Chain Store Age editor Marianne Wilson talks with Michael Schulze, Senior Vice President & General Manager, North America Retail Industry, SAP America, about the motivation behind the "Black Friday and Cyber Monday Consumer Face-Off," and the technology used to uncover its insights, in the following Q&A:
Where did the idea for the Black Friday and Cyber Monday Consumer Face-Off originate?
In today’s retail world, we all know that the shopper is in charge. Engaging with the customer today means meeting them on their “shopping turf” or channel, not waiting for them to come to you. Single-day events like Black Friday and Cyber Monday represent defined ways of shopping during defined time parameters. With omni-channel shopping on the increase, we wanted to see how consumers are using social media to find, track and report deals during the kickoff to the holiday shopping season.
Beyond “how many times” was a retailer or experience called out, we wanted to understand the types of shopper sentiment that was associated with these two days. Were they blasé about the prospects of shopping on Black Friday, or was it going to be a life changing event? Did they love or hate the retailer, the deals, the lineups, or the ease of check out? As shoppers become more tech-savvy and loyalty neutral, retailers need to understand not only what customers are saying about the experience of shopping, but also how passionate they feel about the actual experience.
How exactly will SAP be tracking consumer sentiment those two days? What tools will you be using?
Consumer sentiment, as well as a number of other metrics, were tracked using SAP Social Media Analytics by Netbase. With its powerful Natural Language Processing (NLP) engine, SAP Social Media Analytics stores and interprets the last year of conversations from Facebook, twitter, and 165 million other sites across the social web – including social media networks, blogs, product review sites, forums, news sites, etc. – and provides insights into emotions, behaviors, brands, themes, passion, and sentiment.
By aggregating all these sources of data, we can quickly identify patterns – either by time of day, geography, news events, or