Mary: What’s challenging, though, is how do you unify and pull all of the information and data from all of those systems together to really get a full view of your customers, right? Because the siloed data only tells part of the story. So you’re really missing all of the interconnections and correlations across, right, how the customer journey is actually playing out.
Adil: Hey, welcome to the Hyper Engage podcast. It’s a weekly interview-style podcast series, we will pick the brains of some of the best customer success leaders across the globe, and try to unearth customer engagement beyond onboarding, expansion and churn. So let’s get right in.
Adil: Hey, greetings, everybody, this is Adil, we have Mary Poppen from
Involve.ai, she’s serving as Chief Customer Officer at, you know, in a very short succession, they have a great journey and, you know, great growth metrics from what we’ve observed iat involve. So thank you very much, Mary, for joining us today.
Mary: Thanks for having me, Adil. Good to see you.
Adil: Nice. Likewise, likewise. Okay, so let’s get straight in. And talk a bit about your initial journey within the customer facing teams. And I’m sure, you know, let me tell you more about
involve.ai involve.ai is a dedicated data intelligence platform for your customers and for your CROs, like you can, you can have 360 view of all the data in front of you as a customer success manager or VP, you know, senior leadership as well. And you can definitely have and, you know, craft actionable insights out of it to build a meaningful relationship, and of course, drive more revenue more, you know, retention and all. So could you tell us in in even more simplified manner, to our audience, please?
Mary: Yeah, so, we essentially, are a customer intelligence platform really applicable to the entire business. Because customer data and customer insights really apply across the entire company product can leverage it for innovation and differentiation. Marketing can use it in terms of which kind of use cases and pitches make the most sense for various segments of customers, sales can leverage it to see where value realization is happening for customers, and then you know, be able to share those best practices and use cases with prospects. And then of course, customer success. And account managers can leverage the information to actually be more proactive, more predictive of what the customer needs, and actually personalize every customer experience. So that’s a really cool part of it. It’s applicable to everybody, because we all want to know what is making our customer healthy.
Adil: I tell you what, because I speak to a lot of SaaS founders and, you know, head of customer facing teams, and you know, this in the next three to five years, I would say more than 70% of the SaaS businesses, whether you’re sitting on the series, A or early stage, they will definitely look for for a dedicated, you know, data platform for the customer facing teams, what do you think about it, because a lot of them are already they’ve already incorporated catalyst Totango, Gainsight, they’ve been there for like quite some time. But now, there is an awareness and, you know, customer facing leaders they have, they finally realized that, you know, they need to leverage these tools to save more time, be more efficient and have information ahead of time, and forecast your customers as much as possible. What do you think about it?
Mary: Well, first of all, I think what’s awesome today is that sales, have their own specific systems, marketing, has their own specific systems, in customer success has very specific systems. And you can build those around how your, you know, part of the business should function. What’s challenging, though, is how do you unify and pull all of the information and data from all of those systems together to really get a full view of your customers, right? because siloed data only tells part of the story. So you’re really missing all of the interconnections and correlations across, right, how the customer journey is actually playing out. And so what what’s the nice part about where technology has gotten to now especially AI, and how involve is leveraging you know, the data is to tell a holistic story, to be able to correlate things that you would maybe never even think would be related, you know, so kind of like data science as a service, if you will built into a platform.
Adil: Exactly, exactly. I’m well familiar of data science as a service that you guys are providing, and you’re doing incredibly well. And have some, you know, let’s start with the you know, talking about your, your team. How big is your team right now.
Mary: So my team right now is about 15. And that’s across our technical our data science, can we I have data science consultants, who actually will run all of the correlations and consult with the customer around the insights. I’ve CSMs who are really ensuring that At the value of realization and adoption of those insights is taking place. We have our community and Education team. By the way, we’re launching our community, the end of the month, it’ll be called maybe customer intelligence community. So watch for that. And then I also have our partnerships, and renewals and expansion.
Adil: Great, great. Great. So, you know, you I mean, of course, a lot of your customers, they might need someone technical, like some sort of data ops team, to, you know, help them integrate just like Team catalysts during the strike team Totango these guys doing? So? How how do you guys onboard new customers?
