MarTech Talks #11

00:00 - 01:49 - Opening, episode introduction and presentation of Mike Blanchard

01:50 - 09:19 - Trusted growth, short-term results and sustainable customer relationships

09:20 - 17:42 - AI search, customer agents and the evolution of trust in indirect relationships

17:43 - 20:53 - The addiction to email volume and the case for fewer, more relevant communications

20:54 - 27:27 - From content production to connection, relevance and customer signals

27:28 - 33:58 - Predictive AI, hyperpersonalisation at scale and decision intelligence

33:59 - 40:16 - Making predictive AI accessible to SMBs through SAS Marketing AI

40:17 - 44:39 - Composable technology, faster implementation and the importance of data foundations

44:40 - 52:14 - Data readiness as competitive advantage and the shift from scarcity to abundance

52:15 - 55:54 - The role of SAS Customer Intelligence 360 in the MarTech stack

55:55 - 1:00:29 - Data silos, integration, change management and ownership

1:00:30 - 1:06:34 - What CEOs should prioritise: a North Star, trust, AI literacy and customer experience

1:06:35 - 1:09:07 - Final reflections on customer intelligence and the future of marketing

1. Growth and trust are not opposing goals: Companies can generate short-term results while building long-term customer value, provided growth is based on reliability, consistency and a clear commitment to delivering what was promised.

2. Trusted growth requires measurable outcomes: Leadership teams still need concrete business results. The challenge is to balance immediate revenue with customer lifetime value, loyalty and the long-term predictability of growth.

3. Marketing measurement is changing: Clicks, views and lead volumes are becoming less representative as AI engines answer questions directly. Brand visibility, sentiment, discoverability and trust within AI-generated answers are becoming more important.

4. Brands must remain relevant when AI agents mediate the relationship: Customers will increasingly rely on agents to search, compare and transact. Brands still need to understand the human behind the agent and create an emotional connection through reliable products, service and customer experiences.

5. Marketing teams are addicted to communication volume: Open rates and click-through rates often disguise widespread disengagement. The objective should not be to send more messages, but to make targeting sufficiently relevant to generate more growth from fewer communications.

6. Content is becoming a commodity: Generative AI makes average content abundant. Differentiation will depend less on production volume and more on a consistent point of view, brand experience, relevance and the ability to build real connections.

7. Customer signals matter more than isolated campaigns: Successful companies will connect behavioural, transactional, service and product signals to understand customers as a whole. Their advantage will come from learning faster and acting on those patterns.

8. AI is broader than generative AI: Predictive models for propensity, churn, lifetime value, next best action and next best offer have existed for years. Generative AI attracts attention, but predictive intelligence remains one of the largest untapped marketing opportunities.

9. Hyperpersonalisation has been constrained by operational complexity: The main obstacle has not been a lack of ideas, but the number of handovers between business, data, content, channel, operations and compliance teams. AI can reduce those handovers and compress campaign delivery from weeks to days.

10. Decision intelligence is the next stage of marketing automation: Future systems will not merely execute predefined workflows. They will continuously optimise customer decisions, journeys, budgets and offers according to business goals and real-time customer behaviour.

11. Predictive AI is becoming accessible to SMBs: SAS Marketing AI aims to automate the data science work required to build models such as cross-sell and propensity models. This approach seeks to deliver transparent, near-data-science results in weeks rather than months.

12. Data readiness is becoming the real competitive advantage: AI tools are increasingly commoditised. Differentiation depends on the quality, uniqueness and accessibility of customer data, as well as the organisation's ability to interpret it and turn it into decisions.

13. Better data does not always require a perfect transformation programme: Missing information, limited datasets and privacy constraints can increasingly be addressed through modern applications, data generation and synthetic data. Companies still need solid foundations, but more barriers are now technically solvable.

14. Automation should release human capacity for growth: The goal is not simply to reduce costs. By automating repetitive work, organisations can redirect people towards strategic questions such as customer expansion, acquisition and the design of future relationships.

15. Customer Intelligence 360 acts as a decision layer: Its primary role is to connect data with activation systems and customer experiences. It can centralise segmentation, propensity modelling and decisioning while remaining compatible with composable CRM and channel stacks.

16. Technology alone does not solve organisational problems: Data silos, fragmented ownership and weak alignment between business, data and IT remain common causes of failure. Strong projects combine a clear strategy, change management and close cooperation between business and technology leadership.

17. CEOs need a clear customer and brand North Star: Leaders should define how the brand should be known, what customers should feel and how the company should behave consistently across AI engines, digital channels and human interactions.

18. AI literacy is now a leadership requirement: Executive teams need to understand that AI includes more than chatbots and generative tools. Customer experience and CRM provide practical areas in which companies can test AI, learn quickly and generate measurable returns.

00:00:00 you know, if you make your targeting so um powerful, there there's no reason why you're not going to, you know, lift your response rates. We had a um yeah, great customer, one of my best references from a customer said, you know, we grew our business by 50% and we sent 30% less communications, >> right? So sending 30% less and growing 50% more.

00:00:45 Forecenders Auto marketing. Hello and welcome to Martekch talks by uh Limino. Uh today we'll explore explore what it really takes to build growth on top of customer intelligence and where trust fits into that equation. Our guest is Mike Blanchard, vice president of customer intelligence at

00:01:50 CES. Welcome Mike. >> Hey Thiago, thank you for having me. It's great to be here. >> Such a pleasure having you also Mike. Thank you so much for your time. Um Mike has you know more than two decades of experience across CRM data marketing technology as a whole. He has worked closely with leading brands at the intersection of analytics, AI, customer experience.

00:02:16 Um Mike is the one to help companies um to move from fragmented data to connected datadriven journeys and and obviously this has to to mean uh more business and better relationships with customers. Of course Mike recently you shared a story I found that very funny uh when somebody asked you what do you do for a living? Um and instead of saying I work in software, he said I work in the business of trust.

00:02:46 So >> and this is a strong statement. Uh we know that in reality most companies still optimize for conversion and revenue. Uh so where does the business of trust actually conflict with the business of growth? >> Oh, what a what a great question, Thiago. I can see you've been following my LinkedIn posts because that's >> that particular story still drives me crazy now because um and and just for your listeners I I am I'm developing a house in the UK.

