#041: The Productization of Customer Intelligence with Blair Reeves

What IS customer intelligence? What is a customer? Is the customer best understood by breaking the word down into its component parts: “cuss” and “tumor?” Would that be an intelligent thing to do? Will these and related questions some day be answered by self-aware machines? Will any of *these* questions be answered on this episode? Give it a listen and find out!

The mish-mash of companies, products, and miscellany mentioned on this show include:

 

Episode Transcript

The following is a straight-up machine translation. It has not been human-reviewed or human-corrected. We apologize on behalf of the machines for any text that winds up being incorrect, nonsensical, or offensive. We have asked the machine to do better, but it simply responds with, “I’m sorry, Dave. I’m afraid I can’t do that.”

[00:00:24] Hi everyone. Welcome to the digital analytics power hour.

[00:00:28] This is episode 41 Hey Tim Wilson. Hey Michael how when.

[00:00:34] You know I’ve been thinking about the customer lately and you know to really understand them you’ve got to break it down to the two words that make that up Kuss and Tumer but no seriously you know in the world of customer analytics and customer intelligence lately everybody’s moving into this space and trying to productize it but who’s really getting it right. You know customer analytics is kind of a big bear and we’ve got a lot of moving parts. So we wanted to talk about that on this episode. It was something we thought we might need an expert opinion on. So with us is our guest Blair Reeves. He is currently the principal product manager for SAS customer intelligence. And you probably remember him from his time at Demandware and IBM as well. He’s got a lot of great opinions and we’re excited to have him welcome.

[00:01:26] BLAIR Thanks a lot. Thanks a lot. And I’m sorry your expert didn’t work out for you about having to fill in in a pinch.

[00:01:33] See that’s that’s the kind of can do attitude that’s going to help us figure this out today. All right. So maybe first maybe we should define what we’re talking about in terms of customer intelligence or customer analytics.

[00:01:48] Yeah. So I mean I sort of think of it kind of conceptually right so you think about who the customer is. So who are the customer I used to be used to involve you know way back in the battle days. It was just you know you’re in analog days this is the guy came in near your store or whatever. Then it became kind of you know your online visitor and then different companies started splintering and having a different kind of view is that right. So you how you’re you know I’m sure that the world is sort of think of this and the web analytics sort of way you know the person who is on the Internet here whose behavior you’re going to analyze. You know that’s really who your customer is and then you can tie that back to whatever data you have about him or her off line. And the sales force is kind of a different view of this and the whole see around of view that around who they are in terms of the information transactions sort of way all the e-commerce companies and this is one thing that I got to. Hear a lot about when you work in e-commerce for the platform companies you know Demandware Hybris even even WebSphere ETG those folks they think about who that customer is pretty differently than than the Martek people do. How so. Oh they think about it and think about customers. I don’t know. They don’t really target visitors.

[00:03:02] You know if you’re on know using an e-commerce platform and you care about your visitors your unconverted visitors Horsman you care a lot more about making transactions right. I tell you that the e-commerce platforms see the marking technology platforms as areas to colonize to subsume and the marketing platforms think about the commerce platforms the same way and they think of it as a add to cart widget essentially. There’s a whole kind of battle going on there.

[00:03:29] But again whole other discussion but as part of that what are the data actually lives. I mean is that some more. You know what I mean when these platforms that say sure we integrate with lots of stuff we’ll put other people’s data in but not necessarily as easy to get our data out or is that not how the battle manifests itself. Yeah.

[00:03:46] You think about it. A No. If you’re an e-commerce platform you’ll naturally expand to capture all parts of their customers journey customers you know interactions on the site and that kind of it very quickly gets to where you know your marketing clouds are going and the market clouds will say well you know we already have onsite offsite email transaction know all your different kinds of channel interaction histories as well. We’d like to make commerce one more component of that. So there’s so that was all of that then you have the kind of traditional data science kind of systems right. So you have people who’ve been working particularly in like telco or financial services with Kustra information customer data warehouses doing. What we now call big data used to just be data using any number of tools for that. And those are all questions customer intelligence and they’re all kind of have their origins and their strengths in different areas and what I think we’re seeing now is a convergence of all those things and no one’s really there yet. And what we’re trying to figure out is how we prioritize a lot of the kind of dirty work that goes on there.

[00:04:59] So is that everyone. All of those are it’s not really moving up market or down market you’re saying is everybody’s expanding. Everybody generally has a sense that they could be the central spot for the entire journey from pre awareness all the way through acquisition and loyalty and tracking that person.

[00:05:20] Yes. So I think some certainly do. So you have. So you have the consolidators out there right. You have your IBM Oracle Adobe sales force a lot of them to to some extent or another offering you a consolidated package say take this platform and we will do all parts here earmarking stack that work for you that’s great for most companies that is not the case right. I mean Scott Brager talks about this all the time but now the future of most marking is to act even though even in the enterprise you know here is using best breed modular technologies right. In reality either approach necessitates you and you’re going to have a whole lot of data from different channels much of which does not actually integrate together either at all. If it’s no best of breed or very easily even if it’s a consolidator because all the consolidated clouds are made up of you know modular technologies are mostly acquired and you know integrated post facto. Right. So there’s a real tension there and then of course you have all the historical data as well. So right now you know there are there’s a whole new breed of data scientists out there.

