2017 is shaping up to be the year that artificial intelligence (AI) goes mainstream, impacting an ever larger share of our daily life and transforming just about every business and industry, including retail. Combined with the power of big data analytics, AI opens up new possibilities for retailers to understand consumers at the individual level and to differentiate themselves from their competitors by creating a truly personalized, omnichannel shopping experience for each of their consumers.

Getting that consumer experience right is critical. Even retailers who have shifted to discount understand that competing on price alone is no longer enough and that driving experience is the new battleground in the fight to attract and keep consumers. The key is making that experience relevant to each individual. That means retailers must understand each consumer’s path to purchase and completely personalize the journey.

That path to purchase has changed dramatically in recent years, as digital technologies have enabled consumers to interact with retailers and brands across more touch points than ever before, and for longer periods of time. While some consumers still shop the old way – enter a store, look around, make a purchase and leave – many more, especially younger tech-savvy consumers, engage with retailers over several days through the pre-purchase, purchase and post-purchase phases. And they do that across multiple platforms, including company-owned websites, mobile apps, third-party marketplaces (such as Amazon) in-store, and via social media platforms where they share information and discuss products and the consumer experience with their friends.

Fortunately, retailers have never had more access to data about their consumers or more ways to get their message to them and measure responses at each of those touch points. But having all that data is useless unless you can sort through it all to gain meaningful insights and then be able to act on those insights in a timely manner that resonates with a particular consumer.

Artificial intelligence is a key tool to help retailers aggregate data from all of their touch points, understand and digest that data, and communicate with individual consumers in the most effective way via the most appropriate channel. This is particularly important as we move away from simply segmenting consumers by demographics towards segmentation based on social values, and as more consumers acquire Internet-connected devices that become part of the path to purchase for a growing array of products and services.

These new paths to purchase have many places where consumers can come on or drop off. Retailers who want to keep consumers on a path to purchase from them rather than from a competitor need to recognize that each path is as different as each consumer and design their omnichannel offering to be flexible enough to allow each consumer a personalized journey to the same end point. That means a compelling, seamless experience that includes both traditional and digital commerce and allows consumers to move from one channel to another, leave for a while and pick up where they left off on whichever channel they want when they come back.

Here’s one example of how that might work. A consumer sees a print ad or receives one via email or a social media platform. Curious, she goes online while commuting to work and checks out the retailer’s website for more information, then searches on Google for product reviews and consumer testimonials. Her interest piqued, she does a price check during her lunch hour via the retailer’s mobile app and compares that with offers available from some competitors’ sites and Amazon. A few days later, she visits a store to see the product for herself, then does another price comparison using her smartphone. Before making her final decision, she consults her friends on Facebook and Twitter. Finally ready to buy, she returns to the retailer’s mobile app on her commute home, places her order and arranges to pick it up at a store near where she lives.

At any point along this path to purchase, she could give up in frustration if it’s not easy to navigate any of the channels offered by the retailer or if the information she needs isn’t readily available. She could be deterred by bad reviews of the consumer experience that she gets from Google or her social media contacts. Or she could be lured away if, during her price comparison on another retailer’s site, she finds that consumer experience more pleasant than the one she is having through the original retailer. And she might not have even begun this particular journey if another retailer had used its detailed insights about her to target her with a more enticing offer in the first place.

In 2016, Deloitte’s Omnichannel Consumer Experience Index found that, while some retailers already have extensive omnichannel offerings and are reaping the rewards of their efforts, some have barely started and many others are somewhere in between. The Index also found that retailers in the United States have more extensive omnichannel offerings than those of Canadian retailers, a particular concern given that Canadian shoppers—three-quarters of whom regularly cross-border shop—want digital offerings and experiences in the same way U.S. shoppers do.

It’s critical for retailers to find and nurture omnichannel consumers, as they are between three and five times more profitable than non-omnichannel consumers due to their loyalty to a retailer, the frequency of their purchases and the lifetime value of the relationship. Retailers can use a resource like the Omnichannel Consumer Experience Index to see how they stack up against their competitors—where they lead, where they lag and, most importantly, what investments they need to make—in technology, systems and talent—to have the maximum impact.

No matter how they compare, all retailers need to constantly refine, learn and adapt by continually monitoring their consumers along the complete path to purchase. This includes using leading technologies, including artificial intelligence, to bring all of the data together, drive insights and deliver outstanding consumer experiences.

This isn’t as daunting as it sounds. While some retailers may want to do it all themselves, others will prefer to rely on the current big AI providers such as Amazon, Google or Apple. Both approaches have risks, however. The complexity and cost of integrating AI and analytics into a retailer’s operations means only the biggest retailers can contemplate the go-it-alone approach and even then they may lack the skills needed to pull it all together. Those who align themselves with an existing AI provider could find they are left behind if another, better provider comes along in future.

The best option is to build a model that is always plugged into the network and ecosystem and which relies on being flexible and nimble through partnering with others to help meet each consumer’s personalized needs. By using a proof of concept approach, retailers can start small, fail fast, learn from those experiences and quickly move on to try new things.
Wherever retailers are on the omnichannel spectrum, taking steps now to harness technology to provide a better consumer experience will lead to a better payoff in the form of more loyal consumers, higher sales and a greater ROI and ROA.

This article originally appeared in the March 2017 issue of Direct Marketing.

Previous post

Environics Analytics launches mobile analytics services

Next post

What is mobile direct marketing?

Jennifer Lee

Jennifer Lee

Jennifer Lee is the national retail & consumer analytics leader at Deloitte. She helps companies leverage analytics to drive a differentiated physical and digital experience. In addition to improving margin and efficiency, Jennifer sits on the global retail exectutive team at Deloitte.

No Comment

Leave a reply

Your email address will not be published. Required fields are marked *