00:03:20 I'm building a very old modernizing a very old house here in the UK and and as part of that I had some build work done in in my garden and and I was I was incredibly let down by some some builders and it it's just you know it just brings home the most frustrating thing is about people doing what they say they're going to do um and and not lying to you or not, you know, pushing you away or finding excuses, you know, and and and it just, you know, with everything that I do, I I I try and, you know, I meet lots of our customers and, you know, some of these projects are

00:04:02 challenging. We all work in this market. It's, you know, there are some fantastic opportunities and use cases. There are some some hairy challenges that we confront on the way. Um, but I, you know, try and just be the the reliable kind of voice of saying, look, you know, this is something that either we'll take away and get better at or, you know, we'll investigate with you as our kind of partner on this journey together.

00:04:29 And I I I think it's, you know, I I think as a just as a way of working, um, you know, you've got to have those foundations in place. um particularly in the businessto business space you know despite all this world of AI you know people want to trust the the the people they're doing business with um but actually I I I was going to pick when you when you put this in your I didn't realize you were going to go with that story which I found interesting but um when you put it in your in your notes to me it sort of reminded me about um um I don't know if

00:05:06 you had it in Portugal there was an experiment um many years. This was back in the 60s. They called it the marshmallow experiment where they got some children together and they put a marshmallow on the table and they said basically, you know, if you leave it for 15 minutes, we'll bring you lots more marshmallows.

00:05:26 And the test was whether the children would, you know, eat the marshmallow now or, you know, wait for the 15 minutes, right? And there's been various sort of there was a there was one not not so long ago in the UK which was just a an evolution of the same experiment. But I I I find that it's quite interesting, you know, do you you know, do you do you satisfy yourself now >> or do you kind of trust that there's something better in the future coming? And I I think, you know, they they worked out reliability um of the future state is important. Um

00:06:00 so I look at those, you know, are we in the business of trust or the business of growth? I mean the markets will tell you they want trusted growth, right? you know any any CEO wants to build predictability of growth you know and predictability means there's some trust there there's a reliability there um I I I think it probably is the ongoing always the biggest tension of any project is you know how much how much do we invest to get revenue in a reasonable time frame or growth in a reasonable time frame versus, you know, some future

00:06:41 point. Um, you know, and we'll dig into lifetime value, I'm sure, a bit more, but you know, how how do you measure lifetime, right? What is lifetime? And most companies will probably tell you any model is a a maximum three-year model, you know? Well, that's not lifetime, is it? That's three years, you know? So it's um I I I think that's uh you know I I think I think measurement is is is is super important.

00:07:09 You know what what what gets measured ultimately dominates. >> Um I was I was last week with with um with a customer. You know I was with the the leadership of that customer and you know what they want is results. They want very clear concrete results and you know we all we all have the same from our from our leaders right they you know what are the results that that you're getting and and the results can't be well we'll have nice loyal customers in three or four or five years time so it's a balance so >> yeah definitely the difference between

00:07:44 growing a business or developing one I mean just being focused on shortterm um and uh uh financial uh KPIs or um the starting with the purpose of a brand of a business and making sure that we are trusted not only now but uh in in the next generation. Uh that's what's what makes great brands great, right? And this is a challenge for small companies who have to to pay um their employees uh you know every week, every u month, but also for big corporate companies that go public and then they have all the stakeholders and stockholders

00:08:27 pushing them to to to to grow. Uh >> it is but I think it's reasonable to assume that you can have both, right? You can you can grow in the short term and the long term if you choose to set a strategy that is not trying to rip people off in the short term but build sustainable growth with your customers that you >> they they come to rely on you right they come to trust you they come to be loyal all those things are important so um I I think it it is reasonable to do that I'm lucky at SAS we we work for a private

00:09:01 company so actually we >> we're not chasing the quarter results all the time, right? Um, and and you know, that that is a challenge for many big American companies, right? Or big corporate companies. So, you know, we we have some freedom to build relationships over the long term. So, >> yeah. Mhm.

00:09:20 And and so it's so funny because uh in performance marketing we are also shifting away you know because of the serial click phenomenon because of the AI answered results we are shifting away from a purely um you know how many leads have you generated, how many were sent to sales, how many close, how many views, how many likes to a you know are you what is your your overall sentiment whenever um Gemini mentions your brand, >> is it positive? How many times do you come up in AI answers? And this is also a shifting from uh growth KPIs, I would

00:10:02 say, to trust uh KPIs and visibility. Do you agree with that? >> Oh, totally. I mean, and by the way, it's a good thing, right? It's a good thing we're not chasing clicks and those things anymore. I I you know I think you know the challenge at the moment is how does a business reorientate itself from you know it's measured those things year on year and month on month and and and week on week and it's use those as indicators in its business of performance you know of of the of the ultimate >> uh outcome of revenue performance. So,

00:10:37 you know, at at the moment there's this kind of, you know, just rewiring that has to take place where, you know, people need to understand that that that's that market is is drying is is is changing because consumers are now, to your point, >> um going to use a summarized form of of a of a search or or the AI agent itself and and so having your content discoverable, having your content in the way and the shape that the AI engine can interpret it is is you know every marketing department is frantically trying to make you know and and our own

00:11:15 marketing team in SAS a lot of the big enterprise customers will hide that content behind you signing up you know so I'll let you have access but you've got to sign up with your details you're having to now make it discoverable in the first place >> so um so that's a it's an interesting shift in the B2B markets as Well, but yeah, I ultimately um you know, us all as consumers are not going to click through anymore.

00:11:46 We're just going to read read the summarized form, right? >> Yeah. So >> that's definitely I would like to come back to this subject later on in our conversation but uh uh what we are already seeing is I mean AI agents obviously helping marketing teams and and business people um doing more things faster and probably as good as it was before hoping that this is true but uh we are also seeing that uh consumers u you know citizens we are all adopting AI agent and they are acting on our behalf.

00:12:22 You know, searching as we mentioned uh deciding even transacting the customer relationship is no longer uh direct and this is a scary future for for business people like us. So uh how does trust evolve in this context? And are brands still building trust with people or do we now need to to be concerned with earning trust from AI agents acting on our behalf and how can we do it? I mean >> yeah I look so my my my view is that that that shift to the indirect side was happening anyway right I mean if you think if you just sort of step back and

00:13:04 sort of said I you know I I used to talk to individuals directly or send content to their houses that they picked up and then the world went digital right and we were sort of trying to trying to connect your digital identity with your human identity and we've spent you know, huge kind of sums of money trying to kind of bring all these views of you together in a in a digital world.

