Many companies lack qualified personnel. Management has to take on many additional functions, and minimal time is left to control the team. However, this task can now be entrusted to artificial intelligence. Read the case study on how Ringostat conversational intelligence saves Keramis several working hours daily and money on salaries. After all, AI performs functions that would require several additional employees.
About the company and why it became interested in artificial intelligence
Keramis is an online tile and sanitary ware store. Also, on its website, you can find:
- floor coverings;
- wallpaper;
- lamps;
- door.
The store has branches in all major cities of Ukraine: Kyiv, Kharkiv, Dnipro, Odesa, and Lviv.
The peculiarity of the Keramis niche is the small number of repeat customers, as people mostly make repairs once every 10–15 years. Another challenge is that automating many processes in the plumbing industry is difficult. For example, it is impossible to receive stock balances for all suppliers automatically. Therefore, even if the product is available on the website, the customer has to call to check its availability. Because of this, 70% of orders are received by phone. Also, a sales rep’s advice is often needed because the repair is a large-scale event and involves many products. During a call, a specialist can identify the client’s needs and offer precisely what is needed.
Given the vital role of calls in business, Keramis wanted to understand what kind of advertising generates them. Therefore, they first turned to our platform for call tracking.
What data does Ringostat AI provide to control the work of employees?
Ringostat’s artificial intelligence analyzes each call and collects the results of the analysis in:
- Ringostat report — where key information is displayed when you hover over the desired column;
- the call card that opens when you click on the date and time of the call — here, the data is presented in more detail.
The information provided by artificial intelligence is mainly used by Keramis’ co-owner. Although the company is currently unable to implement a quality control department, AI helps to notice all the crucial points in time. The sales reps themselves also work with conversational analytics data to understand and eliminate their mistakes.
As described in the article, AI analytics can be flexibly customized in your Ringostat account. However, the basic settings available by default are enough for effective control. Let’s examine how Keramis uses them to improve its work.
Call transcription and call summary
Artificial intelligence automatically converts audio conversations into text and translates them. Therefore, you don’t need to listen to the dialogues — you can quickly view their transcripts. And by clicking on a specific line, you can hear how it sounds in audio. For Keramis, call transcription is incredibly convenient because employees can copy the product code from it, which the client or sales rep has voiced.
Keramis can receive more than 300 calls in a week. The company’s co-owner can’t listen to or read all the dialogues because he has other responsibilities. However, this is unnecessary with AI, as the solution automatically captures the call’s summary. To see it, you just need to go to the call card or hover over the corresponding column in the AI report.
Artificial intelligence also provides separate lists:
- a structured summary of the call;
- key points of the dialog.
Thanks to this, Keramis can save time on monitoring. You can quickly review the call summary and immediately understand whether you need to listen to it or read the transcript.
Evaluation of the call, identification of errors and positive aspects
Ringostat AI takes into account that any conversation usually consists of four stages.
- Opening. The online store representative should say hello and name the company at this stage.
- Anamnesis. During this stage, the employee identifies the customer’s needs, asks what product they need and for what purpose.
- Presentation. At this stage, the sales rep describes the goods and their benefits to the buyer.
- Closing. At the end of the dialog, the sales rep should talk to the client about the next steps. For example: “I’ll send you a price list on Viber right now. And I’ll call you back tomorrow at 15:00.” The employee should also politely say goodbye.
Artificial intelligence understands the current stage of the dialog from the context of the conversation and considers whether the employee has voiced all the necessary remarks. The fewer mandatory actions an employee takes, the lower the score for the dialog.
Based on this, the company’s co-owner can immediately see problematic dialogues and analyze what went wrong. The AI shows the sales rep’s mistakes in a separate block and additionally detects whether the employee uses parasitic words or foul language. The Keramis team then analyzes all the employees’ mistakes in online meetings.
AI also records what went well in the conversation. This information can be used to emphasize employees’ strengths when handling calls. Or use examples of successful dialogues when training new sales reps:
Analyze the mood of the conversation
Even if you follow all the steps, a client may still be disappointed. For example, if they were put off by the sales rep’s manner of communication or if the consultant remained indifferent and inattentive even though he formally voiced all the necessary remarks.
To detect such cases, AI captures the general mood of the conversation and the mood of the sales rep and the customer. The Keramis co-owner pays attention to this when analyzing the dialog. This way, he can quickly notice a service problem and fix it.
Next steps
Artificial intelligence automatically analyzes the agreements between the sales rep and the customer. Based on these, AI makes recommendations on the best next steps. Employees use these recommendations in future conversations with customers and to remember what actions to take after a call.
Keywords
AI records what topics or products were discussed during the conversation. It displays this information in the Keywords block. Data on such words can be used to customize contextual advertising. It will also help you understand the products discussed during the conversation even faster.
Transferring call data to CRM
Keramis transfers almost all the data artificial intelligence collects to its CRM system. For this purpose, they use Webhook, a mechanism for notifying third-party systems about events in Ringostat. This technology sends the data to the Keramis server, and from there it goes to the CRM. Also, thanks to the integration, all calls made and received by the teams are recorded in the CRM system.
Artificial intelligence data is transmitted as comments or immediately attached to a lead or transaction, depending on what is open in the CRM system for a particular phone number. The sales rep also automatically receives AI recommendations and a list of errors in the CRM.
Thus, Keramis management can analyze almost all calls related to specific transactions. Often, it turns out that the employee was not persistent enough. The team quickly reviews and corrects such cases, which helps to bring customers to the point of purchase.
Results of Keramis and Ringostat cooperation: customer feedback
Thanks to AI, we can cover all dialogs and free up much time for other things. Artificial intelligence replaces almost half of the service quality control department for me, i.e., a couple of employees. On calls with the team, we discuss AI analytics and improve customer communication.
The result is a gradual increase in the conversion rate from calls to sales. It was disappointing initially, but I hope it will soon double or more. Speaking of money, it is a lot. Therefore, according to our experience, AI pays off many times over.”