Mary: Yeah, so great question. One of the key focus areas for us that we wanted to make sure that we targeted early was taking over the heavy lifts for our customers. So in terms of data integration, unification, tagging, mapping, you know, just point us to the data, and our team will do all that work. That is a huge differentiator, because you don’t have to get on the engineering roadmap to build an integration, we’re internally, right. So you can actually move very quickly, we’re able to unify customers data across and create insights within two weeks of getting access to the data. And then we have that operational component kind of up and running for the CSM and account managers, for example, within, you know, the first 30 days. So it’s a really rapid deployment, because we sort of, you know, do this day in and day out with the data integration. And then our models are proprietary AI models are trained on all of the post sale data. So we’re very quickly able to bring benchmarks and insights and segmentation once we get access to customer data, so
Adil: That’s interesting. So I have one more question as well, because I have some technical background as well. And, you know, heaps of experience, like more than 10 years experience in the customer relationship. And, you know, we building other tools as well for you know, b2b SaaS businesses. So now, a lot of your customers, you know, they must have, they would definitely come up and say, Okay, we have all of our like, source for data for our customers lies in, in Salesforce. So do you have range of different engineers or data ops team that actually, you know, that is certified or maybe skilled on the configuration for, especially for the Salesforce, HubSpot is easier, you know, other CRMs. What about Salesforce? I’m sure that you will have more than 50% of your customers definitely would have Salesforce and you. So how did you come up with that, you know, initial challenges and how to overcome that time?
Mary: So one of the things that, that I think it really works in our favor is we are tech stack agnostic, we don’t, it doesn’t matter to us what type of CRM what type of customer success management platform, what type of support platform you have, we can get as we as long as we can get access to the data. And even if that’s CVS or CSV files, we can take it from wherever it lives. You’re right that a lot of our customers have Salesforce as a CRM, but we also have some with HubSpot, some with dynamics. So we’re really agile in that respect, the configuration of the customers’ data and their systems of action, we don’t, it doesn’t matter to us in terms of how we get the data and are able to run it and create these insights. So any type of custom configuration doesn’t really matter. We do that data mapping upfront. So our team is able to assess like the common data elements for customer health, as well as any unique elements that our customers, for example, like Gone scripts, even just qualitative data too.
Adil: Yeah, exactly. Because we’re using like for our business, we’re using, you know, some CRMs and, you know, chat platforms and, you know, product usage platform like Segment and Mixpanel. So, you know, I’m just thinking, creating a thinking model, if we are to use involve. So, you know, just, I’m trying to understand like all the data points that we have inside segment and Mixpanel. Of course, you can automatically pull it up and basically map it to some of the signals or maybe highlight some of the data points that you have on your end. So what about some of the If I miss out on any of that is compatible or that is something that involve needs in order to be able to generate powerful insights for my team to take actions, you know, of course, data is always going to drive actions. That is your mantra, you know. So in that case, like, do you ask me questions? Do you help me map those data points inside my, you know, product analytics, and as well as the CRM?
Mary: Yeah, what, what we do, we’d rather cast a really wide net, then narrow to start. And so we want to understand all of the data that you have, where it lives, and we’d rather start. So what we do is pull all of your historical data together, anything you have collected on your customers, we generally take a year’s snapshot of all of your customer, your product adoption and utilization, customer satisfaction, support tickets, your CRM data, and community data that you might have if you have a community, right, so we pull everything we could get our hands on, including emails, Slack, you know interactions with customer. And we do a regression analysis on your specific data. So we actually take your historical run it through our proprietary AI models. And so the models are actually trained on your data. Some customers, some customers only have, as long as we have three data elements, we can find some pretty interesting insights. But we have some customers that have you know, more than 150 points of data. So again, agility is sort of our 11:44
Adil: Basically, that’s the best way to do it. Yeah, that’s the best way to do it, to basically take all the data and train it on on your engine and then generate events that are generating, you know, tasks. So is that can I rephrase involve AI in a way as a CSM, customer success manager that involved that whenever I log in on 9am, in the morning, I’ll see involved, are they giving me all the test? Is that right?