00:13:30 Um, and then the kind of challenge of, you know, how we've used that data, whether it's, you know, third party, second party, first party or even zero party um, data. So, that that >> that's just been happening, right? you know, as channels evolve, people people have, you know, gone to influencers on social that they trust more than the brand voice, right? And and so that that shifts happening.

00:13:58 I think what what you see now with an AI agent is just a further um obfuscation if you like of the of the indirect nature. Um I mean I I come back to like you know how do you build trust with an AI agent? I mean, you know, I I I I come up dry on that one. I I I step back and say it's you still have to understand the human behind that's using the agent, >> right? And and I think what the more that you do understand the human behavior that you understand particularly the human behavior towards your market your brand >> that is enabling you to shape your

00:14:38 products your service your customer experience to the market. Um and it goes back to what we were saying earlier pe people will will go back to the brands they rely on that that they trust that they find reliable. Now, you know, the agent will just do some of the hard work.

00:14:57 To your point, you know, we we are creatures of trying to we we don't want to click through. >> It's it's funny, right? If you think we we've gone through this this digital era and I I see it with, you know, my children growing up, your you you've shown me your children growing up. You know, we we didn't have the internet, right? So you know we we grew up with information availability in a certain way going to libraries and reading books right >> yeah exactly then then the kind of world exploded you got information everywhere and then suddenly it's really hard to

00:15:28 navigate all that information so the AI agent is just helping us simplify that navigation experience right um but ultimately you know the the you know I I think understanding how you show up there is important which goes goes back to your question of the B to you know how do how do brands make themselves discoverable by AI agents but but ultimately you know you come back behind the scenes say well your role now as marketing is to make sure there's a continued human emotion connection between your brand >> and the people

00:16:04 >> right because AI agents are just going to pull whatever they can read and you're going to be in a battle to kind of make that discoverable but you know if you rely on your bank if you rely on your retail businesses and they do good service, you know, people are going to come back to you, right? That's the nature of humans.

00:16:24 So, >> um, it it always makes me laugh. I I shared with you when we met in in Lisbon, the that marketist example where where you got the AI agents on the left hand side saying it's going to make marketers much more engaging and on the right hand side, right, consumers are going to ignore more marketers, right? Carson is great.

00:16:44 >> So true. It's so true. Um and and whilst we just spoke there about about the consumer side, you know, that the as I shared with you that the impact on the automation uh for you know for your uh technology your marketing technology it's just incredible. I mean you we are talking you know we're we're talking step change which is you know five to 10 times the output >> with AI agents that you get >> pre agents.

00:17:19 >> So if you just you know there aren't many things that give you a 10x >> kind of like whether that's efficiency or effectiveness >> and and that's what we're seeing with the capability. So it's it's just incredibly powerful both sides, you know, from the from the marketer out, but also as you can see from us as consumers and simplifying information.

00:17:43 So >> yeah, thinking about automation, uh it's obvious that um probably the first automation use cases in software that uh were widespread among us marketers were marting uh email marketing automation tools like Mailchimp, naturally, HubSpot, and so many others. Um and I remember you said something last time we worked together in Lisbon, which I I found so funny.

00:18:12 You said that uh sending emails is like taking drugs and that's a a strong an analogy. So what exactly are companies addicted to? Uh volume delusion of activity. What do you think? >> Well, you raised it at the start. You know, how many clicks am I getting? You know, it's I I it makes me laugh. I mean, I think later I think we were going to pick up on this point, but I'll I'll raise it now.

00:18:38 Um, I I I still find it I still find it incredible that we celebrate 30% open rates and 10% clickthrough rates. And I look at that and go, "Well, that's 70% of people are ignoring you, right? And 90% aren't doing anything, right?" And so, you know, we we've we've grown to accept these, you know, phenomenally low rates of engagement, right? Um, and and and email's sort of in the middle, right? you look at display and you know you're talking of fractions of percentages right um and so I you know my my philosophy is kind of the alternate it's

00:19:16 well how do we how do we connect every every communication to something tangible you know or at least more communications are tangible than less so flipping that 30% of people opening to 70% right for me That's that's our what what we see as our um our focus in the industry. Um you know if you make your targeting so um powerful there there's no reason why you're not going to you know lift your response rates.

00:19:52 We had a um yeah, great customer, one of my best references from a customer said, you know, we grew our business by 50% and we sent 30% less communications, >> right? So sending 30% less and growing 50% more. So that's kind of, you know, so I often say, it's funny, we go into this whole this, you know, email drug sort of space.

00:20:20 I'm I you know our focus of conversation is look how many emails do you send out this year this year or last year and you know if it's 1 million 10 million a billion because we have customers that send that much you know I I'm I'm starting with well how do I half that number and give you a better response right and I I think that that is just our philosophy with where we come into the industry looking to drive better targeting um and and ultimately less less but more powerful engagement.

00:20:54 So >> and that's a very hard thing to to to get into because uh uh we are all just hardwired to think that more is best and more touch points and more uh I don't know visibility and engagement or supposed engagement with people and um you know I I heard recently and and it's a a matter of relevance not of you know repetition I heard Seth Gordon recently arguing that content is no longer a scarce asset.

00:21:31 Of course, this is obvious and it has become a commodity due to gen AI. You know, we can produce content um at the press of a of a button and uh so content is and in thought leadership content it's no longer a source of differentiation. Okay. Uh so competing he was saying that competing in volume uh competing on volume frequency or uh average quality it doesn't make uh any strategic impact.

00:22:00 It's it's just uh the new you have to to do it uh because everybody's doing it. But uh the real shift comes from building content to building connection in his words. I would say relevance of course connection uh again trust and real uh relationships. Um so from from your p perspective uh Mike how should companies rethink their content marketing strategies? What adaptations should we make to the old inbound playbook? Well, I think um you know content marketing is still is just to to that point I was making about the the

00:22:41 10x increase. You can build it a lot easier and quicker and then you know the discoverability of it is just changing with the nature of way an AI AI agent works. You know, I think ultimately, you know, your content, your voice, your opinion, um, goes back to, you know, how you build that brand trust.

