Global annual spending on artificial intelligence (AI) by retailers is expected to surpass $7.3 billion by 2022, according to Juniper Research, and IBM estimates that 79% will implement AI for customer intelligence and 75% for marketing, advertising and campaign management.
Traditional retailers are hoping to reap the benefits of employing AI in these areas, but with the growth of direct to consumer (D2C) retailers – 40% of U.S. internet users expect D2C retailers to account for nearly half of their purchases within the next five years – traditional retailers must find efficient ways to catch up.
As AI continues to further integrate into retailers’ internal and customer-facing processes, it’s critical to understand how traditional retailers can create AI focus areas to see the best results.
Barriers to AI Entry for Traditional Retailers
When exploring the adoption of AI for any area of the business, buy-in from senior-level management can be a challenge. Although AI use for businesses has been around for nearly two decades, some upper management, particularly those with an old-school thought process, think it’s just a phase.
With the significant investment it takes to implement AI from the ground up, it’s difficult to understand how the results will impact their ROI before investing the time, money and hiring power.
Understanding the data infrastructure is another barrier. Stuck in a pattern of fading strategies, one could compare the adoption of AI to the dot-com bubble.
It’s scary for established businesses to take on a new challenge and invest the resources needed to completely revamp how their business functions. Many don’t believe they’ve seen the results needed to take on that challenge and risk an established company.
Emerging D2C retailers don’t have a fallback plan based on decades of success, so pursuing new tech is a no-brainer.
A third barrier is a lack of innovation. Lack of success with AI initially may be due to a lack of patience and a lack of innovation.
Because AI evolves and learns, retailers must understand the limitations and continue to try new processes, teach the technology and innovate to find the next big breakthrough rather than accepting the status quo.
Opportunities for Retailers to Shine Through AI
AI plays a major role in expectation setting for consumers. To be successful, the technology must be able to create predictive inventory feeds, understand sell rates, know how to proactively take a product off the website and recommend the next product.
A greater understanding of the overall customer persona is critical to provide these features that resonate with the customer.
To develop these personas, AI can review all elements of customer interactions and look at several trends at once versus just one conversation. This data can help retailers better align on which products customers are most likely to buy, when and why.
D2C retailers already do this well by ingesting customer base persona data using AI to confirm what they already have and fine tune their models for truly autonomous strategies. These retailers can also enhance their persona modeling and expand it out to micro-persona base modeling, creating strategic approaches to add on sales, position products up front and run micro-marketing campaigns to drive a higher ROI.
Implementing AI in the Supply Chain
Using AI to understand purchasing patterns can help retailers stay on top of the supply chain by monitoring product availability and stock the products their customers actually want. AI looks at data at a much larger level than one interaction or conversation. It allows retailers to group like elements together and form trends to predict seasonal changes, hot topic items and everyday products.
One example is the feature that tells customers, “Based on other products you might like...” Retailers can utilize AI for this feature to understand what the customer is buying, and likely to buy in the future, helping to clear out older stock and bring in new stock to sell faster.
Zappos, a company known for their great customer service, has reached the top of their market through smart channel routing and AI assisted logistics. They use more than 200 algorithms between CRM and distribution to create a better customer experience.
They use AI to understand drop rates and ZIP code management as soon as an order is placed, and create proactive routing paths using the most cost effective distribution center to get it to the customer’s doorstep faster.
Creating Effective Data Management
Organizations using data analytics are 23 times more likely to outperform their competitors in terms of customer acquisition and nine times more likely to surpass them in customer loyalty. But only 3% of business professionals say their organization is able to act on all of the customer data they collect, and 21% say they can act on very little of it.
This exemplifies the challenge of successful data management when employing AI technology. It’s vital that retailers are continually evolving their ML (machine learning) algorithms with version control testing on an ongoing basis to create the most successful algorithm.
By indexing and archiving, making the data usable in the future, retailers can train new models on an ongoing basis. This helps them mature past monitoring ML paths to unmonitored paths, allowing the technology to quickly reset the base approach when something isn't working, and get back on track faster.
Cris Kuehl is VP, analytics and client insights at customer experience management company Sitel Insights