[00:06:28] There’s a whole constellation of great consulting firms and they’re working with very large companies and say we have enormous sums of data and we have to you know put it together and derive some server value out of it and to some extent they used to use the traditional tools for us but it’s becoming that with more digital data and more hosta data and data that belongs to the proprietary vendors if they’re finding it harder to do that. So that’s kind of what I mean by customer intelligence I really mean taking all that channel data historical data online data putting it you know and in doing something productive with it which is still really hard for a lot of companies and practically out of reach for a lot of. Still a lot of companies both in terms of finding the right people to do it and find the right tools and do it. A lot of what downlow says I think is where a lot of this is headed. I don’t think they’re there yet but I think a lot of what they’re talking about where the type I’m going is you know being the the vendor that puts all you know any data you have from any channel in one place that any so anybody in your organization can use it and Darius’s or inside out of it.

[00:07:38] Domos story is kind of a little more almost generic it seems then you they’re saying we will provide the connector and any data anybody wants they can use that to me is very different from we will help you kind of get the right data from the different sources and actually think about the customer.

[00:07:59] I mean where does customer intelligence meet up with marketers so coming from the marketing side. Oh you got to be customer centric we have to be customer centric to the point that it’s it’s almost a meaningless term. And then you’ll hear people rail about what truly being customer centric means.

[00:08:16] And to me there’s a piece of that that says hey when you’re saying customer intelligence or when you’re saying when you’re saying customer there’s sort of two things that I think can get lost in that one when you say customer do you really mean somebody who has already purchased from you. Or do you mean a prospect or a customer. And I’m not asking you right now though if you have thoughts let me know and then the second thing is when we say customer the immediate thing comes to mind as a person. Whereas where does customer intelligence fall when you’re talking about your B2B and your customers and organization or your B2C but the household matters as much as the individual person. And so even when I hear something like customer intelligence I think crack. There are already definitional right out of the gate. Two things that I bet you say customer intelligence to 10 different people while you won’t be able to attend different answers but there’ll be kind of assumptions even like what that is. Yeah definitely.

[00:09:05] Yeah. No that is good points. So I’ll give you guess for the two and half right. So intelligence a lot of the people doing this right now there are people who can purchase from different sides intelligence ideally should mean take your data and making some drive some sort of insider value. Right. So that means that by definition that would mean more than reporting more than simply reporting what data is there and then visualizing in some sort of way data visualization reporting is really important. Right. It’s really hard. And it’s really you know it’s a really big area for really big and important area for any kind of company that’s why you know data studio Google name has spent a lot of time working on that. It’s a really really cool tool. Tablo is out there and that’s an area that I see a lot of growth in. It’s not really what we mean by intelligence. You know I think that when we talk about customer intelligence in the market I think people generally mean taking your data and doing something useful with it like prediction discovery of you know either patterns or segments or affinities for example that sort of things we classically use kind of analytics to do.

[00:10:10] Yeah because I mean when I think about it I basically my mind immediately goes to customer acquisition as one major category. Yeah customer value as the second one and then customer retention is a third and sort of you spend time in your analysis. Hitting one of those three. Usually all the time so you know just in the examples you gave. Those are all fit in one of those three categories. Yeah how I get new customers how do I make those customers more valuable. Whether that be through affinities or stuff like that and then how do they keep those customers around. So

[00:10:45] you churn out of my customer of all things on some level require some interrogation or the data as well as some cell reporting to do that right it to make it known to me that people in the company.

[00:10:58] Right. So could we even stack it up like there’s sort of data visualization data exploration and data querying. I don’t know if that’s the right word for it. Like at the deepest level and then various tools let you do more or less of that.

[00:11:13] It seems to me that there are a lot. So you know there is any number of companies right now that have that have a channel strategy and say if your data comes through this channel we can we can take it we can do all kinds of we can make it stand up and do tricks right when you want to combine that data with another channel or with another set of data or what have you. That’s when it gets really tricky. Yes that is correct. And there are any I can say there’s any number of people trying to do this in different ways. And right now it’s frankly a really kind of manual process for a lot of companies. And you know there’s like I said you know giving way to a whole industry of people who do this for on a contractual basis. So when I say privatization I think that this is an area where I think every vendor in our industry is working as hard as they can to privatize a lot of that work because that work is where the future lies for any company that wishes to be competitive.

[00:12:09] So what’s your sense on getting to that and you started to talk to it earlier of sort of get all the data and centralize it into a warehouse or into what you call an aggregator that go as an aggregator. Give us a platform. I didn’t know.

[00:12:26] I don’t even know if they’re thing anymore or let’s say let’s say mestiza all abilities of data we are okay with throwing on a big query.

[00:12:36] I mean there’s sort of 1 1 way to go is to say we have a powerful maybe expensive maybe not expensive but can handle large sets of a range of data type structure and unstructured and we’re just going to go and do an assessment of all of the data sources all the systems that have something that might be useful and we’re going to pump it all into some repository that we feel like we can then query. Yeah. Or I guess on the other extreme is it. I’ve got one system. What would be the most valuable second set of data that would compliment that. I’ve got my web analytics and stitching that together with my CRM is going to give me a step function increase the level of insight or intelligence into my customer and I’ll just figure out the best way to do that with some eye to the fact that I’m then going to come along and throw my fulfillments system or ERP into any kind of taking an incremental approach or even a fair distinction to say that’s kind of the range of the spectrum of its bite this whole thing off and say we’ve got to solve customer intelligence and there may or may not be a product out there.

