If ‘Customer experience’ is fast eclipsing product and price as the retail differentiator, intelligence and technology are rallying around retailers in their quest to strengthen Retail Personalization – a successful differentiation initiative to say the least.

As POE (Point of Experience) scores over POS (Point of Sale) in terms of customer-relevance and customer engagement, retail success is more about promoting personalized customer experiences that resonate with customers and make customers feel valued.

The ‘Point of Experience’ paradigm-shift also means engaging customers in a way they want their experience scripted. Decathlon, a French brand, pioneers customer delight in its own way, in allowing a kid to cycle around the store before walking away with the cycle as the proud owner.

The age of experiential retail has also raised the bar in terms of knowing who the customer is, what he wants to do – an imperative that calls for retail intelligence fuelled by technology including big data, advanced analytics and machine learning to facilitate experiences in a way customers want them.

How ‘I’ makes up for an intelligent personalization?

Intelligence from all customer angles is what retailers need to win ‘moments of truth’. This translates into looking at every possible customer dimension – from attitudes, activities, taste, interest, behavior, values to wants and needs. Personalization begins with customer intelligence.

With customer intelligence, retailers are equipped to bridge the gap between what customers actually want and what retailers actually deliver, made possible by way of personalized messages, recommendations, personalized automation, personalized customer journeys and experience.

Empathy, intent and context

Personalized customer experiences also take ‘empathy’ into account. An ill-timed and irrelevant message or an offer can only serve to repel customers – as in the case of Tina, a weekend grocery-shopper, who is not all that pleased to get a Monday morning special grocery offer. Personalized shopping experiences stem from intelligence around customer attitudes, need state as well as shopping occasions.

While predicting what the customer wants, reading the intent of customers with behavioral data is about the progress made to understand the purpose behind a customer action – with that, retailers have intelligent inputs to earn customer confidence and trust. Context is another important piece in the personalization jigsaw. Context translates into intelligence about where, When and Why, gels well with customer actions to promote personalized customer experiences.

For a customer who shows interest in buying apparels, contextual personalization becomes a reality only when intelligence – about what type of apparels does the customer buy, frequency of purchase, propensity to buy other items along with the identified product, brand consciousness and other decisive contextual factors – is made available to win moments of truth.

Everything customer – From propensity to issues

As customers now have the choice of channels, devices and touchpoints, integrating the physical and digital world and making the most of customer data has grown into a retail requisite to enable personalized customer experiences. Gaining intelligence on customer propensity to buy, items purchased, frequency of purchase, how customers buy and what they don’t buy is another way to promote personalized customer experiences, prompt new buying behaviors and build customer loyalty.

Retailers also reap rewards through early detection of potential issues using a range of engagement metrics, as that of clicks, likes and conversions as also by leveraging social listening. The key is to engage customers in a relevant, timely and a proactive manner to enable personalized experiences.

How the ‘T’ shapes personalized customer experience?

John ploughs through a retailer’s website, buying a pair of boots as a result but letting the sports shoes languish in the shopping cart. When John enters the nearby retailer store, the retailer offers a tempting discount for the sports shoes, guided by appropriate ML model that helps know if John is a price-sensitive customer and take the Next Best Action in luring John to buy the sports shoes left abandoned a few days before.

This is one of the many cases where big data, advanced analytics and machine learning have teamed up to help retailers acquire customer intelligence, predict customer actions and roll out the Next Best Action to win customer loyalty. Data from various sources like the online data, CRM, in-store, supply chain, behavioral and contextual provide the fodder for ML algorithms to create fluid experiences, spot opportunities to personalize customer experience.

Attribute analysis, propensity modelling

Every attribute of a customer matters today to create personalized experiences. With attribute analysis, retailers not only gain rich intelligence about customers but also roll out personalized communications and promotions to create enriched customer engagement. Where retailers wants to know what has driven a customer to take a specific action, positive or negative, analysing the sequence of events gets the advanced analytics push to unearth indicators for a customer action, facilitate prediction with sequences and tap into the opportunity to personalize every customer event.

Travis is a frequent-shopper, Sara is the at-risk customer and Mark is an infrequent but steady shopper. A retailer buckets all of its customers under these three segments and applies propensity modelling on all of these customer segments. And the retailer makes the proactive move to cross-sell and upsell, deliver personalized messages and experiences, strengthen customer relationships and retention.

Personalized recommendations and collaborative filtering

In the world of experiential retail, experiential marketing also taps into the data and advanced analytics connection. Big data and real-time analytics come together to help retailers make real-time adjustments to messages, leverage context to facilitate personalized customer engagement. A retailer wanted to make personalized recommendations to users landing on their website. Behavior data and collaborative filtering technique rose together to build the recommendation system, allowed the retailer to recommend the right product for a customer and promote personalized experiences.

Think Personalization, think customer behavior and personas and on the technology-front, think big data, AI and machine learning. Saksoft helps retailers reap the harvest of personalization – whatever it takes, from personalized communications, recommendation systems, omnichannel analytics to customer engagement behavior prediction, guides retailers to implement an intelligence-driven personalization strategy to meet customer demands – Hyper-personalization.

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