Allison Hartsoe: [00:01] This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue generating customers, then this is the show for you. I'm your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let's go! Welcome everyone. Today's show is about 2019 predictions for customer-centric marketing and to help me discuss this topic as well, me. I am Alison Hartsoe, the host of this podcast and also the CEO at ambition data, and I love CLV, that's customer lifetime value, because it is the connection between the empowerment of customers, predictive data, and the companies who realize that the future is figuring out convenience, ease, speed, ways of being of service to customers and not just shoving more product down their throats.
Allison Hartsoe: [01:14] Marketing in this way really puts customers at the heart of a data-driven business, but it also provides a clear connection between business value, sometimes we call that equity, and marketing actions. So that means we get to an actual long-term ROI business value on marketing, and I just, I just love that because if you've ever been a marketer and you've tried to put a value on the work that you've done, it can be very difficult, and I love this strategy because it allows us to make that connection. At ambition data, we are a company that helps other businesses make this connection. We don't sell platforms or lock you into our technology, but we can tell you how to pick the right data to properly hear your customers such as implementations and reporting, how to learn from them, which is their behavioral intent, highly predictive, testing and analysis, and ultimately how to lead with customer-driven CLV strategies.
Allison Hartsoe: [02:22] e can help you score your maturity or measure the unlocked value of your customer base and use that stratification to make good customers happier and stop annoying the others. Now, why should you care about this? So many people do predictions, especially this time of year. Why listen to me, because I have one superpower, which is, I can stretch to five times my normal size. Nope, that's just after Thanksgiving dinner. But people do tell me that I can see around corners, which I take to mean that I have a pretty sharp sense of where the industry is going, and I think I was attracted to entrepreneurship precisely because I can see those market trends taking shape. I love to think about the future. I love it so much that my signature actually tilts over as if it cannot wait for tomorrow. So let's dig into these fantastic predictions and what they might mean for you.
Allison Hartsoe: [03:26] Prediction number one, insights proliferate like rabbits everywhere. So, you know we've topped out at about 6,800, probably more Martech tools and consolidation, platform integration is starting to take shape. So we're seeing a lot of companies starting to acquire, and we're seeing lots of, um, major platforms just saying they can do it all for you. You might remember back in 2011. There were just a hundred and fifty Martech tools. Now, here we are seven years later and almost seven x that number, so what can we take from this? Well, we know the customer green matters. And think about for a minute who knows that, and especially on the show, customer value, customer empowerment is what we talk about a lot. But when we look at the Martech tools, which companies actually know more about the customer than anything else, and you might immediately think, oh, it's the CRM system, but I actually think there's another powerful segment and I'll tell you why, because I think there's a segment that has privacy nailed and the CRM systems don't have it nailed like this group does.
Allison Hartsoe: [04:46] That group is the email service providers. They have a huge advantage to bolt on to their core systems and become larger plays. Take a look at HubSpot for example, that actually started out as predominantly email and then it expanded out to all these other systems. So these tools, these ESP's, email service providers, could seriously challenge the clunky CRMs. Now you might say, hey Allison, what about customer data platforms? CDPS, and it's like a whole alphabet soup now. But that sector seems to be leaning more toward intelligence or injection systems. They figure something out, and they stick it into another system. I think that's largely because of where they came in the market. They came on late stage, whereas email service providers have been here since, Gosh, at least the 1990's. So this late stage play with CDPs injects data into the system. That makes sense. But what's totally surprised me this year was that several customers told me that their ESP was a marketing data warehouse for them.
Allison Hartsoe: [05:58] When I asked, well, where do you keep all your data? Where do you keep all your customer data? Oh, you know, we use Marketo, we use Bronto, and they named any number of ESPs. Now, what does this mean for you ultimately? Privacy regulations are serious business, and ESPs have handled this for years. They are well positioned to keep you on the right side of the law, and as ESPs expand their capabilities to include analytics and predictions, marketers will face a dilemma of overwhelming insights I believe, each with limited sight, excuse the pun. Now which tool or even which agency should you believe? You have ESP/warehouse. You might have another platform with analytics, you might have maybe media analytics, maybe you have your own data science team. So you've got insights coming from all these different angles, and that means essentially that we've moved from an avalanche of data to an avalanche of insights and still no one can tell you how to run your business, but you. And this is why, CLV driven marketing is so powerful, it helps sort out a multitude of choices, particularly from different perspectives by the opportunity cost. Prediction number two, a data operations cloud fabric.
