Forecasting & Planning for Facebook Messenger, Whatsapp, Telegram, Viber, WeChat, Line…

Messaging is the most frequent digital activity that a person does, there is no contention. If you’ve ever messaged someone to ask if it’s a good time to give them a call, you’ve already experienced this shift.


The meteoric rise of Facebook Messenger, Whatsapp, Telegram, Viber, WeChat, Line, and IMO (if you live in Turkmenistan) has been nothing short of stunning. Why has this adoption rise occurred, how are customer service areas responding and how do you forecast and plan for this channel type – all questions I hope to provide some insight into with this article.


Firstly, let’s start with defining this channel type. Facebook Messenger, Whatsapp, Telegram are all examples of asynchronous communication. This is opposed to Phone, LiveChat, or face to face conversations which would be classified as synchronous communication.


Synchronous communication is immediate with people responding to information at the same time that it is shared with them. Asynchronous communication is simply where you don’t respond to information as soon as you receive it. We’ve actually being using it for millennia by sending letters across the world.


In customer service the rise of the internet in the 1990s and 2000s brought the email and livechat channels to life and moved the call center to the contact center. 

Enter the rise of the mobile messenger apps – according to a Hoosuite 2019 report WhatsApp, Facebook messenger, and WeChat (the top 3 most popular apps) have 4 billion monthly active users. The rise of the smartphone has of course been a driver of adoption, but the hectic nature of modern life has also had a major influence; people do not want to spend large quantities of their time on the phone, computer or mobile waiting for a livechat to conclude. For those who consider time their most precious resource, the asynchronous nature of messaging may be appealing; they can respond whenever it’s convenient, send a message from their mobile device, and then continue with other tasks whilst waiting for a response. Because these conversations are not carried out in real time there is less  expectation that you will get an immediate response.


Similar to Email (another form of asynchronous messaging) this channel type also reduces failure demand because the conversation history can be saved within the case history, something more difficult to achieve with a phone call or a live chat since the conversation is session-based. A solid CRM system and speech/text analytics can certainly help with synchronous communication challenges but it is still common that customers are required to repeat information the next time they contact customer service. Asynchronous messaging on the other hand eliminates this inefficient repetition, allowing agents to gather customer information faster, engage other colleagues and solve the customer issue that much sooner.


So, there is no doubt that asynchronous messaging or fire-and-forget conversations provide customers the convenience that matches today’s hectic life’s. It is therefore surprising that adoption so far in customer service has not been as meteoric as it has been in people’s everyday personal life. However, with launch of WhatsApp for Business, Apple Business Chat (which enables customers to talk to businesses via the Messages App) or the advantages of implementing in-app messaging strategies to both App adoption but also the ability to identify customers thereby offering a smoother experience, I predict this is about to change very rapidly.


The main challenge for a contact center to truly adopt asynchronous messaging lies in leveraging context, duration/timing, and business rules to determine if a message is part of an existing dialog. It is also important to enable seamless transitions between automation/bots (AI and directed dialog) and deliver the entire asynchronous messaging conversation to an agent.



Dynamics and Behaviours of Asynchronous Messaging

The first step in forecasting and planning for a customer channel is to understand its dynamics and behaviours. So, let’s break it down…


Like the email channel asynchronous messaging conversations are long-lived and can span days, meaning there is the possibility that more than one agent or self-service would be handling the same case. Defining assumptions around desired length of time an agent stays dedicated to a case is critical e.g. if a customer has not responded in the last 2-8 minutes should the conversation be “timed-out” to ensure agent capacity is efficiently managed?


Asynchronous messaging is not real-time, so no need to apply occupancy assumptions meaning it has significant potential to decrease staffing requirements vs say Phone or livechat channels.


The fact that messaging can be asynchronous means that the pressure of service peaks can be spread out.


Asynchronous messaging shares similarities to Livechat in that agents can deal with more than one conversation concurrently.


Asynchronous messaging when combined with intelligent routing has greater potential to apply case ownership principles.


When setting how quickly you want to respond to the last customer reply, it’s worth noting that with asynchronous conversations not every interaction is like for like i.e. like email customers expect a quicker response to their first inquiry but are willing to wait longer between messages once they are confident their inquiry is being dealt with. Unlike email however there is a greater expectation that you won’t be waiting days for a reply – so critical to have regular checkpoints to reassure the customer their query is still being dealt with. Mapping these checkpoints is critical for forecasting purpose to understand channel hopping behaviour.


As a minimum you need to break contacts into two broad classification – first contacts (that require a quick response) and the rate and frequency of the subsequent contacts related to that conversation end to end. You will find each type of contact reason will have a different pattern and number of subsequent contacts. Thus mapping out these contact reasons is critical to ensure you are not lost in an average. With this in mind forecasting at both the interaction and the end to end conversation level is important depending on the granularity of the forecast i.e. the closer you get to real-time the greater the importance to adopt interaction level forecasts.


Like email Handle time can be broken into three components: time spent actually writing to the customer, time spent researching answers or enacting a process, and wrap up time to complete case notes.

From a WFM perspective there is lot yet to be discovered and understood about how to forecast and plan for Asynchronous messaging – there is no best practice yet established. What is however certain is that this channel is set for big growth in customer service in the coming years and I for one look forward to embracing the challenge with open arms.

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