Artificial intelligence (AI) promises category managers the ability to act on real-time insights delivered by customer behavior data. But, to reap the rewards of leveraging shopper insight to instantly inform decision-making, they need to move beyond the limitations of traditional legacy system and manual process approaches. We all learn to crawl before we can run, and it’s no different for category managers when employing new technologies.
First, they need to ensure they have access to a 360-degree view of customer behavior patterns as they relate to both digital and brick and mortar environments, from owned sources and through third parties. Doing so creates an agile category development structure that frees decision makers from rigid, top-down directives and creates smarter, more customer-directed assortments, allowing for real-time adjustments based on vastly complex signals that the human mind struggles to identify or react to.
In order to determine the perception and maturity of customer-centric category management approaches in the U.S., we partnered with EIQ Research Solutions to survey 50 leading retailers. Our research found that while many understand the value of customer-centric category planning and the foundational need to create a 360-degree customer view, many have the inclination but don’t really have the capability. At least not yet.
Roadblocks to customer-centric category management
To emphasize that point, we found that 75% of retailers claim that customer-centric strategies create significant to above-average in-store customer performance improvements. Furthermore, nearly 70% of retailers do this by using internal and external data to develop localized store marketing programs that target various customer segments based on actual shopping behavior. Fifty-six percent use this data for either accurate demand forecasting or customer-centric promotions and 46% use it to create segmented assortments, an approach that can significantly positive outcomes.
However, while customer-centric category planning has proven to be an effective driver of potential business success, many retailers struggle because they can’t leverage their existing data sources to work effectively for them and are limited by other factors, including:
- 54% - Inconsistent or incomplete data
- 52% - Insufficient data analytics talent and capabilities
- 38% - Legacy processes and disintegrated systems
- 33% - Lack of collaboration among business functions
- 29% - Lack of an integrated omnichannel strategy
Artificial intelligence drives customer-centric category efficiencies
Retailers are becoming more aware that they can address many of these issues by ensuring their core infrastructure becomes highly agile — only two in ten retailers will still manage on-premise solution models within 24 months, compared with 9 in 10 today. Cloud-based, SaaS models will allow them to integrate AI-enabled, data-driven solutions that can assimilate data from all sources in an omnichannel or unified commerce environment, while helping them become more predictive of consumer-driven trends, prescriptive in how to leverage that insight and scale faster and upgrade capabilities in real-time.
AI is often thrown around as one of the revolutionary buzzwords in retail. By itself it is not a silver bullet that saves retailers from the pressures of Amazon or the demands of increasingly savvy, brand-agnostic shoppers. AI-enabled solutions, combined with a 360-degree view of the customer’s behavior across all channels, can deliver the ability to transcend the limits of traditional category planning systems and support the delivery of an approach that generates the right actions in real time that drive tangible and sustainable results.