– Retail Activity Optimization (RAO) – This uses recent (daily or more frequent) point of sale (POS) data to determine which stores to visit, what to do while there to drive the greatest return on resources, and the best sequence for reducing drive time. RAO not only optimizes in-store execution, but also reduces cost factors, such as fuel and the personnel costs associated with visiting locations where there is low or no need.
– Image recognition – This captures an image of the store shelf and transmits it into the cloud where images of manufacturers’ and competitors’ brands are analyzed to determine compliance to approved schematics, compliance to some promotional agreement, key performance indicators (KPIs) such as share-of-shelf, and so forth. This drives the activities the salesperson will engage in at that store after the few minutes it takes for the results to come back to that person’s mobile device.
– Guided selling – This utilizes branching logic, store-specific data, fact-based selling, and graphical delivery to direct activities and maximize impact at the individual store level.
– Social capabilities – These harness the power of social networks between peers and from managers to peers to give immediate feedback, solve problems, and share best practices from within the retail execution solution without having to resort to a separate tool.
– Gamification – This utilizes leader boards, compensation simulation, embedded games, as well as daily/weekly contests as motivators for the field force.
– Voice enablement – This enables a field salesperson to speak into a Bluetooth headset for taking orders or entering observations about the store shelf, rather than keying the information into a mobile device. Conversely, it enables the solution to “speak” to the user to share instructions from managers that come in via social, share insights about an upcoming store visit, and anything else that would allow a user to multitask by have a device speak as opposed to reading from it.
– In-flight analytics – These are capabilities that provide insights that can be harnessed immediately by a field salesperson to improve results. We refer to this area as “in-flight analytics” because it provides a stream of insight about performance and opportunities. Surprisingly, many solutions we have evaluated cannot do this because they cannot work off-line or must send transactional data to the central server and back to the device to provide these insights.
– Virtual reality – This allows the visualization of an image such as a display module or cooler within the context of an individual retail outlet to provide the best possible understanding of appearance and fit.
– Predictive analytics/ optimization and pattern recognition – Enable a user to understand probable outcomes that are not within the realm of the human mind to understand. Examples include the optimal time to bring product to a store before it becomes unavailable or the best route between 15 stores that have been chosen for a visit today so as to reduce drive time. Pattern recognition, on the other hand, is the ability to identify relationships between variables and answer questions that people have not thought to ask. Examples include identifying that a store consistently sees an increase in sales during the first 3 days after a merchandising visit despite no product availability issues or identifying social triggers that impact sales that nobody ever thought to consider. These capabilities are often provided under the banner of machine learning or artificial intelligence interested in learning more? See the full report here. Reach me at mkantor@P-O-I.org
We’re back at the Westin Galleria! Join us and 300+ CPG and Retailer peers! Attendees include: Pepsico, Sargento, Rich Products, Bellisio Foods, Massimo Zanetti, DPSG, Deoleo Bertolli, Musco Family Olive, Daisy Brand, AAFES, Johnsonville, Kimberly Clark, Ajinomoto Windsor, Walgreen’s, Blue Buffalo, Pinnacle Foods, Del Monte Foods, Mondelez, ConAgra, Ainsworth Pet Nutrition, Red Bull, Hormel, and more!
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