1197: How to Take a Data-Driven Approach to Customer Support w/ Robert Johnson

WEBVTT 1 00:00:05.799 --> 00:00:10.310 A relationship with the right referral partner could be a game changer for any BEDB 2 00:00:10.429 --> 00:00:15.070 company. So what if you could reverse engineer these relationships at a moment's notice, 3 00:00:15.070 --> 00:00:20.469 start a podcast, invite potential referral partners to be guests on your show 4 00:00:21.149 --> 00:00:26.940 and grow your referral network faster than ever. Learn more. At sweetfish Mediacom 5 00:00:32.140 --> 00:00:37.250 you're listening to BB growth, a daily podcast for B TOB leaders. We've 6 00:00:37.289 --> 00:00:41.049 interviewed names you've probably heard before, like Gary Vannerd truck and Simon Senek, 7 00:00:41.409 --> 00:00:45.609 but you've probably never heard from the majority of our guests. That's because the 8 00:00:45.729 --> 00:00:50.479 bulk of our interviews aren't with professional speakers and authors. Most of our guests 9 00:00:50.520 --> 00:00:55.039 are in the trenches leading sales and marketing teams. They're implementing strategy, they're 10 00:00:55.079 --> 00:00:59.799 experimenting with tactics, they're building the fastest growing BB companies in the world. 11 00:01:00.479 --> 00:01:03.439 My name is James Carberry. I'm the founder of sweet fish media, a 12 00:01:03.560 --> 00:01:07.230 podcast agency for BB brands, and I'm also one of the CO hosts of 13 00:01:07.349 --> 00:01:11.629 this show. When we're not interviewing sales and marketing leaders, you'll hear stories 14 00:01:11.670 --> 00:01:15.349 from behind the scenes of our own business. Will share the ups and downs 15 00:01:15.430 --> 00:01:19.099 of our journey as we attempt to take over the world. Just kidding. 16 00:01:19.739 --> 00:01:29.340 Well, maybe let's get into the show. Welcome back to be tob growth. 17 00:01:29.420 --> 00:01:32.739 I'm Logan lyles with a sweet fish media. I'm joined today by Robert 18 00:01:32.780 --> 00:01:37.090 Johnson. He is the cofounder and CEO over at team support. Robert, 19 00:01:37.090 --> 00:01:40.569 how's it going today, Sir Logan? It is going gl gay. Today's 20 00:01:40.650 --> 00:01:42.329 so much fun of a chance to talk to you today and talk about team 21 00:01:42.329 --> 00:01:46.810 support. Absolutely, sirs. So we're going to be talking about how to 22 00:01:46.969 --> 00:01:49.719 take a data driven approach to customers support. You know, as you and 23 00:01:49.799 --> 00:01:53.159 I were chatting offline, on this show, we've taken a lot of time 24 00:01:53.359 --> 00:01:59.000 to speak about a data driven approach to marketing, data driven approach to sales, 25 00:01:59.359 --> 00:02:02.709 but customer support, customer service and success, I think, you know, 26 00:02:02.790 --> 00:02:06.750 maybe get the short end of the stick when it comes to taking this 27 00:02:06.829 --> 00:02:10.150 data driven approach. So I'm really excited for you to share some some tactical 28 00:02:10.189 --> 00:02:14.469 advice for folks and how they can leverage a lot of the data. The 29 00:02:14.550 --> 00:02:16.740 good news is a lot of it is is there for the taking in the 30 00:02:16.900 --> 00:02:21.139 in the platform that they may be using. So we'll get into that in 31 00:02:21.219 --> 00:02:23.060 a second, but for some context, for a little bit of lead up, 32 00:02:23.259 --> 00:02:25.939 Robert, give us a little bit on your background and what you and 33 00:02:25.979 --> 00:02:29.740 the team at team support are up to these days. For some context. 34 00:02:30.289 --> 00:02:32.849 Sure. Well, we've founded team support just about eleven years ago with the 35 00:02:32.969 --> 00:02:38.129 mission of being a BB focus customer support platform and the primarily for technology companies. 