Allison Hartsoe: [07:25] Now, last year I closely watched the platform plays of salesforce, adobe, and a few others versus Amazon, Google and Microsoft data services. Because I love data, I could never get fully comfortable with the friction in the Frankenstacks, which are the acquisition heavy bolt-on systems that promise the world, but simply don't work well together. You know, force and adobe. Sorry about that. So when I saw Thomas Tongues at redpoint venture put mono clouds in his 2019 predictions, I really perked up. He said, from their infancy through their adolescence, these mono clouds played primarily in the infrastructure world, databases, hosting, compute and storage, but now they're moving into applications. In addition, all of these mono clouds invest aggressively in commercializing research. This *Ola gobbly* on machine learning talent releases advances faster than any startup could. Now Thomas was talking about the startup world and really looking at the services and the way that companies might be just, you know, rolling out a minor innovation sitting on top of one of the cloud stacks.
Allison Hartsoe: [08:52] So his approach was a little bit different. But I think if we look at what Scott Brinker was saying over at Chief Martech, you'll start to see how this fits more into the stack, the overall stack that's relevant for individual businesses. So, Scott who's going to join me on the show in a few weeks, says on his blog post, the average enterprise today now uses over 1000 cloud services across every department. Marketing, sales, customer service, finance, it, hr. And while I'm pretty sure marketing has the largest vendor landscape of all of them, there are hundreds and hundreds of SAS vendors serving all of these other departments too. As a result, innovation is happening not only within individual SAS applications but also how companies are combining them together to create their own unique digital operations fabric. This is why microservices and APIs are, in Scott's opinion, one of the biggest disruptions happening in marketing and business in general, and I completely agree with this concept that there's a limited amount of people who really deeply understand the machine learnings, the AI systems, and everyone who says they understand these are basically calling other libraries.
Allison Hartsoe: [10:18] So when you have these mono clouds investing in the research and sucking up that talent to create, you know, new libraries for everyone to call on, that is an incredible advantage. And then Scott goes on, and, I love how Scott calls it a fabric and Thomas calls it a mono cloud, but as they go on, Scott says, salesforce quickly has gotten the game when it acquired Mulesoft last year. So it's a really big toss-up as to who's going to win, and in any case, I think the number of startups who are coming out and filling up your inbox with all kinds of offerings is probably going to be reduced. In fact, Thomas goes on to say, for hundreds of startups in the software as service world scores of product launches at Amazon's web services reinvent conference where a warning shot across their bows, we are coming, we are coming right after you with tens of billions of dollars on our balance sheet, hundreds of salespeople and the broadest suite of software and infrastructure since Oracle.
Allison Hartsoe: [11:30] Now that's scary. If I were a software company on that side, I'd be. I'd be shaking in my boots after I saw those Amazon web services releases that came out. Now, what does this mean for you? It just means that one, it's not a good time to be a startup in a space where Amazon's, which is, you know, widely acknowledged as the most customer-centric company in the world is how they build themselves. If you've got Amazon coming in trying to go after this space, I would not want to be a software company competing with them, but for you, it means that the cloud is really not just about servers, security and compute power. Start taking a close look, a serious look at the full suite of offerings because you don't want to have to build an analytic stack from the ground up by yourself if you don't have to.