[00:13:41] There will be lots of companies saying they have a product and that product maybe technology. It may be people you know they may be a service to solve customer intelligence versus a more incremental approach to say we need to slowly be moving to where we’re bringing more and more stuff in. We don’t want to paint ourself into a corner so we don’t want to pick something that’s Uber partyer proprietary and restricted. And that seems like more of kind of an incremental approach. Probably more driven internally as opposed to using outside.

[00:14:12] So I think that traditionally there have been a couple of approaches right. So you want a approach that’s been around for a while as well. You take all your data through it and a pied piper box right and then get someone you know give them six months you buy a PC statistician and a couple developers and give them a whole lot of time and let them run models over it and drive some sort of value out of you know all the all the stuff in your warehouse. And you know traditionally you know that’s kind of I don’t know 10 20 30 years ago. That’s why banks started buying you know data wouldn’t call data science and we don’t mean data solutions right. That’s why SAS is a company that’s why IBM had a big business in that Microsoft had big business and that lots of those still do. I think that’s a architecture that might work for some people while this thing is not necessary anymore and is probably not the best approach anymore. Then if you look at the attribution people what they’re doing today they’re also kind of two different approaches. One is we’ll sell you a whole lot of hours in these ph deals who are real whiz bangs with you know putting together disparate sources of data. Put all together and model it to develop some sort of value from it. And there are companies that do that today.

[00:15:26] The other approaches we’re going to apply analytics to it in an open sort of ways and you give it different you know sums of data and you know through the wizardry of technology we will deliver our value from it. And of course it’s not really that simple. Ziza a lot messier than that but that is a message and between those two I think you see kind of the direction that the general market is headed as well. So as we go forward you’re going to have a lot of sources of data. Yet more than we did just a couple years ago you’re going to have more in the future. And I think we’re we’re running out of runway for the approach in which you know any one any vendor can you know build a special solution to integrate every data source you you know into it. And I think that different kinds of companies are going to have to find ways of feeding data and some sort of way to make sense of them into a system that applies and it likes to it it doesn’t necessarily require specially designed models for any particular data source. That’s kind of what I mean by privatization. The other was just a manual process. We were doing that already by privatization. You know I sort of see this kind of going the same way about legs has gone which common tasks and web analytics around reporting around segmented discovery of value you know things like this have been largely privatized and now that settles down a path of ever more sophistication.

[00:16:47] Huh. So I was very skeptical. Well I mean I wonder.

[00:16:51] So a challenge with web analytics is that you know what bailiwicks is data being 100 percent data being collected using internet protocols that were not designed or are ever really intended for data collection. So major challenge of digital analytics is that it’s taking technology taking a standard taking the Internet standards and figuring out how to get data out of that. Using

[00:17:18] mechanisms hey we can use javascript to pass stuff all in our understanding of the customer. Basically

[00:17:23] the browser cookie right. Yeah

[00:17:25] I mean so that’s yeah I mean that’s actually the that’s the essence of the big challenge there is it wasn’t designed for that.

[00:17:31] So I’m wondering and I’m thinking in kind of SML and my limited knowledge of SML you know you can start defining schemas that are you know a customer definition or not an SML guy.

[00:17:45] Going back 10 years from where I was learning about it you know you could you could have a scheme you could have a language or a structure that is generally accepted and you could have systems start to speak and whatever that languages. I don’t know if I’d go that far. I think that’s one of the challenges now with any of these solutions is you’re taking these point solutions systems that are collecting data and everything about them is structured to what works well for their specific process.

[00:18:16] Well yes I think I think I kind of I can’t interact with that with what you’re saying here a little bit. So given that handcuffs came from you know core metrics and the web Battler’s kind of world as well. No one actually have web balances is a little bit similar to what you have and like CRM or or actually e-commerce and that is that every implementation is different every implementation between different vendors and also with us with the same vendor one implementation is going to be it’s going to be quite different from another and have certain similarities as well. But each one is different. How do you take the data from what from from one of them Taishan then apply to another and what they gets back to is applied to a data model that you know that runs systems or analytic process over that data that delivers. Like I said those kind of cases that we talked about and no one we can think about this is by is by opening your scheme by saying no doing doing some sort of mapping. So just in the same way you do an e-commerce or Web analyst’s implementation you say well you know this event means this or or you know this you know variable property might be calculated might be custom or might be standard and it means it means this thing that we can open up a can a kind of data schema. And that part is kind of what I call the kind of the connective tissue or the connective tissue of doing this between for just for one data source but then for many other ones is a big challenge.

[00:19:39] And I think that some people have begun to get really sophisticated with this and the sales force and Adobe do that really well and the rest of us you know we’re still working on it. The other part of it is really hard is the actual analytics behind it because it now analyzes becomes buzz word we’re like people call reporting analytics and it’s like that’s like everyone knows that right. So like an edit isn’t. But if you can actually apply actual analytical models of that actual analytical technology then you can begin to derive a lot of value. And so both parts. So some companies history are really good at the connective tissue stuff. And I think that for them than applying you know true analytics to the data that they’re able to plug into you know that sort of schema is kind of the next place that they’re going to be headed for other ones who are kind of the connective tissue part of this and collecting that data together and then you know applying kind of ethical sincerity have that’s going to be the next the next challenge and one issue that the other issue that I can’t see here is that all that data isn’t going to live in the same place right. So if you’re a very large company you probably don’t want to recreate or replace your entire data warehouse in the cloud. Right. You have sensitive customer information you probably don’t want to like updo Apple and that sums up proprietary cloud Domos kind of getting around this in some interesting ways.