36 00:02:38.569 --> 00:02:42.409 My background is running other software companies and one of the things that I 37 00:02:42.530 --> 00:02:46.960 realized is that there really wasn't the tool out there design for that type of 38 00:02:46.039 --> 00:02:52.520 company. I do think that the customer support for the BB world is vastly 39 00:02:52.599 --> 00:02:55.400 different for the than for the BTC world, and we created the industries leading 40 00:02:55.520 --> 00:03:00.669 be tob platform for customer support. So it's going a great ride. We've 41 00:03:00.669 --> 00:03:02.150 got a lot of fun doing it and we're growing like a weed. So 42 00:03:02.349 --> 00:03:07.550 life is good awesome. Well, what you mentioned there actually, you know, 43 00:03:07.629 --> 00:03:09.469 you team me up for my opening question for you is, you know, 44 00:03:09.590 --> 00:03:15.099 you mentioned that customer support in a BDC environment is different from a b 45 00:03:15.259 --> 00:03:20.500 Tob Environment and you know we talk a lot in the marketing world about how 46 00:03:20.780 --> 00:03:23.620 we can learn lessons from BBC and approach it more like be Toc in a 47 00:03:23.659 --> 00:03:28.250 B tob environment, but when it comes to customer support there are some very 48 00:03:28.530 --> 00:03:31.650 distinct differences that you like to call out from B Toc to be tob. 49 00:03:31.770 --> 00:03:35.449 Can you speak to those a little bit? Sure, I love to. 50 00:03:36.169 --> 00:03:39.210 As I said, it really is there's some several fundamental difference, BUT DIFFERENCES 51 00:03:39.250 --> 00:03:44.639 BETWEEN BE TOB support and B Toc Support, and my backgrounds always been be 52 00:03:44.759 --> 00:03:46.560 to be. Really never been to B Toc Guys. So I really understand 53 00:03:46.560 --> 00:03:52.560 the B to be marketed very well and I think it's absolutely important that we 54 00:03:52.680 --> 00:03:54.949 treated differently. So several things that are different in the market places. Number 55 00:03:54.990 --> 00:04:00.069 one is that we are looking at and this is going to sound obvious, 56 00:04:00.150 --> 00:04:03.229 but when we talk to a customer, we're not talking to an individual, 57 00:04:03.270 --> 00:04:08.789 we're talking to a corporation and a customer really is that corporation. So we 58 00:04:08.909 --> 00:04:12.860 need to understand the relationship at the corporate level, which is going to include 59 00:04:12.860 --> 00:04:18.420 a lot of different individuals across potentially multiple divisions in a company. So understanding 60 00:04:18.579 --> 00:04:25.089 that relationship at a company level is absolutely critically important. One of the things 61 00:04:25.089 --> 00:04:29.649 that really makes team support different goes back to the very first architectural decision made 62 00:04:29.649 --> 00:04:32.490 in the product going back to two thousand and eight. In most customer support 63 00:04:32.569 --> 00:04:39.040 systems the core construct is the ticket. In team support the core construct is 64 00:04:39.120 --> 00:04:43.720 the customer, because that's the single more most important thing. Sold tickets is 65 00:04:43.800 --> 00:04:47.199 great, but understanding the relationship, in improving the relationship with that customer really 66 00:04:47.279 --> 00:04:50.199 is the most important thing. A couple of things that are important on the 67 00:04:50.240 --> 00:04:57.230 B Toc side seeing the Betb side is that the volume of tickets generally in 68 00:04:57.269 --> 00:05:01.389 the BTB side is lower than the BBC side, but the complexity is much 69 00:05:01.389 --> 00:05:05.189 higher. If you think about a normal transaction in the BTC world, it's 70 00:05:05.230 --> 00:05:11.579 going to be high volume simple questions, repetitive questions that get get asked over 71 00:05:11.699 --> 00:05:15.100 and over again. Generally speaking. In the BTB world it's not that way. 72 00:05:15.139 --> 00:05:17.699 It's going to be more complex questions but a lower long of those quest 73 00:05:17.740 --> 00:05:25.850 questions. Finally, in a B Tob side, each potential interaction has potentially 74 00:05:25.889 --> 00:05:29.610 a lot more value associated with it. Again, if you think about a 75 00:05:29.649 --> 00:05:35.600 BEDC transaction, one individual consumer for most brands is not a material conversation. 