Allison Hartsoe: [12:20] If there all these whizzy wig tools and ways of doing it that that can help you. Now, I don't think they're there just yet, but like Thomas says, it's a shot across the bow, so this prediction number two, it'll be very interesting to watch in 2019. Okay. Prediction number three, personalization is injected with smarter, more contextual nuance. I love this literal quote from E-consultancy. "Personalizing content has been shown to generate a seven-point eight increasing conversions, so it's probably worth trying." How great is that? Now maybe you've been guilty of this. I know I have, I've thinking that personalization is basically binary, either something is personalized, or it's not. For an example, um, an email has my name, some suggested items I might like, or it's a generic message just sent to basically everyone like your local political campaign. Now. Last year I ran across a framework for personalization maturity from Mckinsey that I really liked and it feeds directly into this prediction.
Allison Hartsoe: [13:29] I think about email as I walk us through this framework again from Mckinsey and they talk about five stages. The first stage is broadcast, which is basically the same message going to almost everyone, so that's pretty much the 1990's and, you know, early 2000's. And then we have macro segmentation which is the same email to two or three broad groups. I think those broad groups really matter, you know, if you're looking at people who completed the cart, people who didn't complete the cart, that's an easy place to start. You could also do that by customer lifetime value, which I would advocate as a much stronger way to do it or by behavior, which again is much stronger and more predictive. And that brings me onto micro-segmentation, which is different emails by a group with intent. So I might be looking at people who didn't complete the cart, but when I pull in intent, I can see, well, this person really looks like they were a shopper and this other person doesn't really look like our shopper profile and maybe they, you know, they came in and threw one item in the cart, but we don't really know if they're a shopper or if they're just a one and done, if they've had a long-term relationship with us.
Allison Hartsoe: [14:43] So the micro-segmentation takes an intent and starts to cultivate a little bit more prediction on top of the personalization. And then the fourth stage you have the one to one trigger. This is different emails sent to everyone by trigger. So that means that instead of sending it in batches where you might have some delay, the trigger is very precise and very fast at sending out information. Um, you put this item in your cart, you didn't complete the sale, I'm going to send you a trigger right after that says, hey, maybe you'd like this 10 percent off coupon and complete, come back and complete the cart. So I, I don't know if you've ever actually carted something and just to see if something like that happens. I can tell you most retailers are not doing that, but there are some that are out there that are doing it and and I would expect that their conversion rates are naturally higher.
Allison Hartsoe: [15:41] Then the fifth stage is dynamic, and that means that we've got different email formats, different content going out at different times to different segments. So this is really, really requires a different type of tool. You're injecting a lot of information at the right time and the right place. I really think the dynamic is almost an AI exercise. I'm not sure how you could program enough rules to make that fly. I'm sure you could on a basic level, but in order to really make it powerful, you almost have to have that system be learning from itself as to what's working and what's not. So anyway, those are the stages. Now, I think many companies are doing a blend of these different levels, and that makes sense because you're using different types of email for different reasons is perfectly acceptable. You might have say 40 percent broadcast, the order has shipped kind of notifications and 40 percent macro, the buyer's carted but didn't buy and 20 percent experimental micro, uh, there's a product category intent versus a product confused buyer intent and it gets a different type of email for each type of intent.
Allison Hartsoe: [16:58] This year I think this personalization mix will mature and make a stronger showing around intent, which again is predictive and this makes sense. First, you optimize the checkout path, and then you optimize the quality of traffic coming in, and then you optimize the paths through the store. Then using intent, you can build out search and rescue missions and via paid ads or emails for the individuals who are likely to convert but didn't. So this is kind of a natural progression of where people optimize them. What's the next thing to optimize? The more this is customized and automated for the individual level and sent in a time-sensitive fashion, the closer we get to an increased level of sophistication. That one to one trigger rules on top of rules which are difficult to manage and even harder to anticipate and adjust for edge cases means that AI will eventually make a much stronger showing here, but still we have some distance to go, so the prediction for this year is that personalization is injected with smarter, more contextual nuance, but not that AI takes over all of the personalization yet.