Mary: You would see the ability to prioritize all of your portfolio based on the health of the customer, and how it’s changed. All of the data elements that are feeding, remember, it’s a data driven health score, which is different than what you know, has been available historically, where it’s been more of a gut driven health score, right? This is actually data driven, and it’s individual by every customer. So if a customer’s health changes from yesterday to today, you can go in and drill in and see what changed, was it a drop product adoption? Was it an escalated support ticket, was it right? And so then the CSM, the AI will actually generate action require or recommendations as well. And so it will say, you know, hey, Adil, this customer escalated this particular issue yesterday, you may want to reach out to Bill Smith, to help resolve the issue. And it will be that specific and give you the name of your contact in the organization to reach out to to it’s very
Adil: I wish Will Smith, which will smith could have resolved that day. But so, okay, so Mary, I’m finding it’s so interesting, because I’m looking at these platforms, from the last three years, all these platforms, there is no good or bad at every SaaS is solving a problem for a specific segment of customers. So as you said that it’s basically AI driven data driven signals based on which you get recommendation. And then you make action is that right? That’s right, and that those data points are basically taken from from from your system from your CRM product usage data, you know, chat data, of course you might be taking from payment gateway as well as stripe and you know, revenue coverage and everything. So all of that you guys pull in and then you train you know, you basically trained that data and will insights on it, that insights in the form of signals and recommendation and so how many customers you guys have?
Mary: So we have close to 70 customers with a pretty big focus on enterprise and large mid market.
Adil: Okay, so looking at your stack and you know, when I first look at your website a few months back, so I thought that you must have some customers mid market to enterprise so that’s great. So of course as you grow in you know, the later stages growth phase of your SaaS Of course, that that small becomes enterprise and enjoy phase becomes a large enterprise anyway. So you have like 15 team members all together in the customer facing team under you.
Mary: Yes, right now. Yeah. And we’re growing, we’re growing pretty significantly, that will double, at least double by the end of the year.
Adil: Okay. Wonderful. That’s great news. That’s good news. So this is more about the product. And also, of course, you might be using your own product for your own customer success team. How do you what is the setup, because that is a story a lot of folks listening to this episode would love to love to know that How involved is using their own product for their own customer facing teams?
Mary: Yeah. So we do, we actually use our own platform in several ways, we are able to onboard our customers using what we call workspaces within our product. It’s a collaboration workspace with actions and tasks, and you can create different boards, you can share, you know, with customers in and internally as well. So there’s the ability to have the a lot of visibility, as well as collaborative action. So you’re not stepping on each other’s toes, right? Plus, all the actions that are taken in the workspaces is fed back into the AI. And so it’s actually generating which actions you’re taking have an impact on customer health. But we use it for that we use it, I do a Health Review, every week with the executive team, I use the product for that. I have a QBR report established in the product that actually emails, the different leaders in our company, and they can see real time what’s happening across our customers, and our CSMs, in our account managers use it to manage their specific portfolio of customers. And the actions generated, of course, by the AI or what they take action on in the workspace.
Adil: Yeah, well, I’m asking so many questions pertaining to your product, because it’s, I mean, this podcast is about customer success, and your product is about, it’s more about success of the customer. And it has to be because product lies these, their source desperately needed for, especially for the mid market to enterprise segment, where they have so much high touch and you know, account managers, they’re just working really, really hands on with their customers, and they don’t have data at the right time to forecast the journey. So how do you guys map the journey for different segments of customers? You said, it’s more enterprise and then you moving towards large enterprise? So how you guys segment the customers like you have around 70? I’m sorry, I forgot?
Mary: Yeah, yeah, we tell. And our delivery approach is actually very similar across all of our segments. And the reason is, because we can do, you know, a pretty rapid deployment, again, like once we had the data, the model and the processes setup pretty efficiently. It’s really the adoption of our customers and the change management that’s required. So whether they, whether their system of action is Salesforce, or like a game site, or turn zero, or another right catalyst or something like that, or they’re, or they don’t have a system of action, maybe they’re using spreadsheets, they can use involve because we have an operational side as well to be the system of action. And so we segment our customers ultimately on how, you know, we can build the best practices and change management most easily for them.