76 00:05:35.639 --> 00:05:40.199 If the consumer walks from the brand is says I'm never knew business with that 77 00:05:40.319 --> 00:05:43.759 company again. Most companies are going to care too much however, in the 78 00:05:43.839 --> 00:05:46.920 BB environment, if a million dollar recurrent run your client walks, that's a 79 00:05:47.079 --> 00:05:55.269 major and material event. So the level of materiality on each interaction is potentially 80 00:05:55.350 --> 00:05:59.069 much, much higher on the BB world. So again we shad on in 81 00:05:59.110 --> 00:06:01.350 two thousand eights and dollars for the preminute be to be customer support tool and 82 00:06:01.470 --> 00:06:05.819 it had a great success doing that. Of the last love plusumers. Yeah, 83 00:06:06.019 --> 00:06:12.259 I love those three specific components that you talk about there and the complexity 84 00:06:12.500 --> 00:06:15.259 and, you know, the stakes being higher are two that really stand out 85 00:06:15.300 --> 00:06:20.730 to me because if you are going to look at customer support being different in 86 00:06:20.930 --> 00:06:27.290 B tob you need to understand a number of the dynamics affecting the relationship that 87 00:06:27.410 --> 00:06:30.129 can be there can be more dynamics at play and, as you pointed out, 88 00:06:30.209 --> 00:06:33.399 more complexity. So what is some of your advice, Robert, for 89 00:06:33.720 --> 00:06:40.399 customer service and customer support leaders when it comes to managing all those different dynamics 90 00:06:40.439 --> 00:06:45.519 of the relationship and applying data to it so that they can manage them effectively, 91 00:06:45.759 --> 00:06:47.350 because it is, you know, so complex. At the same time, 92 00:06:47.829 --> 00:06:53.069 as I said, the in the bdb world is much more about understanding 93 00:06:53.149 --> 00:06:57.350 the relationship with that customer as opposed to the B Toc World, worth probably 94 00:06:57.350 --> 00:07:01.500 a more about personall resolution ticket times and managing it very, very efficiently. 95 00:07:01.819 --> 00:07:04.980 Of course efficiency is important in the be tob world from a support standpoint, 96 00:07:05.019 --> 00:07:09.939 but again it's go back to that relationship. So as far as managing the 97 00:07:10.060 --> 00:07:16.689 relationship with data, understanding the potential distress of that customer is key. So 98 00:07:16.850 --> 00:07:20.329 in team support we have a cool tool, a well it's cool tool call 99 00:07:20.769 --> 00:07:26.529 the customer distress index, or CDI, and it actually measures the level of 100 00:07:27.250 --> 00:07:30.720 potential distress with a company. We do that through a really cool algorithm that 101 00:07:30.800 --> 00:07:34.800 actually measures the average of all the customers and looks of the standard deviation of 102 00:07:34.879 --> 00:07:38.959 one customer compared to the rest of customer base on a whole bunch of different 103 00:07:38.959 --> 00:07:43.959 factors. In so from running that algorithm we can rise up to the top 104 00:07:44.430 --> 00:07:47.430 the customers that we perceived to have the most distress. And we call it 105 00:07:47.470 --> 00:07:50.870 the customer distress index because in the customer support side we really never get a 106 00:07:50.910 --> 00:07:55.870 chance to look at customer happiness. Nobody ever calls to the customer support department 107 00:07:55.870 --> 00:07:58.860 that says Hey, we let you guys, things are going awesome. Just 108 