Allison Hartsoe: [18:14] Prediction number four needs a name, I'm going to call it *holiday oil*. You heard it here first. This is new team-based, temporary organizational structures that become more common as organizations need to make changes fast, *holiday aisle*. There is more pressure on organizational silos, and we're seeing more about new agile ways of working. It's clear the data is spread across the organization, but in addition, skills are spread across the organization, and people with certain skills like data science skills can be hard to find. They can be thin across the organization. We need a Holacracy style cross-functional teams to bind and unbind per project. Remember Holacracy was that landmark book written by a software CEO who applied theories of software development, Hello Agile to management, maximum agility and flexibility for an ever-changing market was really the heart of it. Some say what agile lacks is given by Holacracy in the form of purpose and what Holocracy lacks is given by agile, which is organizing the feedback loop and learning from it.
Allison Hartsoe: [19:36] Now recall that the org structure that companies use today actually comes from the railroad. Yes, the 18 hundreds railroad command and control structure, and I guess if you're company moves as fast as a railroad car, then that is the system for you, but if you need to operate in the 21st century or better yet, if your customers expect the convenience and service of the 21st century, then consider establishing an empowering these autonomous cross-functional two pizza teams with a very specific goal such as optimize this campaign's performance while in flight. I'm raised conversion by two percent or even find customers who we are about to lose and help reactivate them. Put these teams together like like small Swat teams and watch them fly. This is a low-risk way of creating powerful focus within a company without having to do an overall boil the ocean reorg.
Allison Hartsoe: [20:39] It's a great way to get things done with low risk, so that's why prediction number four, Holo Jile, which is these temporary team org structures, is going to make more of a showing in 2019 for companies especially that need to make changes so fast. Um, prediction number five, Channel goes out of style of this is wishful thinking on my part, but for several years I've been saying to clients, channels don't buy products. Customers do. When you frame all your data around channels, even multi-channel or Omni channel, it creates this internal naval gazing as people go about adjusting the channel without enough thought to who the customer is and what they truly need. Now, to be fair, it was previously very difficult to connect the data, but this is rapidly becoming a non-issue. Customers expect that you know them and you want to serve them and don't forget that if you don't, they simply go away, or worse, they exercise their power to block you.
Allison Hartsoe: [21:47] The generation is much more likely to do this than Gen Xers like me, but make no mistake. Customer empowerment is here, and the tolerance for inaccurate bad marketing is decreasing. I received a great example of this at home a while back when I received a postcard from a local real estate agent who wanted us to set an appointment with him to discuss selling our home. Okay. Yeah. I get it. The inventory's low and the realtors are hungry for homes to list, but the postcard had a very friendly fashionable couple on the front horse smiling and talking to a guest in front of them. The card says, quote, you've seen your children off. You're gearing up for retirement. Your whole life is in front of you and quote, okay, yes. I live in a neighborhood with an older demographic. Yes. I've seen my kids off, but to school this morning and I'm keeping an eye on my retired parents, but my kids were in elementary school at the time I got this postcard, and we are nowhere near retirement age, in an age where you can simply browse across website, and instantly catalogs for vacation cruises show up in your mailbox. This kind of sloppy marketing is no longer acceptable. Now, okay, local realtor, I give them a pass, but it's really still a great example of incredibly poor, a broad-ranging segmentation that was probably done on the zip code level. What does this mean for you? Don't be that guy when all your internal data reporting sounds like channels. You are not customer-centric, and you risk making mistakes like this that further annoy your customers rather than delight them. Channel-centric analysis says, if we email customers five or six times with the same message, they are more likely to open. Yes! And probably press unsubscribe. The customer-centric analysis says, we notified the whole customer base, but we sent different incentives or personalized messages to customers who love us versus the customers just getting to know us versus the ones we haven't seen in a long time.
Allison Hartsoe: [23:58] So may 2019, please be the year that channel goes out of style and the year that customers really began to take over in the language that we use internally about our data. Now there are two premature trends that I am still watching, so I'm not putting them in my list, but I'm going to flag them anyway. Trend number one is the chief customer officer. And we've seen chief analytics officers, chief data officers, chief marketing officers, and the change is slow going, but we're still seeing, I think it was in February of last year too many CEOs, chief analytics officers, that are being appointed that have no idea what their job really is. So I'm not sure if the chief customer officer will hit this here, but I think it is a very interesting trend to watch because it's more than data. It's more than marketing. It's more than analytics.