Adil: That’s beautifully put. So, you know, I was also thinking like, there are a lot of things coming on top, because this is something I really am interested in, you know, using not only using but promoting, and letting people know that this this platform like these exist that said, like, you know, sit looking at what you’re saying, I can easily say that it saves 50 to 60% of the time for Customer Success slash account manager in a day, this is huge time is a big, big, big aspect in this customer visiting teams and these operations. So how smart is your system in terms of flexibility, smart is a system in terms of, you know, adding your customer brands, a lot of tools, you know, they’re allowing customers to add their customer events along along the line when they see any pattern in the during the customer journey. So do you do also allow your customer to have their own task or have their own maybe data points integrated? Once Once the onboard after the onboarding during the adoption stage? You’re ready to do stage.
Mary: Yeah, we. So the solution evolves based on the customer’s evolution and maturity. So the more data that they collect, we add that into the model ongoing, we also revisit with them the actions that are taken or the changes they make in process internally. And make sure that the way the models are trained, are still reflecting accurate health. Because, for example, if you roll out a digital strategy, right, and automated kind of self serve motion to customers who were scoring on sort of high touch interaction, we need to make sure that we’re training the model to now look at a different type of interaction with a customer segment. And so we’re kind of constantly partnering with the customer from a data science consulting perspective, at least a quarter quarterly review. But if something if they have an acquisition activity, you know, ad hoc, I mean, they’ll know about it, but it’s something that we will work with them on pretty quickly, to bring that new business and so we can build insights on that as well. So it’s definitely an ongoing partnership.
Adil: Exactly. And at the same time, of course, you know, involved as a product needs to, you know, as time goes by, it needs to mature because it’s an AI, which is consistently evolving. So I’m familiar with GP three, and a lot of my friends like Jarvis now named as was in recently, he had conversion AI in the first place two years back when he started, then he had renamed it to Jarvis. Now, Jasper, so Jess, for Dottie, so it’s an AI, intelligent, AI based AI powered content writing platform for copywriters and marketers. So that’s, they’ve grown really, really big in the past few years during the pandemic, so And they’re also empowered by GP three and some of the AI models that are more advanced. And they have similarly they are evolving, and they’re consistently improving trading. So that’s great. So are you also running NPS? Video customers as of now, because you’ve been there long enough?
Mary: Yeah, we do actually have, we have a partnership MPLS that we do. But we also do product MPLS. So in product, I like to, I like to separate the two, I also like to make sure, you know, when we say MPLS, it’s truly MPLS methodology, whereas customer satisfaction or CSAT. You know, we measure that separately based on milestones along the journey. And try throwing this out there. Because to me, it’s super important to make sure there’s the distinction between what NPS is really meant to measure just the loyalty or a and, you know, kind of the bit of lagging indication of whether the customer is going to renew, but customer satisfaction along the journey, you’re asking different roles, it’s super important to be able to measure that from more of a emotional side. So I think I think both have to come into play as part of the story with all the other data that we’re collecting along product usage, you know, sentiment, those things.
Adil: Exactly. So using your own product for you on, of course, Chief Revenue Officer also monitors the high level stats on monthly or maybe quarterly basis, using your product, and also the chief customer officer like yourself, you guys also monitored reports and everything generated by your system, and which is already a poor data driven, least human touching. So like, how do you guys measure the relationship? Like a lot of sales businesses, they are they’re having some data learning, relationship capital or, you know, RPS relationship perceptions for these kinds of, you know, terms that they use to gauge the relationship, of course, mainly on the data, but it has to be based on the communication data as well, you know, so do you have any sort of AI applied to that, like on the web, when you take data from intercom, let’s say intercom, or HubSpot, like all the chat or email, trails and communication. So do you guys empower those kinds of based on keyword analysis that is a part of data science, keyword analysis, sentiment analysis?