[00:21:03] And you know every vendor in our industry you know kind of has ways of uploading a certain style certain segments or whatever and that works kind of right. It doesn’t work terribly well but in same time we know we’re kind of we live in a cloud world we don’t really necessarily want to rely on an on premise on premise technology anymore say wilier what you’re starting to talk about is a hybrid solution where some companies can be perfectly perfectly fine using all hosted customer information. That’s great. That makes things a lot easier right for a lot of other companies especially larger companies specially older companies. You’re a bank airline. Now you know telco whatever public sector obviously helps healthcare. GS You know all these companies you probably don’t want to or can’t do those things. So to make these kinds of solutions scale you need a way to make those two work together in a way that doesn’t force you to buy a whole kind of data management solution.

[00:22:01] Well I was thinking as you were talking that’s the other thing we haven’t talked about is that customer data is notoriously messy and short lived. So even as you’re pulling from these different systems you can say well I’ve got Blair Reeves is you know I’ve got from when I interacted with him a year ago. I know that Blair Reeves is in Raleigh North Carolina and that is pumped into the system know it’s out of whatever the source system I find. Blair Reeves somewhere else I stitch them together and I’ve got something that is factually inaccurate because you’re not in Rahmi right now. So I mean in a sense there’s there’s the quality of the underlying data and customer data management master data management with customer data management specifically has been around forever with much simpler systems when it’s all one Oracle platform and you’re still screwed with the decay of the quality of the customer. So I just Raos we haven’t really hit on that we’re saying getting the data from these different places I’m claiming that a lot of these your kind you’re kind of getting what data you can. But it wasn’t really generated and it’s not being provided to help this larger greater good of intelligence so it’s got that stitching that you have to do that connective tissue is having to do that transformation hopefully without losing information along the way just you know by the nature of it. And then you’ve got the time factor that really when you start talking about customer intelligence and the lifetime value of you’re talking about lifetime value you’re trying to figure out things over periods of time and over periods of time.

[00:23:32] A mobile society or many people are remote workers. I mean we’ve got much squishier and more fluid data.

[00:23:38] Yeah the signals that we’re generating are more from internal.

[00:23:43] This is depressing. I’m just going to go counts. I was going to go report on some traffic to my Web site.

[00:23:48] It’s so funny because the principles for marketing and things like that are exactly the same as they used to be. But we are constantly having to figure out new ways to handle all the different ways that we are connected.

[00:24:02] I am very skeptical of the approach which I think larger older companies are more prone to take which is we’re going to go solve this we’re at point X now and that point and it’s laughable. You’d think it would be six months like large companies. You know they’ll kick off a two and a half year project to do to get to customer insights and maybe they bring in a couple of Ph.D. and the Ph.D. start crunching on the data in the warehouse and quickly turn up that the data is complete and utter garbage. And they’ve got to go fix their underlying systems. And I mean I’m I’m not I’m not arguing against this being where we want to head. I just feel like there’s something missing that makes it a practice to get to a practical reality. It is a enormous lift to get to that.

[00:24:50] You go to like a marketing technology conference and you say master data management and people will clear the room and I was like Go write a fart in church.

[00:25:00] And I mean that’s kind of where we are because like a lot of the market technology field is you know cluttered by gosh we diplomatic about this great companies right. But they kind of do one thing and they know it. One thing we’re all real well assuming use their solution and their data and your account and they have the classic you know that’s their hammer and all the world to them is a nail. So no matter what you throw at them they’re going to frame it in terms of the scope of what their solution to and big companies have been doing massive data management for and just data management generally real well for a long time and a lot of those solutions just aren’t they’re not really appropriate for the world of customer data. We live in today and like I said some the data is going to be live at rest. So that data is going to live in the cloud is going to live maybe in cold storage somewhere. Now a guy you know either on a tape or even a glacier somewhere or whatever. So when you start talking about the intelligence of scale I think the one issue you run into real fast is how you work with on premise data and cloud data. Or whatever data that the client is feels comfortable keeping in the cloud and for how long and how you do it. And the other thing you mentioned there is identity identity is a real tough tough nut to crack because you need some way of identifying who customers are.

[00:26:27] So for a long time we talked about kind of you know cookie ID mapping and that’s just kind of you know something that you know everyone can do. But it gets to a lot more than that. Right then when you start talking about ballistic matching or fuzzy matching you know with like phone numbers and emails and names and addresses and all this stuff which again is really new unless you have a data management solution that can match whatever data you have in your warehouse with whatever is in your web Alex Day there your e-commerce data somewhere. And those are things that I don’t think that the marketing clouds and the e-commerce platforms can really do. I don’t think that they have their reach to really do that very well and they don’t necessarily have the incentive to really reach into those different sources to do those things. That’s fundamentally not really what they are. It’s not a first order of business for for those kinds of platforms at least not today maybe in the future will be but for what they’re doing right now I don’t see it.

[00:27:24] Yeah I think I agree with you because a lot of the companies were talking about Adobe and some of the other ones they don’t in my mind don’t really touch customer analytics as much as they’re doing some of the things around it. And then you take that data and stick it into where you’re looking at customers.

