Kinguin improves customer support processing time by 50% with data agents

Key Takeaways

Hello and welcome to the AI impact customer showcase. I'm joined here today by Mario from Kinguin. Mario, will you please introduce yourself? Yes, of course. Thank you very much. My name is Mario Shaginski. I'm head of data and analytics uh in Kinguin. Um King focused on providing the best uh offer of digital version of video games on our e-commerce platform kingwin.net. So we are a company who are a linking pin of the merchants with the customers who are seeking for the best deal. >> What was the main motivation to start your AI journey? >> Yeah. So first of all uh we do not want to miss the train. Yes. uh but on the other hand uh we want to use the proper level of the enthusiasm of the AI. So in short words we would like to implement and use design AI based solution in the areas when it makes sense not because the technical aspects and components uh relies on AI or LLM components. >> It's great. Uh so what what were some of the key use cases or the key problem statements that you were looking at or um evaluating across the company that you decided to focus on? >> Yeah. So we have many frankly speaking hundreds. Yes. Because we started our journey from um writing the all ideas what we as a data analytics team sees but also we ask our colleagues from all departments what they about their ideas yes of using the AI. One example is uh building some kind of uh automations based on data linking uh data joining from or internal sources and external when from technical point of view that two sets of the data are not uh merible it's not possible to join but based on implementing the modern Gemini suit uh AI engines No code solution can be implemented within one two weeks which is great. >> As a AI impact award recipients, one of the use cases that you decided to focus on was helping uh was helping to enable uh employees. You built an employee agent. C can you describe a little bit about what the before and after of that was like? >> Yes. So so you very important point for us. So treating AI uh technology as an enabler. Yes. So uh in that case uh after gathering a lot of great ideas from the organization, we started to focus on doing some kind of uh research PC or or shortly implementing some small part of it and we face it with with some kind of um uh risk or or uh not proper awareness of our colleagues or or simply threat. that that uh AI based solution were caused to the situation that they will need to focus on on finding a job in another company. So shortly they their job their daily activities will be automated by AI which is not right from the perspective of the company strategy goals and the AI based solutions are selective uh can can be used for automate the selection or or some part of the process executed by the human. Yes. So the main benefit is synergy and summing the all small pieces of automation together. >> Can you describe some of the practical use cases that uh changed like the workflow for for employees in the in the company whether it was like searching for like information or like like how like what was the customer what was the customer experience like for the for the people in your company that that were using these agents? >> Yes. So AI agents uh help us or or we treating it as additional channel for example which is treated as access to knowledge database. For example, in customer support team, uh the AI agents significantly allows decreasing the the time for customer support specialist uh to confirm some exact element of of the procedure, exact part of the procedure. Example, customer refound. Customer ask us to provide because of many reasons and we found and there are specific condition dependent on product group product type even the date when initial purchase order was uh created. Yes. Which triggers the end uh answer if refund can or cannot be processed and if yes what are the exact details? Yes. And before introduction the agent the the whole knowledge about the procedure and details of execution such operations were spreaded across few places in our organization. Uh so technical data stoages and right now after typing the proper prompt the answer is within few seconds. >> Amazing. So, so how did you measure success in this in in this in in this use case? >> So, I would say we have two vectors of the success. First is more measurable uh which is represented uh by for example measuring uh two types of the benefits. Uh first one related to performance and time. So execution or amount of the resources uh required to uh perform particular uh process or or or time box on the process set and second one which is uh more difficult to measure is the internal and external satisfaction. Yes. Because one thing is data is uh everything which is related to metric how fast the execution of the prop uh particular process uh is. But second is internal and external satisfaction because one of the main uh motivation or or uh objective what I'm trying to implement and spread across the organization is increase the level of the satisfaction personal interest or possibility to use AI within daily work because if we have more fun during execution of our daily task and and simply doing the job everything will be better for customer us and our colleagues. Amazing. So it it's about it's about you described first being very open within the within the organization trying to like ask the organization like like what are the key problems you want to solve building some trust um up and down the uh the uh chain of command within the company understanding what are the key use cases from them um and aligning that to customer satisfaction scores not not just to the external customer but to the internal customer as well um what uh so many things to choose from what why did you choose Vertex AI in this in case. What were the main reasons for this decision? >> First reason is simplicity and secondly very high uh number of the benefits due to synergy with our existing uh processes or or architecture of the platform. Yes. because almost 100% of our transactional uh system components so the the heart of it is kubernetes in is based on Google cloud platform. So decision about using vertex AI allows us to implement seamless integration uh which is counted sometimes in minutes. Yes. And second uh or third one very big benefit is is uh safe environment with very good suite but of control from the organization perspective and flexibility in terms of the usage it by our colleagues from for example operations. >> Amazing. So now that you kicked off these initial PCs in the in the company what's next? What what does the future hold for you? Yeah. So shortly we are going to continue. So we would like to continue the journey and uh do the same things but uh more and better uh because uh production implementation or or first phase of it was happened a couple of weeks ago. So this is the very fresh in terms of some kind of vision. What I would say uh for for next year is uh treating AI agent as additional channel for data access. We have very advanced analytics which is based on looker. So this is business intelligence service and we have more and more data components and having possibility to ask our agent uh for for example correlation of one part of the analytics let's say sales with the marketing and have the result within few seconds is amazing from the data perspective linking that two business area is not easy >> amazing so thinking about how you not just expand uh the use cases but how you can make the use cases applicable across the organization. Amazing. Um leveraging everything that you've learned so far like in your in your in your journey here, what would be the the key takeaways for a decision maker to take to start implementing a PC or a solution in the next 30 days? >> Yeah. So firstly, uh prepare some choose the lowhanging fruit. I know that this is the common term but I I do not have any better idea which came to my mind. Yes. So start something uh which result with high number of the benefits. benefits can be countable or can be uh related to this this personal and company expression about the capabilities of the AI and do something even small part present something and my second advice and takeaway is invite colleagues from the other department to be a part of this development stage and thanks thanks to Vertex AI we have a few uh ways of creating the new uh agents based on the nood solution which is great amazing. So starting simple look at a very simple business case um and this idea of like keeping it cross functional like invite cross functional teams across the business to understand how the use case could make sense getting some feedback from them. um you touched you touched a bit on um the adoption curve like like how how to earn trust with the organization, how to educate the organization on on the benefits of how AI can improve the business case um as well uh of of course like how it can advance the workflow. I I did want to ask you like what were some of the components that you you used to help educate the organization especially where there may have been some kind of resistance. What were some of the strategies that you implemented and you would recommend? So the components I would say is some kind of organic work based on the meeting awareness meetings uh awareness session at the company level uh daily cooperation with the leaders enthusiasm and people spread it across the organization who are really keen to use this new piece of technology. Yes. And based on some iterations, increase awareness, perform some piece of the solution, check the result, and start again. >> Excellent. Mario, thank you so much for joining us today. Thank you very much.