According to statistics, e-commerce companies were leaders in AI adoption in 2022. Now, when the pace of this technology is only growing. 84% of e-commerce representatives are either actively adopting AI or consider it a priority. So what makes such a digital assistant so attractive to businesses? Ringostat’s team discusses the benefits of using AI for e-commerce call handling examples.
- Example 1: assessing only those conversations that really need to be analyzed
- Example 2: Contact center control for e-commerce operating in different markets
- Example 3: advanced level — analyzing conversations by dozens of parameters
- Example 4: the near future is a chatbot that learns from your team's experience
- Conclusions: what opportunities AI brings to e-commerce contact centers and sales teams
Example 1: assessing only those conversations that really need to be analyzed
Sales departments in e-commerce often handle dozens or hundreds of inquiries per day. And here the management faces a severe problem. On the one hand, without full-fledged call listening, controlling how employees communicate with customers is impossible. On the other hand, analyzing such numerous calls is also impossible. Therefore, the manager has to listen to conversations selectively, and there is always a chance of missing something important.
Knowing about this issue, Ringostat has found a way to solve it. Artificial intelligence, unlike humans, can quickly analyze large amounts of information. Therefore, it can point out exactly those challenges that require management attention. There are already solutions built into cloud telephony that work according to this principle:
- the virtual PBX makes a recording of the conversation;
- AI decodes the recording into text and analyzes it;
- based on the meaning of the conversation, it makes a conclusion about the general mood of the conversation, as well as about the mood of the customer and the operator separately;
- records the mood of each conversation in telephony reports.
This allows the supervisor to immediately see dialogues where something went wrong. It is enough to set up a filter that shows only those conversations where the client is disappointed. This can happen, for example, if the AI “understands” from the dialogue that the customer is dissatisfied with the quality of the consultation. Or the customer, for example, is upset because they were not offered an alternative to a product that is out of stock.
There is another option for management to understand what to pay attention to. AI can infer whether a customer request has been resolved. For example, if a customer:
- tried to find out if the product was suitable for use in certain conditions, and the sales rep could not answer and promised to check with colleagues;
- asked several questions, and the employee did not answer all of them;
- called to complain about a faulty product, but the employee did not offer a solution, such as a replacement or a refund;
- wanted to exchange a product but did not receive comprehensive information on how to do so, etc.
After listening to such calls, you can find out whether, in a particular situation, there was an employee’s deficiency. Or whether the client’s task really cannot be solved. For example, if he wants to buy a product you don’t sell.
Example 2: Contact center control for e-commerce operating in different markets
AI can “erase borders” when it comes to analyzing calls from residents of different countries. Hiring native-speaking operators in foreign markets is not a problem, but it is much more difficult to supervise them. You must find a separate supervisor for each country, which is not always justified. Especially if a particular direction is just being developed and there are few calls from local customers.
Thanks to AI, this is no longer a difficulty. As we wrote above, such solutions turn audio conversations into text. But they are also capable of translating dialogues into English.
For example, a supervisor from Poland can quickly analyze an operator’s conversation in German and then a dialogue in Romanian or Italian. It is enough to understand the English subtitles that are automatically added to the conversation. For example, this is how Ringostat’s sales manager controls the call handling of employees working in Bulgaria with the help of our AI.
The availability of transcripts is especially useful when a supervisor needs to analyze only dialogues about a specific product. When necessary, he can check whether the employee mentioned a promotion, discount, free delivery, etc., in the conversation. It is enough to set a filter on the desired phrase, and only those calls where it was mentioned will be pulled into the report.
Example 3: advanced level — analyzing conversations by dozens of parameters
In primary settings, AI can still do a lot of valuable things. For example, noticing an employee’s mistakes or advising the best next steps. For example, to study competitors’ offers more deeply to emphasize the advantages of your online store. Or to call when a specific product arrives in stock.
AI can now be configured individually according to the needs of a particular company. Thus, you can get a comprehensive assessment of the contact center’s work. Not by general parameters but by those that are fundamentally important in your business processes. For example, one of Ringostat’s clients, a consulting company that helps build sales departments for e-commerce companies, asked Ringostat for such a solution.
Let’s briefly examine how the AI can perform under these settings.
- The company needs to determine which lines or questions the employee must be sure to voice during the call. For example, it is necessary to say hello and say the name of the company, identify the customer’s needs, ask how the customer found out about the online store, etc.
- After the call, the AI will check the course of the dialogue with the script of the conversation. If the sales rep voiced the right line, the solution will assign 1 point for it. If not, it will be 0.
- The AI will assign an overall score at the end of the conversation. This is another way to quickly find dialogues worth paying attention to.
- The AI can also rate the operator’s overall performance by analyzing the scores for his or her conversations.
This approach allows you to identify problems systematically occurring in certain employees. If you notice it in time, you can tighten the manager’s skills and increase his conversion from a phone call to a sale.
Example 4: the near future is a chatbot that learns from your team’s experience
AI-based chatbots already exist to relieve the burden on the sales team. Such solutions have not just pre-prepared answers to the most frequent customer questions. From the conversation context, they understand the “usefulness” of the request for the company, segment leads, and decide which ones to pass on to an employee. This approach lets you filter out untargeted traffic and provide basic answers to questions without human involvement.
In the future, this system will be improved by learning from all possible sources of information. Let’s say a company has documentation on products or a knowledge base about them, data on phone conversations and chat conversations. The AI will learn product characteristics and pick up techniques from highly rated dialogues. This allows it to serve customers just as well as a human — and sometimes better. After all, the digital solution does not suffer from mood swings, inattention, or forgetfulness.
AI-based chatbots can also be used for additional tasks.
- Automatic classification of requests by subject. This allows you to quickly find all dialogues on the same topic — for example, about the return of goods or wholesale purchases. Thanks to this, the head of the sales department will be able to analyze such requests and develop an optimal conversation scenario for the team.
- Identifying and solving customer problems. AI can identify keywords and phrases in the conversation that indicate that the customer has specific difficulties. For example, if a client says a product doesn’t match the description, the solution automatically creates a return request.
- Helping the customer choose a product that fits their needs. A chatbot can ask customers about their budget, goals, and interests and then suggest suitable products.
- Checkout. It’s not uncommon for a customer to not understand how to order a product — even if the site is quite user-friendly. Or he doesn’t have the time or desire to do it themselves. AI can ask customers for their contact information, shipping address, and payment options and automatically place the order.
Conclusions: what opportunities AI brings to e-commerce contact centers and sales teams
- Automated call analysis. AI can quickly and accurately analyze large volumes of call data to identify problematic issues in manager-customer communication. This allows contact center employees to quickly learn where action needs to be taken to improve service.
- Monitor contact centers operating in international markets. AI can translate conversation text into English, allowing supervisors to monitor agents working in different countries.
- Advanced conversation analysis. AI can be configured to custom analyze conversations based on a company’s specific needs. This provides a comprehensive assessment of contact center performance and identifies problems that would be impossible to spot using standard settings.
- Offloading the sales team. In the future, AI will be used for an ever-widening range of customer service-related tasks. For example, AI-powered chatbots can provide more personalized assistance to customers, solve their problems, and even place orders.