[00:27:43] You know if you look at it so I know that it is not really cool to talk about analyst but they do a lot of like you seen a lot of this kind of surge in interest and like customer analytics digital marketing analytics and those sorts of things as distinct from kind of marketing hubs or the marketing clouds or whatever sort of research. So there’s research coming out now more about kind of customer analytics and what that means. Google has been talking about this for quite a while.

[00:28:11] Yeah. She moved to the concept of a user or a concept of a personal.

[00:28:16] Yeah I think they do it well. I think they do a lot of really. There are people there who are like no tasks specifically doing that which is really interesting. Google of course comes at it from an advertising perspective which makes a lot of sense from where they are and that means very kind of a different thing for a customer identity their in their use case for it. And there aren’t a whole lot of you know there aren’t a whole lot of people out there who have that kind of reach. But there’s a lot of research coming out. Gardner did a thing for sure did a thing of course some other ones done other research around what customer analytics means what it’s going to mean technologically for vendors to to meet and then for yet to do. And I think that kind of going back to the purpose of the podcast here. I think that we’re we’re in the very early days of how that productize is right now it is a lot of elbow grease of analysts knee deep in a data warehouse I guess I’m still fuzzy on what the productize of all aspects of the whole challenge are.

[00:29:15] You know if we’ve got that we’re not going to necessarily make the data cleaner. So you’re going to need that connective tissue piece. I mean you could argue that well that’s kind of you know Domo and other D’Adamo trying to build in connectors. In a sense that’s maybe connective tissue.

[00:29:31] They’re trying to productize that piece of it and I think is what you’re saying that if you get it to where you’ve got some common language where you can have repeatable across different industries across different customers ways of going generating that analysis make it super simplistic analogy like a market basket analysis from 30 years ago the concept of a market basket analysis was something that okay this is what it is this is what it gives you and here’s how you do it.

[00:30:02] And once that was kind of outlined people who were trained in that could go with anything where there was you know more than one product put in the basket and they could do a market basket analysis across industries. Is that part of what the product ization of customer intelligence is what are the different ways that this gets approached.

[00:30:20] So I see privatization as happening on two ends. One one of those is the connective tissue like you just talked about. So instead of doing manual Joynes of numbers and tables in your data warehouse or instead of dumping all those into some sort of solution where you have an actual attack detection manually you prioritize joining joining different customers to get it together and Dolos take in one direction and doing that which is build build a billion connectors right from a product perspective. I see a lot of difficulty in doing that. And you know when God bless them they have a lot of smart people over there and they got a lot of money so you know they can they can make it. Alec Baldwin is on the we’re on the job right. He could do that. The other the other way you go about this though I think it’s more to think about it as a web analytics or even a CRM implementation. Right. So if we end up with Asians and those kinds of systems you kind of are to some extent kind of given a menu of you know here are the kinds of events we can pick up and here’s what you can do with them. But then there were also customer events calculator fields calculated metrics and this sort of thing the kind of build yourself and if you can take some of those and you have an analytical solution that. Schema is open right. So there is not a black box solution it’s kind of an open schema solution.

[00:31:45] So you say well you know here’s here’s the kind of fuel I want to fit in. No in this slide and here’s kind of like that in this slide here’s here is this in these sets of fields and sensor information that also fit into it. You know them it’s probably more scalable. And that kind of way it’s hard to build. It’s probably more scalable than building and connecting for every possible data source you could ever possibly have.

[00:32:09] Waving a magic wand would that be a. I don’t think consortiums and collective efforts tend to work for that when it comes to. But would there be kind of a a body of an evolving schema that kind of gets to this consistent. I think this is where I was trying to go earlier that is there this master definition. Think the 3C. But instead it’s just around the customer to say these are the definitions. This is the relationships. This is how we’re going to talk about the relationships between the data elements here’s the standard and now hey pick your technology if you want to play nice in this ecosystem you need to be able to.

[00:32:54] Provide data in that mechanism and that could be latency quality. You know certainly the structure the model and it’s tough to generically to find something like that across across industries across across business models. But is that would there be like a standardized just just definition. Like a bunch a bunch of web pages saying this is this is that schema and now you’ve kind of dissin or mediated between the people where the data is being collected and the ones trying to crunch and get analysis or store the data.

[00:33:25] Well maybe no never happened. I don’t see vendors wanting to cooperate to do that. You just invented the new world order. Are you are you saying wave a magic wand like you can’t get here. You can’t get there from here.

[00:33:40] Yeah but say think about it you’re using SPSS or SAS or other tools that exist.

[00:33:46] No. Why would you take one. So you know it used to be a solution today. Right. What do you do. You know you feed it data right. And you have to map that data to us to a certain schema within that tool and you do differently for each tool. I will see why it wouldn’t work or why it wouldn’t work the same way here.

