tag:blogger.com,1999:blog-34368959.post8809706635259721634..comments2024-03-25T04:32:02.396-04:00Comments on Customer Experience Matrix: Campaign Management Is Dead. Here's What Next-Generation Marketing Automation Looks Like.David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-34368959.post-18992500409788708152015-07-04T10:04:26.534-04:002015-07-04T10:04:26.534-04:00Great article. I'll start using the "MADt...Great article. I'll start using the "MADtech" term now too. I think the progress you describe from where we were/are toward "plays" is a fulfillment of the idea of 1:1 conversations ... and by that I mean more the "conversation" part than the targeting. If you line up marketing for a car, say, from broadest TV ad to series of a flow of targeted emails about a specific car someone's had digital interactions with to an actual human conversation w/ salesperson in a showroom, the movement is more toward this "what state is my prospect in *now* and what is the best 'content' - or thing to say - right now." It's what a salesperson naturally does, in real time, making massively complex calculations about the optimal thing to say ... based on his own accrued learning, changing in real time as data points (body language of the would be buyer, what he says ...) continue to flow in to his senses, or the machine in machine learning. More and more MADtech becomes something like a machine having a digital conversation w/ a prospect, understanding the best thing to "say" in a given moment.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-34368959.post-16950481996980647342015-06-29T15:00:32.668-04:002015-06-29T15:00:32.668-04:00Thanks Pini. Your post covers the topic very well...Thanks Pini. Your post covers the topic very well. I look forward to seeing what you come up with. But don't make that machine too smart...I still need a job.David Raabhttps://www.blogger.com/profile/03489754392712536104noreply@blogger.comtag:blogger.com,1999:blog-34368959.post-77510607733621376252015-06-29T14:29:44.938-04:002015-06-29T14:29:44.938-04:00Hi David,
Great piece, enjoyed it a lot.
I truly...Hi David,<br /><br />Great piece, enjoyed it a lot.<br /><br />I truly believe that Optimove is the closest company to realizing this vision.<br /><br />I have already blogged about this topic a few weeks back (http://www.optimove.com/blog/managing-infinite-customer-journeys), but still I want to emphasize that the true challenge is in the data science! Can one make the machine as smart as a talented human, or even more? We have made a significant breakthrough recently and are close to bringing this to life. Wish us luck and stay tuned...<br /><br />Cheers,<br />Pini Yakuel<br />CEO, OptimoveAnonymoushttps://www.blogger.com/profile/04018121213672734646noreply@blogger.comtag:blogger.com,1999:blog-34368959.post-66271895752042667462015-06-29T09:47:42.536-04:002015-06-29T09:47:42.536-04:00Thanks John. There was a parallel discussion of t...Thanks John. There was a parallel discussion of this post on CustomerThink, which prompted a bit more thought on what the next-generation interface would look like. I might as well share it here are well:<br /><br />My guess regarding interface is that there will be a map that shows the flow between states (represented as boxes). Inside each "state box" is a grid with different contexts (events, locations, need-states, etc.) across the top and different customer segments down the side. Each cell then represents a situation in which you'd execute a play. The big advantage here is you could easily see where no play was available or review whether the assigned play really makes sense. (Remember that the premise is marketers primarily think about one play at a time.) You could let the size of each cell represent the number of customers who move through that cell within a given period, and maybe let the color show the value of a successful play (i.e., the change in lifetime value if the play fails or succeeds). That would be a pretty effective heat map for prioritizing your play development. To some extent, you could add complexity by splitting rows or columns to represent more detailed distinctions between situations or segments. This might get ugly from the top view but, again, remember you're most likely to look at subsets while actually working.<br /><br />A roughly equivalent alternative would be to use a tree with the first node branching on situations and the second level branching on segments. You could use size and color the same as in the grid to help with prioritization. The tree makes it easier to add splits that apply in only some circumstances: say, in some situations you want different plays for people of different genders, while in other situations you don't. It's also visually prettier. But I think it loses some of the visual connection of segments across situations, which intuitively seems useful. David Raabhttps://www.blogger.com/profile/03489754392712536104noreply@blogger.comtag:blogger.com,1999:blog-34368959.post-14681029710074366362015-06-29T08:53:49.169-04:002015-06-29T08:53:49.169-04:00David. I agree wholeheartedly with this. From a ...David. I agree wholeheartedly with this. From a systems perspective, you describe a convergence of AdTech and MarTech capability (I'm pleased that you've picked up the MADTech coinage too ;-). From a methods perspective, you've pointed out where humans can still add value. In my work, I found quite early that the nuture stream approach really started to collapse in on its own complexity (Jenga-ish). I think the set-play concept can work and that the sooner marketing organizations incorporate this into both their preparations for Intent based Marketing and SALES, the better they will do. As you alluded to, set-plays are like little bits of code, objects or "services" that can be called on to fulfill a customer need. They can be aligned as a prescribed nurture or they can user-defined, where the user is the customer.John Steinerthttps://www.blogger.com/profile/11675064043080996314noreply@blogger.com