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Problem: The airline had problems with the speed of reaction to message requests from their customers, which were contacting them via online chat. A not processed message request from a customer quickly turned into a "lost message request", because the customer was leaving the site of the company due to a long waiting for an answer (the expected reaction time to a message request in online chat is 2 minutes). The company decided to take a risk - it disabled the online chat and left social networks and messaging apps as customer support support channels, despite of the fact, that before disabling the online chat 50% of text message requests were coming from it.
Result: The volume of text message requests after disabling of online chat didn't change - it was redistributed towards other channels (social networks and messaging apps). Hence, Belavia has won the battle with "lost message requests", because the contact point of the customer shifted from the site to social networks and messaging apps. When working with social networks and messaging apps, there is a 100% probability that the customer will get a response, the open rate of such messaging is not less than 96%. Transfer of customer support to social channels helped the company to reduce the speed of reaction to message requests of the customers, without making satisfaction with the company's service worsen (the expected reaction time to a message request in social networks and messaging apps is 5-10 minutes). In the mean time, it helped the company to process all incoming message requests with a 100% result.
Problem: The internet shop KupiVip (.ru, .by, kz) had a problem with high costs associated with support of telephony, which their contact-center was using for customer support:
Result: The company integrated social networks and messaging apps for customer support. After 6 months of work, 50% of phone traffic shifted to social channels (social networks and messaging apps), which reduced the costs associated with telephony by about 50%.
Problem: BSB-Bank in summer of 2017 year temporarily lost its license in the National Bank of the Republic of Belarus to conduct transactions with individuals, which caused panic among client, which in turn created maximum load for the bank's contact-center. That day it was close to impossible to reach the contact-center by phone, the customers had to remain in a "queue" (when a robot tells you on the phone "Your call is very important for us, hold on the line), hence even more stress was created for customers.
Result: Based on the situation (peak load on the contact-center), some clients chose alternative communication channel - social channels (that day bank also decided to disable online chat due to the potential effect of "lost message requests"). This tactic enabled the bank to evenly redistribute the load, process several message requests of the customers in a single moment, who contact vie those channels, and respond to all customers, which were worried about the question of safety of their financial savings.
Problem: Before Blinger.io integration, BelVEB didn't support customers in social channels by themselves. It was done by a third-party company, which was also doing their social media. Due to that the quality of consultations on the bank's services suffered (the quality of the response and speed of the response).
Result: Due to Blinger.io, customer support in these channels was transferred to the contact-center of the bank, which enabled the bank to follow SLA, introduced in the bank. The social media company was "fired". A similar case was resolved with Gazprombank.
Problem: All Forex brokers care about the quality of customer support and try to be in close touch with the customers. Many brokers work with customers worldwide. To improve the quality of customer support, some brokers decide to start supporting customers in social channels. The man task of RoboForex and Exness was to redistribute message requests from messaging apps to separate language agent groups - English, Spanish, German and Chinese.
Result: Flexible settings of Blinger.io allowed to redistribute message requests to a specific language agent group in automatic mode, which helped the companies to process the message requests from their customers even faster.
Problem: The problem of the company was lack of fast and effective routing of the customers, which contacted the company from its social channels, and wanted to buy a tour or have already bough it, but have questions. It's important to note, that Teztour is a tour operator, and doesn't sell the tours by itself, the tours are sold through their agents.
Result: The company got the opportunity to connect to Blinger.io all sales office of their agents, convert each message request from social networks into a lead and quickly route it to the closest to the customer sales office. The second benefit - with such an approach, TexTour created a decentralized contact-center from their agents' sales offices.
Problem: MTBank was one of the first in Belarus to start using Blinger.io for customer support in messaging apps and social networks. Initially via this channels the bank conducted only consultation services by the contact-center (issuing bank cards, loads, deposits).
Result: In a year of working with Blinger.io the bank made a decision to scale its service and created full-cycle customer support on the base of Blinger.io (front- and middke- office). Meaning , 1st line - contact center of the bank, 2nd line - transfer to the sales office, solving questions of current clients (for example, technical questions).
Problem: Yandex.Taxi gets more than 1000 message requests a day on various matters from their drivers. The company chose WhatsApp as a contact channel. Yandex.Taxi provided a number for the drivers to contact the company. Customer support agents used to process them directly in WhatsApp Web interface, which caused the company several problems: it's difficult to process these message requests by one operator (WhatsApp Web can be authorized only on 1 device), such message requests are difficult to classify under SLA.
Result: The company contacted Blinger.o, because the solution has integrations with WhatsApp and Zendesk Support- a helpdesk-solution, used by Yandex.Taxi. The company managed to deploy the omnichannel module from Blinger.io in Zendesk SUpport, where the default communication channel is e-mail. With the help of the omnichannel module the company connected a WhatsApp number so that the drivers were able to contact it. All message requests from them started to transform into the habitual for the agents tickets and get processed based on the SLA, approved in the company. As a result, Yandex.Taxi managed to connect more support agents to support WatsApp and make the work process with message requests transparent.
Problem: One of the largest banks in Russia - SOVCOMBANK - had difficulties when working with mobile managers. High staff turnover in this group of employees prevented solution of the problem of low quality of knowledge of the bank's products.
Result: Bank created a support group to solve the questions, which arrived from those managers. WhatsApp became the main communication channel. Now a mobile manager of SOVCOMBANK can contact support anytime via a WhatsApp number, and the agents will receive the message request in the unified interface of Blinger.io. The huge benefit of such communication is in the fact, that agents and managers can exchange other content types, which simplifies communication in many aspects. Besides, lack of the knowledge level of the bank's products for mobile managers is compensated with the qualified support in the headquarters.
Problem: Profi.ru actively searched qualified employees for various projects. Before recruiters used to use the phone for calls, but that used to take too much time, especially if the candidate didn't pick up the phone at the first call attempt.
Result: The company connected a WhatsApp number and activated in Blinger.io the possibility to send outgoing messages to the candidates with vacancy proposals. In that case, the company changed the business-process: a call, if the phone wasn't picked up, outgoing message from the Profi.ru number with links to additional materials, in the case of a response, the candidate is moved to the next stage, if the candidate didn't pick up then you made an additional attempt. Sometimes it's better to send a message before the call if the number is not familiar, so the person would know why he's getting a call.