To build customer engagement in today’s connected world first requires a deeper understanding of exactly what customers are looking for. Some businesses use focus groups, others turn to research surveys or buy in demographic data, but savvy enterprises are turning to conversational applications to make the difference.
When people communicate in a natural, conversational way, they reveal more than just the words they’re saying. Their individual preferences, views, opinions, feelings, inclinations and more are all part of the conversation. This information is one of the reasons that makes the data collated during human-machine conversations so valuable.
Understand the intent
It’s like being able to listen in on every sales assistant conversation and every customer support agent interaction, and understanding people’s intentions, actions and behaviors. Armed with this level of information organisations can gain unprecedented levels of insight into the “voice of the customer” and use it to build customer engagement.
Real-time interpretation of natural language data allows enterprises to automatically personalise responses at an individual level. This type of interaction can be customised even further by including other information from internal and external data sources. It enables businesses to respond immediately to opportunities arising during the conversation, and realise revenue that would have been missed in a “point and click” style of interaction.
Someone shopping for a suitcase presents an opportunity for a business to cross-sell anything from insurance and suntan lotion to a good book. However, if they respond to your simple question of “Going somewhere nice?” with “I’ve got this awful work thing I have to go on”, then intelligent interaction allows you to switch tactics and instead suggest ways to treat themselves to something special as a way of consolation.
Personalise the conversation
Moreover, the conversation can be personalised further by using data from other sources such as information from past purchases or items recently viewed as part of the response. For example, a user may ask for recommendations of restaurants. Using information already learnt implicitly about the customer’s preferences from previous conversations; the application can combine this with recommendations from other users of similar profiles to offer highly informed, bespoke responses.
By analysing conversational data enterprises can identify a wide range of concepts, trends and relationships: “What are your customers talking about?”, “Why are they placing an order?”, “What else interests them when they ask about a subject?”. It can also uncover hidden associations, such as understanding that when users ask “how much” they do it in conjunction with “product X”. All this information can then be used to build customer engagement across the entire business.
Analyse data for greater insight
Take for instance, Carry Me Airlines. A fictitious name for an airline, but the following is based on real data. An analysis of Carry Me’s conversational data is achieved within seconds through a few clicks of the mouse. Using machine-learning algorithms, the software identifies concepts within the conversations, as well as associations between those concepts, and visualizes the results as a block of tiles with labels on them.
Since the analytics software ranks the concepts from the most frequent and down, it is possible to quickly see what stands out in the data. Questions about baggage are one of the more frequent topics, but when we drill down it’s possible to see that customers use the terms “baggage” and “luggage” differently. Luggage is much more likely to refer to carry-on bags. This type of information is tremendously useful when building a conversational app that is sensitive to the expectations of customers.
Start using facts, instead of intuition
But this type of information isn’t the only thing that analysing customer data reveals. Our intuitions about conversational data are often wrong, and businesses need facts to guide them, otherwise they risk misunderstanding the voice of the customer.
For example, the stemming feature within the analytics software groups together all grammatical variants of the same word, in this case the word “book”. What is it that people book online? Intuitively, we’d think of booking flights, because that’s what airlines are all about and because there’s such a strong bond between those two words in everyday speech. And there are a lot of inputs about booking flights.
But analytics shows that “book” is most frequently used about seat reservations.
Using the data from conversational applications enables businesses to understand the “voice of the customer” as never before. Not only is it possible to build customer engagement through more meaningful interactions, but it presents revenue opportunities that would have been missed in the past. For the modern business the art of conversation isn’t lost; it’s just waiting for you to digitise it.