Retailers know that they need new ways to engage and retain shoppers, technology teams are searching for new skills and better tools to achieve that, and shoppers are ready to buy from retailers that personalize the shopping experience, new research shows. The confluence of these needs has the potential to blow up retail – with money landing in under-shopped retailers' pockets or with shoppers leaving the brand for good.
Three new surveys show that artificial intelligence (AI) has the potential to resolve the frictions among these groups.
In the last three months, Coveo used outside research firms to survey 600 tech professionals (46 of whom identified as in retail), 4,000 consumers, and 89 retailers. Here's what we found.
9 out of 10 Want Online Equal to/Better Than In-Person
For the second consecutive year, more than 90% of shoppers say they expect their online experience to be equal to or better than recently reopened brick and mortar stores.
These shoppers told us that their shopping journey begins on Google/ search engine, the brand's website, and Amazon. For those who gave one answer (44%), the picture became much clearer: 32% start through Google or a search engine, 30% start on Amazon, and only 16% start directly on a specific retailer's site.
As such, retailers continue to hone an Amazon solution - while throwing more money at search engine marketing. Neither of which is working out well.
Brands Want Acquisition, But Can't Keep 'Em When They Get 'Em
For our retailer survey, we partnered with RSR Research - which segmented respondents by those they had previously tracked as "winners" vs "others." Retail winners' number one business goal is acquiring new customers (91% vs 73%). Yet, we found that many retailers feel they spend too much on SEM/SEO - and that might be because 95% of them claim that "we have a high bounce rate once shoppers reach our site."
That brands are failing to keep shoppers once they get to their sites is one of the glaring gaps haunting not just merchandisers, but the tech professionals who support them.
Why are shoppers bouncing? Consumers across the United States and United Kingdom cited lack of personalization, lack of relevant recommendations (29%), difficult website navigation (32%), search deficiencies (29%), post-transaction problems (27%), and, vitally, customer service shortfalls (48%).
Making Relevance a Priority
Retail tech professionals agree that the importance of search has increased, while 99% admit they're struggling to provide relevance.
Part of that struggle is a lack of alignment among departments. Teams responsible for catalog search differ from teams that create and curate content, like text and videos. Additionally, management teams and technologies vary. More than 72% of companies manage multiple search indices for different applications and 61% support and tune countless search systems.
Nine out of 10 professionals say that AI is essential for solving such disconnects. However, their lack of internal expertise and mistrust of machine learning has kept them from fulfilling merchandisers' – and shoppers' – needs.
Gen Z Is Defining the Future of Relevance
All of this comes at a high cost as a new generation of shoppers is willing to flex its buying muscles. Gen Z shoppers are more likely than other generations to pay more if they could find products quicker (60%) if they could discover something new (54%), or if they received tailored recommendations (53%). Gen Z is also the generation least loyal to retail behemoths - 53% of Gen Z respondents listed reasons they would veer away from Amazon.
Opportunity is there for brands willing to make technology investments.
Low-Code, Pro-Code to the Rescue
What has been most anxiety-producing to techs is the fact that most say (93%) they KNOW shoppers expect their experiences to be as good as Google's. But without Google's deep pockets and brain trust, they have been floundering.
This is why 72% are optimistic that low-code/pro-code applications are an ideal approach to solving search and relevance problems. Off-the-shelf AI that provides purpose-built machine learning is the quick and easy solution to this shopping dilemma - without making tech professionals do any heavy lifting.
Customers will always be finicky about their wants. And you just can't create enough merchandising rules to meet their needs. Nor can you (guickly) develop enough code to solve it automatically.
A SaaS-based solution that offers turn-key AI - can finally make everyone happy.
