In today’s episode, we chatted with Cynthia Kellam, Global Senior Director, Digital, Data and Customer Experience Center of Excellence at TE Connectivity, again to discuss how collecting and using digital data can create better customer experiences. She explains their journey of building a better Voice of Customer system to help all business units in TE Connectivity make customer-focused decisions. Cynthia explains how she avoids survey fatigue, uses automation techniques, and visualizes data for actionable success.
Building a Voice of Customer System to Enhance Customer-Centric Decision Making Transcript:
Announcer: You’re listening to The Kula Ring, a podcast made for manufacturing marketers. Here are Carman Pirie and Jeff White.
Jeff White: Welcome to The Kula Ring, a podcast for manufacturing marketers brought to you by Kula Partners. My name is Jeff White and joining me today is Carman Pirie. Carman, how you doing, sir?
Carman Pirie: I am delighted to be here, Jeff, and you?
Jeff White: I’m doing great. Nice to see you.
Carman Pirie: Yeah. Nice to see you, as well. Happy summer.
Jeff White: Indeed, it is. It’s that time we get a few weeks of nice, sunny weather in Halifax.
Carman Pirie: Yeah. You always know us Canadians here, we talk about weather for the first couple seconds.
Jeff White: Yeah. It’s a common thing. For sure.
Carman Pirie: Yeah. There you go. At least we got that obligatory weather commentary out of the way, and we can begin to turn our attention towards something maybe a little bit more entertaining, I think.
Jeff White: I think so, and certainly a very interesting perspective on something that a lot of people would probably like to be able to do, but maybe aren’t really diving into yet.
Carman Pirie: Yeah. And meaning no disrespect to any guest we’ve ever had on the show, but today’s guest is one of my favorites, maybe my favorite. This may be the best guest we’ve ever had on the show. We’re having her on again. Yeah. And I’m delighted for the conversation. I’m really looking forward to this.
Jeff White: Yeah. And last time she joined us, we were talking about product data, which sounds not that interesting, but the way that they went about it is a level above just about every other manufacturer I’ve ever experienced.
Carman Pirie: I will tell you what. If marketers can’t get excited about product data, then nobody’s gonna get excited about product data, so we may as well get excited about it.
Jeff White: Yeah. Exactly. So, we’re really glad to have Cynthia Kellam back from TE Connectivity, and Cynthia’s title is a long one and I’m gonna try and make sure that I get the whole thing, but it’s Global Senior Director, Digital, Data and Customer Experience Center of Excellence. Did I miss a word?
Cynthia Kellam: You did a great job with that.
Jeff White: Whew.
Carman Pirie: I think it basically means she runs the place.
Jeff White: Yeah. I think so too. I think so too.
Carman Pirie: Welcome to The Kula Ring, Cynthia. Lovely to have you back.
Cynthia Kellam: Thank you so much. I’m happy to be back.
Carman Pirie: Yeah. But look, let’s just jump right into it here, because the subject for today is really the notion of customer experience transformation, what it means to implement, nurture, and grow a voice of the customer system, and make wise customer-focused decisions on the backbone of solid data I think is kind of where we’re at today. I’m really curious to see where this goes.
Jeff White: Yeah, so tell us a bit about your new role.
Cynthia Kellam: Yeah, so I’ve been at TE for about nine years, and I’ve been leading digital and more recently also leading our customer experience center of excellence, and just recently, in the past month and a half, have taken on leadership for our marketing data insights and analytics function, as well, which was really quite good timing, because in our role around leading the customer experience center of excellence, we’ve been starting to focus more and more on the need for better real-time data and insights around our customers, and exposing and democratizing access to that data and insights to our businesses so that they could make better customer centric decisions.
At the end of the day, customer experience is all about putting the focus on the customer whenever you can, making decisions based on what’s gonna best benefit the customer in service to the business, as well. So, the addition of focusing on data insights and analytics has been, again, well timed, and we’re really excited to take that on.
When it comes to the customer experience piece of this, we’re really at the beginning of a journey in building what we were calling a voice of customer system. So, at TE, we have historically listened to customers in a couple of key ways. One, we’ve always listened to customers by capturing feedback through our website, TE.com, so it’s pretty common to see their feedback tab on websites. We have one of those so customers at any time can let us know what may be going well, or what’s not going well. Typically, with a feedback tab, it’s what’s not going well, because a lot of times customers aren’t gonna proactively give you feedback unless they’re feeling some pain and they’re irritated about something.