00:23:05 You know, if you were, you know, if you were bouncing around trying to say too many things to many different people, you'd be like, I don't, you know, whereas that consistency I I I think is is hugely important. So, you know, for me, a a north star of any business now is well, how how do I want my brand to be perceived? How do I want that experience to, you know, and the brand, you know, the perception of a brand is not just about what you're saying, it's about the experience around it. It's about the product qualities and

00:23:37 all the things that you associate. So, it's all those things coming together. Um, which is why I think the um, you know, I guess picking up where I think Seth's coming from or is where where we're coming from is is is actually understanding those patterns of signals that bring together, you know, someone's experience of service versus, you know, the product experience they've had or, you know, when they've called in to us or actually, you know, you know, when they've made a repeat purchase.

00:24:11 I I think bringing those signals together, you know, from a from a an intelligence point of view is is is the bit where the market is going to be. Um, you know, I we we talk about this from our side to say, you know, we we're moved from this digital, you know, we talked about this information digital era >> and and you're moving into an intelligence era, right? And and you know, and that's both on the consumer side with the way AI is going to interpret things for you.

00:24:39 Um but it's also on the brand side because the brands that are going to be um more successful are the ones that are going to increase their learning rate. So they'll be able to understand you know the signals in their business >> right >> and these are the challenges I think somewhere else we said these aren't linear anymore these signals right these are these are multi-dimensional kind of things um and and and a reality is is you know how do we connect and stitch together all those signals but that that's the power right you know we

00:25:15 we you know too many today too many silos still exist in these companies. We need to break those down. We need to make sure that that these decision systems are kind of understanding of all the different patterns that are happening. Um because you know the compute power is there, you know, the AI power is there, right? you just we need to unlock some of those things and I think that that bit so so so content you know understanding the voice and things not so much the generation to your point but then the signal you know

00:25:45 understanding the intelligence of interpreting signals is is going to be huge so >> yeah and this is something uh amazing because everybody thinks about marketing automation as you know of course if this then that if a customer you know looks at our pricing page and put him in into in a a nurturing uh workflow with the purpose of accelerating the buying uh uh process or something like that.

00:26:17 Now we can understand that if this then that is not just the the name of the game. We can have agents doing that for for ourselves. Um but uh uh uh uh the other part is that what we see the market and marketers doing is thinking of automation of content alone. Again going back to genai the geni possibilities of increasing productivity for you know how many newsletters can I do today or blog posts whatever but you're touching upon something that we've that we've had for so many years which is machine learning algorithms that help us uh cluster

00:27:00 uh uh behavior um patterns and uh um respond to them with things like next best action, next best offer. We've had this for so many uh uh years now. Uh predictive AI, not just uh generative AI. And I think that we are missing this which is probably the the greatest opportunity for us in um bringing AI to our companies.

00:27:28 not just generative AI, not just if this uh uh if then um uh workflows, but also all these predictive uh abilities that we've had for so long. >> Yeah, you're you're right. I mean we we we sort of the market sort of has got hot on AI because it's seen, you know, a generative AI and consumers can sort of understand it because they can sort of see what's happening in a search agent or um you know and and I think that's sort of in some people's mind that sort of narrowed the perception of well but that's sort of one branch of it but you're right that the the the discipline

00:28:07 of AI has been around for some time. Um the the main challenge that we've seen is because you know we you're right. You're absolutely right. We are companies have been already benefiting from these technologies for some time. the the the key the key challenge has been the scale of those things because um what what what has tended to happen is if you think about you know the whole concept of AI targeting we're making things smaller right we're not you know we're not blasting the same message out to everyone we're you know ultimately

00:28:41 getting to a oneto one you know people talk about the pursuit of onetoone personalization right you know where everyone's getting bespoke content and a bespoke message and potentially bespoke pricing and all those things, right? And the the systems have have been struggling to keep up, you know, and so what what you've seen in sort of the whole hyperpersonalization market and there's a load of articles, >> you know, it gets you so far, but then there's been no ability to scale.

00:29:11 And so a lot of our customers are still struggling. You know, their their main their main kind of challenges are just purely efficiency things. you know, there's there's too too many handovers. There's too many handovers between teams, you know. So, you think about the the business teams trying to create ideas, then you've got data teams trying to, you know, analyze customers, and then you've got operations teams, you've got content teams, you've got channel teams.

00:29:43 Um, and then you know, you go into some of the regulated space, you've got compliance teams that that you know, and so it's the handovers of all those things that that slow you down. Um, and then they kind of stop you getting so much of your >> your hyperpersonalization. I had a retailer big retailer in the US that were um you know they've got I think they've got sort of seven product categories on you know say you know jewelry apparel home those kind of things right department type store you know and and ultimately they underneath those main categories they've got lots

00:30:22 of subcategories but they're they can only get to personalization at the category level where they want to get to is personal ization at the subcategory level and then kind of then you sort of think so you get these exponential need for customer journeys for AI and modeling and I think that that's the bit that's been holding the the industries back because >> you know no one's getting an army of people to come in and help you build those it's the same people you know trying to do more um >> and that's where we've seen I think

00:30:52 really the for me the really heavy lift um of of the AI side on the vendor side just to take away a lot of that manual intervention. I mean, we we're working now our I sort of challenged our team and said, "Look, I I think the tool should be able to do everything from a marketing brief." You know, the marketing brief should define what the campaign is for.

00:31:18 You know, what are my goals? What am I trying to sell? What are the um uh what are the the revenue? and and and and it should be able to map out all of the things that we we go through all the steps and and I would say we're we're quite far down that journey now of saying right >> um you know the brief should do it all all these handovers should go away you know I I I was with a I was with a bank last week that that said that their supply chain from from the business to their output was eight weeks >> I said right we need to target that as

00:31:55 two weeks, right? I mean, that's the reality we should be and AI will get you there. And I I'm fairly confident that the handovers and things will get so what that what that means is suddenly we are going to unlock a lot more hyperpersonalization. I think you're going to see you're going to see the promise of personalization just get a lot better because the content can be easily generated, the journeys can be easily mapped, the modeling can be scaled.

