Know what content to send with Predictive Intelligence
5 Minute Read
In Cordial’s second volume of our Predictive trilogy, we answer the question, “what content do I send?”
The stakes dramatically heighten anytime a marketer sends a customer a message. The stakes of providing hyper-relevant content rise, the chances of creating loyal customers elevate, and the possibility to hit your quarterly goal is now a few customers away.
However, there are two sides to every coin.
The stakes of providing completely irrelevant content rise, the chances of coming off too strong elevates, and the possibility of creating a poor-performing campaign becomes real.
In this interview, Bailey Busch, Sr. CSM, talks about how Cordial’s predictive capabilities help answer this tough question: what content do you send that will wow your customer?
If you’re interested in learning more about Cordial’s predictive capabilities, feel free to reach out or schedule a demo.
Read the interview with Bailey
The second part of this conversation is, what do I show these people that we’ve identified and identified as a high engagement user or a low engagement user?
There’s then a question of; now we’ve got to serve them content. So what content are we going to show, and that’s based on what they’ve ordered, what they’ve carted, what their interactions have been with us. So that same sort of data that we’re using to kind of categorize their engagement, but now we can shift our focus to, okay, what have they purchased? And what are these things that are very similar to what they’ve purchased based on the whole universe of contacts that are giving us this purchase data?
So we can then go, okay, this is what a recommended product might be, or this is what some recommended content might be from an article perspective. So this works in many different industries and scenarios. But that’s the part we need to get to is what are we going to show them as the secondary portion?
So it sounds like the power is really in that personalization, right? The power of predictive is in connecting all those data points to personalize content in every message.
Yeah, that’s where the rubber meets the road. That’s the personalization, the content is what is going to ultimately drive the revenue that you’re getting from the program.
And in this realm of predictive, you have this kind of mentality that oh, this is just a black box, and I just turn this thing on and this thing’s gonna start spitting out recommendations. It’s automatically going to do everything we just described. Just find the right customer and give them the right content, and having it in this black box model is a little tricky because it is marketed that way in some cases, so you can find that type of solution. However, that generally becomes problematic in a program as you start to scale because that’s not aware of subtle nuances like, Oh no, his black box is now recommending gift cards to somebody, and we’re not allowed to give gift cards with discounts.
It’s really common that you can say, exclude these products. Sure, you can type them in by hand. But then what happens if there’s just different business logic. So you need to have a toolset that can then adapt to a variety of different business needs, which is going to be we can’t show these things that are low in inventory, and not just out of inventory. One, we don’t want to send an out of inventory product. But we don’t want to even sometimes get close to showing something that’s low in inventory. Or this brand new category that we’re not allowed to then start discounting on until a certain timeframe is passed. So it becomes difficult to scale a model like that in this somewhat black box little scenario that takes into account all of the different business logic that needs to be taken into account as you scale a program,
Otherwise, you’re ultimately creating a bad experience, right? Whether it’s offering a product that’s low in inventory, and they can’t get it, or combinations of products that don’t fit together. Now you’ve got customer service issues.
Yeah, and that’s a good point. Because that could have further downstream implications. If you just adopt some things. Great, let’s take this off-the-shelf thing, plug it in, go. Now we have customer services getting blown up, and that’s a that represents a business cost, and there are discounts that have to be given there and then returns, and that can really hurt something if we don’t have the ability to tune this thing upfront. Having something that you can tune to your business needs and make it something that that you can adapt and grow it with your program as you scale is is the most important function.
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