Experience shops tend to resemble pop up boutiques like Orchard Mile (seen in the picture in the middle), which lets store shoppers experience and engage with over 150 brands…
Experience shops are low cost, require minimum inventory, are easy to update. This is the perfect scenario for brands because they are able to engage with new customers and test products with a very low barrier to entry.
Another example is the Fordhub, which is now open in the New York Westfield World Trade Center. It is meant to engage with consumers and show the future of transportation. Without being a dealership or store.
Image source: Flickr (https://www.flickr.com/)
For example, facial scanning software can be a good fit for a business that caters to humans’ more spontaneous purchases. This only works when it is connected to strong analytics in the backend.
Tie to analytics section - Its the source of data that helps create those digital store engines. While this made it to our hotlist this was the bottom of the hot list.
insert graphic of digital store tech radar - most things are in survival mode. not many up to the left. digital store tech investment not about one thing they are about finding when works for their customer - digital signage may work for a hip store, in store apps may work for high end retailer but not others.
General theme we heard from retailers was – I don’t have anything in my budget for AI on the commerce suite side, I don’t think its ready to be customer facing, and there are people in other parts of the org that have budget for this. I’ll let them learn about this before I start diving into it. Orgs are considering AI budget for the ecommerce pro but aren’t investing in it just yet.
Similar to chat bots this was conditional. The theme we heard was my budget line doesn’t have chatbots in it, maybe BI, logisitics, labor management. Not ready to invest in it. I am going to see what my operations (Warehouse management, store operations, data analysis) can do with it then I will see if it can become a customer facing solutions.
Seen by most ecom professionals as something they don’t have in their budget line this year, but the org has some appetitie for AI mostly for BI for operations teams and also in a test and learn pilot phase.
When we asked about customer facing tools, when you are talking about a big solution for customer engagement, not necessarily a chatbot, there isn’t a need for it right now.
The case for AI does exist. On the right we see Forrester’s Case Study on The North Face, who in just under a year launched IBM/Fluid's XPS conversational commerce solution, which utilizes IBM Watson's Natural Language Processing to analyze text from shopper's typed responses.
However this solution required rigorous testing, and investment in production (from actual checks to time spent perfecting the tool).
Need an example of the ops case mage and video analysis. Thanks to the increasing availability of cloud-based image and video analysis platforms from major vendors and startups alike, companies from the retail, insurance, market research, and security industries are now taking advantage of the insights in video feeds, marketing content, and other image data sources (see Figure 9). A minor level of investment can create applications that determine foot traffic. Or these apps can identify customers' age and gender for content targeting, comprehend some human emotions through facial expressions, or identify suspicious actors to both reduce risk and enhance customer engagement and experience.