This repository was archived by the owner on Dec 12, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 0
OKRs and KPIs
Chris Pondoc edited this page Feb 13, 2024
·
1 revision
To help us measure success, below are our OKRs and KPIs
| Objectives and Key Results (OKRs) | Scores and Notes | |
| O | Help restaurants diversify their wine lists by connecting them to smaller vineyards looking to diversify their revenue streams | - Have been working on talking with restaurants and wineries alongside trying to make our initial MVP (see KRs) |
| KR1 | Reach out to 5+ small- and medium-sized wineries to understand pain points around selling to restaurants + distribution in general | - Have had one call with a former partner at 3 Steves Winery and have a call set up for 2/16 with Leaf and Vine Winery β continuing to reach out to more wineries to identify more pain points |
| KR2 | Reach out to 3+ restaurants to understand problems around sourcing diverse wine lists. If possible, connect with sommeliers, and establish strong relationship/potential advisorship | - Have reached out to multiple restaurants more on the high-end side in the area and have received a response saying that we can email over questions |
| KR3 | Build out simple MVP, also onboard restaurants + maintain relationship through creating custom curated lists as well as launching initial rec algorithm | - Have discussed the MVP structure of using a vector database alongside OpenAI GPT to make initial MVP |
| Key Performance Indicators (KPIs) | Details | Relevant Metrics and Notes |
| Number of searches | Number of users who returned and made a search within one month | - Need to Ship MVP to get more details |
| Number of clicks | In particular, clicking on a search result for a wine and click rate for promotional emails | - Need to Ship MVP to get more details |
| Recommendation algorithm accuracy | Recommendation algorithm will predict a score out of 10. When a user rates a wine that was recommended, we can store the average percent error of the userβs wine rating to the rating predicted from algorithm. | - Need to Ship MVP to get more details |
| Number of transactions that occur on the platform | Look at periodicity β what is the habit of restaurants buying wines | - Need to Ship MVP to get more details |
| Number of bottles sold | Relatively simple evaluation metric | - Need to Ship MVP to get more details |
| Number of new users signing up both on vineyards and restaurants side | βChicken and egg problemβ β validates who has more pain points | - Need to Ship MVP to get more details |
For other information, check out our team's Google Drive. For a daily stream of thoughts, check this document.
- OKRs and KPIs
- Team Coding Standards
- Real Customer Profile
- Launch Week Recap
- Unusual Academy Pitch Event
- Final Reflection
- 1/23 - Jay Borenstein
- 1/26 - Chris Oh
- 1/30 - Glenn Reid
- 1/30 - Adam Heher
- 1/31 - Samantha Phillips
- 1/31 - Chris Tsakalakis
- 2/2 - MZ Zaveri and Kasey Zhang
- 1/25 - Introduction
- 2/1 - Proposing a Product
- 2/13 - OKRs and KPIs
- 2/27 - Catching up for Last Weeks of Winter
- 3/5 - Demo + Discussion
- 4/2 - New Quarter
- Strategy for Search for Shopping
- Ideal Customer Profile Brainstorming
- Outreach Messaging
- Tracking Outreach
- Discovery Call Outline
- Feedback from Unusual on Slides
- Meeting with Seena from Nike
- Meeting with Mike from Launch
- Meeting with Heather from Walmart
- Meeting with Sandy from Walmart
- Demo Call Outline
- Prototype v0: A New Shopping Experience
- Prototype v1: Updated Bookworm for Demo Day
- Prototype v3: SUPost Battle for Software Fair