Overview
In this quick but comprehensive demo, Feifan Wang (Founder of SourceMedium) unveils the official Gorgias integration, designed to transform siloed customer support data into actionable business intelligence. Feifan walks through SourceMedium’s out-of-the-box Looker Studio visualization template, showing how brands can instantly audit their CS performance without needing a data engineering team to normalize the raw feed.
The demo highlights the power of SourceMedium’s “unified data model,” which allows brands to seamlessly swap in other CS vendors in the future without breaking their reporting. Feifan showcases key capabilities like tracking “one-touch resolution” rates, analyzing ticket volume by channel, and—crucially—drilling down into agent-specific performance. He also demonstrates how the underlying BigQuery data access allows for advanced joins, such as linking support tickets to Shopify customer data to see if subscribers are receiving better support than one-time buyers.
Key Takeaways
- Unified Data Model: SourceMedium ingests Gorgias data into a standardized schema, meaning your reporting structure remains stable even if you switch support vendors later.
- Out-of-the-Box Scorecards: Instantly track critical KPIs like Average Resolution Time, One-Touch Resolution %, and CSAT Response Rate relative to previous periods.
- Granular Drilling: Ability to slice data by channel (Email vs. Chat), priority, agent team, or individual agent to identify bottlenecks or high performers.
- Time-to-Resolution Buckets: Visualize ticket velocity (e.g., “most tickets resolved in <7 hours”) with customizable time buckets to match your SLA goals.
- Customer-Level Context: The integration includes customer identifiers, enabling you to join support data with Shopify transactions to analyze CSAT scores by customer value (LTV, subscriber status).
- Open Access: Beyond the dashboard, users get direct SQL access to the
OBT customer support ticketstable in BigQuery for unlimited custom analysis.
Transcript
[00:01] hello everyone this is Fay the founder of source medium today I am excited to announce our official gorgeous integration this is a totally Preparatory integration that we have where we’ve created the ability to ingest all of your gorgeous data normalize and make that usable by the end users whether that is data analysts or data scientist or a visualization
[00:27] template and primarily what will be walking through today is our out of the-box Open Source visualization template on looker Studio as you can see here we have many many different templates that works directly out of the box with our data sets so we won’t have time to go through everything so I’m just going to go through the gorgeous
[00:50] dashboard here and as we integrate with additional customer success vendors in the future you will be able to continue to leverage this same visualization and data model uh thanks to our unified data model approach when it comes to data aggregation so at the very top here we have some um quick help text here as well as some quick
[01:14] definitions and as we add more Integrations you will see more of the logos here and then right at the very top is some of our Global filters most importantly the date filter which filters by either the ti a Clos day or the open dat depending on the metric that we’re looking at and then any filter that you apply here will apply to
[01:38] the entire dashboard we already have a lot of different ways of filtering the data whether that is tickets that are coming from a particular Channel or tickets that have a particular priority or cesa scores or by a agent team or a particular agent as well as whether or not that ticket is being marked as spam I won’t go through every single
[02:04] scorecard here but some of the really cool ones that I like on average how many hours is it taking for us to resolve our tickets what percentage of those tickets are being resolved with just one touch on average how many tickets are we seeing on per customer basis what is our average seat score what is our average csat response rate
[02:25] so that gives you a lot of very very useful high level overview in terms of how the Cs program is doing relative to the previous period and then you can get to some of the Deep dive here underneath the scorecards starting first with the amount of tickets that we’re seeing being created by channel so then of course if there is any spikes from any
[02:49] particular Channel you can very quickly see that as it is the case with all of our time series visualizations you can also drill this up to a weekly granularity a monthly granularity etc etc so we’re defaulting to the Daily granularity here and then to the right of it we’re seeing the overall distribution of ticket counts by Channel
[03:13] and then we can further see how that sort of breaks down when it comes to multi-touch versus OneTouch ticket resolutions we also get to see our average resolution time over time relative to a previous period so if there is any particular spikes we definitely want to be watching out for that but we’re actually seeing a decrease which is a really good sign as
[03:36] well as the average cesas score that we’re seeing down below here we see the distribution of ticket counts by the amount of hours in terms of hours to resolution so this is a bucketed view so you’re seeing for example most of the tickets are being resolved in the first 7 hours since creation but because this is look Studio you can actually also
[04:01] customize these buckets so if you wanted to look at 24-hour buckets or 3-hour buckets you could basically will have the ability to do all of that as well and then down here we have the dist the distribution of tickets that have received a cesa score so good thing is we’re seeing most of the tickets receiving a five but we do definitely
[04:24] want to look into what these one tickets are all about so we’ll be be able to actually examine the underlying data in a couple different ways which I’ll show you in a little bit and then to the right here we also see the distribution of tickets by the amount of messages that they’re generating so in this case
[04:43] we’re seeing most of the tickets are receiving about 3 to six messages followed by 6 to9 so and then second and last here we have a overview of the agent performance by either the team or the actual individual agents so you can see a lot of really cool stats around how many tickets are open closed average seat score and then if you wanted to get
[05:09] to the actual agent level performance you can very easily toggle that here as well and then last but not least is a way for us to access the underlying data that is being shown so all you really have to do here is right click and click on export and then you will be able to export it to either a CSV or Google
[05:33] Sheets but you know all of these particular columns are actually totally customizable so you’re not limited to what’s default here and then you can also do some quick filtering here in terms of open versus closed or everything together this only sort of represents a tip of the iceberg in terms of what you can access through our data
[05:56] sets because you will have access to all of the underlying data in our hosted bit query instance and the table name here is going to be called OBT customer support tickets so as you can see this is going to be at the ticket level granularity and each ticket will be one row of data but they will have a lot of
[06:16] metadata associated with it so what we have done here is we’ve included the actual customer level identifier so you will be able to then join that further Upstream with the Shopify customer data and for example be able to filter by subscribers versus onetime purchasers versus firsttime customers etc etc uh and then you can see that all of the
[06:42] data will be here totally totally accessible so the amount of visualizations that you will be able to make will be a lot bigger than what’s shown in this visualization but we believe that this will be a great starting point for most brands when it comes to visual izing their gorgeous performance excited for what’s to come and thank you so much for your time