[00:34:07] I guess maybe that is productize it means that I want to have the product that does all this. So it does wind up with this super high stakes with a lot of money behind it for a lot of different companies that are trying to come up with like the best solution. It’s good for the analysts or the organization that wants to use the solution because competition is good and that means innovation and they’re are going to be chasing each other. What sucks is still going to be a pretty high high dollar investment to say this is the horse that we’re going to ride because we think there schema and their approach and the product they put out is the way to go. And it’s just it’s tough by the way. So

[00:34:49] I sort of think about it in the same way that no we did. We went through this process nine times before right. I mean this is literally like the web analytics market 10 years ago or five years ago whatever was. This is literally like what the attribution market has been like for a number of years. Right like this. You know you have to you know it’s a it’s a special isolation. But if you want to derive more value or the data you already have. You know this is one way of doing that. And the added benefit of doing that I think is that it also it was a Desser intermediates the marketing stack that kind of sits around top of that stack right. So in addition to your e-commerce layer and your CRM your web Alex later or whatever. No you have a Cusk. Intelligence layer that is able to consume those different data sources and do so repeatably and scalability. Then you don’t have to rely on any you know any in India. There are those layers in the stack to provide a channel specific gear you can get a you know omni guide I will sit on the channel Dingding another of whiskey over here so I don’t know if you can get it if he can get it gets a channel like Nasik is going to I guess I’m saying from a from the connecting standpoint and then also apply some sort of no real analytic value to that to provide actual business night to the whole purpose of doing that and that is that gets us closer to what we call it.

[00:36:21] You know what I would call Customer intelligence or kind of what we’re living towards.

[00:36:25] OK so you definitely are advocating for this layer of customer intelligence application of some kind.

[00:36:31] Oh I think it’s a good thing. I think this is a number of vendors kind of there right.

[00:36:36] So you think about IBM talking about you know Watson and these kinds of terms right. You know IBM said it will give us. Anything in Watson that is where we can wave a magic magic wand do it. Domo is sort of saying that we mostly on the reporting side but I think that they have much larger ambitions beyond that as well. Google gave they’ve got to be you know not a very short a far ways off. I’m sure they’ve got an AI solution in the works there and it’s going to be and Google X premium and you’re going to give it all kinds of data and it’s going to come back with value in it right.

[00:37:13] And that’s kind of inevitable I see Adobe. I mean there are a lot of smart people over there I’m sure they’re working on something like this. They already have. Of course a lot of you know analytical capabilities and they already kind of bring to bear in that sector as well.

[00:37:26] So do you mostly see them sort of companies drilling down from the top layer.

[00:37:33] The digital analytics are sort of a transactional layer down into customer told us as opposed to drilling up from the customer data into it in terms of where you see movement then I think it kind of depends on where where they are right like if you are a dominant digital analytics layer then yeah then I move into those downward into the stack if you are a CRM player you know and that’s probably where the sales are done this right. They built during Webelos tool. They bought a marketing kind of cloud and they’ve done a lot of work and in time two together Phills for us has an analytics like a digital analytics tool. Yeah. Yeah I mean it’s not very well known as it is. It’s there.

[00:38:17] Yeah yeah. I have no idea what it’s called Find.

[00:38:22] Neither do I I just I’ve seen I’ve seen power points that they have because they have there are their wave you know analysers cloud. Now they have now they have a commerce clause and they have a different kind of modular parts that and to what extent they’ve now they work take no tie the connective tissue there. They have the assets I should say. To extend their cash or tissue I know it has to be seen yet. So I guess it depends where you where you sit and where you sit in the sac today if you’re if you’re in the second all here and there then you either drill down to dissent and mediate the ones below your if you’re if you’re below and you know you can work up market.

[00:38:57] Yeah I think that’s interesting because I think as I have observed these kinds of things and I certainly I sit and look at this all from the perspective of a digital analyst I don’t see a lot of connectivity. Most of the time between customer analytics and digital analytics unless you do a lot of hard work yeah like we’ve been talking and most of those companies don’t really collide at that intersection or do a good job of doing one of the other like the Oracles and the sales forces do a great job down here in the CRM and the customer but do a real crappy job up here in the digital place whereas Adobe and Google are doing a great job appear and giving us great tools but don’t live down here where all this other data is. So it’s interesting to see you know to your point where this is all going to go for those who aren’t don’t have the video version of the podcast. There

[00:39:51] are investors like that you can just imagine when he set up here that his hands were over his head and when he said down here his hands were much lower.

[00:40:01] There’s about a 30 percent chance of digital analytics data coming in from the east and translating green screen he’s forecasting.

[00:40:09] Here’s what I got I got I’ve got Unfrozen Caveman sort of looked at that. You know if the person that’s good to know.

[00:40:17] That’s right. I am just a caveman lawyer.

[00:40:23] There we go with the pop culture references. That was Phil Hartman and it was like probably in the early 90s that there’s been a long time has this been pop that ferment. Yeah yeah.

[00:40:32] It still counts stuff for historical pop culture historically. I want to know when we got the watch.

[00:40:37] Eight is Enough once a week it was my own TV.

[00:40:41] All right are we reaching a point in which we are going to put our hands up in despair and realize that we just don’t care. I mean we do care. I think we care deeply. I think probably the three of us should start that company that’s going to do this. I think we’ll be rich.

[00:40:55] Rich I tell you I think a hundred people have started this company now and they’re going to get squashed like bugs by companies that are much larger. That will do a piss poor job of actually pulling it off of integrated but present company excepted really. Not really.

[00:41:10] I mean I’m thinking more of what the oracles in the adobes and the sales forces have done from the boardroom drawing of why this acquisition is going to give us this next thing like it just it doesn’t seem like they’re ripe for the innovation that requires to get there.

[00:41:29] I have nothing but positive things to say about my Adobe acquisition experience.