00:32:19 >> Um, and that those are the things I think you know that increase your learning rate. Um and but but but ultimately the system sort of becomes intelligent to do those things and you we we um it was funny we built a um we built a future state what do we think our software looks like in in uh I think it was like five years we were debating and effectively it's just a it's it's just a dashboard right with with just some which is telling you where your constraints are where if you spent more budget you would get more return,

00:32:54 >> right? >> Um and and ultimately the system is self-optimizing around those goals for you. All of the handovers, you know, you know, so it's an it's just it's a fascinating time for for for change and potential. So yeah, sorry. that that that's that's a vision close to and correct me if I'm wrong close to what we uh would expect from a marketing mix modeling but with uh the decision uh part integrated there or the semi-autonomous decision part >> the CRM component of that. Yeah.

00:33:28 So whereas the marketing mix is that sort of esoteric kind of you know how am I shaping my spend um you know for for the year for whatever this is the sort of micromanagement of CRM decisions where customers are coming to your web or coming into your mobile or you know and and how does your equally how does how you prompt customers through your your outbound kind of activity match when they come to you and how do those things sync synchronize between them.

00:33:59 So that that for me is what we call decision intelligence. So >> that's very interesting. Um and and all these uh um features from AI and all these predictive AI uh uh use cases. Uh again we've seen them work for many years now and mainly in uh companies that have enough data and enough budget to to uh uh to invest on this.

00:34:27 Um uh I want to get uh in detail about this topic later on our conversation but uh how do you see that uh um an SMB company um you know how can an SMB company get inside this predictive AI uh world that has been around for so so many times. I mean we work every day with uh uh vendors that you know deliver CRM uh uh automation uh some some sort of intelligence in business to SMBs but we are not seeing these software vendors touching on these use cases and I think it's a waste of uh opportunity.

00:35:09 >> Yeah. Well, I I'm glad you said that cuz we think it's a waste of opportunity as well. And we're we're about we're about to release a new product. I think that fits and covers this um really well. Let you know, let me say I so here's where I see the options are in the market today, right? So if you want to go and build um good um uh AI models for things like um propensity to buy or cross-ell or you know even looking at things like you know elapsed spend churn those kind of areas or lifetime value those kind of

00:35:44 measures you know you're you're typically kind of in a any enterprise you're you're going to either you know leverage or employ your own data science kind of resource or you're going to buy an agency that is bringing those kind of skills to you, right? Um, and so it's a very human ccentric form whether you're doing in-house or or using an agency and and you're you've got those humans kind of, you know, producing those um models and and the challenge for marketing is is those that that that process in itself is is quite a um a long time and

00:36:25 it makes sense in sort of things like where you're evaluating >> fraud or risk or big decisions, right in in in marketing, you know, it doesn't need to be the sort of perfect decision. You just need to have, you know, 95% of the people would do this, right? Um, so what what kind of happened is that that the vendors then went all the other end and and they created, you know, AI models in their software as a service applications and and and effectively they became um what you describe as black box. So you you kind of get an

00:37:00 output of the model but you don't understand how it came >> to the the those criteria. >> Um and then what you would see is if a if a software as a service platform was just for example looking at web data you know it would it would build the model just on clickstream data but that might not be representative of human behavior because guess what >> you know I'm web is part of my you know clickstream data is part of what you see about me not everything.

00:37:29 So I think you know you you you had those two extremes and you know we we most times what we see is customers are taking months to build just simple models um and and relying on that and that data science is expensive it's um you know it's hard to maintain and and for your SMB question it becomes out of reach right and yet they don't want to use these sort of AI blackbox models because they sort of you know >> I'm I I know enough about my data and AI to think I need to understand how it's making a decision, you know, because

00:38:03 >> so so we've kind of come up with an approach that's that's sort of in between the two. It's very very innovative. Um and we're just literally coming to market now with this product this year. um we have some early adopters and effectively what we're trying to do is is um automate all of the all of the data science heavy lift work which is the underlying creation of the the data um for modeling um and so we we we've managed to automate that in a way where uh someone who is more um uh I guess you know more comfortable in

00:38:44 data science can build what we call a recipe, but then business users can now democratize and build multiple versions of this model. >> And so what we're what we're seeing is like a normal cross-ell model for, you know, might take somewhere between three and five months for an enterprise business. We're doing it in two weeks.

00:39:04 >> Um, but we're not trying to we're not trying to emulate the data science person. >> Mh. Um, what I'm trying to do is give you a data science close to a data science result in a fraction of the time. >> And if you want your data science team to then go in and you've got those people that want to understand how the model is created, what the data it used, etc., you can do all that.

00:39:27 So, it's not blackbox. So, it's kind of it's playing halfway house. So, we're we're really excited because I think I I think that in the data in the SMB space is going to suddenly unlock capability. Um so you don't you don't need the the the data science skills. You do need data data people I would say but you can use a agency or external resources but ultimately we're just trying to make the process um sort of somewhere between a data science and not a black box.

00:39:58 So um yeah, we've got we've got customers that are trying it at the moment and they are getting >> they're doing they're doing it in tandem with a data science team and our results are pretty close to the data science and it's taken us like week, you know, two weeks. So um yeah, it's called a it's called marketing AI, SAS marketing AI.

00:40:17 So we're going to be releasing that in July. Um but yeah, I'd love to share the demos with the team. Um yeah love to love to get some clients in Portugal. >> Wow that that's exciting because we are used to think of you know this use cases as you said in a data science pipeline typical long uh implementation cycles you know large platforms a significant investment in people and in tools.

00:40:45 uh we're thinking all mainly in the category of um CDP customer data platform that many times solves for these issues and it's a a hot topic uh um also here in Portugal because companies are trying to figure out where does my CRM is uh great enough and when should we bring to our mart tax something more like uh a CDP and many many midsize companies are making these calculations right now.

00:41:19 Uh you mentioned a case uh last time we were together of an SMB company in Holland that completely breaks away from that model like you were saying. What was deliberately ignored? I mean I can see the innovative part of your solution at SAS. Uh but besides that was anything deliberately ignored simplified you know in order to to deliver that value faster? No, this is just about um I I guess it's it's just the nature of us making our enterprise software more composable um and leveraging um the the capabilities of not not just cloud and

00:42:00 software as I said we have we have quite a unique model where we our philosophy is not to build a data model in our tool and not to copy data into our tool. Um so we we focus on integrating to the data where it wherever it resides at source. Um but what we're what we're trying to do is you know just simplify the the generation whether it's of journeys or whether it's of AI capabilities or decision capabilities >> um and and you know in essence then just um push that back.

