people pretty much to self discover, and the things that you have put the effort into surfacing there. So, so do you rely on a product team to go to the same dashboard that you’ve carefully crafted and make the same make the decisions you hope they would make, based on the data that you have worked so hard to present

Simone Secci 29:19
and collaborate on? I think it’s important to clarify the goals together. So, before you set up like any sort of, you know, mechanism we talked about before with like a source and endpoint and an automation in the middle, like ask, what their goal is, what the strategy is, what they what the categories are, for example, and see how even matures very important because like your score categories, you know, the more they match like in the language they will the team speak, the better your message can be understood. So I think this preliminary meetings Before, you know, investing a lot of time and technical effort in into building this, this dashboards of data being separated or being something more visually compelling, like, are necessary. And then, you know, looking for level operational of your team, of course, because, you know, you’d go into or rely on them on understanding their business priorities in order to tag correctly. And so the tagging for the team is not just an after forward, it’s like, you know, I’m trying to get to do my job as fast and as efficiently as they can that like tag is sort of like a hurdle that isn’t in the middle of this.

Charlotte Ward 30:43
Yeah, absolutely. You might as well simplify it for yourself as much as you can at the start by by aligning as much as you can. Otherwise, you’re going to have battles of alignment further down that actually involve battling the data rather than just the concepts.

Simone Secci 30:58
Yeah. So yeah, they are Time is fundamental for you do not like never expect anyone Never assume anything or expect anyone to know what you know or what is apparent to you.

Charlotte Ward 31:12
Yeah, yeah, absolutely. And I think this has been a wonderful chat. I think. We’ve talked about data on and off in various conversations recorded and not over the last two, three years, maybe.

But one thing that I would like if you, if I’m going to put you on the spot now, just to close out this conversation, is Do you have a favourite story around data that you can share? Maybe as a big data success as a support? I appreciate I appreciate it. I have put you on the spot there. Yes.

Simone Secci 31:51
that are that are a few. I’m trying to think of like something significant. Yeah, well, I will put something simple. And let’s say, underlines collaboration and empathy among, among teams. So one of my teammates in the, in the tier two, team, you know, they help with, like structured data, among other things to make the strategy came to me with like a request from a product manager will help this person with an OKR that they have in order to understand a certain percentage in order to guarantee a certain percentage of bugs submitted by our team to be solved. So how do we help this person measure that? Right? And so at this, we’re talking about two different tools. And so they’re saying To think, Okay, how do I put this tool in this tool? How do I connect them? So you start thinking maybe I don’t like permissions, and maybe I need to get somebody else to help me out with this other way, do it. I have this specific piece of data there, but not this. And I have this piece of data there, but not there. So thinking aloud, I put them in and I do them in touch. And then the most effective thing in the end, the simplest thing was like using charts on a Google spreadsheet. This is something I mentioned actually beginning of this conversation. You know, I add the names of the projects that this person was working in, and the bugs and for each Park, I had tickets that were you know, there were associated with each bug. So we could see the number of bugs submitted by project. We we see, because of the weather, we set up the data with the team When a bug is resolved, we mark this song as part of like the data, the tabular data that we have on this on the spreadsheet, right? So we get all these columns, we feed this into a chart. And we’d split this by month when we have to see the quarter Well, the, you know, the, the end of the, let’s say, the actual, like okrs see what is the key results was successful, which is grab three months of data. We align them on a column, we feed into a chart and we have that data. It was as simple as that. And we can see what what the percentage of like a, you know, solved bog by project management Kevin Rooney was.

Charlotte Ward 34:47
Yeah, I like that. I think that it’s it doesn’t always have to be automations and and, you know, data data teams necessarily does it because, frankly, you know, you’re lucky if you’ve got those tools available. And if you have, you should really utilise them. But, but sometimes all you’ve got is a Google Sheet and two tools that don’t talk to each other. And if you’re willing to put the time in and the effort you can, you can do some quite some quite amazing things just in a single chart. Right?

Simone Secci 35:20
You’ll have that, you know, the Golden Circle of like, good process, and the good data, you put them together to have a simple solution.

Charlotte Ward 35:33
Absolutely. I think that’s a wonderful passing sentiment. I don’t think you can take that as a closing remarks m&a. So I’m, I’m just gonna take this opportunity to say thank you for joining me today. It’s been wonderful to date, your data insights. And thanks again, I look forward to you popping up on the podcast again in the future, which I’m very sure you will, but now, you

Simone Secci 35:58
shall always play

Charlotte Ward 36:04
That’s it for today. Go to customer support leaders.com forward slash 104 for the show notes and I’ll see you next time.

Transcribed by https://otter.ai