Vijay Kotte: Sorry, I did miss that in my first response so I appreciate you bringing it back up. No, the net impact of changing the plan types, et cetera, that does not change the general reimbursement structure. The benefit design is independent of how we’re compensated.
Greg Aurand: Okay, thank you very much. And then about the constraints issue? Thank you.
Jason Schulz: Yes. No, happy to. So this is Jason. There is a couple of components here, I think, that are worthwhile to talk about. Number one, we have actuarial models that go ahead and produce our LTV calculation. And related to that, there are various assumptions that are built into it. On top of that – and we’re appropriately balanced in our estimation and build in the appropriate conservatism there. The second thing is we have constraints based on both our internal and our external channels. And those numbers – those are individual constraints that are different for each component. The short answer, I’ll get to how to think about this, is if you look at our prepared presentation on our Investor Relations site, if you look at Q3, we have about a 15% RPS decline year-over-year.
And as I mentioned in our prepared remarks, the large component of that decline is related to the changing constraint year-over-year. And so 15% would be an overstatement of the constraint, but it’s within that range.
Greg Aurand: Great. That’s a great answer. Thanks you. I appreciate it. And just a quick follow-up with that just so I understand this correctly. Will this constraint number change if it’s based on actuarial considerations? I would assume it would, but tell me if I’m wrong.
Jason Schulz: We have evaluated at every quarter, we would only change the things if we see something materially differ in the actual performance. And the quarters that are most relevant are coming out of AEP and then also OEP, so Q4 and Q1.
Vijay Kotte: Yes. I think it’s important to highlight in that process that as we assess those LTVs and the constraint, part of it is what you’re seeing in the actual data retrospectively and also the application of management discretion of understand we’re anticipating to come. And those are not going to be seen in the numbers yet. And part of the constraint is trying to see that, right, before the data is telling you. So for instance, all our references to increase shopping behaviors, et cetera, are more – that applies to math more to the constraint of prospective expectations as opposed to what you’re seeing in the actual data itself.
Greg Aurand: Terrific, thank you for the explanation. I really appreciate it.
Jason Schulz: Thank, Greg.
Operator: Our next question is a follow-up from Sandeep Soorya with Delaware Street Capital. Your line is open.
Sandeep Soorya: Hi, thanks for allowing me to ask another follow-up question. So can you talk about your comments about the PlanFit Checkups? You said you did – I think you said you did 5,000, I lost track of that. And the breakdown of the potential responses, can you just go over that? And then my question was, what is the – what do you guys find is the outcome – the proportion of the different outcomes? Is it usually a third, of a third, of a third between the three? Or do you find that the majority of time, what are the outcomes is what happens?
Vijay Kotte: No. I love that you brought it up because we are really proud of what the PlanFit Checkup is doing for consumers. The way – just to highlight it, the PlanFit Checkup is a systematic, technology-driven, standard, uniform experience for the consumer. So, we have live agents that will ask key questions of the consumer that will be fed into the PlanFit tool that is demographically where they live, what are their eligibility status is, which we’re verifying and integrate into the tool. Then we apply their – the physicians or clinicians they utilize, the drugs that – or prescription medications that they are using and need and then their prioritization of other key benefits, vision, hearing, OTC or otherwise. And then through that processing, we will attest that against their current plans as well, and we will present to the consumer, whether the plan they are on according to our proprietary algorithms is a better rated plan versus those that are otherwise available or they’re eligible for in the marketplace.
And again, our scoring also takes into account the quality score, star scores and some of our own proprietary information on retention year-over-year. And then as you alluded to, there are three likely outcomes that you see. One is we find out that they’re on the best plan, right? And there’s nothing to be done and we recommend that to them. And I’ll talk to you about what our agency compensated on secondarily. But they are on the best plan already. There is nothing to be done. We tell them that. They leave with peace of mind and we do nothing. We just tell them call us again next time when you’re ready to do PlanFit Checkup again when plan benefits change or our circumstances change. The second scenario is we make – well, I would say the second bucket is really going to be broken out into two other outcomes.
One is we find that they are not on the right – the best plan available in the marketplace, and we present them some alternative plans that are better for them. And then the consumer made us say, “You know what? I see the difference. It’s not significant enough or I just feel more comfortable saying with my current plan.” We say, “Okay, great. That’s your personal choice. We completely support that. If you ever want to reconsider that or think about other options, you call us back we’re happy to help you.” And again, no enrollment would take place in that circumstance. And then finally, it’s in that scenario where again we presented all the different plans. Plan they’re on isn’t rated as one of the top plans available according to our algorithm, and then our agent presents one of those other plans.
And they say, “You know what? I’d love for you to help me enroll in that plan.” When that happens, we then transfer to our Tier 3, our resolve agents. And those agents then re-verify that it’s the right choice for them according to all that same scoring methodology and then they take the application. So, as you think about the distribution between that it’s – again, we did 5,000 PlanFit Checkups in AEP – or sorry, in Q3. And we’re early in AEP, so I don’t really have great stats to provide you just at this time. But I would say that the key is getting the consumer to get through the entire process because it’s not always about the distribution in each one of those dispositions. It’s more important that we provide a high-quality experience and planted seeds or really kind of helped develop the relationship with consumer.
So we’re building that long-term relationship with GoHealth. So I think that’s a big piece of how we think about distribution. But what I’ll tell you, in all three of those scenarios, when a good PlanFit Checkup takes place regardless of what, as I said, the disposition is, our agent is getting paid. They earn money in all three of those dispositions because they did their job. They built trust with the consumer. And I think that’s the most important piece of the bundle as well.
Sandeep Soorya: Can I follow-up on that in terms of agent payment? So does that mean they get like a base salary? And then how do we think about bonus productivity? Is that – like walk me through, when you say that they’re paid regardless and they can maintain their independence, walk me through how to think about that from a kind of a base pay and a bonus that’s driven by productivity basis?