Allison Hartsoe: [24:58] It's a person inside the company that has the back of the customer that's saying, hey, how does this affect the customer across the board and how do I create a win-win for both teams, both my organization and the customer. Trend number two is about chatbots. They were really popular last year, and I've seen chat bots just be a great example of ways to engage visitors and guide visitors and oftentimes they're supported by an employee whose role is to understand the current and potential customers through data. I think this is one that's very interesting, but the technology, the heart of what makes it work is still a bit early. There's something inherently risky about having a non-human bot have a conversation with your customers. You might remember in 1950, the test bot, I mean you might not remember 1950 but it goes back to 1950, but there's a test of a machine's ability to exhibit intelligent behavior which is equivalent to or indistinguishable from that of a human.
Allison Hartsoe: [26:07] Yes, I'm talking about the Turing test. And futurist Ray Kurtzweil predicted that the turing test and turn test capable computers would be manufactured in the near future. In 1990, he said that that would be around the year 2020, which we are just on the doorstep of, but in 2005 he revised his estimate to 2029, adding another 10 years, and that is why I think chatbots are fascinating with a lot of potentials, but right now they're still little more than automated fax with good NLP, natural language processing capabilities. So chatbots, great trend, not willing to put a prediction on it for 2019. So let's say I'm convinced I love these trends. I want to do something with it. Hey, great call ambition data. Happy to help you. Free shameless plug. I'm firstname.lastname@example.org. Drop me a line. I'll be happy to send you some information. We can talk some more, but let's summarize on the predictions.
Allison Hartsoe: [27:15] Prediction number one. Insights proliferate like rabbits everywhere, and that's because clv driven marketing can help us sort out those different choices. Prediction number two, the advent of data operations fabric. The solution here is to get closer to the cloud. I can't believe that I still hear some people talking about security issues here. I understand, you know, if you're hp, that makes sense, but this is so 1990s for other companies. It's time to really consider using the cloud unless you have a considered issue where you really can't. For 80 percent of the companies out there, it's probably the right thing to do. Prediction number three, personalization becomes more sophisticated, that means that you need to think through your mix and your strategy now on how you're going to get there. Do you have the right technology? Do you have the right structure to work through a personalization system, which by the way goes hand in hand with testing?
Allison Hartsoe: [28:21] Prediction number four, I call it *holiday aisle*, new Team based temporary Org structures become more common for those companies that need to make changes fast, but don't want to do a giant reorg. You can imitate this with these temporary cross-functional swat teams. It's a great short term win and a fantastic way to show that your team can get really good things done. Prediction number five, this is my, my wish, channel goes out of style, and the customers become all that we talk about all the time, how to serve them, how to innovate for them, and how to make customer centricity the heart of our businesses. Now as always, links to everything we discuss are @ambitiondata.com slash podcast, and if you missed my show summary at the end of the year, you might want to catch that. It summarizes 2018. It's a great way to just kind of pick through the episodes from the prior year and maybe pull out one or two that you might've missed. So thank you so much for joining me today. Remember, when you use your data effectively, you really can build customer equity. It is not magic. It is a very specific journey that you can follow to get results. We'll see you next time on the customer equity accelerator.
Allison Hartsoe: [29:40] Thank you for joining today's show. This is your host, Allison Hartsoe, and I have two gifts for you. First, I've written a guide for the customer centric CMO, which contains some of the best ideas from this podcast, and you can receive it right now. Simply text, ambitiondata, one word to, three, one, nine, nine, six, (31996) and after you get that white paper, you'll have the option for the second gift, which is to receive The Signal. Once a month. I put together a list of three to five things I've seen that represent customer equity signal not noise, and believe me, there's a lot of noise out there. Things I include could be smart tools. I've run across, articles I've shared cool statistics, or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and The Signal. See you next week on the Customer Equity Accelerator.
Key Concepts: Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research
Who Should Listen: CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers