1 00:00:03,420 --> 00:00:06,640 Sean Aylmer: Welcome to the Fear and Greed daily interview. I'm Sean Aylmer. 2 00:00:07,060 --> 00:00:09,440 Sean Aylmer: Anyone who listens to Fear and Greed would know I 3 00:00:09,440 --> 00:00:12,840 Sean Aylmer: love talking about data. I love it because almost every 4 00:00:12,840 --> 00:00:16,010 Sean Aylmer: business is using it and the best businesses are using 5 00:00:16,010 --> 00:00:19,220 Sean Aylmer: it really cleverly. But how do they actually do it? How 6 00:00:19,220 --> 00:00:22,000 Sean Aylmer: do they take reams and reams of data gathered from 7 00:00:22,000 --> 00:00:25,270 Sean Aylmer: sales or online tracking or surveys, and turn it into 8 00:00:25,270 --> 00:00:29,620 Sean Aylmer: actual usable insights. New Zealand company Yabble works with companies 9 00:00:29,620 --> 00:00:33,360 Sean Aylmer: like Officeworks and Carlton & United Breweries to turn data into 10 00:00:33,360 --> 00:00:36,840 Sean Aylmer: information that businesses can use. Kathryn Topp is the founder 11 00:00:36,840 --> 00:00:39,570 Sean Aylmer: and Chief Executive of Yabble. Kathryn, welcome to Fear and Greed. 12 00:00:40,120 --> 00:00:41,730 Kathryn Topp: Thank you. It's a pleasure to be here. 13 00:00:42,380 --> 00:00:45,210 Sean Aylmer: We're going to start at the very beginning. And it 14 00:00:45,210 --> 00:00:46,930 Sean Aylmer: might sound like a simple question, but I'm not sure 15 00:00:46,930 --> 00:00:49,159 Sean Aylmer: it is. What is data? 16 00:00:49,590 --> 00:00:53,570 Kathryn Topp: So that's a good start point. So look, for us, 17 00:00:53,570 --> 00:00:57,570 Kathryn Topp: data can be quite varied. In terms of Yabble specifically, 18 00:00:57,570 --> 00:01:01,950 Kathryn Topp: we work with brands to unlock value from mainly customer 19 00:01:01,950 --> 00:01:05,069 Kathryn Topp: based data. So we work with a lot of feedback 20 00:01:05,069 --> 00:01:08,770 Kathryn Topp: data, product reviews, call center data, so calls that come 21 00:01:08,770 --> 00:01:12,930 Kathryn Topp: in to an organization, and also social data. So any 22 00:01:13,140 --> 00:01:16,429 Kathryn Topp: data where we can really unlock what a customer or a 23 00:01:16,430 --> 00:01:19,280 Kathryn Topp: marketplace is thinking and feeling about a brand and its 24 00:01:19,280 --> 00:01:21,899 Kathryn Topp: products tends to be the data that we work with. 25 00:01:22,270 --> 00:01:25,270 Kathryn Topp: Obviously there is broader data in terms of financial data, 26 00:01:25,600 --> 00:01:27,960 Kathryn Topp: sales data, all of those things. And we can link 27 00:01:28,010 --> 00:01:31,360 Kathryn Topp: into those data sources. But in terms of the data we process, 28 00:01:31,360 --> 00:01:35,900 Kathryn Topp: it's generally more focused around understanding marketplaces and understanding customers. 29 00:01:36,240 --> 00:01:39,340 Sean Aylmer: Okay, so the data you collect is very much based 30 00:01:39,340 --> 00:01:44,550 Sean Aylmer: on words, what people have said about certain products and 31 00:01:44,550 --> 00:01:46,970 Sean Aylmer: things. How do you collect all that information? 32 00:01:48,720 --> 00:01:51,020 Kathryn Topp: Through the Yabble platform we have a couple of different 33 00:01:51,020 --> 00:01:53,740 Kathryn Topp: options to bring data in. So we do have a 34 00:01:53,740 --> 00:01:56,380 Kathryn Topp: set of creation tools, which is where brands can actually 35 00:01:56,380 --> 00:02:00,580 Kathryn Topp: use our tool set to survey customers in their marketplace. And 36 00:02:00,580 --> 00:02:03,720 Kathryn Topp: that is a marketplace around the world of around 62 37 00:02:03,720 --> 00:02:08,280 Kathryn Topp: million consumers. But what we're seeing is that brands want 38 00:02:08,280 --> 00:02:11,220 Kathryn Topp: to actually bring more varied and different types of data 39 00:02:11,220 --> 00:02:14,540 Kathryn Topp: into our platform for analysis. And so they can bring 40 00:02:14,540 --> 00:02:17,380 Kathryn Topp: in their own data from other sources. So it might 41 00:02:17,380 --> 00:02:21,200 Kathryn Topp: be social commentary, review data, all of those other forms 42 00:02:21,200 --> 00:02:24,210 Kathryn Topp: of text data or voice data. And they can bring those 43 00:02:24,210 --> 00:02:27,660 Kathryn Topp: into our platform and upload them for processing as well. 44 00:02:27,660 --> 00:02:30,720 Kathryn Topp: So we tend to be agnostic from a data creation 45 00:02:30,720 --> 00:02:33,309 Kathryn Topp: point of view, and where the magic happens is what 46 00:02:33,310 --> 00:02:35,311 Kathryn Topp: we do with that data once it enters the Yabble platform. 47 00:02:35,311 --> 00:02:35,871 Sean Aylmer: Okay. So what does happen when it enters the Yabble platform? 48 00:02:39,800 --> 00:02:43,000 Kathryn Topp: Well, you can give us text data in any format. 49 00:02:43,120 --> 00:02:46,419 Kathryn Topp: So we bring it into our platform. We have a 50 00:02:46,419 --> 00:02:49,820 Kathryn Topp: six stage process that we take the raw data, it 51 00:02:49,820 --> 00:02:53,980 Kathryn Topp: goes through a cleaning process. It also then gets categorized. 52 00:02:54,260 --> 00:02:56,760 Kathryn Topp: It gets grouped, and it goes through a series of 53 00:02:56,760 --> 00:03:01,150 Kathryn Topp: natural language processing, understanding and generation to provide the end 54 00:03:01,150 --> 00:03:05,200 Kathryn Topp: user with an account of the data. So a quantification 55 00:03:05,200 --> 00:03:08,080 Kathryn Topp: essentially of the key themes and sub themes that exist 56 00:03:08,080 --> 00:03:11,650 Kathryn Topp: within the data. A sentiment code, whether that's a positive 57 00:03:11,650 --> 00:03:14,950 Kathryn Topp: or negative kind of viewpoint. But we can also query 58 00:03:14,950 --> 00:03:17,840 Kathryn Topp: the data. And so within our platform, we have a hero 59 00:03:17,840 --> 00:03:21,570 Kathryn Topp: product called Hey Yabble. And Hey Yabble is designed for 60 00:03:21,570 --> 00:03:24,120 Kathryn Topp: a brand to be able to ask their data questions. And 61 00:03:24,120 --> 00:03:26,889 Kathryn Topp: so I can say, Hey Yabble, what are the main 62 00:03:26,889 --> 00:03:30,930 Kathryn Topp: things customers are happy about in their last supermarket shop, for example. 63 00:03:31,210 --> 00:03:33,350 Kathryn Topp: And it will mine all of the data that's been 64 00:03:33,350 --> 00:03:36,690 Kathryn Topp: uploaded and give you a really key summary insight of 65 00:03:36,690 --> 00:03:39,300 Kathryn Topp: what customers are thinking and feeling, and then it will 66 00:03:39,300 --> 00:03:42,100 Kathryn Topp: also provide you account of that data as well. So 67 00:03:42,500 --> 00:03:44,630 Kathryn Topp: it gives you a really good summary, but then also 68 00:03:44,630 --> 00:03:46,410 Kathryn Topp: a quantification of the output too. 69 00:03:46,810 --> 00:03:49,790 Sean Aylmer: Okay. So who's using data best? This type of data 70 00:03:49,790 --> 00:03:52,280 Sean Aylmer: that you are talking about, what sectors are using it well? 71 00:03:54,420 --> 00:03:56,890 Kathryn Topp: Look, this is quite an emerging field for data. So I would say we're at the 72 00:03:56,890 --> 00:03:59,720 Kathryn Topp: kind of start point of brands really starting to use 73 00:03:59,720 --> 00:04:02,350 Kathryn Topp: this data well, and tools like Yabble and Hey Yabble 74 00:04:02,350 --> 00:04:06,180 Kathryn Topp: specifically will enable them to do that. So what we 75 00:04:06,180 --> 00:04:08,600 Kathryn Topp: are seeing is brands are starting to move away from 76 00:04:08,600 --> 00:04:11,470 Kathryn Topp: some of those more traditional data sets like your survey 77 00:04:11,470 --> 00:04:14,790 Kathryn Topp: data sets. And they're looking