The impact of digital was first felt by retailers through the evolution of online shopping over the past decade. In conjunction with this, the retail industry witnessed the ubiquitous adoption of smart devices by consumers. However, due to many practical business reasons the physical store continues to be slower to digitally evolve despite all the hype in regard to automation and AI. Digital technolgy inside stores benefits both customers and retail teams equally if prepared for properly.
It is challenging to provide “one-size fits all” advice on the best approach to driving personalization in physical stores because each retailer’s store estate can be complex in respect to the number of stores, store locations, differing sizes of stores across the estate, and the format of the store (i.e., fashion, food, general merchandize, or mixed). However, the growth of online shopping has continued to increase the consumers’ expectation in regard to receiving an ever more personalized and seamless shopping experiences across online and in-store contexts.
Best-practices recommended for consideration when developing an in-store personalization engagement strategy are not simply a case of replicating what has been successful online. The physical store is a more complicated environment to engage consumers within than the tightly focused and curated experience of online. An in-store shopping experience has numerous in-person social dynamics, and other multi-sensory factors, that are not reflected when using an online store so the approach, while similar, has very different dynamics.
LEF advises retailers to be pragmatic and modernize the store environment first before looking to bold new technologies to transform personalized in-store engagements. The following challenges must be addressed to embed an operational foundation for delivering personalized in-store experiences:
Inability to understand in-store micro-trends – To drive in-store personalization retailers must understand the granular behavior of specific consumers both within individual and across multiple stores. These behaviors include consumer type (i.e., comparison, time-limited, or attention-deprived), visit frequency (i.e., first-time, infrequent, and often), duration, cross-department journey, conversion rate, demographics, and purchase preference (i.e., purchase in store, click and collect, purchase and deliver). Marketers' need to utilize the aforementioned granular data to deliver targeted per-consumer information and offers either direct to a consumer’s smart phone or to a sales assistant’s mobile device. Allowing the opportunity to plan and perform flexible marketing in quick, or ideally real-time, cycles as consumers traverse a physical store to aid in improving conversion rates. The in-store application of customer analytics is the real competitive differentiator covering the store estate, per store dynamics, and consumer behavior within a store as well as across multiple stores. The data generated from new digital touchpoints, in each store, allows retailers to optimize merchandising, marketing, and shop floor teams to better understand how granular consumer behavior impacts a stores performance and the dynamics across the retail estate.
Empowering not burdening store staff - It is common for retailers to have frequent staff turnover, on a per store basis, which increases with temporary staff employed during peak trading periods throughout the retail calendar. To this end providing staff with mobile devices to better engage and support customers must be carefully thought through. Just providing mobile devices for sales assistants to search a retailer's online inventory falls a long way short of the business opportunity presented. A consumer in a physical store engages with a sales assistant for information that is not readily available to them and often loses interest in a purchase if a sales assistant cannot help. Devices for use by sales assistants should provide access to non-personalized product information and offers, product availability both in-store and across the whole retail estate (including online), an ability to reserve or order products, and information about the consumer (if agreed by them) to provide personalized product recommendations, offers, and/or further information. By empowering sales assistants, retailers have the opportunity to increase consumer purchase conversion and gain an invaluable new source of data to provide a perspective on store and customer dynamics in support of merchandisers. Sales assistants need devices with intuitive software interfaces that adapt to the specific role, within a department, of the employee using it. Devices with complex and time-consuming user interfaces will deter employees from using them and diminish the business value of the investment by the retailer.
The importance of in-store data connectivity – On a per store basis determine the existing network bandwidth constraints in contrast to the requirements of deploying digital technologies that require integration with centralized systems. No matter how great the user experience on a mobile device is, either for sales assistants or consumers, without fast Wi-Fi with perfect coverage across each store the benefits of an in-store personalization strategy will not be realized. Very few commercial retail spaces were designed in anticipation of providing good wireless data coverage and many retailers who adopted in-store Wi-Fi did so without anticipating usage growth by both staff and consumers. In addition, Wi-Fi coverage per store requires segregated networks, with reserved bandwidth, for store operations as well as "free" in-store Wi-Fi for consumers to use. The configuration and building materials of commercial retail spaces can vary considerably and impact the ability to effectively deploy in-store Wi-Fi. Extensive planning and co-ordination are needed on a per-store basis to address cost and complexity and ensure ease of operation/manageability.
Culturally embracing rapid in-store experimentation - Rapid experimentation, at scale appropriate to the retailer, is an iterative method of gathering in-store customer engagement data to determine how and when consumers are being influenced and what is changing their purchasing patterns. Gathering such data allows marketers and merchandisers to incrementally evolve and improve in-store personalization engagement and also stop those that are not providing the desired results in faster and more frequent cycles that keeps apace with the consumer. Retailers must become comfortable with iteratively, and rapidly, evolving the retail use cases derived from in-store personalization exercises and design them from the start for rapid deployment across the whole store estate, if deemed successful.
-Spencer Izard, researcher and advisor at Leading Edge Forum