First, search the browser for Pinduoduo merchants' login background, select Pinduoduo official website to open the login, then click the option of Pinduoduo customer service on the right, and select Customer Service Data to open. On the customer service data page, you can directly view the data of store consultation and reply, and click View Customer Service Performance Data to view the specific reply data of customer service.
What is the core data?
1. Pre-sales customer service core data
①30-second response rate: During 8:00-23:00, the number of messages that customer service responded within 30 seconds/the total number of buyer messages.
You know: the reply is ten seconds slow, and the traffic runs out. The customer service doesn't answer, and the promotion fee is wasted. The sooner you reply to the buyer, the greater the hope of retaining the buyer ~
② Average manual response time: During the period from 8: 00 to 23: 00, the average waiting time for the buyer to send a message to the merchant customer service for manual reply.
Everyone's time is precious, and you are not the only one selling this product on the platform. Buyers come to consult pre-sales customer service. If you ignore it, he may choose someone else's shop.
③ Effective response rate: the formula is the total number of consumers who responded effectively/the total number of consumers who consulted×100% =/the total number of consumers who consulted×100%.
Online shopping can't see the real thing, so communication is particularly important. If you can reply to customers in time, there will be more orders. And the registration of many activities is related to this indicator, so do you value it?
④ Inquiry conversion rate: the number of people who come to the store for consultation and finally place an order is calculated according to the percentage of the number of people who come to the store for consultation, that is, inquiry conversion rate = final number of groups/inquiry number.
The buyer's consultation refers to the intention to buy, which can be converted into actual profits as long as it is properly guided, so the inquiry conversion rate is a very important indicator reflecting the professional skills of customer service.
⑤ Customer unit price/customer quantity: customer unit price = sales volume/sales buyer quantity, that is, the average amount of each purchase transaction by the buyer. Number of customers = sales volume/number of sales buyers, that is, the average number of goods purchased by each buyer.
The unit price and the number of customers reflect the customer service's familiarity with the buyer's demand and product association, that is, whether more products can be associated with a buyer's demand and whether more packages can be recommended are important indicators reflecting customer service skills.
6 Complaint rate: Generally, buyers will only complain about customer service if they are not satisfied, so the complaint rate is the most direct indicator to measure customer service attitude.
2. Core data of after-sales customer service
① First reaction time.
② Average response time.
③ Effective recovery rate.
④ Complaint rate.
⑤ Refund rate of disputes: If the platform successfully intervened in the refund and was judged as the responsibility of the merchant, it was a dispute refund form. If the refund rate of disputes in your store is too high, it means that the quality of after-sales service in your store is not good.
After the refund is generated, the customer service can actively communicate with the buyer to deal with the reasonable needs of the buyer. Customer service has played an important role in reducing the involvement rate and disputes in stores.
⑥ Average refund speed: the average refund time of all successful refund orders in the last 30 days.
This part of the after-sales is mainly to make buyers feel worry-free after-sales, so the faster the refund, the more satisfied they are and the more willing they are to buy again.
When analyzing the customer service data of Pinduoduo stores, we should look at it from two aspects. First, the data of the same customer service at different times, and then compare these data. The second is the comparison of different customer service in the same time period, so as to have a comprehensive understanding of customer service.