00:07:58.980 --> 00:08:01.139 want to let you know that. Thanks. Good by click. That phone 109 00:08:01.180 --> 00:08:05.220 calls never happened the history of customer support. We always get the complaints, 110 00:08:05.379 --> 00:08:11.100 the issues, the questions. So we always get the distress and our tools, 111 00:08:11.220 --> 00:08:16.449 CDI, allows us to measure that and report on that, and then 112 00:08:16.970 --> 00:08:20.569 what most of our client companies do is use that as a way to understand 113 00:08:20.689 --> 00:08:24.050 where to deploy resources, how to best treat that client and potentially change that 114 00:08:24.209 --> 00:08:28.120 interaction with that client. So that's one of many tools we use in team 115 00:08:28.160 --> 00:08:35.559 support to allow our customers to use data to better understand, manage and improve 116 00:08:35.600 --> 00:08:41.990 the relationships with their customers. Today's growth story is about a brand we all 117 00:08:41.149 --> 00:08:46.230 know well, air BNB. When they were trying to maximize growth among work 118 00:08:46.350 --> 00:08:50.950 travelers, are BNB new they needed to develop and execute a content strategy to 119 00:08:50.029 --> 00:08:56.139 reach multiple personas at different stages of the customer journey. Enter hub and spoke, 120 00:08:56.220 --> 00:09:01.980 marketing hub and spoke managed creative content development and crafted a custom publishing process 121 00:09:01.299 --> 00:09:05.980 that allowed airbnb to develop more content in less time. The end result a 122 00:09:07.340 --> 00:09:13.129 lot of content across multiple channels, all strategically nurturing, leads through to conversion. 123 00:09:13.529 --> 00:09:16.610 Within the first six months, are BNB nearly tripled the number of companies 124 00:09:16.690 --> 00:09:22.129 enrolled in their AIRBNB for work program they also saw huge increases in user adoption, 125 00:09:22.289 --> 00:09:26.840 with work travelers booking longer stays and more guests per booking. If you're 126 00:09:26.919 --> 00:09:31.960 looking for strategic content at scale, I've got a hunch hub and spoake can 127 00:09:31.039 --> 00:09:37.200 help. Head over to hub spoke dot marketing growth to schedule your consultation with 128 00:09:37.320 --> 00:09:43.870 a content specialist today. That's hub spoke dot marketing growth. All right, 129 00:09:43.909 --> 00:09:50.470 let's get back to the show and I think the methodology there is worth pointing 130 00:09:50.470 --> 00:09:54.059 out because when you, you know, mention your index that you mentioned the 131 00:09:54.220 --> 00:09:56.259 the CDI customer and distress indexes, you guys call it, you know, 132 00:09:56.340 --> 00:10:01.460 I think of other tools, other reporting methods to gage customer satisfaction, like 133 00:10:01.620 --> 00:10:05.580 a net promoter score in ps, those sorts of things, and you know, 134 00:10:05.620 --> 00:10:09.330 as you and I were talking offline, you know you mentioned that. 135 00:10:09.490 --> 00:10:11.129 Yeah, those, those things are important. You want to measure those. 136 00:10:11.169 --> 00:10:16.529 Those have, you know, great implications, but specifically in customer success, 137 00:10:16.769 --> 00:10:22.399 customer service and customer support, you you're dealing with customers when they are at 138 00:10:22.480 --> 00:10:26.480 that distress point, and so you want to manage the the ones that are 139 00:10:26.720 --> 00:10:28.559 in the greatest distress. So I love the way that you guys are tackling 140 00:10:28.679 --> 00:10:33.600 that and I think that that mindset shift of thinking about overall customer happiness but 141 00:10:33.679 --> 00:10:37.590 then looking at the other end of the spectrum and and how can you, 142 00:10:37.710 --> 00:10:41.029 you know, improve both to move the pendulum, you know, on both 143 00:10:41.110 --> 00:10:43.389 