Leveraging Analytics and AI to Better Understand Customers
Originally Posted: Fibre2fashion
‘The Customer is King’ — is an age-old business mantra as old as time, but it has never been more relevant. In today’s complex and hyper-competitive market, businesses need to truly understand their customers; what they are thinking, what they want, their needs, etc to come out on top. By keeping an eye on customer experience (CX), businesses can improve shopability and engagement levels, and use insights to capitalise further and attract and retain customers.
With data analytics and AI advancing at a mind-blowing pace, businesses can learn more about their customers, and use these technologies to improve shopping experiences and engagement levels. At a time when most of purchasing is happening online, especially in the midst of the ongoing stay-at-home pandemic, organisations — be it small, medium, or large — can turn to data analytics and AI to gain insights into their customers preferences and provide support they need. Datasets are a key source of truth from which to develop an AI-based project, and by aligning the two with business objectives, companies can maximise impact — resulting in a more substantial ROI.
Data analytics and AI can help companies with smart segmentation to identify their target audience, build a relationship with customers and deliver tailored and personalised content to each segment.
Using data analytics and AI to improve customer experience
It is finally time to move away from gut instinct to predictive analytics when it comes to making business decisions. By optimising social listening opportunities and understanding purchase patterns, businesses can garner contextual information about their customers, and use that information to create personalised offers, better retention strategies and out-predict competitors. Predictive analytics helps companies increase the customer lifetime value, and maximise inventory, among a host of other opportunities.
Organisations today are interested in analysing both enterprise and external data to identify previously unknown relationships that may drive operational efficiency, increase sales, and provide a competitive marketplace advantage.
Organisations can create a feedback loop via focus groups and consumer insights research in order to track how consumers feel/ think. By capturing emotional and cognitive responses companies can prioritise their actions. Further, data analytics and AI can show companies what they are missing. For example, based on the behaviour of customers, it is easy to identify the shoppers from the window-shoppers and offer targeted incentives to drive a purchase.
So, what should companies consider while designing such a system:
• To capture and measure value across the customer moments that matter, it requires a complete ecosystem, across channels and data sources
• In order to paint a full picture of customers, organisations need to access and integrate marketing, sales, service, social, and survey data
• They then need to tie this to back-end system data to be able to draw connections between a customer’s experience value and their business value
• Finally, to make the most of the CX insights, they need to make use of analytics and data visualisation tools to display these real-time dynamic insights in a clear and actionable format to feed into customer strategies.
By leveraging customer analytics and AI, organisations can lower customer churn and acquisition costs, drive conversions within mobile platforms/ apps, personalise the online customer journey, anticipate consumer needs and behaviour, improve, and automate processes for both employees and customers, and understand new customer and overall brand sentiment. Armed with a 360-degree view of a customer, organisations can place themselves ahead of the competition.
Today, customers expect a quick and seamless experience when it comes to online shopping. And, with the power of customer analytics businesses can not only connect with their target audience but also garner insight into behaviours and patterns enabling them to create more engaging content, adverts, push notifications and communications. For example, customer analytics and AI can provide information on whether customers are price conscious, enabling companies to identify the same, and introduce discounts, or a seasonal sale, etc.
The rise and rise of hyper-personalisation
The most important question brands should be asking themselves is how they can become more personalised and relevant to their customers. The bottom line is that a customer coming on a digital channel or physical store should not only be uniquely identifiable, but organisations should know his/her tastes, likes/dislikes, behaviour etc to create more personal experiences and eliminate pain points in a customer’s journey.
The key point is to understand what an organisation is trying to solve for their customers — and whether that can be achieved more effectively with the use of data and AI.
With the increased popularity of live-commerce, shop-tainment and the rise of D2C brands, businesses can truly transform shopping experiences by using personalisation as their go-to strategy. The world of shopping has changed, and businesses need to continuously evolve and find effective ways of creating value and better customer experiences.