Stored Advantage - Why Leading Retailers are Doubling Down on Stores
|零售領導者的商店投資、商店策略 Stored Advantage - Why Leading Retailers are Doubling Down on Stores|
|出版日期: 2019年01月03日||內容資訊: 英文||
本報告根據北美的首席零售品牌為對象調查的結果，提供領導企業的優先的IT投資領域，商店部署、商店整修趨勢，預算分配，電子商務趨勢、影響，新興技術 (AI、機器學習、邊緣運算、IoT、處方的分析、巨量資料等) 的投資趨勢等資料彙整。
2018 ended on a very high note in retail for North America. As an economy in the US, retail sales were up over $242b for the year ending 2018. More than the entire retail GDP of South Korea. What does that mean for 2019?
In this collaborative study with RIS News you will see a view of what winning retailers are investing in for IT in 2019 vs. average or laggard retailers. Are we in the midst of a POS Refresh? How are changes in customer experience driving customer engagements and how retailers are fulfilling orders from various touchpoints? How is BOPIS driving store investment? How many are not only growing stores but growing the sales in those stores vs those who are simply accepting a lower sales per store? What about ecommerce growth, how much of this is expected to be desktop vs mobile? And finally, which emerging technologies such as AI, Machine Learning, Edge Computing, Conversational Commerce, SDWAN, Beacons, IoT, Prescriptive Analytics, Big Data integrations and RFID are seeing growth in investment vs. declines?
With responses from 220 top retail brands in North America, we have produced the results in a detailed, but very easy to read study. You also get the raw data to do your own analysis by segment.
The report is designed for use by Retailers, Hardware Providers, Software Providers, Service Providers and others who might have a vested interest in the North American retail market. The complete outline with chart titles is below.
Along with research study analysis your license also includes the raw data in spreadsheet form to do your own cross tabs. You can cross tab any answer to any other answer for unique insight.
We break down the data to find answers to questions that many in our industry are asking, like the following: