3 Back to School Season Tips for the Data-Driven Retailer
The Back to School Retail Opportunity
In 2018, ahead of the Back to School (B2S) shopping season, Deloitte issued a report estimating that total spending for this single shopping season would surpass $27 billion. By including university students in this figure, the National Retail Foundation put this number at over $82 billion. With B2S shopping affecting a quarter of households in the US and accounting for 50% of annual school–related spending, retailers must think critically and strategically about meeting B2S consumer preferences, drawing in buyers and solidifying a long-term relationship that will attract loyal customers back year after year.
Important to everyday retail operations, Big Data and business intelligence are ever more crucial around popular shopping seasons. Instead of formulating and executing plans based on assumptions about consumer behavior, data insights empower retailers to engage with their customers based on actionable analytics.
To support the shift towards data-driven retail, merchants are both adopting new IoT sensors – such as smart RFID-enabled shelves – while considering how to maximize the applications and ROI of already–integrated technologies, such as surveillance cameras and point of sale (POS) devices. They are identifying new data sources, new ways of leveraging information and new opportunities for data fusion – merging the data aggregated from different sources to gain a fuller, deeper perspective into business activity.
Retailers use these droves of data to gain insight into who is shopping at their stores, which products and displays are most popular and how consumers navigate the store. Based on these insights, merchants execute plans to deliver an engaging and compelling shopping experience that will drive brand loyalty in the long-term. Here are the three top data points you should be considering, as you formulate your B2S plans:
1) Identify Demographic Data
It is crucial for retailers to know their audience – by identifying and segmenting engaged shoppers, merchants can both cater efforts towards those audiences and determine how to better attract other types of shoppers. Furthermore, retailers must identify the advertising, promotions and products that would best appeal to these demographics.
Utilizing video analysis software, a merchant can aggregate video surveillance content captured over time and analyze it to evaluative customer interactions with promotions, products and store layouts. Using visualizations and dashboards representing trends in video data retailers can pinpoint merchandising and advertising efforts that were successful with demographic segmentations of interest and formulate plans and future decisions based on actionable intelligence.
2) Optimize Merchandising Decisions
As demonstrated in the previous example, video content analysis can help retailers understand which products drew the most attention in-store. Video content analytics software can illustrate – using heatmaps and navigational data – consumer activity around product displays, such as whether consumers were dwelling next to certain products, loitering outside the store’s window displays or – in a single shopping trip – returning multiple times to the same displays. You can even determine whether shoppers dwelling in a certain area of the store, spend more time in the store overall.
By integrating this intelligence with other technological data sources, retailers can gain critical insight about the highest performing merchandise. For instance, the merchant might rely on RFID tags to understand how often certain objects were picked up off a shelf, indicating interest in the product. Furthermore, this item interaction data could be correlated with visitor navigation data from video analysis, as well as inventory and POS data, to indicate the conversion rate for the particular item. The holistic and comprehensive intelligence provided by this data fusion could help inform future pricing, merchandising and advertising decisions to drive conversions and business performance for the retailer – without speculation.
3) Maximize Store Layout
Data-driven retailers leverage their smart infrastructure and IoT devices for long-term data aggregation and analysis. In preparation for major holidays and expected traffic, these merchants should make it a habit to evaluate navigational trends in-store and maximize the floorspace , display placement and even employee distribution throughout the store based on consumer interaction data. Using heatmaps and path data visualized by video content analysis, retailers can pinpoint obstructions, popular paths and underutilized spaces (included entrances and exits) and optimize floorplans in anticipation of increased shopper traffic.
Not only is this critical for ensuring that consumers can comfortably navigate the store, maximizing store layout is also key for preventing congestion and crowding, which can influence even motivated shoppers’ decisions to purchase. By reviewing past data and identifying the impact of crowds on conversions and cart abandonment, retailers can plan and update their strategies to ensure a smooth shopping experience for B2S shoppers.
Of course, data-driven retail strategies are key beyond the back to school shopping season. These principles and strategies can (and should!) support everyday retail decision-making. That being said, with at least 60% of parents likely to begin shopping before August – a demographic which on average, according to the NRF, spends $100 more than shoppers who tackle B2S purchases closer to the school year – it’s never too early to transform your business with business intelligence and analytics-driven solutions.
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