Ananth Siva

Many retailers have struggled with the pace at which the multichannel world is moving, with consumers increasingly expecting the same levels of service across a variety of different channels. Just when most retailers thought that they had a handle on delivering on their multichannel strategies, along comes omnichannel and a whole new set of challenges.

In the omnichannel world, customers will visit your store, shop and research online, ask friends their opinions about products and brands via social media, and purchase via their mobile — and they’ll want to transition seamlessly between these channels.

They’ll expect the retailer to make their experience continuous, consistent, and contextually relevant. When retailers fail, customers become frustrated and take their business elsewhere.

How can retailers offer an omnichannel customer service experience and prevent their customers from taking their business elsewhere? Fortunately, retailers already have a valuable asset that can enable them to dramatically improve quality and deliver best-in-class customer experience in this new omnichannel world.

The only way to make it simple for consumers to connect with brands in today’s omnichannel age is by leveraging big data gleaned from every customer interaction including calls, chat, online, mobile location, social media and in-store transactions.

Retailers can apply predictive analytics to this data to anticipate what consumers want, simplify their interactions, and learn from those interactions in real-time so that future experiences are constantly improving, whatever the channel.

With big data and predictive analytics, you can model the customer’s journey, anticipate their requirements and maintain continuity across channels to deliver an omnichannel experience that generates benefits for both the customer and the retailer.

By utilising information from a customer’s journey across multiple channels, retailers can predict a customer’s likely requirements, whether to anticipate a question or problem or to improve sales conversions by presenting more personalised product options in a timely fashion.

For example, if a shopper had called about a TV, then went online and began shopping for TVs, alternating from one brand to another — you could utilise their phone and web history combined with buying experiences from others who had displayed similar behaviour. This would enable a retailer to intelligently chat with the customer online and be ready to answer questions about what is likely to be their preferred brand and hand-hold them through to a successful outcome.

Simply providing product-options based on data that solely shows that ‘others’ bought the same is no longer enough, we should be able to personalise recommendations based on a consumers style, buying preference, or personality.

Another example would be where a customer has previously called a free phone number about a refrigerator while visiting a website and then begun a chat session. In an omnichannel world, their account information, website history and other information can be provided to the chat operator to improve and make the chat experience quicker.

As a result, the customer will not have to repeat information they’ve already given in-store, and retailers can put them in direct contact with the right specialist at a contact centre depending on their place in the customer journey.

Retailers must use big data to anticipate consumer needs and provide omnichannel customer service if they are to survive and thrive in today’s highly competitive retail environment where customers are moving between channels and devices.

Retailers that don’t keep pace with their customers may find they go to a competitor that can understand their needs, anticipate their journey and provide the same level of personalised service across all channels.

Ananth Siva is the Asia Pacific managing director of [24]7, is a customer service consultancy that “predicts the end of customer service as it is known today” and “envisions a new world of intuitive consumer experiences”.