[00:41:37] If anything I would say sales force is the one where it’s just laughable. Like I mean Radian6 I don’t think they’ve spent more than 27 minutes of development since they bought Radian6 four years ago. It is exactly the same product. So yeah that was in a sense you’d say if I’m going to be how my CRM there’s all the promise of all this social data and I’ve got things that are connected in these platforms and I should be able to bring in some of that. And as I’ve never heard of anyone actually doing any sort of integration between Radian6 and sales force but that was the that was the press release was Hey we’re bringing in social okay sorry it text box rather I mean yeah.

[00:42:16] It’s hard. I mean no matter what.

[00:42:18] And that’s my fear for the products division of customer intelligence is that they’re going to be checking a lot of boxes that say in theory if we bring X and Y together and then if we just if we just define this schema and then we just go out and tell the world that we’ve got it figured out then everyone will pick it and they will you know race to us and they’re going to ignore and skip all the realities of all the messiness around it which I guess is where I’m finding myself on this discussion going more and more saying God I don’t want to be I don’t want to be within a hundred miles of somebody who’s trying to bite it off that way.

[00:42:55] I would much rather say here’s the data you got that’s in good shape. Here’s the other data you got in good shape. Let’s stitch together and the sum of the parts is greater than the whole analysis. Add a third thing. And for the next decade solve it incrementally and not buy into it we’re going to pull this in for a myriad sources and get glorious intelligence. But maybe that’s just the second bear talking.

[00:43:20] Yeah I mean part of the for the measured perspective in a large company I mean they know the trick is always that you want to stay as focused as possible right cause you you know you have a use case you are very clearly defined use case you want to satisfy and you will meet and totally you know crush and every man in the world and say yeah I’ll do that. And then of course you have you know a dozen other customers out there who say well I’ve got this other thing that I needed I need to you do to tweak that model a little bit and you have another dozen customers or you say well I’ve got something new kind of like that I need to go do something you know like it as well. And everyone’s got to want to see a little bit different so you get enough of those kinds of use cases getting together and suddenly you have a much more general approach where you know where we are where you are trumping one use case for you know one very focused application of analytics or application of customer data.

[00:44:21] All of a sudden you need to consume much more you know different types of customer data and many more times they will. So that’s a balance that no one has figured out how to. There’s no there there is no solution for you. You do the best you can. So we’re working on that. IBM is working on that. Oracle is working on that Adobe Google are definitely working on that and I think there could be a lot of different approaches to different vendors will have. And we don’t need no one really knows who’s right yet. So we’ll see.

[00:44:50] Now this is good. And I think now all of us are working on that.

[00:44:54] So what you get and you get the whole weekend here. So I mean yeah it’s it’s not that complicated really by Monday.

[00:45:03] Least have a mockup.

[00:45:05] And it’s really I don’t think we designed it this way but our our last episode having really been kind of exploring and digging into the big query Google query and really just trying to get an understanding of what it was and if anything given the nature of this podcast and the background of the host who were coming at it from a web analytics perspective.

[00:45:24] And as I recall towards the end of that episode there was a little bit of a pivot where Michael Healy the guest was sort of making the comment that that that is one of those places that you can bring in disparate data and you start to get kind of magnified value. But that’s absolutely kind of where you’ve been talking where that is like take a sharp data scientist or a few sharp data scientists and get some technical chops and wire this stuff up. That is not productize that is using emerging technology that is very powerful that can store this stuff and query it very quickly. But it is not productize customer intelligence it’s just one of the avenues to try to get to the intelligence part of that.

[00:46:05] Yeah we’ve been talking a lot about digital intelligence AI AI back in the day I pounded the table I don’t know how many times that like digital analytics is the control point for all your digital intelligence. A company has. Right. Because it is the it is the one place where you have no true raw customer business information that you can then that then leads to everything else leads your e-mail here. Adelies and socially everything else and being in control of that place is incredibly Geibel place to be and that’s a model that is still true. The mobile changes some of that. I mean channel fragmentation changes and some of that but it’s a really really big place to be and that’s why I ask why Google Alex had to be so dominant right there like they did because they had control those there’s a big control points there.

[00:46:55] And you know where I think Cosper intelligence changes that a little bit is you know where where that lives in the stack. So whether you know you applied intelligence downward into the marketing stack or whether this is actually a new layer on top of that stack. And both those approaches kind of mean different things. No. From a technology perspective this is good stuff.

[00:47:19] All right.

[00:47:19] Well as much as I think we still haven’t solved this problem they have to show pretty sure that was that wasn’t we weren’t that aspirational we’re so close guys we were so close.

[00:47:30] I’m sorry but as you’ve been listening if you have figured this out please Dewdrop let us know. Please do let us know. We’ll cut you in. I swear. No but it is a really interesting conversation and it’s one that pretty much every single company is grappling with on some level. So I know it’s applicable. So as you are thinking about it and you’re listening we’d love to hear from you on slack or on our Facebook page or wherever. Also you know check out Blair Reeves on Twitter. He also writes a number of articles over a medium and frankly they are great reading so I would encourage you do that. Players or anything you want to plug or talk about that you’re excited about that. Is this your one moment to sell something to everybody listening so o to sell stuff.

[00:48:20] Yeah I no you can sell it can be a free slack role playing game that as a person if you like.