sides, I think makes a lot of sense. You know, you mentioned 144 00:10:43.429 --> 00:10:48.750 a little bit there, Robert, on reporting. I imagine that there's some 145 00:10:48.909 --> 00:10:54.379 regular advice that you guys are giving to customer support leaders where they may have 146 00:10:54.620 --> 00:10:58.940 some lowhanging fruit of data that they could just be looking at a different way 147 00:11:00.019 --> 00:11:03.980 or changing the way that they collect it. That could give them some insights 148 00:11:03.100 --> 00:11:09.529 that might yield some specific changes that could be big levers for either customer happiness 149 00:11:09.529 --> 00:11:11.769 or reducing that distress. Right. What are some of those areas where you 150 00:11:11.809 --> 00:11:16.250 see folks can change the way that they look at data or the way that 151 00:11:16.330 --> 00:11:20.759 they capture data and then leverage that for a vast improvement in customer support? 152 00:11:22.159 --> 00:11:24.799 Sure, let me go back to the last point is for one second, 153 00:11:24.840 --> 00:11:28.000 though. It's we talked about in the CDI and we brought up the MPs 154 00:11:28.080 --> 00:11:31.600 and some other survey data as well, and that's all absolutely critical. It 155 00:11:31.759 --> 00:11:35.190 is, as I said, the CD I really looks at this one section 156 00:11:35.269 --> 00:11:39.309 of that customer relationship, in we're big believers, and also looking at things 157 00:11:39.389 --> 00:11:43.269 like MPs, things like transactional surveys and getting that whole list of view of 158 00:11:43.350 --> 00:11:48.059 that customer. So it's the CDI is really one data point there which we 159 00:11:48.139 --> 00:11:52.179 incorporate with a lot of other data points to try to understand that overall relationship 160 00:11:52.259 --> 00:11:58.419 and it's there's no one tool that gives you all the inside the customer. 161 00:11:58.740 --> 00:12:01.929 Were working on it. We love that idea. Yeah, Robert, I 162 00:12:01.009 --> 00:12:03.529 mean it goes back to what you said earlier is that, you know, 163 00:12:03.649 --> 00:12:07.769 the relationship in the BB environment is more complex, and so what you're saying 164 00:12:07.850 --> 00:12:13.690 here about having multiple tools to be able to capture data about different aspects of 165 00:12:15.049 --> 00:12:18.960 the customer relationship, you know, just reinforces that point you made earlier. 166 00:12:18.000 --> 00:12:22.120 I'll let you speak to kind of the the data question and some of the 167 00:12:22.240 --> 00:12:26.080 reporting things that customer support leaders might want to be thinking about. then. 168 00:12:26.600 --> 00:12:31.549 Yeah, the data. Obviously, having a lot of any customer support system 169 00:12:31.549 --> 00:12:35.029 you've got a lot of data locked up in the system and hundreds of thousands, 170 00:12:35.070 --> 00:12:41.389 if not millions of interactions with customers is a treasure trove of potential data 171 00:12:41.389 --> 00:12:48.100 right in the ability to look at that and really mind that for intelligence becomes 172 00:12:48.179 --> 00:12:52.580 a very, very valuable tool. So just a couple kind of basic examples. 173 00:12:52.259 --> 00:12:56.740 One thing that's fairly easy to do certain INTA in support is to look 174 00:12:56.740 --> 00:13:01.289 at a histogram of the days of week, in times of day when your 175 00:13:01.370 --> 00:13:07.690 customers are contacting you. So from that it's becomes literally about three clicks to 176 00:13:07.730 --> 00:13:11.529 understand what time of day is is very busy. So you can't, from 177 00:13:11.570 --> 00:13:15.679 a staffing perspectives, start understanding, okay, we need to start the day 178 00:13:15.759 --> 00:13:18.240 earlier, start the day later, or stagger lunches, for example. Let's 179 00:13:18.240 --> 00:13:22.200 say we