Modern technology advances have given Retailers access to exponentially more data about what customers do and want. It is an amazing opportunity for them to use analytics to unlock the goldmine of information to cater to their customers. Retailers have been struggling to give customers an engaging experience with them. There is a misconception that just developing digital channels to access their account is enough to get customer loyalty. In order to provide an engaging experience, it is essential what contextual information should be given to their customers to make their journey fruitful. For that, retailers must invest in a technology stack which will help them to understand what customers want. Artificial Intelligence and Machine learning will play a crucial role, if not the crucial role to get customer intelligence.  Artificial intelligence (AI) allows retailers to ultra-personalise the shopping experience at scale, using significant volumes of data.

In this era of mass customization, customers have become more connected, more demanding and less loyal. Easy availability of similar and competing products/pricing different retailers mean easier comparison and faster switching between brands. The relationships become brief and largely transactional. The demographic trends have scary implications for conventional retailers as the millennials & younger generations become the least loyal.

The What & Why of Customer Intelligence:

Retailers need to adopt customer intelligence strategy because it keeps the customer at centre of all operations. They have a deeper understanding of customers through their purchase and browsing history and transactions. Customer Intelligence investments gives insights about the customer to understand their persona.

It helps in building the customer persona through which retailers can segment customers to improve CX and to have a targeted messaging. It helps them to engage at an emotional level and strengthens the relationship with the customers. Customer Intelligence is the path to true loyalty.

It’s important for retailers to become an indispensable partner for consumers throughout their life. Which means becoming truly Omni channel, offer ultra-personalised care, provide a compelling shopping eco-system. How can cutting edge technology contribute to the above goals?

How to create a Customer Intelligence Strategy with technology:

For starters retailers need to create a customer centric hub. Create a unique customer profile depicting all relationship maps. Record customer references, customer transactions etc. Use Master Data Management (MDM) to get one version of true customer data. Once the customer centric hub is ready, connect it to the digital experience center to get the 360 & 720-degree view of the customer. Once this happens, map individual customer journeys by creating behavioral and emotional customer personas. Using Artificial Intelligence/Machine Learning models create the ideal customer profile and have the relationship manager prepare for the “Next Best Action” for the customer. This can help in providing ultra-personalization for the customer.

Retailers can adopt big data architecture like Hadoop to store client data from various touch points, both online & offline. Use a Master Data Management tool to store the single version of truth of the customer. Tools like Kafka can give real time streaming data of the customer actions. Along with this utilize some ETL and analytics tools to create the customer 360/720 view. Retailers can use RFM analysis and Machine Learning to extract real time data for each customer segment. Automate the process of personalization and provide insights with the help of customer intelligence.

Retailers are already using artificial intelligence (AI) to experiment with services that are ultra-personalised.  Retailers are using AI engine by priming all kinds of data on customer transaction & interaction history, online & offline data, cart abandonment, online heat maps policy and more, to provide contextual service to their customers.

Some of the key uses of Artificial Intelligence & Machine learning are Stocking inventory, Predicting and tracking customer behaviour, and Dynamic Pricing. One of the key advantages of having a superior customer intelligence program is that retailers can streamline the stocking and inventory management process. Machine learning can offer retailers the ability to predict inventory needs in real time which results in faster response time for the customer and improves loyalty. ML algorithms can generate contextual product recommendations that are complementary to items than simply pushing a hot item that is completely unrelated to what they are purchasing.

Machine learning can track customer behaviour based on the browsing pattern and how much time they spend on certain items and or by detecting walking patterns in a store and measure interest of various products. These data can be used to design future customer journeys both offline & online. By ensuring an item is priced right, retailers can reduce cart abandonment. ML has the ability to offer dynamic real time pricing options to customers to increase sales closures by changing the price through algorithms. Now retailers will have more flexibility to provide the correct price at the right time to enable sales closure and achieve both top line and bottom line growth.

We are just scratching the surface of how AI can disrupt retail customer intelligence.  AI, machine learning, and customer analytics will become the key player from a client engagement perspective in the coming years. These new cutting-edge technologies will find a way to integrate themselves in customer’s lives. The chances of this becoming a reality is high as it will be ultra-personal with accurate intelligence gathered from data about consumer behaviours, choices, and preferences.

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