Marketing is undergoing dramatic change, driven by shifts in technology and the availability of digital data. Among the most significant changes is the heightened ability for marketers to discern what customers and potential buyers care about and then act on that information.
Marketers today are watching as buyers leave digital tracks - the web pages they view, buttons they press on mobile devices, comments they leave on Facebook or Twitter. By observing how consumers act, marketers can learn what buyers care about and what is important to them.
By aggregating this digital data, and applying the right algorithms, marketers can recommend products, deliver interesting offers, and create personalization to segments of one rather than to batches of thousands.
Machine learning is well-suited to this type of data aggregation, analysis, and recommendation. To learn more about the role of artificial intelligence in marketing, I spoke with two experts. Sameer Patel is the CEO of Kahuna Software, and Andrew Eichenbaum is Kahuna's head of science.
This conversation was episode 209 of the CXOTALK series of discussions with the world's most important and prolific innovators.
If you care about the future of marketing, AI, and machine learning, then dig into this discussion.
Watch the video embedded above and read an edited transcript below. Or hop over to CXOTALK and check out a complete transcript from the entire 45-minute conversation.
What is Kahuna Software?
Kahuna software is a B2C marketing automation provider. We have built a real-time platform that allows brands to be able to understand the interests and preferences of their consumers. Literally within seconds, and put meaningful offers in front of them. This is the new way of using artificial intelligence to engage with your consumers on the right device at the right time.
We look at convergence and the need for consumer brands to rethink how they engage and transact with the consumers.
We're in this new era, where you can market to anybody, probably 14-16 hours a day. People are that connected to their cell phone, it's always there, there are multiple channels to reach out to them, and that's all through one device. This connectivity has become ubiquitous, at least in the US market, over the past five years.
Now that being said, it's easy enough to spam them, and nobody wants to do that because people have become hypersensitive to spam. So, it's not just not sending them spam; it's knowing what to send them when to send to them, how you send to them because there's a range of things. What message do you want to send to them? And, it just extends out.
We're now in an area where we can think about trying to increase the expected long-term value of all my customers. I want to increase their overall engagement stake, and this is what marketers can now reach to. It was a nebulous goal before but is now something we can move forward and try and act on.
Is this just marketing automation?
Marketing automation was created a decade ago. How does stack up? The market's over ten billion dollars in size, yet there is over two hundred and eighty billion dollars of goods left in abandoned shopping carts every single year. Two hundred eighty billion dollars.
That's how much you and I go, and we almost buy, and we put it in the shopping cart, and we leave it there. You're effectively nudging the consumer to the finish line, or providing them with handholding, information and research that might persuade them to finish buying.
The conversion rates are 2-3% on e-commerce. That's how bad it is. All this investment in what seemed like the right offers lead to 2-3% of conversion.
The point is tailoring the message, timing, and channel to the consumer?
That's the bottom line!
How does AI help?
Artificial intelligence can solve many problems. The question is can you define what you really want?
Most of modern AI are "supervised learning systems." We have historical data, where we know the outcome. So, to get a particular outcome, we should look at this data beforehand. It's a constant process of refinement and improvement and improvement.
The point of all of this in plain English is moving from "lazy segmentation and coding." The technology has never been sophisticated, so we kept putting random people into buckets just to make ourselves feel good that these segments matter. The goal is moving in a direction where you're starting to engage and transact with an audience of one.
Data is great and is the center point of data science. But, if the data is junk, the data science coming out will be junk as well, so a large piece of any data scientist's life is making sure the data's coming to the system is properly being stored, planned, and verified. So we can believe in the results coming out because if not, why bother doing it?
How is this different from traditional marketing?
Historically, we would figure out the optimum time to send emails or notifications to the entire consumer base. You can plot out over time for the entire base that it's best if we send out the last email at 10 AM on Tuesday morning. And so, we backed out, starting a little beforehand until then, and it was all good. We could see a significant lift.
But now, we can do this on an individual basis. We saw an individual come in and respond to messages, or not respond to messages, through various channels over the past couple of months. And we know how they respond to what type of message.
So, we're no longer blasting out to the entire group at a single time of the week. We can set up our campaigns so that an individual user will be sent out right before the expected time or act, and in a public channel. We have a huge amount of processing power, so it's not the limiting factor anymore.
Kahuna processes half a billion events today. It's a linear, scalable system in the cloud, so we can expand simply by adding more computerization as we get more data.
Email delivery systems built a decade ago are the predeccors of Kahuna. We've now reached a point where the number of engagement and touchpoints for us as consumers has gone from one, which was email, to many. And we haven't even seen this play out yet.
Today the dominant ones are still email and mobile; SMS. We're going to have beacons tomorrow; we're going to have IoT after that, we're going to have chatbots. The places where we engage are going to increase.
Every one of those engagement touch points is going to start sending different events to us, that email never sends back. We can accept signals from these different engagement touchpoints, make sense of them, add them to the user's profile.
Machine learning can tell us that the right way to engage with Michael is 7 PM on Thursday nights, via email, because that's when he seems to be on his laptop and he seems to want to engage and buy stuff.
The technology has fundamentally shifted from what were nothing more than email delivery machines. Batch and blast, batch and blast, batch and blast. We allow marketers to engage in a fundamentally different way.
This notion of being cross-channel at your core, where you can engage with people in a way that respects the pace at which they want to go through this journey
This is a problem that every marketer and frankly every CEO and consumer brand have been waking up to for decades, right? The amount of money they plow into customer acquisition costs, to drive new customer acquisition, has only gone up. But the amount of focus and available technology, once they've acquired that customer, to get from that, from the point where you are now a customer to the first purchase. What is the actual work required to get you from first purchase to second purchase?
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