We also capture feedback in more of a customer satisfaction type of feedback through a randomized survey that we serve up to a certain percentage of our customers on the website and we also have other types of transactional surveys, so when you do a search for product information we ask you to let us know if you had any issues finding the product you were looking for. When you order a sample or place an order through our eCommerce process, we ask you how that experience was. If you abandon your cart, we ask you why you might have abandoned your carts, right? So, we always have been listening to our customers that engage with us online.
We also have a survey, a CES survey, which is customer effort score survey, that we use when customers engage with us for help and support, so when they contact our customer service teams, after they work with our customer service teams and we’ve resolved that issue, we send out a survey and they let us know how hard it was to resolve their issue. So, that’s another type of way that we capture feedback today. And then the other way, the third way, is through an annual NPS survey, which is a net promoter score type of survey. And when we took a look at all these different ways of listening to customers in a standard fashion, or what we’d call a scalable fashion, through these mechanisms, we felt that we’re really missing many of the touch points customers have with us across their end-to-end journey with TE. And largely those touch points that might be occurring through for example sales conversations, or when a customer receives their order, for example. Today we don’t have any common or standardized way that we capture customer feedback about those key moments in their journeys with us.
And I always look to other industries when I’m looking for best practices, because industrial manufacturing may not be… is not really the leader, I think it’s fair to say, when it comes to customer experience. And so, I like to look outside of our industry. And you go to a hotel for a visit these days and as soon as you check out and you’re on your way home, you get an email that asks you how your stay was and to… oftentimes, it’s an NPS type of survey. A lot of companies, you place an order with them, you get a survey. You receive your order, you get a survey. And these companies are using that insight to better understand what are we doing well that we want to do more of, or we want to scale further, and where do we have opportunity areas?
So, we look at those best practices and we’ve said how are we gonna apply that within TE? And so again, we have a vision around developing a voice of customer system where we collect customer feedback at key moments all along the journey and we aggregate that insight along with our operational data to understand where we have true opportunities to improve our customer experience, and especially improve it in a way that’s gonna drive greater business results for us.
Carman Pirie: Is there any concern, Cynthia, about-
Jeff White: Survey fatigue?
Carman Pirie: Yeah. That’s where I was going. Yeah. It’s actually exactly what I was gonna say. It’s like we’re talking about four very specific ways that were already in place to collect customer feedback and information. I appreciate that that certainly is not going to capture the entire journey. So, yeah, has that been a concern? And how are you thinking about it/mitigating it?
Cynthia Kellam: Great question. So, we’re a company that is made of 11 different business units, and what we’ve learned as we’ve taken on the customer experience center of excellence role is that different business units were surveying customers in different ways and using different mechanisms, so one business might have been sending a monthly survey. Another business might have been sending a survey every quarter. And first thing we did was start to build an inventory of all the different ways that we were surveying customers and we also started to speak to our businesses about what they were doing with the results and what they were doing with the insights.
And what we found is that in many cases, these surveys were happening and actually those businesses were concerned about survey fatigue, but they weren’t necessarily doing anything with the insights that they were gathering back. And so, we have worked to put together this vision for more of a standard approach to surveying that allows us to better manage and monitor how many surveys any one person may be receiving. But at the end of the day, we’re a B2B company. It’s typically not one person per customer. It’s actually typically a team of different people that are involved at different stages of the journey. So, if we do a survey to a person at a customer when they’re looking for products, that may be an engineer on an engineering team. But then when we survey the person who is placing the order, that’s maybe someone in purchasing from a different team. And then when we survey a person who is actually on the receiving dock receiving the delivery after a production size order, it’s a different person.
And so, in order to manage the potential risk of survey fatigue, you have to have visibility to all those different people and the roles that they’re in, and also be able to manage and control and govern the surveys that are going out so that you address survey fatigue. So, I think it’s something we keep in mind, and it’s only something that we can manage with a system that gives us visibility so that we can control it and put rules in place that say we don’t survey a person more than once a quarter, let’s say.
Carman Pirie: So, is it fair to say that you may actually not be even surveying more. You’re just surveying-
Jeff White: Better?
Carman Pirie: … better? And at the right times. And making better use of the information as you get it rather than just getting it and letting it collect dust.
Jeff White: Yeah. And I have to think too what’s interesting about what you said, of course, because you have these, in these B2B purchasing arrangements, you of course have all of these different… What was the last estimate? Like 13 people involved in a standard B2B transaction. How are you modeling the organization so that you’re getting data from the receiver, you’re getting data from the procurement person, you’re getting data from the engineer? Are you structuring that and kind of modeling the purchasing team or the buyer?
Carman Pirie: Yeah. Are you able to wrap your arms around the decision-making unit in the organization? Or is that too ambitious?
Cynthia Kellam: Never too ambitious, but I would say we’re again really just at the beginning of our journey on that path. And at the core of it, of course, is data, right? If we don’t have our data, our customer data, and I’m not talking about what’s the account number and what’s the revenue associated with that account number. I mean the human element of who are these people, what’s their contact information, what role do they play. That we must have well structured, governed, managed data before we can start doing what you’re describing, and this just reminds me of the journey that we went on around product data when I think about, “Hey, we want to solve this product findability problem, but we have to start with core product data. It has to start with structure, and you have to have it filled in and it has to be managed.” And it’s the same topic, same solution, when it comes to designing this VOC system and getting good insight.
So, I would say that’s absolute. The ambition is to be able to have such good customer data that we can map out these decision-making teams. And of course, that doesn’t just serve the voice of the customer practice we’re talking about. That serves account-based marketing, and account-based experiences, and all of those things. So, yes, an ambition. No, we’re definitely not there yet. And we’re learning about it day by day.
I mean, we had a call recently with one of our operations teams in one of our businesses who’s interested in surveying customers after they receive an order, and the idea of designing a survey and sending it out from our new enterprise VOC system is easy, right? That’s something that it’s easy for us to do. The hard part is what data is gonna trigger the survey to go out? Where is the data that tells us who that survey should go to? Those are two key questions that we need to answer that have less to do with how we send the survey out and where the results go and all to do with our internal operations and data management and making sure systems are connected the way that we need them to be.
And so, we get so excited when a business partner reaches out to us about something like that, because one, it shows that they’re interested, so they’re pulling, and two, it’s gonna send us on a path of discovery that hopefully will lead us to maybe solving for this use case in a way that can scale to our other businesses. Because that’s also always what we’re looking at, which is how do we solve for a problem for one business but do it in a way that ultimately we can then scale across our enterprise, so we really maximize the value of the solution?
Jeff White: When you said, Carman, is that too ambitious, I would have said like this is the one company that there’s never anything that’s too ambitious in terms of creating systems to understand, whether it’s product data like you worked on before, or now this customer data, and customer experience data, you folks really seem to take it seriously around crafting not just doing this better or what have you, but actually creating systems that can be used, and analyzed, and improved upon. You seem to create systems that you can also test and see how the systems are working, not just-
Carman Pirie: Yeah. There’s more vision, I think it’s fair to say, than what we often experience. Yeah.
Jeff White: Yeah. For sure.
Cynthia Kellam: And it may just have to do with our size and scale. And again, I know this came up in our product data discussion, but one of the important ways that TE has differentiated in our market is just the massive size of our portfolio, the variety of the products that we make, and the many different technology areas that we focus and the application areas that we focus in, and we have this global footprint, and so if we want to really maximize value, we have to think about how we do that at scale and through systems thinking and system-based design.
And I also just happen to have a real bias for those types of… That’s the stuff that I get excited about, so I’m probably naturally drawn to those challenges, as well. Because a custom solution that only serves one percentage of your customers may be great for that 1%, but if you’re trying to tackle a much bigger portion of your customers, you need to have something that will work systematically.
Carman Pirie: Cynthia, when it comes to collecting customer information and kind of getting these insights, I know we’ve talked about surveys. I’m assuming that call mining technology and things of that nature maybe is also at play?
Cynthia Kellam: That’s right. So, we have… currently using, and I’ll mention the platform names. We’re using Call Miner in our call centers specifically with our… We have a team called the solution center and the really serve, take customer calls, and chats, and emails at scale for our businesses, and it’s a voice and text analytics capability that does real-time classification and sentiment analysis of those calls so that we kind of better understand what are the major call drivers, what types of call drivers are driving particularly negative sentiment versus positive sentiment to help us identify opportunity areas, because you might have a large volume of calls in one area, and so you want to think about, well, their cost to serve opportunities and creating better self-service for those types of calls.
And then you might have a smaller volume in another area, but that smaller volume may be particularly negative sentiment, and so you want to figure out how to adjust that. And we’ve started to look at how to tie the data from that sentiment analysis and classification analysis against key performance indicators like churn, or revenue, or loyalty. Ultimately we want to be looking at customer lifetime value. So, yes, we have that for our call centers, and then we also leverage Qualtrics as our enterprise VOC platform, and they have a great capability called Text iQ, which does a similar type of classification, automated classification and sentiment analysis of any open field text or verbatim comments from customers. And one of the things we’re trying to figure out is whether we need to ultimately, and we do do some sharing, knowledge sharing and machine learning between the two, but do we need to create a taxonomy around the classification system, and a taxonomy around the way that we look at sentiment, and the way we maybe tag verbatims to moments in the journey, so that we can standardize?
Because again, ultimately we want to bubble and roll these things all up into customer journeys and look across the whole journey, and even at some point drive more customer journey orchestration. So, not just leveraging this data for better insights and understanding and better decisioning, but actually automate some of the decisions. So, if we know a customer, typically customers are gonna call and be unhappy about something, can we automatically proactively do X, Y, Z in order to avoid that negative experience for that customer? Or can we use data from our customer insights or from our customer preferences around which channel they prefer and use that to direct our particular type of outreach or routing of the customer contact?
Carman Pirie: And I’m curious, it typically would be a bit more of a B2C type thing, but it may be at play here, as well. Are you looking at social sentiment analysis in addition to-
Jeff White: Direct interactions?
Carman Pirie: … direct interactions? Yeah.
Cynthia Kellam: Great question. So, we do have a social analysis platform, but we just don’t see quite as… and it’s on the major social media platforms, and I would say it’s primarily we focus quite a bit on social media for more of our employment brand and employer brand. Not as much so much for business engagement, so we have it, but it’s not as much activity as we have just in our own first-party data and engagement that happens on our own platforms is just massive and significant. The number of sales calls, the number of sales emails, the number of customer service calls and emails. There’s just such a rich source of insight and data that we already have on our own platform, so that’s where we’re starting.
Carman Pirie: Very cool.
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Carman Pirie: You spoke earlier about the democratization of the information, as well, getting it more in the hands of people who can use it to make decisions. I guess what does that look like? Is that about just being more open when a business unit comes to you and says, “Hey, I want to know about X, Y or Z?” Or is it about creating dashboards and things of that nature to give them that data more quick glimpse?
Cynthia Kellam: It’s more the latter, although it’s always both, because there’s both a consultative piece of this and then there’s also the self-service piece of it. And it also goes back to the point that was made earlier about the fact that it’s not necessarily about doing more service. It’s about taking what we already have and exposing it in more meaningful ways within our businesses so that it can be leveraged to drive insight and decision making. So, it is about making sure we’re centralizing that data and exposing it in dashboards, and charts, and visuals, and KPIs that our businesses care about so that they pay attention to it and want to take action on it.
Like I mentioned, we’ve been doing surveys for a while here at TE. The question is what are we doing with the information that we’re getting out of the surveys. So, when we think about democratization, we focus quite a bit on this one platform I mentioned, which is Qualtrics, in order to build dashboards, and we’re in fact for the first time this year we’re gonna have our annual NPS survey results in Qualtrics and our businesses will all be accessing their results and interacting with their results using filters and interactivity that’s part of the platform in Qualtrics. So, we’re gonna learn a lot through that. We’re gonna learn how well they’re able to self-serve, how well they’re able to identify the key insights they want to focus on on the platform, what they still need to go back to raw data in order to analyze, and I’m sure we’ll grow from there once we get through this process.
Jeff White: I wonder, I’m gonna have to tease this out because I don’t have a direct question about it, but one of the things that you often hear from software companies and things like that is… I’m thinking of like 37signals, and Basecamp, and folks of those ilk that they don’t just implement something when somebody asks. You know, so if there’s a feature request, or a problem or something, they wait until there’s a critical mass of it, or it fits with their vision for where they’re going to take the product.
Carman Pirie: And it kind of suggests that doing less is better than doing more poorly.
Jeff White: Yeah, for sure. So, I guess what I’m wondering is are you making decisions based on kind of hearing things a number of different times and a number of different ways through varying channels, or would you be actioning individual questions, or individual requests, or individual feedback directly? Or is it more kind of a once we sort of see the overall shape of the sentiment, then we’ll make a decision on what to do, but we’re not just gonna fight every fire?
Cynthia Kellam: Great question and very relevant. So, a number of years ago, when I first joined TE, one of the first things I did was actually ad the voice of the customer elements that I described on TE.com, because I was focused just on our digital channel, and we immediately started sharing on a monthly basis, “These are the types of things that we’re hearing. This is what we’re seeing.” And we’d start to identify trends.
And sometimes we also shared verbatims because everyone knows when it comes to customer experience, the best way you can start to build empathy for your customer is to literally have your business leaders read the voice of the customer or even watch a video of a customer kind of describing an issue that they have. So, we started doing that too, and one of the things we immediately learned was that the challenge with that is that sometimes you have folks that get just really focused on, “Well, this is the biggest problem we have because I heard this customer say it. We must solve this problem.” And so, we hit the empathy trigger, and someone was feeling like they really, “Oh my gosh, this is terrible. This must be an awful problem.” And we’d have to always balance that with yes, it is a problem, and we should have empathy for that, but ultimately we need to look at more of the trends, and what segments of customers do we want to focus on. Is this just a one time issue? Is it something where we just need to call and talk to this customer and have empathy with them on the phone to let them know we’re sorry that they had this bad experience? Or is there a systemic problem that’s impacting 40, 50% of our customers that needs to be solved?
And it’s really always I have found in this role to be always balancing those two things of wanting to, needing that empathy. You need your business leaders to really care about their customers and what’s causing pain and what’s not. But balance it with a data-backed insight about what’s really gonna have an impact or what’s really having an impact on-
Carman Pirie: How widespread is that problem?
Cynthia Kellam: Exactly. Absolutely. Exactly. The same thing. And in fact, and I can’t help reflecting back on the product data discussion, but this even happened with product data where when we first started tackling the product data problem, we were sharing at a part level. We’d get a customer who would complain about a part and say, “This part is missing a document.” And we’d hear, “Hey, are you making sure that document got added to that part?” So, imagine every week you get hundreds of individual comments from someone saying, “This part’s missing a document,” and we said, “Look, we can focus on chasing every one of these different pieces of feedback to make sure the document was added to the part or we can look at all of our products, and all of the documents, and see at scale how many are missing documents, and instead focus on this kind of higher level KPI that’s really more about the system.
And again, this is you’ve always got to figure out where’s the right place to put your energy, and time, and capacity that’s gonna make the biggest difference. And there’s a Bain model of customer experience that talks about… which is where NPS came from. The inner loop and the outer loop, and the inner loop is about how do I address the individual customer and the issue that they brought to me, and then the outer loop is really how do I look at trends? How do I zoom out to 10,000-foot view and look at the trends and identify where I need to really do root cause analysis and start fixing our system overall versus just that individual customer problem. And you want to close the loop in both cases, and you just want to be careful to always not think just because someone’s being really loud about their individual issue that that deserves that larger outer loop analysis and resolution.
Jeff White: Yeah. It’s just that we’re putting the cover sheets on the TPS reports now is what I had in my head when you were saying that.
Carman Pirie: You mentioned I think you’re about a year into this now?
Cynthia Kellam: The CX, yeah, so the CX role, about a year and a half, and the data analytics and insights role just about a month and a half.
Carman Pirie: Okay, okay, so this is… In terms of the overall kind of time horizon or timeline that you see for this project, I’m sure that it’s an ongoing iterative process that never truly ends, but is there a time horizon for the heavy lift? Getting the systems in place, et cetera, et cetera?
Cynthia Kellam: So, great question, and I would say what we’re focused on now is making sure we have the systems in place and that we feel pretty good that we’re 80% of the way there and having the basic system in place, which is Qualtrics as our one enterprise platform for VOC. That doesn’t… We also have other systems. I mentioned Call Miner. We have some other systems that we may use. But that we want to use our primary platform, and it’s where we have our TE.com feedback. It’s where we have our CES data and feedback. And then it’s where we have our NPS data and feedback, and those are the three main enterprise-wide ways we capture customer feedback.