[00:48:30] I saw these guys before we started recording that I did. I made a mock up of this old PBS role playing game that I made called Legends and plaid dragon which is on the slack app directory. The Legend of the Pied dragons on there but also go ask them Tell them where it’s a lot of fun so you know check. Check that out.

[00:48:51] So yeah we’ll be adding that to the major slack I’m sure.

[00:48:54] And don’t worry. Is not collecting data on you to make you a future customer. It’s about you.

[00:49:00] So much intelligence. All right. Well as usual we go around at the end of the show. We do what’s called last call something we think it’s cool that we’ve seen recently. So let’s go around who wants to start. You want to start. I love to start. You know this is totally and another topical area but there’s something that we’ve been using as search discovery that I’m growing in my appreciation of in terms of employee feedback and satisfaction. So we use this new tool and it’s probably been around a while but it’s new to me. Newish called Office vibe and it goes in surveys everybody in the company like on on a weekly basis and asked a few random questions and then collates all the results and gives them to you and it’s anonymous and it’s super super helpful to get feedback like that and start to know what you can do to kind of make your employees lives better. So anyway as I’ve been loving that and as hard as it is for me to see where we’re not doing things amazing it’s the most valuable kind of feedback because it allows us as a company to consider and get better. And I’m like I literally spend like time every day looking all the new feedback and being like interesting. OK. OK. What do I do with this. So if you are running a big team or are thinking about running a big team I highly recommend office vibe.

[00:50:24] Yes. Should I go next.

[00:50:27] So I this will not be the last time that I last call ties to 538 dot com but based on some of the stuff of the new with our son I’m reading statistics. Good old p values. And it’s kind of a multipart plug for 538 dot com has written some kind of hilarious articles getting statisticians to actually define what a P-value is. There are some kind of totally statistician nerd humor but they also have the hacking side where you go and you have to basically plug in different different values and see if your result is significant. And it’s kind of making the point that boy the P-value can be abused but it’s kind of fun to play around with and I think actually is somewhat sort of elucidating and informative around looking at things and relationships of data. So it’s kind of a little bit more refined than spurious correlations. Not quite as broad reaching but 538 various various takes on p values as my last call. Why are you smirking at me Mr. Holbourne.

[00:51:32] I just I think that’s an amazing last call and I can’t wait to go read some of those articles and figure out or refresh my memory of what a P-value is.

[00:51:42] Well I’ll say one of my favorite parts of it is when he says the American Statistical Association got 26 statistician’s members to sit down and try to say look we’ve just got to come with one simple definition of a P-value. They came up with starts with the where it says informally and then it goes on for like a modest length paragraph defining P-value and even the author of the article is like yep that didn’t help. And I kind of look at it saying you can’t just say you can’t start by saying informally and make it all of a sudden be like simple and clear. So fun times. Blair.

[00:52:15] All right. So my thing is I have to apologize. It is from Andreassen Horowitz. It is a video AI deep learning in machine learning a primer dude named Frank Chen. It’s on their website but basically it’s like 30 minute 35 minute or so little primeur video presentation that this dude Frank Chen gives about the history of AI. Kind of where it comes from. It has roots in computer science and you know how different approaches to Ai Ai and machine learning have worked. What has changed for you. Kind of like lead to the breakthroughs we’re seeing today in the new applications of AI and what deep learning is and how it differs from machine learning and different kinds of applications and I’ve learned a lot from it. Usually a lot of fun so I highly recommend.

[00:53:07] Nice. There are some high value. Last Call. This show.

[00:53:12] So nice work if anybody I got some real stinkers lined up for the next episode. The five people that made it this far end of the episode No. So thanks for listening.

[00:53:23] BLAIR Thank you so much for coming on the show. It’s been great to discuss this topic with you. It’s comforting on a certain level maybe that it’s not. Others haven’t totally figured this out. And the rest of us are being left behind. And it’s alarming. On another level. That we’ve all got to solve this big problem so we can really do a good job for our cause.

[00:53:44] But let’s keep the truck and end for all of you listening and my cohost Tim Wilson. Keep analyzing.

[00:53:56] Thanks for listening. And don’t forget to join the conversation on Facebook Twitter or measures Slyke. Great. We welcome your comments and questions. Facebook dot com slash analytics or analytics on Twitter. Joel be made up.

[00:54:16] Word. Yeah I’m almost ready to talk. How’s. It going. Make me a fine customer intelligence. Got you man. OK Tim let’s focus. I do not want to be dragged kicking and screaming into this case. All right. You don’t want to do it. I’ll just tune out. I don’t remember what we did was what intros. I’m just a talent. Like seriously. Thank you for coming on the show. We have to redo this whole section. All right. Monday’s will be mid afternoon on the. You then and got it. God bless you out of it too. That’s like a southern hero. So private. Yeah it’s entirely on byde. I get it. We’re also private we were. Just kidding. Hello. I also analytics and honestly I feel good. We’re going to have 150 analysts inside of a year. I was like Where are you going to find those people. Because I can tell you 10 companies that want just one. I don’t think we need to stoke people’s egos. I mean he’s on the show he gets it. He’s part of. All. Right. We’ll fix it in post. Listen I want to hear your sense. Exactly. Does that joke work a lot. Where are you where you’re from. Oh I’m just being an ass. You are.

[00:56:11] Rock the flag and cussing tumors mostly words I I that.

 

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