How Customer Behaviour and Analytics Shape Our Customer Support Models

August 9, 2020

For centuries, customer support has been an ever-evolving entity that continues to develop alongside technological innovations. Where traditional customer interactions were aided by the arrival of the telephone, which provided instant assistance and the flexibility for consumers to troubleshoot their issues without the need of leaving their respective homes, the emergence of the internet and big data has reshaped the quality of experience businesses provide for customers.

In today’s hyper-competitive landscape, brand interaction has taken on a new level of importance and businesses are wholly intent on making it simpler than ever to get in touch quickly and efficiently in order to resolve their queries.

Keeping customers happy is of paramount importance when it’s becoming easier for customers to switch their allegiances to competitors. In the 21st Century, analytics has become an essential part of customer experience. Learning from historical insights and current customer records has become a reliable way of creating sustained success for brands.

Analytical technology has developed to the point where key figures have the power to draw on big data insights in order to improve their customer experience models. As these technologies become more accessible online, the emphasis will be on more businesses to identify trends and act on them faster than anyone else within their industry.

Let’s take a deeper look at the invaluable role that customer analytics plays in identifying trends and shaping customer support structures:

 

Real-Time Intelligence

Artificial Intelligence is an essential part of customer analytics and the technology allows businesses to mechanise their customer support initiatives while maintaining a more personalised human approach.

The vast potential of AI allows customer support operatives to track consumer relationships, address queries more efficiently, and swiftly sift through both historic and current data.

(Image: Microsoft)

AI also allows users more time to add layers of personalisation into conversations in a way that could build relationships between the customers interacting with the company. Chatbots and analytics combine here to interpret data points that instantly identifies and addresses users by name and adds a recognisable level of formal, or informal communication.

According to Gartner research, by 2022 marketing AI will have freed up more than ⅓ of data analysts, allowing them to spend longer analysing data rather than collecting it. Such developments can help customer representatives to better understand the behaviour of their customers.

In future, AI will be capable of making data-driven insights that will become essential for businesses to utilise the mounds of big data that it’s possible to gather regarding individual customers. With worldwide data projected to reach a mass of 175 zettabytes by 2025, AI will grow to become an essential part of business workforces. Companies that fail to adopt technology that’s capable of interpreting big data will be left behind.

Today, in the ever-competitive digital marketplace, we can already observe how businesses effectively utilising AI are offering enhanced customer experience models to those that are running on a more traditional basis. The risks associated with losing out on access to insightful customer data are already creating something of an imbalance. 

 

The Potential of Big Data

So, how exactly can AI help to bring to light key indicators within the behaviour of your customers? Big data helps to provide a wealth of information that would otherwise be impossible for a human to track efficiently.

Given the steady evolution of analytical technology, businesses can now have a wealth of information at their disposal that would once have been dismissed as superfluous, that can now be interpreted by AI in a way that adds value to their customer experience models.

Insights can be valuable for adapting your sales funnels to suit the expectations of individual customers, and technology has grown powerful enough to understand the behaviour patterns of target audiences. Where traditional customer support setups were based on a more generalised experience model, big data allows companies to tailor to customers on a more individual level.

Big data effectively aggregates the sentiments of customers, providing an exponential picture of a company’s branding and its relationships with target audiences. For instance, it’s possible to utilise big data in order to conduct concentrated surveys and study comments left on social media platforms – as well as record the conversations had in customer service calls. These can all be automatically interpreted to provide clarity on public perceptions, trust, and purchase intent.

Transversely, big data can help to optimise a business’ marketing strategies, advertising campaigns and their respective tone of voice.

 

Learning From Analytics

As companies begin to step away from the more traditional principles and practices of collecting customer data and gathering insights, it’s becoming increasingly pertinent for businesses to utilise advanced tools that are capable of providing more intuitive and tangible information about customers. Luckily, increased demand has leveraged a rich environment for marketing tools that are capable of compiling data and reporting to decision-makers. 

Technology has made customer communications much more valuable to businesses, owing to the quality of information that can be measured. Contact centres are capable of offering real-time reports on consumer waiting times, their path through your sales funnels, and the accuracy of call rerouting.

This helps to provide insights into how to optimise the conversations that are taking place between teams and their respective customers. AI and big data can combine to help businesses identify the areas that are underperforming within a customer experience model, and allow businesses to begin fixing them swiftly before it begins to undermine the company’s overall efforts.

Analytics software is widespread and already capable of making accurate predictions based on the wealth of data that customers produce during their website experience.

Tools like Google Analytics are capable of offering deep insights into the respective traffic performance of businesses by interpreting the wealth of data that customers leave.

That said, Google Analytics may not be so straightforward when it comes to tracking conversions and analysing funnels.

In cases where we need to analyse conversions, we use Finteza, which identifies key audience characteristics, where the sales are coming from and where potential customers drop off.

Many AI-driven analytics tools come with omnichannel support when it comes to collecting data – allowing them to freely mine information from Facebook, Twitter, and other social accounts alongside company websites and brick and mortar locations. This paves the way for a dynamic insight into how your business caters towards different customers.

Other analytical tools like Bitex help to automate the customer feedback model used by businesses. This is particularly important for companies that are aiming to utilise stronger insights at the end of a customer’s purchase journey and could gain great value from their respective feedback. This tool helps to automatically access and process a wealth of feedback data without any risk of human error coming into play.

The future’s certainly bright for businesses that are conscientious enough to actively draw on the wealth of insights that customers are capable of providing. Today, there are already scores of tools that are able to automatically collect customer impressions across a range of touchpoints, such as email, internet reviews and comparison site feedback, in order to actively seek out areas for improvement.

In a time of unprecedented competition, the act of improving customer support and steering clear of ineffective modifications is vital in gaining ground on rivals. With the right set of analytical tools, your customers can effectively become as vocal and constructive as you would like them to be. Where big data was once indecipherable, analytics engines can rapidly interpret scores of valuable data and automatically suggest ways of improving the experience of online shoppers on your pages. With this technological development, it’s already possible for customers to feel more valued and respected by your business.