to other data that's sitting 78 00:04:14,790 --> 00:04:17,589 Kathryn Topp: in their organizations and how they can unlock insight from 79 00:04:17,589 --> 00:04:20,300 Kathryn Topp: it. So we are having brands kind of come to 80 00:04:20,300 --> 00:04:23,000 Kathryn Topp: us, they see our tools, they say, we're sitting on 81 00:04:23,000 --> 00:04:26,560 Kathryn Topp: this wealth of data within our organization already, and we really 82 00:04:26,560 --> 00:04:29,200 Kathryn Topp: want help to unlock that. So I think we're on 83 00:04:29,200 --> 00:04:32,500 Kathryn Topp: a start point. Brands have become really aware of how 84 00:04:32,500 --> 00:04:36,130 Kathryn Topp: to use their data that's already quantified. So what I mean 85 00:04:36,130 --> 00:04:38,870 Kathryn Topp: by that is it's already in a numeric format, but the 86 00:04:38,870 --> 00:04:40,960 Kathryn Topp: new world is what they do with all of their 87 00:04:40,960 --> 00:04:45,260 Kathryn Topp: unstructured text data and how they create that into meaningful 88 00:04:45,260 --> 00:04:46,990 Kathryn Topp: insights in their organizations. 89 00:04:47,339 --> 00:04:50,900 Sean Aylmer: That's not an easy job. My experience in this is I've worked in 90 00:04:51,110 --> 00:04:54,900 Sean Aylmer: the media and media relations. And media relations teams have 91 00:04:54,900 --> 00:04:56,930 Sean Aylmer: been doing this for quite a few years. And it's 92 00:04:56,930 --> 00:05:00,479 Sean Aylmer: basically the sentiment towards company ABC, let's say, and company 93 00:05:00,480 --> 00:05:04,029 Sean Aylmer: ABC has negative, positive sentiment. They talk about what people 94 00:05:04,029 --> 00:05:07,380 Sean Aylmer: think of them. So it's kind of along those lines. 95 00:05:07,630 --> 00:05:09,150 Sean Aylmer: The question is what you actually do with it if 96 00:05:09,150 --> 00:05:09,420 Sean Aylmer: you are the company. How do you manage that? 97 00:05:11,920 --> 00:05:15,940 Kathryn Topp: So with the tool set, essentially we are providing companies 98 00:05:15,940 --> 00:05:18,210 Kathryn Topp: with a whole lot time back in their day, so 99 00:05:18,210 --> 00:05:21,589 Kathryn Topp: to speak. So the processes that we are introducing through 100 00:05:21,589 --> 00:05:24,990 Kathryn Topp: the Yabble technology right now is around a thousand times 101 00:05:24,990 --> 00:05:27,900 Kathryn Topp: faster than the manual processes that exist in a lot 102 00:05:27,900 --> 00:05:30,580 Kathryn Topp: of businesses right now. And that's why the brands we're 103 00:05:30,580 --> 00:05:33,660 Kathryn Topp: talking to are getting really excited about Yabble. And so 104 00:05:33,660 --> 00:05:35,400 Kathryn Topp: what it means is it means that all of a 105 00:05:35,400 --> 00:05:39,039 Kathryn Topp: sudden, not only can their individual insights team members become 106 00:05:39,040 --> 00:05:41,550 Kathryn Topp: a whole lot more productive, they can actually be more 107 00:05:41,550 --> 00:05:45,260 Kathryn Topp: productive as a broader organization. So instead of spending hours 108 00:05:45,260 --> 00:05:48,789 Kathryn Topp: processing data, they can actually spend time activating it. So 109 00:05:48,790 --> 00:05:51,289 Kathryn Topp: they can say, hey, look, this is what the market's 110 00:05:51,290 --> 00:05:53,320 Kathryn Topp: saying about our new products. They can be much more 111 00:05:53,320 --> 00:05:57,240 Kathryn Topp: responsive to the marketplace. They can be really agile and 112 00:05:57,380 --> 00:06:01,000 Kathryn Topp: iterative in the way that they're designing communications. But equally 113 00:06:01,000 --> 00:06:04,680 Kathryn Topp: for employees, we're also working with organizations looking at employee 114 00:06:04,680 --> 00:06:07,890 Kathryn Topp: feedback and how a company can be more responsive and 115 00:06:07,890 --> 00:06:12,150 Kathryn Topp: reactive to employee needs, especially in the current environment. So 116 00:06:12,150 --> 00:06:15,440 Kathryn Topp: what this technology does is it allows a business to 117 00:06:15,440 --> 00:06:18,350 Kathryn Topp: be a lot more in- tune with the current marketplace 118 00:06:18,400 --> 00:06:20,729 Kathryn Topp: and be a lot more reactive and faster in the 119 00:06:20,730 --> 00:06:24,080 Kathryn Topp: way that it changes its strategy or behaviors based on 120 00:06:24,080 --> 00:06:24,550 Kathryn Topp: that feedback. 