00:42:36 So let let me be practical with this. you know, if you when you sign a contract with SAS, you know, we can have an environment up within an hour. Um, so our environment comes up within an hour. You can map your data to it in five steps, you know. Um, so you know, and and so within two or three hours, you can have an environment with those things mapped.

00:43:00 Now, the power of these agents becomes super important because suddenly, you know, I want to build a customer journey out. I just now upload a diagram of what I want to do and the tool starts configuring on its own. Um, we are as we kind of go to the back end of this year, we're going to have an a an AI agent that will just tell you, you'll be able to tag to it and say, tell me who are the people I need to target for this campaign and it will go away and it will look at your data and assuming that your foundations are there um then it will

00:43:33 run really quickly. So just the speed of of going from uh you know a starting project to campaign. So this one example that I mentioned was six weeks from contract to their first live email campaign. >> Um so so suddenly you know barrier to entry I I think for SMB has significantly changed. Um now you know I'm not I'm not sort of saying you know you have to still get your data foundation >> right and we have in this case in Holland we had a we had a partner that provide a managed data service alongside our application

00:44:16 >> um so you know as we know that the application in this space will only ever be as good as the data that that you're feeding into it. So um but there are so many powerful ways now to get the data right. You know it's not difficult to get clickstream data. It's not difficult to create email response data back from from your campaigns.

00:44:40 And that that is you know your greatest source of opportunity is to understand you know who's clicking through and why and you know how do I then go and understand and tailor my campaign so that I get more people engaged. >> Oh here he goes. It touched the I believe the most important uh point uh in our age of AI because we see companies rushing to adopt the latest generative AI tools or whatever AI comes as part of the brand uh while uh overlooking what actually creates differentiation which is uh their unique customer data. You know, given that AI

00:45:18 is increasingly becoming commoditized, um, you know, to what extent do you believe competitive advantage is shifting away from tools towards data ready readiness? >> Yeah, I think what what we've seen is the the software as a service industry uh and this I was reading some stuff from Scott Brinker actually on this topic.

00:45:46 the the software as a service industry to run it had to it had to create these sort of interpretations of data to to to scale. So um you know every of these big vendor solutions sort of had a data model within it and then you know what in a project a customer would have to map their data into this software as a service application data model and then everything works beautifully.

00:46:15 Um but what that typically did was it ignored a lot of the the cool stuff in the customer's data right so where now application building for AI apps is is coming in is customers uh or clients now seeing well I can build an agent directly on my own data so what's all this software as a service stuff now right and Scott Scott describes those platforms becoming more infrastructure which I I I agree with >> um And I think from our side, we've we've sort of focused on we've always focused on the infrastructure of being able to connect to the data, being able

00:46:51 to use all of the data available because guess what? That's what AI companies want, more data, right? That's, you know, they they don't want to squeeze data down, you know, they want everything. Um, so getting that balance of how we can, you know, access everything but, you know, interpret what's needed.

00:47:11 Um and then when you get into runtime because you don't have to get your data right all up front you you know when you when you work out because marketers are always like you know right I've got a new campaign new things I've got new I want to try this new data I want to I want to bring that in you know in the past that was really hard right you know I I knew you know certainly you go to regulated markets again I banks that would tell tell me that it takes them 12 months to get a new data variable into their source systems for marketing you 12 months. It's like

00:47:41 crazy, right? You know, whereas that speed now in is is, you know, we we can adjust on the fly. I mean, we can literally drag and drop new variables into our tool for uh either AI, for targeting, for journey building. Um so yeah, I I data readiness becomes um becomes foundation. Yeah, intelligence of your data is where the differentiation is, right? So your data readiness is one thing.

00:48:13 If you can't interpret it into understanding what those patterns are in your data, then then almost then and I think that that's the bit I think which is which is exciting. You know, how do you build strategies to better use information that in the past, you know, how many execs would tell you I really struggle to get basic questions answered? Well, how how about I just give you an agent that you can ask the questions to now and it just looks at your data.

00:48:39 That's where we're going. So, >> yeah. Great. So, this is a call out for, you know, every business leader. Please first solve for fragmented, lowquality, disconnected data, customer data, which is >> well, let me Yeah. And what I would say is um don't don't invest, you know, there are applications that can help with that piece itself, right? So your so give example so you know your data might have missing fields and things we can we can manage that you the tooling can manage that if if your data is not um big

00:49:17 enough um representative enough we can generate data right you can generate synthetic data to actually look like what so so again these kind of barriers of data readiness become solvable through applications as well. And you know, we're getting, you know, again, that's really exciting because, you know, I don't need to worry about personal PII anymore, right? I can just take a take a snapshot of your data and just create and I can tell you I'll give you a a model that will be pretty darn accurate for for marketers. So,

00:49:52 >> this is gold. What you've just said, it's it's gold. uh you know uh and makes a huge difference when we uh think about these projects and when we want to uh compete using data AI and all these tools not just with the scarcity mindset as C and friends R mentioned I I believe we've read that paper that scarcity mindset that puts us on a an optimization and reducing um uh you know costs but competing on a abundance mindset.

00:50:28 I mean differentiation, innovation, uh customer relationships. >> Oh yeah. It's it you know it you know to today the kind of I would describe the market as you know to towards that scarcity mindset is kind of how do I how do I do more with the resources I've got? Yeah. Um, >> and I I think what you'll see is that shift to well, let's automate the tasks, the core things that that make you inefficient.

00:50:57 Um, and then let's reuse that human capital into thinking, well, what's that abundance mindset look like? How do I do more with more? >> Um, so actually we I we have our major conference next week in Dallas and for for my customers, I'm saying exactly that. I'm saying look I I don't have all the answers here because part of this is you rethinking how your teams are going to operate when I collapse the barriers between those handoffs right but ultimately what I hope we'll be doing is we'll be saying we'll be stepping back and saying right let let's automate the

00:51:32 scarcity stuff so that you get the the stuff that's on your queue out the door and then you can get on the front foot of your business and start saying right Let's look at how we strategically grow our customer base this year, next year. How much of it is already, we have the customers that we're expanding.

