Daljeet: Hey everybody, my name is Daljeet, and I'm the CTO and co-founder of cast.app.

Cast.app helps businesses scale Customer Success using automation. Today I'm super excited to have a cast have customer Dallas Frazer from Goodtime.io join us.

We just wanted to have a quick conversation about what Dallas has accomplished using cast.app.

Before we get started, Dallas would like to introduce yourself?

Dallas: Thank you first of all for inviting me along happy to explain what I've done so far, but to introduce myself, I'm Dallas, a Customer Success Manager over at Goodtime. Goodtime is a tool that helps customers with their interview scheduling leads, and I've been at the company there for about eight months or so.

Before that, I operated within the recruiting field, so it was a natural transition for me to go from the recruiting space across to recruiting logistics, effectively helping people schedule interview events.

Daljeet: How did you find out about cast.app?

Dallas: I learned about Cast from a Customer Success Leadership Network webinar where there was a mention of cast.app by another one of your customers, which piqued my interest. I did some searching from there and stumbled across your website.

Daljeet: What problem were you looking to solve when you started looking at cast.app?

It was primarily the fact that as a CSM we have a fairly large book of business, so we're often meeting with customers our customers at goodtime, and one of the things that we love to do is take the time to share back their data to them, to explain to them how many interviews they've been scheduling, how many candidates they've been engaging with, etc.

This is something that takes a lot of time, particularly when you've got a large book of business and we're trying to find a way where we could summarize all of these data points so that we can get them to the customer in like you know in a quicker more seamless fashion so that we can actually spend more time not searching but actually talking about the data.

Daljeet: What you built was a digital business review using cast, which is the workhorse of cast, and many customers' first use case. Our customers then add on additional things. i'm really excited to dive into how you built it. Maybe you could share this cast that you built and just walk us through it in in the cast designer.

Dallas: Certainly so, let me share my screen. So you mentioned the digital business review being one of the the key use cases for cast. We do plan on using the digital business reviews but in the short term what we actually have planned here is for a monthly cast which shares data back to customers, just more consistently. Our customers are often too busy scheduling to actually look at their own data themselves and certainly to look holistically at the big picture so we are planning on a monthly cast going out to our customers and then on a quarterly basis that's when we want to slip into a more traditional business review where we can look quarter over quarter and show their their growth from there. Here in the cast builder what i've done

is a standard welcome scene and then we go through and talk about interviews conducted, unique candidates engaged, monthly active interviewers, the the quantity of interviewers from the company who are actually involved in the interview process, and then a summary. You can see this is intentionally a very short cast just meant to share back the basics of that month-over-month data with our customers as a means of starting a conversation.

Daljeet: Amazing and so i'll just give a little bit more context for our viewers. So this is the cast designer, it's our self-service product and here Dallas has built a cast which is just consists of a series of scenes which are pertinent to his business. I think before we go into those scenes which will be super interesting to look at, how did you kind of connect the data and what data sources did you use to personalize this cast for your users?

Dallas: Yeah so there's actually quite a few different data sets that we ultimately would like to bring into our casts. At the moment we are using some fairly basic ones which are all around usage data effectively usage data from within the good time scheduling tool we have a data warehouse where we consolidate all this data on behalf of our customers. Ultimately it's their data that we're sharing back to them. To build out our cast we actually just used a csv where we exported that data and pushed it to cast in order to build the cast itself.

We have already started the process of using a a SQL connection directly into Goodtime's database in order to pull this information more readily.

Daljeet: Very nice yeah, we connect to over 55 data sources. A lot of them are data warehouses like what Goodtime is using here, which is microsoft sql server, but there are a lot of other data warehouses we connect to like snowflake or redshift bigquery and then a lot of csm tools like gainsight and crms like salesforce. Wonderful, so now that we got the data into cast, let's go into those scenes. Could you share with us one of the scenes that you built and walk us through a little bit of the narrations, how you structured it, and how it comes together.

Dallas: Yeah certainly! So, jumping down here to the monthly active interviewer scene...what we've done primarily is make a bar scene and you can see here in the preview behind. We simply want to show the customer that month over month how many interviewers from their company are involved in actively involved in the interviewing process. So, let me play the Cast.