Mary: Yeah, we do have a natural language processing capability built in as well. And that makes up the qualitative portion of the analysis. And so it actually is fed as elements into the overall health score, which is also unique As far as I’m aware, there are other health scoring mechanisms that are taking qualitative data and sentiment into consideration. But we will look for very specific product objections to competitive mentions to escalated language, you know, we look at those kinds of things and then flag them right away for the customer facing teams to be able to take action. And we can separate the sentiment of the comments from the quantitative scores. So the nice part is you might get a promoter for NPs, but a lot of their comments might reflect challenges and adoption with the product, right. And so it’s really important to be able to analyze all of the customer feedback in that respect.
Adil: And give an accumulative data point and insights that is actually meaningful and relevant. So you know, your CSM can also you know, they can simply build a conversation, and, you know, start building relationships, and have everything, you know, on the dashboard to measure ahead of time. So that’s great. So this is the question I asked to all of the Customer Success leaders, when they go on and build the customer success ops and Customer Success teams from scratch. They from ground up, just like seems like you as well, when you started. They were it was small team. So what was the biggest challenge? Could you share? So it could help us because a lot of early stage companies, tech businesses there, they’re going to be listening this podcast. So what is that challenge that you think you should share and within twice to overcome it while building a customer success team from ground up?
Mary: Well, the first thing I would say is the understanding the customer journey, the ideal experience you want your customers to have. And building that foundation early, is really important, because that is what is going to align the types of roles, the type of talent that you’ll hire to be able to deliver that. But also putting some systems and visibility in place early measurement is really important to know if you’re on track or not. And it’s really easy to skip over a lot of the foundational pieces, because you’re so busy just trying to build the business, right. But if you don’t put these things in early, there’s kind of a lot of chaos, and people are figuring it out as they go. And then you have to backtrack quite a bit to get the foundational layer in place. So if you define that journey, everyone understands and is aligned to it, they know what their roles or responsibilities are, the handoffs become seamless, the customer experience becomes seamless. So I would definitely encourage you to take the time and build out what does that differentiated ideal customer journey look like? And get everyone aligned to that,
Adil: that says so? Well. You know, you’re trying to you know, it’s, it’s very important to be visible, and build an ecosystem, before inducing any customer success. Individual the first time build an ecosystem, build, you know, a measurement where you can be visible on the road, on your customers, even on your on your team members, and all the data starting from, you know, the revenue to, you know, the entire customer journey. And also, you know, their lot, lots of tools, and everybody is aware of it, it’s just about, you know, building, streamlining the ecosystem, you know, having the product use everybody knows segment Mixpanel, you know, HubSpot sales versus cash more expensive, but, you know, people still prefer to use hotspots starting off and then, you know, customer data is pretty cool, too. For, you know, for the feedback, you can use intercom for the same case as well, for the documentation and everything. So thank you very much for being putting up with some genuine and practical knowledge. That’s why as to the leaders who have been who have done the hardcore work nicely, while building the team from scratch, so, you know, anybody wants to connect with you, we’d love to share the credential so people can reach out on LinkedIn, via email, whatever you think is amicable for you.
Mary: Yeah, I mean, I’m happy to have definitely LinkedIn connect with me if we’re not already, please follow me. And that’s probably the best best way to to connect with me.
Adil: And then kind of percent 100% Thank you very much, very for taking the time out with us and you know, playing a part In this in this system and in this activity that we have to help people help early stage startups help, you know, growth leaders impact leaders moving into the Customer Success space, and it will get paid off someday.
Mary: Thank you so much for having me. It was great to chat and I could talk about this stuff all day long every day. So definitely reach out.
Adil: Sure. Likewise, marry you have a wonderful day.
Mary: You too. Thanks so much.
Adil: Thank you so very much for staying with us on the episode, please share your feedback at
Adil@hyperengage.io We definitely need it. We will see you next time with another guest on the stage with some concrete tips on how to operate better as a Customer Success leader and how you can empower engagements with some building some meaningful relationships. We qualify people for the episode just to make sure we bring the value to the listeners. Do reach us out if you want to refer any CS leader. Until next time, goodbye and have a good rest of the day.