have to spike at twelve to one when we might want to move 180 00:13:22.200 --> 00:13:24.000 the lunches around so we have your lunches earlier or later, as we don't 181 00:13:24.000 --> 00:13:28.990 have the whole staff gone when we're seeing a big influxive tickets. Certainly, 182 00:13:28.190 --> 00:13:33.190 one of the things we've done is we actually have a customer support operation in 183 00:13:33.429 --> 00:13:35.710 Tape Count South Africa, and we use our own data to understand that. 184 00:13:35.830 --> 00:13:41.059 We had a lot of issues and customer questions coming in from both in North 185 00:13:41.139 --> 00:13:46.860 America, in overnight, but also in all our customers in Europe and Asia 186 00:13:46.899 --> 00:13:50.179 as well. So we are opened up a will open up a customers port 187 00:13:50.220 --> 00:13:54.299 operation and take time to be able to address those again based on the data 188 00:13:54.299 --> 00:13:56.250 that we have within team support. Yeah, no, things we can do. 189 00:13:56.929 --> 00:14:01.409 Kind of going back to the survey stuff, is we have the ability 190 00:14:01.450 --> 00:14:07.730 of doing a essentially a ticket closed survey. So we can understand. There's 191 00:14:07.809 --> 00:14:11.960 lots of different ways to surveys. Can Do MPs, we sort have the 192 00:14:11.000 --> 00:14:15.440 CBI, but this is a very transactional survey. Every time a ticket closes 193 00:14:15.759 --> 00:14:18.840 we sent a very basic surveyor that says essentially we said three faces, happy 194 00:14:18.919 --> 00:14:24.029 faced, neutral face and Friday face, and it's a snap shot gage of 195 00:14:24.149 --> 00:14:28.309 how that particular interaction went. But we can do it. We can measure 196 00:14:28.309 --> 00:14:33.870 two things. Everyone that helps is measuring the relationship with the client and actually 197 00:14:33.909 --> 00:14:37.820 that's a feedback into the CDI, but also helps us to understand the agents 198 00:14:39.019 --> 00:14:43.620 as well. If we have agents that have continually very very high percentage satisfaction 199 00:14:43.940 --> 00:14:48.779 from customers and Asians they have very, very low satisfaction of customers, that's 200 00:14:48.820 --> 00:14:52.620 obviously a point for management to go on there and say hey, let's figure 201 00:14:52.620 --> 00:14:54.730 out what's going on here. Yeah, absolutely. I mean I love that 202 00:14:54.850 --> 00:15:01.129 example there of very tactically just looking at a simple data point right of when 203 00:15:01.210 --> 00:15:05.450 our tickets coming in and then how can we staff around those? Or, 204 00:15:05.730 --> 00:15:07.600 you know, in your case, a little bit more extreme than moving lunches 205 00:15:07.639 --> 00:15:13.200 around, opening up a new office, but that can affect some large company 206 00:15:13.279 --> 00:15:16.639 decisions. If you look at the data, and I love the way that 207 00:15:16.679 --> 00:15:20.720 you pointed it out that the data that you have in your customer support platform 208 00:15:20.120 --> 00:15:24.950 is a treasure trove of intelligence and if you're looking at it the right way, 209 00:15:26.230 --> 00:15:28.990 you can use it very powerfully. Speak a little bit more to Robert. 210 00:15:30.230 --> 00:15:33.309 For Customer Support Leaders Listening to this in looking at, you know, 211 00:15:33.509 --> 00:15:37.740 how they manage csms. Again, it might be a little bit different if 212 00:15:37.980 --> 00:15:41.460 you know, maybe they come from a B Toc background and now they're managing 213 00:15:41.980 --> 00:15:46.460 their agents in a Bob Environment. Are there's some dynamics there that you think 214 00:15:46.500 --> 00:15:52.090 folks need to call out and be thinking about when it comes to managing their 215 00:15:52.129 --> 00:15:56.690 customer Support Team in a beb environment? Very much so. So on the 216 00:15:56.769 --> 00:16:00.929 BBC, as we talked about, as much more about volume, speed of 217 00:16:02.129 --> 00:16:07.519 closure, efficiency of agents and things along those lines. In the Bab world 218 00:16:07.519 --> 00:16:14.600 it's much more about customer satisfaction the relationship of those customers in ensuring that you 219 00:16:14.720 --> 00:16:19.830 are getting that customer to especially in the subscription world, to stay and potentially 220 00:16:19.870 --> 00:16:26.070 expand as well. So in many ways the skill sets from a manager but 221 00:16:26.230 --> 00:16:30.870 also from an agent themselves are different between the two. Again, B Toc 222 00:16:32.070 --> 00:16:36.019 much more vally and much more rapid. Closing be to be much more relationship 223 00:16:36.019 --> 00:16:41.460 driven. So when you're looking to hire in manage agents in the BB world, 224 00:16:41.860 --> 00:16:45.659 one of the things we really focus on is that ability to drive that 225 00:16:45.779 --> 00:16:52.009 relationship and I think that's absolutely critical in the Bab will yeah, anything else 226 00:16:52.169 --> 00:16:53.850 you want to add rubber in? You know, some of the the new 227 00:16:53.929 --> 00:16:59.809 technology that folks are leveraging is, as you and I were talking offline, 228 00:16:59.850 --> 00:17:03.880 you mentioned something about sentiment analysis in a part of your platform that you guys 229 00:17:03.880 --> 00:17:08.680 are leveraging it. It seems like you know not only the data but merging 230 00:17:08.720 --> 00:17:15.349 ai with all of this volume of customer information, customer input, there are 231 00:17:15.430 --> 00:17:22.230 some opportunities to add some technology that folks might not necessarily be thinking about yet 232 00:17:22.589 --> 00:17:26.750 in how they can improve those relationships. That coming back to the main point 233 00:17:26.789 --> 00:17:30.059 of managing relationships in customer support in Feb that's been, you know, a 234 00:17:30.140 --> 00:17:34.980 common thread throughout this conversation. Sure a hundred percent agree with what the data 235 00:17:34.980 --> 00:17:38.819 that we have in in a customer support to long team sport should never be 236 00:17:38.980 --> 00:17:44.849 locked up. That data is absolutely invaluable and IT Saddens Ministry we go to 237 00:17:44.970 --> 00:17:48.529 some potential clads of ours and they don't have that data expose. I don't 238 00:17:48.569 --> 00:17:53.250 really understand what information they have. So our ability to report them that understand 239 00:17:53.289 --> 00:18:00.079 that really is a key put different differentiation for our tool. But one of 240 00:18:00.119 --> 00:18:06.160 the things you referenced was we've got a great integration with IBM's Watson and what 241 00:18:06.319 --> 00:18:08.759 that does for us is allows us to do cinnamon analysis so we can actually 242 00:18:08.799 --> 00:18:18.789 understand the intent and meaning of a customers interaction with us. Obviously, as 243 00:18:18.829 --> 00:18:22.430 a human we going to look at a email or chat in pretty quickly understand 244 00:18:22.509 --> 00:18:26.789 okay, they that or not, and that's easy to do with one ticket 245 00:18:26.789 --> 00:18:29.980 or two tickets. The problem as you get hundreds of thousands of tickets coming 246 00:18:30.019 --> 00:18:37.980 in and a thousand agents looking at those interactions, the ability to categorize that 247 00:18:37.660 --> 00:18:47.089 numerically and use essentially turned sentiment data into numerical old data, is immensely valuable. 