121 00:06:25,130 --> 00:06:28,220 Sean Aylmer: Okay. And it's all built around artificial intelligence, presumably? 122 00:06:28,400 --> 00:06:30,020 Kathryn Topp: Yes, it is. 123 00:06:30,020 --> 00:06:31,800 Sean Aylmer: Okay. Stay with me, Kathryn. We'll be back in a minute. 124 00:06:36,420 --> 00:06:38,650 Sean Aylmer: My guest this morning is Kathryn Topp, founder and Chief 125 00:06:38,650 --> 00:06:42,440 Sean Aylmer: Executive of Yabble. So you worked with a company I mentioned, Carlton & 126 00:06:42,440 --> 00:06:46,500 Sean Aylmer: United Breweries, earlier on. What's the sort of information you 127 00:06:46,500 --> 00:06:48,490 Sean Aylmer: are providing? I'm not actually asking you to tell me 128 00:06:48,490 --> 00:06:51,229 Sean Aylmer: what you're doing for Carlton & United Breweries. But the sorts 129 00:06:51,230 --> 00:06:54,640 Sean Aylmer: of information that they're acting on and how are they 130 00:06:54,640 --> 00:06:58,299 Sean Aylmer: changing their ways? Is it around customer service? Is it 131 00:06:58,300 --> 00:07:00,960 Sean Aylmer: around how things are presented? I'm trying to get a 132 00:07:00,960 --> 00:07:04,810 Sean Aylmer: really tangible hold of what the information you're giving and 133 00:07:04,810 --> 00:07:06,190 Sean Aylmer: how the company's responding. 134 00:07:06,800 --> 00:07:11,090 Kathryn Topp: Absolutely. So we have a variety of use cases. A 135 00:07:11,090 --> 00:07:15,050 Kathryn Topp: really simple and straightforward one is that an organization can 136 00:07:15,050 --> 00:07:19,190 Kathryn Topp: use the Yabble tool set to mine large quantities of 137 00:07:19,240 --> 00:07:22,890 Kathryn Topp: customer feedback data, for example. So we have a local 138 00:07:22,890 --> 00:07:26,500 Kathryn Topp: retailer we work with that collects well over a hundred 139 00:07:26,500 --> 00:07:30,510 Kathryn Topp: thousand pieces of customer feedback data in a year. And 140 00:07:30,570 --> 00:07:33,970 Kathryn Topp: what the Yabble tool allows is it allows really fast 141 00:07:33,970 --> 00:07:38,080 Kathryn Topp: processing of that information right down to an individual store level. 142 00:07:38,490 --> 00:07:41,620 Kathryn Topp: And it enables them to know what the local customer 143 00:07:41,620 --> 00:07:45,310 Kathryn Topp: set is thinking and feeling from everything to the layout 144 00:07:45,310 --> 00:07:48,300 Kathryn Topp: of their stores, to the prices, to the product range, 145 00:07:48,330 --> 00:07:52,110 Kathryn Topp: to what impact recent kind of supply challenges with COVID 146 00:07:52,110 --> 00:07:54,590 Kathryn Topp: is having on their organization and how they should react 147 00:07:54,590 --> 00:07:57,870 Kathryn Topp: to that. So it takes a broader kind of emotive 148 00:07:57,870 --> 00:08:01,190 Kathryn Topp: view and turns it into a really tangible piece of data for 149 00:08:01,190 --> 00:08:04,510 Kathryn Topp: an organization to work with. So that's one example. Another 150 00:08:04,510 --> 00:08:08,270 Kathryn Topp: example could be around when a brand's looking at how 151 00:08:08,270 --> 00:08:10,930 Kathryn Topp: it can improve its experience. So we can pull review 152 00:08:10,930 --> 00:08:14,550 Kathryn Topp: data off websites and say, hey, look, of your last 153 00:08:14,600 --> 00:08:18,220 Kathryn Topp: kind of 10,000 customers, these were the key areas of 154 00:08:18,220 --> 00:08:20,940 Kathryn Topp: complaints they had about your brand and the key changes they'd 155 00:08:20,940 --> 00:08:24,630 Kathryn Topp: like to see you make. And here's your competitor reviews, 156 00:08:24,630 --> 00:08:26,200 Kathryn Topp: and this is how you compare to that competitor. 