So, the next step is to work with business units, and it’s probably one-on-one business units, so I mentioned one of our business units came and wanted to add a survey upon product or order delivery. So, it’s basically working with different… We have another business unit who wants to add a survey at opportunity creation, which is when our sales team says, “Oh, I’ve identified there’s a real opportunity here with this customer who’s working on this application and is likely to want to order at volume X number of products.” So, two different businesses, two different surveys, at two different touch points in the journey, but if we work on solving for those and then we look at scaling both of those across the enterprise, suddenly we’re starting to fill in this overall VO system across the journey. That could take years. It depends on how much our business units lean in on that next stage.
We can’t push that from the center. It’s really we’re a BU-led company and it’s really up to our businesses, and at the end of the day if we push a survey to our business, on our business’s behalf, and they’re not interested, then it’s gonna be meaningless because no one’s gonna look at the data or do anything with it. So, we need to have a business that says, “I want to capture feedback at this moment, and I’m committed to listening to what the customer says and doing something about it.”
So, I would say probably a couple years to build up this end-to-end VOC system, where we’re capturing feedback at all these different moments, and what I want to be able to do in parallel is start to tie in more of our operational data. So, that’s something that will come in, and then I think as we have richer and richer data sets across the journey we’ll start to look at opportunity for more predictive insights and more orchestrated journeys. Instead of using the data just for insight and decision making, using that data to actually drive a predictive experience for our customer.
Carman Pirie: And I would have to think that it wouldn’t be the worst idea to at least try to expose some of the successes that some business units have had to try to encourage other business units to lean into this.
Cynthia Kellam: That is 100% how we do. I mean, that’s the only way to drive change at a company that’s organized the way we are, which many companies are, is you have to really celebrate those wins, and also the learnings. Celebrate what worked and what didn’t work, and that’s right, so it’s really a community of practice, where we’re helping folks see that they benefit when other business units lean in, and they should be curious about what that other business is learning about, and they should be ready to be fast followers if they’re interested. And we really… We count on that happening and it’s been a pretty successful operating model for us.
Jeff White: One of the things… You mentioned kind of overlaying, incorporating other data. It makes me think of a lot of these industry 4.0 type platforms that are measuring things and then synchronizing data from different sources and kind of showing, “Well, on this day this happened, but all of these things were going on,” and you can start to make decisions based on that too. Would you ever be kind of mapping this against production data and other types of things like that to get a sense of this is what was happening in the factory at that time while those components were being produced? Or would that not be useful? I don’t know.
Cynthia Kellam: I love that idea. I mean, that’s really, truly aspirational. Compared to what we were talking about earlier I think is a little bit closer in, but what you just described would be… Yeah. I mean, the dream is to have all of that data available so you can really understand the full back office to front office, what’s happening in every different moment, so that you can really use machine learning and AI to understand what’s really making the impact or where is the issue that you need to focus on solving? Because you’re right. That would be… Again, that would be the dream, and I do think it would be useful, but that one’s a little bit even further out on the horizon.
Carman Pirie: How much of the insights at this stage are being driven just simply by surfacing data that the business units haven’t seen before so they can see it and go, “Oh, oh. Well, okay.” How much of it is that versus deeper analysis on the data that you’re gathering and kind of unearthing insights from within it?
Cynthia Kellam: I’d say that 90% right now is just about exposing what we already have and helping folks contextualize it and understand what it means to their business. And then maybe 10% is net new intelligence on top of that data.
Carman Pirie: I would think that would be encouraging for listeners. I mean, it kind of tells you that there’s probably a whole bunch of useful data sitting there that you’re just not making use of today. And it’s just about exposing it rather than trying to get too terribly fancy with statistical analysis.
Jeff White: I wonder, though, is the end… Well, not end state, because as you mentioned, it is an iterative, ongoing thing, but to kind of flip that a bit where you’re actually… It’s not just reactionary. It’s actually looking at and predicting and getting better at just not responding to exactly what… just the things that you’re seeing right now.