248 00:18:47.569 --> 00:18:49.690 That allows to look at begin the relationship with that customer. We can 249 00:18:49.730 --> 00:18:55.799 look at all their interactions across all the tickets, potentially across all the different 250 00:18:55.839 --> 00:19:02.160 divisions, when maybe supporting, and see if there's sentiment is broadly trending better 251 00:19:02.279 --> 00:19:06.359 or worse? Are they happy? Are they frustrated? What is that look 252 00:19:06.400 --> 00:19:08.960 like? They can easier to do for a human looking at one or two 253 00:19:08.960 --> 00:19:12.950 tickets. Very difficult to do when you've got again, potentially hundreds or even 254 00:19:12.990 --> 00:19:18.269 thousands of agents across millions of tickets to understand that. So again, going 255 00:19:18.309 --> 00:19:22.750 back to the thing of data, the ability to use that idea of sindment 256 00:19:22.750 --> 00:19:26.220 analysis has been a great data tool for us. Yeah, that's really cool 257 00:19:26.299 --> 00:19:30.140 that you guys have built that integration, Robbert, and it you know, 258 00:19:30.220 --> 00:19:33.059 it points to this common trend. Right of you know we're swimming in data, 259 00:19:33.140 --> 00:19:37.660 but it's really we need it organized, we need it, we need 260 00:19:37.779 --> 00:19:44.049 the ability to analyze it and take action based on the Organization of that data. 261 00:19:44.089 --> 00:19:47.009 And so, as as you were talking, I was picturing, you 262 00:19:47.130 --> 00:19:51.250 know, kind of viewing customer sentiment trending up or down, and that and 263 00:19:51.450 --> 00:19:52.720 low and behold. That's exactly where you went with that. And so I 264 00:19:52.880 --> 00:19:56.640 think for customers support leaders, you know, one of the big takeaways is, 265 00:19:56.920 --> 00:20:00.720 you know, how can how can you get to that? How can 266 00:20:00.799 --> 00:20:04.960 you start to organize the data and apply some analytics to it in some form 267 00:20:06.000 --> 00:20:08.829 or fashion so that you can look at the key components and how they're trending? 268 00:20:08.869 --> 00:20:11.869 And it kind of came back to, you know, as you talked 269 00:20:11.869 --> 00:20:18.710 about, within customer support, distress and not only just customer happiness, but 270 00:20:18.869 --> 00:20:23.539 looking at different key points along the pendulum of that relationship with the customer become 271 00:20:23.619 --> 00:20:26.740 very, very important. Robert, this has been a great conversation. I 272 00:20:27.220 --> 00:20:32.900 love the experience and the tactical advice that you brought to customer support leaders listening 273 00:20:32.940 --> 00:20:34.930 to this today. If anybody listening to this would like to reach out, 274 00:20:34.930 --> 00:20:38.410 ask any follow up questions with you or just stay connected with you and your 275 00:20:38.450 --> 00:20:41.130 team, what's the best way for them to go about doing that? All 276 00:20:41.289 --> 00:20:45.650 gras a different ways, obvious. The website team Supportcom you can reach me 277 00:20:45.849 --> 00:20:51.400 on twitter at team support CEO. Email our Johnson at team, supportcom and 278 00:20:51.720 --> 00:20:55.799 linked in as well, so I am agnostic value contact rd, so I 279 00:20:55.920 --> 00:20:57.960 appreciate it. Awesome. As you said, make it easy lots of different 280 00:20:57.960 --> 00:21:00.599 ways. Robert, this was a fun conversation. Thank you so much for 281 00:21:00.720 --> 00:21:03.509 joining us on the show today. Logan, thank you so much. We've 282 00:21:03.509 --> 00:21:06.789 got a lot of fun and Gret what has been twenty minutes or so. 283 00:21:06.910 --> 00:21:11.349 Thank you so much. We totally get it. We publish a ton of 284 00:21:11.630 --> 00:21:15.670 content on this podcast and it can be a lot to keep up with. 285 00:21:15.269 --> 00:21:19.660 That's why we've started the BOB growth big three, a no fluff email that 286 00:21:19.779 --> 00:21:25.700 boils down our three biggest takeaways from an entire week of episodes. Sign up 287 00:21:25.700 --> 00:21:32.930 today at Sweet Phish Mediacom Big Three. That sweet PHISH MEDIACOM Big Three