Optimizing Category Management Through a Customer-Centric Approach
The pace of change happens faster than humans can perceive it. Category managers are quickly realizing this as traditional approaches to category optimization are not delivering competitive results. To gain a detailed understanding of this paradigm shift, we surveyed 50 U.S. retailers about their category development strategies, perceptions and future plans.
The survey revealed that while many retailers struggle to put their customers at the center of their decision making, those that have are significantly improving business outcomes. Additionally, the emergence of machine learning and artificial intelligence generates the opportunity for retailers and suppliers to make customer-centric decisions in real time based on deeper insights than any legacy system or expert team ever could.
Customer Performance vs. Operational Performance
The key to unleashing the power of customer data comes from establishing a 360-degree view of customer behavior patterns from all touchpoints — digital and physical, owned and third party. When successfully aggregated and evaluated, retailers can intelligently redefine their approach to planning, pricing, promotions and marketing processes to put the customer at the center.
Ultimately, 80 percent of retailers have found that customer-centric strategies create significant to above average in-store customer performance improvements. It's immensely valuable to recognize, although somewhat surprising, that customer performance improvements were noticeably stronger than operational improvements. A commitment to customer-centric retail builds more rewarding shopping experiences that create more reliable long-term growth conditions than improvements to business-driven efficiencies.
Centralizing and Localizing Category Development
Among the retailers surveyed, 73 percent said that location-based clustering helps them gather insights and perspectives on customer data trends that may be subtle or hard to find. Importantly, AI-enabled category management solutions can identify the subtle cues within the data that make it easy to create new efficiencies across the enterprise and at the store and category level.
By centralizing decision-making processes based on an aggregation of enterprisewide data and tying these insights to key performance indicators (KPIs), category managers can make smarter and more strategic decisions for each store and customer group.
Differentiating Through Technology Innovation
We found that while 90 percent of retailers currently manage on-premise solutions, within 24 months, 80 percent will operate cloud-based or SaaS models. Retailers will then be able to more easily assimilate data from all data sources in their omnichannel or unified commerce operations, scale faster, and upgrade at will. Furthermore, by deploying new technologies like AI and machine learning on top of this infrastructure, they'll be able to predict major and subtle behavior shifts in real time.
How well a retailer or CPG supplier manages and acts on the customer data at their disposal will be a leading critical indicator of future success. To do this, they must establish a single 360-degree customer view and rapidly identify trends in the market that affect the store, category and product levels in real time. This can only be done with scalable, hyperefficient and localized analysis solutions, which have now arrived with the emergence of machine learning and AI capabilities driving a next-generation approach to category planning.
Matt Robinson is the director of solution marketing, Symphony Retail AI, a company that helps retailers and CPGs transform their businesses with AI-powered insights for customer intelligence, merchandising, marketing, supply chain planning and replenishment.
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