{PLAYING CAST}

So all of that to say what we were effectively doing is sharing back that month-over-month interviewer data, as in how many interviewers were engaged in the process, and then you would have seen we offered an indication in terms of a percentage increase in terms of for the month at hand to shout out to our customers that that's a great thing they're increasing the amount of interviewers they have engaged in their interviewing process. That was all built out with our narration process here. We can reference the quantity and then also talk about the change.

Daljeet: Now, I'm going to expand. Let's go back to that design tab. You mentioned that the visualization type you chose here was bars, which makes sense for month-over-month data. Would you want to show us that dataset? Can you explain this relation right here and this preview data?

Dallas: What we've done here is we are pulling the data and personalizing that data for the user, so in building the cast we can link the individual user at this particular company. This way we can tailor the cast directly to them and share data specifically to him linking to his company and his data set. i've used that relation there so when we generate the scene for him, it will present data relevant to him and his company.

Daljeet: i'm going to go step deeper and ask so, how did you how are you able to have make sure that this scene is showing data for them it seems like you did something with the relations over there?

Dallas: Yes, so i used a relation between unique customer ids, so the customer id represents the company they work for in terms of that relation we know that the data is relationally linked to this particular id and that id represents the company that he belongs to, so in doing that i can pick up any of our individual users any of our individual team members from our companies that we support and then the cast always knows in the back end to connect that individual user through that relationship with their company data. What this allows you to do with this relation field is make sure that cast is displaying the data for them in this view here which is their monthly data.

Daljeet: If you were to click that preview data... what you're seeing here is data for a lot of customers. So, with relations we can show data for one customer.Let's go into the narrations to figure out how Dallas was able to create these narrations. So, it seems like on the left you were said "Finally there were some quantity of interviewers engaged in the [customer names] recruiting process last month. Could you describe what these fields are?

Dallas: Yeah certainly, so each of these fields I've configured. What i've done is i've in the background, i've gone here to fields and snippets and i've pulled up all of these individual fields and that allows me to create a relationship between each of those individual pieces of data and what I would like it to represent so in that case when i go to monthly active interviewers, when i use that quantity field it's specifically going to pull the quantity of 53 which is the quantity of active interviewers who are engaged from that company.

Daljeet: That's awesome! I noticed in the second one you had a condition actually here so it seems like you're able to create fields and use those for conditions to hide and show narrations. Do you want to share a little bit about that.

Dallas: Certainly, so what i've done here in this conditions field is if i turn this on i've enabled the narration, as in the narration will only occur when all these conditions are met and the condition is specifically that the change between the monthly active interview account you can see here on the side 55 to 60. Only if the change is greater than zero do i want the narration to occur so in effect i want to be able to celebrate with a customer when there has been a positive change within their account and not necessarily call out if we know it has been a negative change.

Daljeet: Awesome yeah so in cast, we call this controlling the narrative. When we create conditional narrations we're able to control the narrative and make sure that we suggest either the right actions for the user and create a positive story.

In the case of this cast for example, I think we got a pretty good idea of how one scene is built from both the visualization piece and the narration piece

Now, i just wanted to chat with you briefly about your other goals around cast. How do you plan on improving this cast and what's coming next in terms of features that you want to add and other use cases you want to do?

Dallas: Yeah certainly, as i've mentioned earlier we very intentionally want like a very basic Cast to be going out to our customers just keeping them in tune with their month-over-month data on that cadence. We have a more standard business review that we're planning on sending out on a quarterly basis. We are also are hoping to grow into a year in review Cast so at the end of the contract period, once the customer has been with us for 12 months we plan on sharing back back that full year and review how much growth they have had in terms of interview scheduling, how many candidates engaged, how many interviewers have been engaged in the process as well as some more specific data like how quick they have been scheduling, whether there's been an increase in their scheduling speed so have they seen efficiencies, and also some comparative data so how their scheduling compares to peers, effectively other companies who are operating in the same space as them. So, lots of grand plans for our end of year cast!

Daljeet: That's amazing yeah i would just say that like a lot of customers, they start small, and then grow over time either into onboarding use cases or what you were saying end-of-year use cases, monthly business reviews, etc.

Wonderful! I would just wanted to say a huge thank you to you Dallas for sharing the Goodtime cast and the Goodtime story with us. i really appreciate it.

Dallas: My absolute pleasure and a special thanks to you Daljeet for helping me build this cast in the first place and helping Goodtime grow how we're sharing this data with our customers.

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