Posted On: February 3, 2020 | 7 mins read
When was the last time you used your smartphone to place an order? Was it this week? Was it today? When sales and buying journeys are omnichannel, customer support must match pace with omnichannel support and experiences.
With this windfall, companies should structure-in, intelligent and omnichannel customer support right now.
A channel is an interactive open mode of information or ownership transfer between a customer and company. This channel is, simply put, a touchpoint through which the customer gathers and transmits world information, and a company (or any entity) interacts with the customer.
This channel can be through a smartphone, a website, a television or magazine adverts, a retail store, an event, etc. Today, it’s mostly used in two ways: for marketing (communication) and sales. A customer can research any product/service using any channel. They can make a subsequent buying decision using any stationary or direct channel (for example, retail). They can also use an online and indirect channel to do the same.
Companies use either multichannel or omnichannel support and sales strategy for their value proposition. The fine difference between the two is the ease with which a customer moves from one channel to another.
Companies chart out how and in what sequence (of digital or live interactions) does a customer experiences the product/service. This traditionally goes through awareness right through action and then satisfaction.
This journey of a customer from the point of interest to the point of satiation is important. The better control a company has over this journey, the higher the chance they have to predict and direct customer behavior.
Apart from inducing buying behavior, customer journeys are also very important in addressing queries and implementing customer support.
Companies must ensure perfectly-defined customer journeys, interwoven with a robust knowledge management system (KMS). This, and only this, can ensure omnichannel customer support.
Let’s quickly run through a scenario to get a grasp on the concept (as they say, contextual learning is very effective). Sam just bought a laptop from a top e-commerce app, using his phone. He tries to turn it on but it wouldn’t respond. Getting a little irked and concerned, he checks on the customer support through his smartphone.
Now, as he clearly preferred his phone even to place the order, his first instinct to go back to the same for his query or grievance (depending on how the next few minutes go) redressals.
Sam gets annoyed and makes a mental note to always check the customer support options before making future purchases. Either the company somehow understands Sam’s problem within moments, or they don’t. Sam somehow gets hold of a representative (or even an email id) after scouring through the e-commerce website. Now, either the email response he receives is a bland automated reply (or none whatsoever), or he finally gets a fact-finding request from the company. Even on the call, either Sam’s concern is acknowledged by them, or it isn’t. In any case, Sam is starting from a point of ‘annoyance’.
Either the response is automated through an AI-driven information gathering Chatbot, or it’s an actual representative. Sam’s problem is isolated immediately using a ‘Decision tree’ matrix. If the problem/resolution is simple (like putting in the charger or the likes) it’s resolved by pulling out the relevant literature from the KMS. If the problem is complex, it’s seamlessly escalated to a more experienced team for further interactions. Here, the current state of the problem is recorded within the system and Sam doesn’t have to brief the next person in any manner.
You may prefer the second scenario, as a company and as a customer. The main benefits here with such an omnichannel customer service journey management and support are:
… and most importantly,
Companies can ensure omnichannel customer support self-care using decision-tree led app interactions/chatbots or assisted care using hybrid customer support and AI-driven natural language processing (NLP) (just a few among other solutions).
Here, the most important point is that it’s the customer’s choice to interact and process their concerns through a medium they want. There’ll always be product or service errors that induce customer dissatisfaction. If a company engages with these problems head-on using an exhaustive knowledge management system, they would be able to tackle all foreseeable issues within an appreciable time frame.
The main takeaway here is that the expectations of a customer, like Sam, coming in is elevated as they move through their support journey. If the channel ease, contextual awareness, resolution parameters, and timeliness of his query are respected, Sam would be more inclined to come back to the company for further purchases.