Life™

A predictive layer that understands the product, service, and content needs of consumers

To win, we must predictively serve user needs, but 99% of their life happens outside of a store.

1. Retail stores are becoming less and less relevant over time
    
2. Users demand the lowest price, and have near-perfect market information
    
3. Users are beginning to shun traditional, time-intensive, purchase flows

Life = Our Conduit to the Customer

Now

• Near-zero non-store, consumer visibility
• No contextual awareness around needs
• Unable to service consumer needs predictively

With Life

• Able to service the consumer everywhere
• Always analyzing for context and need
• Able to predictively serve needs with little-or-no user interaction

• Awareness of home cycles
• Integrates with connected devices
• Responds to family activity

• Monitors home while away
• Coordinates work activities
• Understands transit flows

• Understands the reason for a store visit
• Provides a channel for store triggers
• Suggests products based on circumstance

• Responds to transit behavior
• Suggests service alternatives
• Updates users about surroundings

• Visibility into non-Target flows
• Comparison shop suggestions
• Alternative good/service notifications

Lil' Billy needs a lift

User Story: Sally is a mom on the go. She balances a work and family like a pro. Sometimes, no matter how hard she tries, things come up. In this case, she has to work late, and can't get to her son Billy in time.

Solution: Target Life analyzes Sally's regular commute windows, and the locations she frequents, to discover that she is not on track to pick up Billy as usual. To help, Target Life offers to send a child-safe ride via Target's services platform. Billy can see exactly who is picking him up, and so can Sally. She's notified every step of Billy's journey home - priceless piece of mind.

Matt's med refill

User Story: Matt is a 56 year old salesman who constantly travels, and must adapt to an uncertain schedule. Matt also has a heart condition that requires medication. Matt is in a different city and his medication is running low.

Solution: smart bottles inform Target Life of Matt's remaining medication supply. Target Life already knows what hotel Matt is staying in, and uses the medication data to select the nearest pharmacy for a refill. Matt confirms with Target Life, and his medication arrives at the hotel's front desk courtesy of a drop-off service Target Life negotiated on Matt's behalf.

How can we make Life a reality?

We need to meet the customer where they are, and that requires a conduit that goes with them on their journey. Here are a few options worth considering:

1. Partner with an existing platform provider to enhance their predictive layer with our UX and ecosystem - Google, iOS.
    
2. Partner with one or many device makers to develop our own predictive layer - Samsung, LG, Sony, Xiaomi, etc.
    
3. Partner with or acquire a launcher/assistant app to develop our own predictive layer