There is no doubt that customer experience has become the battleground where companies sway new customers and ensure already-attained customers stay loyal. In fact, 97% of leaders agree that customer experience management is a crucial strategy for creating long-lasting customer relationships.
Yet, as marketers, we often get captivated by the abundance of available technologies and the evolution of automated marketing campaigns that we often overlook the element that sparks the customer journey the most — emotion.
To understand what great customer experience entails, we have to dissect it into pieces that fit the context of the current state of the world. Customers have been through a lot during 2020, and the global situation has changed us in one way or the other.
What customers crave right now (and will most likely expect in the future) is empathy.
When companies expand the perspective to include emotion into the equation – new possibilities are revealed. At the end of the day, customers experiencing a positive feeling when engaging with a company account for the most loyal and most valuable customers.
Data shows that when customers create a positive emotional connection with a brand, 92% of us are more likely to stay loyal to a brand, 88% are more likely to spend more, and 91% are willing to advocate for the brand.
Yet, providing a personalised and emotion-infused customer experience at scale seems almost impossible. And it might seem unachievable until we consider the next-gen technology resources such as customer data platforms and artificial intelligence.
How Data and AI Enhance the Customer Experience
Single Customer View
Having a single customer view is the first step towards applying omnichannel behavioural marketing at scale. We have talked about CDPs and how using the platform can significantly impact the customer experience and marketing ROI.
The CDP platform allows companies to syndicate the data collected from a myriad of touchpoints, centralise it, segment according to specific characteristics and behavioural signals to deliver hyper-personalised messaging.
CDPs are the baseline from where companies can start developing predictive models using AI to anticipate the customer’s next step for effective optimisation of end-result, whether we are striving to sell, engage or boost advocacy.
The role of emotions in decision-making has been well-documented in psychology, however, not many marketers are tapping into this insight.
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We have all witnessed customers making a purchase decision based on impulse, emotion or current conditions. The emotional aspect explains why some customers purchase expensive branded items when buying a similar but less expensive item is a more rational decision to make.
The brand projects an image that causes an emotional reaction in the buyer, which then, in turn—brings higher profits for the company.
For a customer journey to be hyper-personalised and emotionally charged, the most significant factors in play are context and relevance. For this reason, bringing data-driven tactics along with an emotional tone into the customer experience equation is the ultimate win for companies.
To illustrate our point—receiving a mass email offer to upgrade your data plan after having complained about poor connectivity to your network provider will not end well for the company nor the customer. Without knowing the context of the customer’s situation and the timely relevance of the offer — the company is doing a disservice to itself and the customer.
Machine learning helps marketers uncover behavioural signals for better personalisation and contextualisation of the customer journey. Automated machine learning models can process large amounts of customer’s buying signals paving the way to a more emotionally charged journey.
Calculating the next-best step in the customer journey is an ever-evolving activity. Therefore, marketers that try to gauge customer’s sentiment without obtaining the bigger picture that machine learning provides are limited.
Moreover, human bias is hard to eliminate from the process, therefore it is not as reliable as AI models that can analyse the slightest changes and deliver appropriate messaging.
Predictive models powered by machine learning algorithms can shine the light on the customers’ context every step of their journey so that marketers can act from the perspective of that customer instead of their own.
Ambitious companies that recognise the value and opportunity in marrying science and emotion to deliver a more memorable customer experience can stay leaps ahead of their competition. Today, when we have the knowledge and technology to provide such experiences is the perfect time to raise the standards and connect with customers just like humans do.
What are your thoughts on using data and emotions for a better customer experience? Have you tried this approach yet?
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