Cynthia Kellam: 100%. So, what you described is what I’m learning as kind of the very commonly understood kind of analytics maturity model, where when it comes to insight you really… The foundation, first of all, is the data piece, so governed, trusted, standardized, structured, all those things. And then it’s really reporting. It’s just seeing the results in a way that you can understand. And at the top is prediction and that’s where you get more of that really… and I totally agree that ultimately you want to flip it, and what I’m describing again is that we’re really at that base-level stage. And even the reporting piece requires us to do some fixing of the customer data itself.
You know, our customer data today requires more standardized use and adoption of CRM across our businesses, as an example, so if you want to make sure that you know who your customers are, then you need to make sure you’re capturing key contact information about them and that you don’t have redundant profiles of a single person across multiple systems, right? So, there’s a lot of basic work we need to do to make sure we have that clean customer data so that we can do the reporting, which again, is probably… There’s a lot of… 90% of value right now is in just exposing what we already have in a way that people can make sense of. And then once we have that, and it’s automated, and people know how to use it, that becomes the 10% and the 90% becomes all of the predictive insights that we can do next.
Carman Pirie: Man, I’ve got to think getting to that level with customer data has to be a bear compared to getting there with product data.
Jeff White: Product data. Yeah.
Carman Pirie: You know, because there’s just… There’s a lot more human kind of-
Jeff White: Yeah, there’s a people element here that’s a bit less predictable.
Carman Pirie: Yeah. And some of them are salespeople. God love them. But-
Jeff White: Are you suggesting salespeople can be difficult to work with?
Carman Pirie: Well, I’m just saying it’s… I guess I don’t want to put words in Cynthia’s mouth, but is this a bigger hill to climb than the product data integrity hill? Because it seems to me that it is.
Cynthia Kellam: Yeah, so I think it’s a different hill. So, it’s a different hill, so yes. On the engineering side we had a couple things, tailwinds I would call them, right? So, we were already using common backend engineering platforms and systems for our data. All of our engineering and product data sat in common platforms and our engineers knew how to use them, and so a lot of the heavy lifting had to do with standardization and normalization, which we talked about, as well as filling in missing data and documents.
Now, the hard part about that filling in the missing data and documents, and a lot of it was data gaps, is that it required a pretty highly skilled and knowledgeable person to do that. We tried to use AI. We tried to do data scraping. We tried all these things. None of it delivered high quality engineering data and product data, so it had to be people that were highly skilled and knowledgeable. So, that was a heavy lift when it came to the product data piece.
For customer data, the difference is many of our business units are not using a common CRM. They’re not following standard process. They’re not following, right? So, it is there’s a lot of behavior there. Now, that being said, there may be ways for us to clean and fix the customer data we have in more automated fashions that don’t require highly knowledgeable, skilled talent the way that filling in missing product data did. So, it’s a different challenge. I’m not sure yet whether I’m ready to say it’s a harder challenge. There may be ways for us to fix this that don’t require entirely different behavior from our sales teams. Maybe there’s ways for us to better design the CRM system to only allow the entry of data that’s already standardized and normalized, right? In fact, I know that that’s the case.
But it’s gonna be an interesting journey and I’m excited to take it on. This is the type of problem I like to solve.
Jeff White: We’re already over time because this is so interesting and I think we could keep going significantly longer, but one thing I would like to absolutely do is make sure that we have you back in a year once you’ve had even more time under your belt, and see where you are then, because this is fascinating stuff.
Carman Pirie: And that’s when we can rename it to the Cynthia Kellam show and just kind of… This is a great transition idea, Jeff.
Jeff White: I like it. I like it. Well, Cynthia, it’s been an absolute pleasure to have you on the show again, and to hear about what you’re doing. It’s fascinating stuff.
Carman Pirie: Thanks so much.
Cynthia Kellam: It’s been so much fun. Thank you so much for having me.
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Cynthia KellamGlobal Senior Director, Digital, Data and Customer Experience Center of Excellence
Cynthia leads the hard work of digital transformation with a focus on customer-centric digital product innovation and omni-channel customer experience strategy. With over 20 years of experience constructing enterprise and global digital strategies that have transformed businesses in multiple industries, Cynthia leverages data- plus human-driven insights to understand strengths and opportunities within teams and platforms leading to unmatched and sustainable business results.