Whilst technologies exist for retail category management practitioners to quickly understand the needs and preferences of customers, there has been a slower than expected uptake (“Only 4% of CIOs say their organization has deployed AI”*) of machine learning and artificial intelligence (AI) related capabilities in grocery.
In searching for an answer as to why that is, I looked at my own use of these types of technologies and questioned why I too had been slow in embracing the future. Despite being a modern tech-loving professional, Alexa has only been a very recent addition to my household. I reflected on why I had been so hesitant to welcome her into my home and the basic answer was the perceived value she would bring to my day. The same too for retailers and manufacturers who want to ensure that before any investment, the return will:
- Be tangible and positively affect growth
- Make their working lives easier and more efficient
- Enable them to do more relevant things faster, and deliver a competitive advantage to their organization, not just employing technology for technology’s sake.
So why AI… and can it deliver on its promise?
AI uses technology with advanced analytic capabilities like machine learning to carry out tasks that traditionally have required high human resources to carry out. AI enables us to significantly accelerate the time to strategy, decision making and execution so organizations can do highly complex tasks rapidly, anticipate and respond faster to market demands and new trends.
1. Understand who your most valuable customers are and build the objectives for each category around them
Taking a 360-degree view of your customers is no longer an aspiration for category managers, it’s a prerequisite to driving sustainable growth and success in category development. This is supported by the areas of importance that US retailers indicated as part of our recent retail category management survey conducted by Symphony RetailAI, in conjunction with analysts EIQ ResearchSolutions.
However, while it’s easy to say that focus is required, its traditionally been incredibly difficult to consolidate and search through multiple data sources to deliver assortments, promotional and pricing strategies that meet the needs of your most valuable customers: customers that some retailers are still finding difficult to effectively identify.
The challenge for many organizations is that once they do recognize the customer segments that will support their future growth and margin objectives, how can they best turn insight and strategy into executable propositions instore? As revealed in our recent customer-centric category management survey, retailers in the US lead the way in generating performance uplifts through customer centricity, however they have seen less operational performance when delivering them. The opportunity for US retailers is to increase their cluster and store-level executional effectiveness. In Europe, the gap between customer satisfaction/category performance uplift when delivering localized assortments is smaller – the ability to execute in Europe appears more efficient and helps to justify retailers taking a more granular approach to assortment. AI can help in continuing the identification and tee up assortment optimizations for world-class execution in order to realize performance uplifts.
2. Anticipate category trends before your competitors do
The challenge for many organizations when attempting to identify the next category growth driver has been the ability for its employees to consolidate and then successfully navigate the multiple customer data points in their possession. In our recent category management survey, 54% of US retailers state they have struggled with incomplete and inconsistent data. Employing AI capabilities in the category management process is a critical element in combating this challenge.
Organizations are trying to tie all of this information together along with corporate strategies. Progressive retailers are already embracing a pathway to this holy grail. One of the ways is through AI-empowered solutions such as CINDE. Think of her as the Alexa of FMCG retail for merchandising, marketing and supply chain. Using pervasive insights based on AI and machine learning, she understands retail business trends specific to grocery and hard goods and continually learns, and proactively alerts users to critical issues and opportunities that require immediate attention.
3. Orchestrate your employees by enabling higher productivity and drive an enhanced sense of job satisfaction
Rather than taking on an army of data analysts (who would fall short against AI anyway), organizations can utilize the power of AI to rapidly mine its big data repositories to reveal the needs of the market. AI can discover the product attributes required in the category and align assortments and future new product development to wholly resonate with its most valuable customers. As the following statistic illustrates, in the US, retailers are concerned about the lack of available data analytics talent and capabilities. As the volume of data continues to multiply and the mandate to be closer to needs and preferences of customers increases in criticality, successful retailers will be those that can access actionable insights quickly in order to accelerate decision making and execution.
Many organizations consider AI as they reach for improved productivity however as Bob Hetu from Gartner remarks in his recent article (Using AI in Retail: Start with Intelligent Automation Services, March 2018), “the power of combining new insights from AI with human creativity provides the opportunity to redefine customer experiences.” As technology leads in the direction of efficiency and speed to market, its important remembering that creativity and context delivered by humans is critical to realizing sustainable ROI.
There is nothing artificial about AI’s impact on retail category management
AI is here to stay and organizations are now beginning to understand the full scope of what it offers:
- A quick path to customer insights that would take weeks with teams of analysts who, still, would not be capable of uncovering deep trends or anomalies in the data
- The ability to recommend next best actions based on perpetual learning – since many organizations struggle with execution even after they’ve correctly identified customer in-store propositions
- Predictive analytics that enable retailers to stay ahead of the competition
- An enhanced, collaborative relationship with employees who are more effective and creative in their jobs, as they have real-time access to data and insights that enable true, fact-based innovation and impact to the business
- Discover how AI is enabling retail right now – meet CINDE and have a conversation that matters
- Learn what 100+ retailers are prioritizing by downloading US and European retailer category management survey results.
* Andrews, W., 2018. Craft an Artificial Intelligence Strategy: A Gartner Trend Insight Report, Gartner Jan 2018