Use Data to Blend Online and In-Store Shopping Journeys

The Scenario

With a friend’s wedding fast approaching, Olivia visits the website of her favourite clothing retailer and spots a dress that’s just her style. But she isn’t sure it’ll arrive in time, so she hesitates to place the order. For many retailers, this moment might result in a lost sale.

But in this case, a chatbot pops up and asks if she needs help completing the transaction. When she explains that she needs the dress right away and is concerned about the delivery time, the bot tells her it’s available in her size at a nearby store and offers to have it set aside for her.

Though she prefers to shop online, she accepts the bot’s offer and agrees to visit the store. Later, she receives an email reminder with details about the dress as well as a discount code for 20% off a second item.

When she arrives at the store, an associate has the dress set aside. And because he viewed her customer profile and shopping history before she arrived, he also set aside several other dresses she might like — and a few matching accessories so she can take advantage of that 20% discount.

He also knows that after several previous online orders, she’s contacted support to complain that the dresses run small. With that in mind, he also set aside multiple sizes of each dress.

She loves the collection he’s put together for her and is thrilled with his care in making sure she finds the right fit. After trying everything on, she chooses a dress. It fits perfectly. And because she has a discount for a second item, she adds a scarf to complete the look.

Olivia doesn’t know how much effort went into orchestrating this seamless shopping journey. She’s just delighted with the dress — and the personalised experience. And this is why she’s remained a loyal customer.

AI Empowers New Possibilities

Loyal customers like Olivia are valuable for retailers. To build and keep that loyalty, you need to understand your customers, anticipate what they want and deliver personalised experiences.

Savvy retailers have been working to personalise customer interactions for years. But today’s consumers expect more than personalised moments. They expect retailers to connect those moments to create a seamless tailored journey — one that flows effortlessly across physical locations and digital channels.

To stitch those moments together, retailers need to coordinate data and strategies across all business units that affect the customer experience (CX).

Until recently, delivering a seamless, holistic experience was difficult to accomplish at scale. But today, artificial intelligence (AI) empowers retailers to envision and enable new possibilities. Bots can engage in personalised interactions and enhances associates’ performance with customer information and recommended next steps.

Armed with connected data sources and AI-powered analytics, retailers can know their customers better, anticipate their needs and deliver personalised shopping journeys — at scale.

Listen to the Customer

The best way to get to know someone is to listen to what they say and pay attention to what they do. When you’re face-to-face with a customer, that’s easy. But consumers frequently research and shop online, and they often switch channels midstream. Those digital experiences generate a lot of data — and data speaks if you know how to listen.

With every interaction, customers tell you a little bit about who they are and what they want. That includes every purchase and abandoned cart, every page and product viewed, every search of your FAQs, and every conversation with a bot or agent.

The key is to capture and connect that data in a form you can use for real-time analysis that yields insight into the customer’s intent throughout each shopping journey.

You’ll need to bridge your data silos to capture data streams from disparate systems. Knowing a customer’s purchasing and browsing history helps you understand the products they ultimately choose.

But if you can pair that information with data from support interactions, including conversation analytics and sentiment analysis, you’ll know the customer even better.

Understand the Customer in Context

The goal with data collection is to identify patterns of behaviour. An in-store associate probably wouldn’t be able to guess a customer’s preferences if they’d only just met. There wouldn’t be enough information. But armed with the customer’s online and in-store shopping history, the associate could anticipate which products the customer might want to see.

To understand customers’ needs and predict what they want at scale, you’ll need to spot patterns in vast amounts of data — and for that you need AI. Artificial intelligence excels at analysing big data to identify trends, understand intent and predict customer behaviour.

But to deliver results, it needs connected data; individual data points aren’t enough. Seeing those data points in a broader context provides a richer, more holistic view of the customer.

For example, if a shopper lingers on a product page, that might mean they’re interested in the product. Or it could mean they’re confused.

Without the complete shopping journey as context, it’s impossible to guess why they’re lingering. But with sufficient data, AI can determine the customer’s likely intent. And once you’re armed with that information, you can engage in a personalised way.

Deliver Proactive Personalised Experiences

The most effective sales associates study shoppers closely and choose the right moment to engage. They also take note of what shoppers are doing so they can offer more specific help. With the right data and technology, you can empower all associates to replicate that level of service.

You can also deliver automated interactions that feel just as timely and personalised. With a holistic data-driven view of your customers and an AI-powered understanding of their behaviours and intent, you can offer the right help at the right moment — on the right channel.

For example, a bot that pops up with a generic introduction can feel impersonal and off-putting. But if the bot asks a more specific question based on the shopper’s needs, the customer is more likely to engage: Do you need help selecting a size? Would you like a side-by-side comparison of the products you’re considering?

When you focus on the broader journey rather than just the individual moment, you can provide a personalised end-to-end customer experience. Consider a discount offer. Sometimes it’s enough of an incentive for a customer to make a purchase. But a customer who’s contacted support to complain about shipping rates might find an offer of free shipping more enticing than a general discount.

With this type of personalised engagement, each customer feels seen and understood. That leads to a more satisfying shopping journey.

Build Loyalty and Long-Term Value

To deliver Olivia’s tailored shopping journey, the retailer collected and analysed a mountain of data on her shopping behaviour, leveraged AI to identify meaningful patterns, and used the results to engage proactively with the right information and offer at just the right moments.

To stitch together a seamless journey, the retailer looked beyond the contact centre to coordinate strategy across all business units that directly impact CX.

The result is the continued loyalty of a valued customer. And that loyalty pays off with long-term value for the retailer. To deliver a personalised experience at scale, you’ll need to connect systems and align internal strategy across the entire CX continuum.

But if you can collect and connect the data, analyse it to understand your customers, and engage proactively with the right service at the right moment, you’ll build and maintain that loyalty — at scale.

This blog post has been re-published by kind permission of Genesys – View the Original Article

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: Genesys