157 00:08:27,340 --> 00:08:30,160 Sean Aylmer: Okay. So the last decade, well, probably longer than that, 158 00:08:30,270 --> 00:08:33,640 Sean Aylmer: Net Promoter Scores seem to have taken sway. And a 159 00:08:33,640 --> 00:08:36,059 Sean Aylmer: lot of companies have really built themselves on around Net 160 00:08:36,059 --> 00:08:40,350 Sean Aylmer: Promoter Scores because to them, that reflects great customer service. 161 00:08:40,650 --> 00:08:46,600 Sean Aylmer: Is this sort of program that Yabble puts forward to replace or 162 00:08:46,600 --> 00:08:49,929 Sean Aylmer: to supplement things like Net Promoter Scores and the like? 163 00:08:50,500 --> 00:08:54,150 Kathryn Topp: I say we compliment as opposed to replace or supplement. 164 00:08:54,150 --> 00:08:58,630 Kathryn Topp: So look, we can work alongside existing Net Promoter tools 165 00:08:58,630 --> 00:09:00,950 Kathryn Topp: and we do with a number of our customers right 166 00:09:00,950 --> 00:09:04,280 Kathryn Topp: now, but we can also kind of look to broader 167 00:09:04,280 --> 00:09:06,360 Kathryn Topp: sets of data and say, look, if you don't want 168 00:09:06,360 --> 00:09:09,270 Kathryn Topp: to run an NPS program, we can work with the 169 00:09:09,270 --> 00:09:12,540 Kathryn Topp: data that already exists about your brand in the marketplace. 170 00:09:12,830 --> 00:09:16,179 Kathryn Topp: So we can do both. Ultimately we want to create 171 00:09:16,179 --> 00:09:21,380 Kathryn Topp: a world where we're asking customers and consumers less questions, 172 00:09:21,690 --> 00:09:24,450 Kathryn Topp: but we're taking the data that exists and mining it 173 00:09:24,450 --> 00:09:27,120 Kathryn Topp: more effectively. And that's exactly what the Hey Yabble tools 174 00:09:27,120 --> 00:09:27,670 Kathryn Topp: are designed to do. 175 00:09:27,670 --> 00:09:32,870 Sean Aylmer: Okay. So, what next for Yabble? You're New Zealand based, but expanding internationally. Where 176 00:09:32,870 --> 00:09:34,250 Sean Aylmer: will you be in five years time? 177 00:09:34,679 --> 00:09:37,130 Kathryn Topp: Oh, five years. Well, right now we're very focused on 178 00:09:37,130 --> 00:09:39,360 Kathryn Topp: the US and Australian markets. So those are our key 179 00:09:39,360 --> 00:09:43,920 Kathryn Topp: growth markets for the next two years. After that, look, it 180 00:09:43,920 --> 00:09:48,439 Kathryn Topp: is really about expanding the Yabble tools into other markets. 181 00:09:48,440 --> 00:09:52,059 Kathryn Topp: So obviously European markets where we've got strong English speaking 182 00:09:52,059 --> 00:09:54,980 Kathryn Topp: is a great natural extension for Yabble, but our tools actually are 183 00:09:55,620 --> 00:09:59,069 Kathryn Topp: quite multilingual as well. So we can expand globally. Wherever 184 00:09:59,070 --> 00:10:01,570 Kathryn Topp: there's data is where you'll find the Yabble products. 185 00:10:02,350 --> 00:10:04,610 Sean Aylmer: Fantastic. Kathryn, thank you for talking to Fear and Greed. 186 00:10:04,860 --> 00:10:05,439 Kathryn Topp: No problem. 187 00:10:05,700 --> 00:10:09,090 Sean Aylmer: That was Kathryn Topp, founder and Chief Executive of Yabble. This 188 00:10:09,090 --> 00:10:11,280 Sean Aylmer: is the Fear and Greed daily interview. Join me every 189 00:10:11,280 --> 00:10:13,300 Sean Aylmer: morning for the full Fear and Greed podcast with all 190 00:10:13,300 --> 00:10:15,360 Sean Aylmer: the business news you need to know. I'm Sean Aylmer. 191 00:10:16,050 --> 00:10:16,689 Sean Aylmer: Enjoy your day.