Vertical ML/AI Startup Creation Weekend

Sector analysis

Vertical description

The retail market includes any sale of products to consumers. From eCommerce, Grocery, Gasoline, Ikea, to Stitch Fix. How can we utilize technology to improve the shopping experience?

How big an opportunity space is this, how is it growing, and what’s driving that growth?  

The US retail market is worth $3.5 trillion and growing at 3%. eCommerce only has 10% market share of total sales. The smart retail market is worth $10B but is growing at 24%. Grocery is a $663 billion market in the US. Growth is driven by the adoption of smartphones and changing customer demands (customer service, delivery, one click).

What are the segments/pockets?

There are several different types of technology used in smart retail. Bluetooth, ZigBee, RFID, WIFI, VR/AR, LPWAN. You can also segment it by application: smart label, store navigation, smart payments, robotics, analytics, visual marketing. The largest market size among categories is in hardware as computing power will limit adoption of new technologies and require upgrading. Robotics is estimated to have the most growth among segments.

What has been the VC investing trend in this category?  

What is the technology spend and trend in this category, or the revenue growth rate of companies in the category (whichever is applicable)?

64% of retailers are selling omnichannel in the US. 40% of retailers plan to increase their spend on technology. There is still much more data to take advantage of from shoppers.

What are the proof points that success may be rewarded?

• Previous winners:
  

  • Target
    

  • Costco
    

  • Walmart
    

  • Amazon
    

• Recent M&A:
  

  • Purple by Global Partner Acq ($1.1B) - new age mattresses
    

  • Blue Nile by Bain/Bow Street ($500M)
    

• Rising stars:
  

  • Chewy ($451M raised)
    

  • Brandless ($293M raised)
    

  • Dollar Shave Club ($163M raised)
    

• Early seeds:
  

  • Standard Cognition ($5.5M seed) - get rid of cashiers
    

  • Fourpost ($5M seed) - community experience with brands
    

  • Grailed ($15m series A) - peer to peer NYC marketplace
    

At a high level, what problems are there to be solved using technology?  

• How can we improve the shopping experience?
  

• Why are there ever lines to pay for items?
  

• So much manual labor and operation costs
  

• Trying items on takes too long and cannot be done online?
  

• How can I try out online products?
  

• Can I 3D print products at home?
  

• Can technology improve my style?
  

• How can we better navigate stores?
  

What current trends are driving change in this category?  

• Smartphone adoption/ Mobile Purchases
  

• eCommerce
  

• Social Media as a marketplace
  

• IoT - RFIDs, Beacons, Sensors and cheap tech
  

• Visual Search
  

• Reimagined Brick and Mortar
  

• Customer controlled delivery/search - Buy online, pickup in store, Amazon Day, Same Day
  

• Online Security
  

• Personalized/Predictive Experiences - Yoox Net-A-Porter
  

• Subscriptions - Stitch Fix, BabyGap OutfitBox, Hasbro Party Crate
  

• Augmented Reality - Wayfair, Lowes, Salon Project
  

• Messaging with customers - Facebook Messenger
  

• Checkout Automation - Cashless, Amazon Go
  

How specifically can ML/AI change the game in this category?  

• Optimize store layouts
  

• Personalized recommendations
  

• Speed up necessary shopping
  

• Bring new products to consumers
  

• Integrating different systems
  

• Automate labor - Sales assistant, cashier, returns processing
  

• Improve ad experience
  

Investment hypothesis / rationale

The retail market is so large and valuable there are many right tailed opportunities. We should be able to identify interesting spaces to innovate and capture portions of this expansive market.

What adverse conditions / headwinds are there for a play in this space? What makes it difficult?

• Amazon
  

• Where do we start?
  

• No barrier to entry, price competition
  

Data sets

Your novel business idea should be grounded in real-world data with plausible machine-learning/analytics on top. We've compiled a collection of datasets from which to gain inspiration. Note that you are not restricted to basing your idea on the data sets below. You may discover other open source data sets that inspire your creativity or you may bring your own proprietary data sets if you wish.

Many of the datasets below are from Kaggle, Figure-Eight (Crowdflower), Data.World, etc. The advantage of these datasets is that many have been cleaned and normalized and are ready to be explored with ML and data science tools. Note that the use of these datasets is often intended for research purposes only. Be sure to read any associated license agreements to understand if there are commercial restrictions if you plan to continuing using the data after the workshop is over.

Resources

• https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2018/03/global-retail-trends-2018.pdf
  

• https://www.nvidia.com/content/g/pdfs/deep-learning-retail-primer-ai-335699-v5.pdf
  

• https://www.ey.com/Publication/vwLUAssets/ey-retail-reimagined/$FILE/ey-retail-reimagined.pdf
  

• https://www2.deloitte.com/us/en/pages/consumer-business/articles/retail-distribution-industry-outlook.html