00:51:50 How much of it do we need to acquire new? Um, those are the strategic questions. not, you know, h how do I get my campaign out the door next week, >> you know, to sell this new product I'm launching, >> you know, it should be how how am I growing my customer base over the next two to three years and what are the what are the kind of strategic things that we need to build to that.

00:52:15 Um, so yeah, >> that's a major shift. That's a major shift. SAS solution is called customer intelligence 360. Uh, correct me if I'm if I'm wrong. Yes, >> you've you've been talking about that and and how it changes all of this. uh um where do you see you know in a real uh world stack with CRM automation tools um data warehouse probably uh ERPs um multiple channels where do you see uh SAS solution uh bringing more value centralizing decision centralizing data uh or orchestrating you know customer touch points and activations

00:52:53 >> yeah I mean the the the short answer is is decisioning decision intelligence, right? Um, you know, I I would >> I would argue between your data layers and you you know, you see plenty of examples of this between between your data layers, between your application or your activation layers, you know, that there's got to be some sort of central decisioning, some sort of brain multi and and reality is some people kind of get caught up, do I need one big brain? You know, no, you need lots of types of brains for different types of workload.

00:53:27 Um but but that sort of central piece of a brain um I would describe ourselves at look look we have um you know when I go back to the the I look after that CI 360 component which is exactly that that what we call the middle matters the middle matters is the bit that connects your data to your applications your activations and your customer experiences um because that's the bits companies struggle with right um now we do have activation capabilities.

00:53:59 Um, you know, we have, you know, we're a full scale e email service provider. We have, um, mobile, web, um, social, we have, you know, all of the activation platform, but, um, I'm not trying to monetize that. Um, I'm not trying to come and be your email provider and grow an email business. You know, as I said to you earlier, I want to come in and help you send less emails and and get more value.

00:54:26 Um so that that that's our philosophy actually. It's a it's being that decision intelligence layer. We're very we're very comfortable. I mean as you go down to the SMB space there are lots of advantages of using this across both because you know again simple things like if you use our email engine it's integrated directly to the decision intelligence.

00:54:50 So as soon as a customer clicks or opens or does something in your communication, it's in our decision intelligence straight away. There's no there's no data lag coming back in. So there's some benefits, but many of our big enterprise customers are using us in a composable form. So they'll they'll use us as the the brain, the decision intelligence doing the heavy lift of segmentation, of propensity, of all those things.

00:55:17 and then you know feeding us into you know application stacks that that just send content emails web etc. So um yeah but yeah I would you know my focus as a business is on decision intelligence. Right. Right. So in spite of everything that you've just mentioned, I believe it's important to um you know send a message that we should not underestimate the complexity of data and integration, right? So uh um how can we balance this you know new world of possibilities uh but keeping in mind that uh we don't want to make mistakes and what are the the most

00:55:55 common mistakes you see companies when they're trying to implement this this vision? Um, look, it's a lot better than it than it's ever been. Um, so I I I I I wouldn't I wouldn't be telling you anything that, you know, most of these projects have not found out. you know, siloed data probably is your one of your core challenges and some of those silos in data are formed because of um potential risk that that >> data is going to be misused, right? And I think you've got, you know, you know, there are good reasons for that, right?

00:56:37 And you you look at the the the markets trying to manage risk of of data and consumer data particularly in Europe, right? Um leading a lot of those those things. So they they create those inherent risks and so they sort of put these walls in your data. So I think that that that's something that the industry has to overcome.

00:57:01 I I I I do believe that you can you can produce enough positive and and manage your negative experiences um in a better way. I mean a lot of those negative experiences come from people using third party data or incorrectly captured data right um whereas if you build good practices where customers actually want to give you their own data you know zero party data I think you can you can manage those but the the data silos is is there that that's >> you know that that that's the challenge probably the bigger the bigger challenge before that is just you know change

00:57:38 management basic stuff, change management, alignment to the strategy. Are those things articulated well? >> A lot of clients, you know, we, you know, we've been doing this for 25 years in this kind of marketing automation, direct marketing space. Um, we have a lot of customers that say to us, we just want to migrate from your old platform to your new platform, >> and you know, we just want a custom migration. I'm going, hold on a minute.

00:58:04 No, you need to rethink all of those old campaigns because are they still relevant? And you know, do you not want to rethink how you're communicating and what that new experience you want to create is or do you want to perpetuate >> how you've done for the last? And you know, some some of that goes back to, well, I just need to have my week on week, month on month, year on year reporting intact.

00:58:27 M >> but you know the brave companies that I've worked with will say no I understand if I make this shift you know I'm not going to be able to tell you that you know this year is better or worse than last year because I've changed the measures right >> and that that requires a lot of you know thought leadership people leadership um so so I I think if you sort of step back and say well look getting that three year five year whatever your time frame is and saying get get that clear what you're trying to do >> you understand where your data silos

00:58:58 are. >> It doesn't it doesn't mean you can't, you know, you can't get things going. As I said to you, you can change your data in platforms like ours as your systems. You're not you're not caught up in what you build now against the data you have now. >> So, um I think you can anticipate these things better.

00:59:18 Um the the final thing I'd say is, you know, some of the challenges is uh is ownership. Um, too often you see this sort of fragmented ownership between IT, >> yeah, >> data teams. Even if you go into marketing, I could probably tell you seven different teams that touch my software. and and so you get this sort of fragmented ownership piece, but but particularly between between the sort of business, the data teams and it um I've seen plenty of customers get frustrated and caught up there. But equally, I've seen some

00:59:55 really good um projects emerge and and almost entire I tell you the thing I would say that the the thing that that comes back to me that is a common denominator of the good projects is the the the relationship between the business and the I the data leadership is is strong. you know, when when those two things are are strong, um, you know, and a lot of, you know, my view, a lot of CIOS these days are much more business, you know, friendly as opposed to sort of building stuff and throwing it over the fence. So,

01:00:30 >> that that makes all the difference. Um you know as a final thought uh if you had to advise you know a CEO today obviously with limited time and resources and again thinking uh of the SMB um market which is the majority of of our companies um you know how can a CEO like this prepare for the next uh 12 months 3 years um what they should prioritize first trust we've mentioned that uh data foundation adopting AI.

01:01:05 Uh, anything else? What What is the cost of getting this sequence wrong? Because I mean, maybe there's a sequence here or or or not. What do you think? >> Well, I think, you know, I I I would, you know, any any senior leader really what what I'm looking for them to do is give the the longer term direction, right? Um, and I I think if you you know, forget the short-term stuff for the minute, but you know, the market in the next three years is going to continue to evolve at pace.

01:01:35 You know that there's going to be an exp an exponential rise in the way people use agents, right? And what I would be sort of saying to you is look, that's going to happen. You can't you can't change that. Yeah. So I think what you need to do is say, well, how do I want my brand to be known in the market? How what what's the emotional connection that we stand for? Yeah.

01:02:02 And and sort of create that northstar. I mean, honestly, Thiago, that's no different to having a northstar for your business, right? Is, you know, how does your business touch your customers? What do you want them to feel, to experience? And you know, importantly then that that becomes the the way that you show up, you know, and that's whether you're showing up in an AI engine >> or you're showing up in some digital communications or you're showing up when the customer is coming into your call center to complain, right? You know, I

01:02:30 think for for me, get that tone right and and and and lead by example and say that that because what what people want, I I believe is that, you know, you you said it up front to me, they want they want reliability of what they're going to get from you. >> Um and and I think the the companies that are going to sustain themselves in the long term are going to be reliable and consistent and how they do it.

01:02:54 And and ultimately, you know, you you may not get everything right. you know, you might give me a bad experience, but if you acknowledge it and you know, you come back and say that I'm going to fix it, you know, then I think people go back. Um, so, you know, I think that that becomes a mindset, right? AI is just part of it.

01:03:13 You you cannot you've just got to you've just got to become AI literate. Um, and you need to Yeah, you need to understand that to your point, AI is not just about Gen AI. AI is not just about chat agents. AI, you know, there is you I I love it now. I I tell to my kids there is in the UK market there's loads of great you know uh online learning to become literate in AI.

01:03:41 Um you know SAS actually provides um uh support into these things as well for governments. But you know you've just got to deal with an AI literate and and and one of the things I would encourage you to do is get your executive team AI literate. you know, understanding what it is. The the the big bank, you know, the big companies, the big banks are sort of doing this now that they're they're they're taking their leadership teams through this AI literacy.

01:04:05 So, so that becomes important. Um, and then, you know, you sort of then bring it back to say, okay, so I I know that AI is going to be a part of my future, right? And so now how you want to shape your AI is is is the piece, right? And then that comes back to data foundations about your strategies. Um and and honestly CRM is probably or or customer experience is probably the a place where you can go and try stuff and get a lot of return right not you know you can get a lot of value for your business because you're going to get

01:04:42 value from understanding the way customers are changing. You're going to get value from, you know, actually if I can just be better targeted, I can show up more consistently. You're going to get value from AI being able to um make you more efficient um which generates more revenue for you.

01:04:59 So there's sort of no downside to me. It's just, you know, and and frankly, as I've said on the call, your your access to AI is just going to continue to to, you know, democratize. So I I think um Northstar, come back to it. Get get a north allstar, confront it. Don't you know there's no point in being scared of AI. I I just no, you know, I think there's a lot of there is a lot of people that are concerned, quite rightfully concerned, but human behavior will resolve those things, right? It will it will put guard rails in place.

01:05:35 >> It will it will kind of, you know, those things are going to happen, right? So, you have to worry less about them. You just sort of know that you know you know don't do anything stupid clearly you know don't misuse data but again most most companies kind of understand the foundations are there right now let's now let's build the positive sides of the benefits of AI let's build let's build an understanding that AI is not going to replace human roles it's going to it's going to change our roles >> right and let's embrace what those

01:06:05 changes are you know in the same way you know five billion years ago, you know, farming changed the way that humans evolved, right? AI is just going to be the latest generation of >> Yeah, that's a good >> automation. So, >> undoubtedly and these are, you know, so so precious insights, Mike. Uh maybe the sum to summarize it, you know what uh that that uh sentence um it nails it.

01:06:35 when the middle works. Martin, I would say business works. Whether the middle is uh data is uh trust is the human on the loop. And yeah, >> it's funny. Do you know it is funny the the the data industry's got a bit of a reputation of being sort of down in the weeds a little bit you know and you know a little bit scientific and yeah >> you know a little bit complex because you know we do like to make the world complex right and you know acronyms and technologies and you know yeah it is but you know actually you

01:07:15 know when you think about it it's just understanding patterns you understanding patterns in your business and you know giving you indicators of what's happening and I think if you kind of come back and say look you know you know the the the teams need to kind of confront um the the the grubbiness if you like trying to find a better a better way to explain it but the you know the the sort of roll your sleeves engine room type side of data >> alongside kind of all of the the business and and what shows externally.

01:07:56 I think that those two will naturally just come closer together. Um it's just, you know, it's great. It's such a great industry to be in and and it's exciting the the change throughout now. I I I've never been more excited in in I in in the 20 years I've been doing this to say, "Wow, three next three years is just going to be just incredible opportunity.

01:08:19 " >> Yeah. >> Yeah. Brace yourself. Face it. Just, you know, get get and get good people that, >> you know, can talk to you about about this stuff. You, you know, us, you know, let's let's just kind of confront some of these topics and, you know, it's just all all upside to me. And that's the positivity. >> Perfect. Yeah.

01:08:43 So, so, so, so tonight nice nice note to to to end this. Mike, thank you so much. Was perfect and and delight to me to to have this conversation. Thank you so much for your, you know, uh, uh, your energy and your vision. Um, it was great. Thank you. Thank you. >> Brilliant. Thanks, Thiago. >> This was the 11th edition of Liinal's Martekch Talks podcast.

01:09:07 Liol is the first Portuguese company specialized in strategic consulting and martekch operations. We have been able to raise the marketing and sales performance of several brands through marketing strategies and technologies such as CRM automation AI and BI. In these martekch talks, we will continue to bring you the interviews with those who are at the forefront of the digital transformation of marketing and sales department so that your business can also grow in this new technological era.