Startup Weekend Chicago 2015 - Chews Pitch Deck
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This document proposes a digital menu solution to help restaurants and customers with special dietary needs. It identifies the target market as people aged 22-35 with dietary restrictions, and suggests marketing directly to them through nutrition websites and social media. Customer feedback validated a need for digital menus with pictures, simple navigation, and filtering options. A survey of 179 people also supported testing the solution. The proposed business model charges restaurants $100/month, with the goals of increasing table turnover and sales. The large total addressable market is over $1 billion serving the over 1 million restaurants in the US.
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Startup Weekend Chicago 2015 - Chews Pitch Deck
- 4. SOLUTION
- 5. TARGET MARKET WHO: HOW MANY: HOW TO REACH THEM: 60% of Americans have at least one dietary restriction Men and women between the ages of 22 and 35 with special dietary needs Direct-to-consumer marketing using diet and nutrition websites and blogs, SEO, and social network marketing Advertise with restaurants on tabletops and website
- 7. WHAT THEY SAID... Think something I would like a lot is if there was a digital menu that had pictures of every single item on the menu. Ever try going to a Mexican restaurant where the menu is flooded with food names you can't pronounce, so you basically have no idea what they are?" "I hate to hold and navigate through large bulky menus, would prefer clean simple menus. I wish all menus highlight their signature dish better. If I spend the time looking at the options and making a choice, to hear that it's not available today, is a big turn off." "I liked the ideas of filters! If I could think of generally what I wanted and click a filter that let me see options for what I was in the mood for, and filters my dietician needs that would be amazing!"
- 9. Would love to increase turnover on our tables. Lets please sit down to talk about doing a beta at our restaurant. - Jason Morgan, Owner of JDI Bar & Grille BUSINESS VALIDATION
- 10. GO-TO-MARKET STRATEGY Go-to-market Use a combination of Menu APIs and manual scraping in a targeted area Once, weve reached critical mass, begin partnering directly with restaurants Social media campaign
- 11. BUSINESS MODEL $100 per month per restaurant Improved customer experience Data on user behavior Real-time updates to menus and inventory Increase in revenue Faster ordering = higher table turnover Personalized recommendations = increase in sales per order Approximately 7,000 restaurants in Chicago 5% penetration in Year 1 would translate to over $400,000 in revenue
- 12. COMPETITION ISSUES Hardware Dehumanize Lack of personalization Our solution is unique!
- 13. Build recommendation engine Add restaurants and menus to our growing database Build menu management system for restaurants Refine marketing strategy Point of Sale (POS) and kitchen management system integration NEXT STEPS
- 14. Team
- 15. Thank you!
- 16. Total Addressable Market Number of restaurants in the US: 1 million Per Restaurant Per Month: $100 TAM: $1.2 billion Price Point National average revenue is $60k per month for a resturant We need to increase resturants revenue by 1.7% assuming 10% margin TAM: $1.2 billion Appendix A
Editor's Notes
- #3: This is Maria and she has a problem that many of us share. Imagine this, she sits down at a restaurant and opens up a menu like this...
- #4: The best way for her to see if meals meet her dietary needs is to read the ingredients of each item. She may not understand the ingredient names, she cant see photos of meals. Maria becomes frustrated and she hasnt even been at the restaurant more than a few minutes.
- #5: We are going to fix that. Our product enhances the dining experience of consumers like Maria by making a digital menu that is smart, user-friendly and personal. Marias menu understands her and makes it easier to choose what to eat. This menu is DYNAMIC -Filters (that meet her dietary requirements) -Full-text searching -Up-to-date pricing Chews learns Marias favorite food items over time It removes irrelevant choices -Based on variables like meal time, portions and nutrition Most of all its PERSONAL Our machine learning algorithm learns your tastes with each meal Receive personalized recommendations confidence score Maria cant stand onions so her menu doesnt bother to show that as an option Her menu also knows that because she loved the Portabella she had last week, she would love the Megamess, a chef specialty at (Restaurant name).
- #7: Survey: 179 people; Guerilla research in the streets A/B Tests: layout filter functionality When we went out to test multiple versions of the prototype, those with dietary restrictions were the most excited. Talk about cross-fit paleo gal jumping up and down.
- #9: Other notable metrics 95.5% of our surveyed users use their phone while dining 84.7% would like ther menu to offer meal recommendations personalized to them
- #12: Total Addressable Market Number of restaurants in the US: 1 million Per Restaurant Per Month: $100 TAM: $1.2 billion
- #13: Were definitely not the first ones to try and make the menu digital, but our approach is certainly unique. Most current competitors have been trying to solve a different problem: eliminating the server-consumer interaction.
- #14: We have confirmed our consumer needs this and that restaurants are willing to join. So, how do we reach them? Aggregate menus and add them to our growing database Get restaurants on board Continue refining our algorithm that recommends food choices.
- #15: We have confirmed our consumer needs this and that restaurants are willing to join. So, how do we reach them? Aggregate menus and add them to our growing database Get restaurants on board Continue refining our algorithm that recommends food choices.
- #17: We have confirmed our consumer needs this and that restaurants are willing to join. So, how do we reach them? Aggregate menus and add them to our growing database Get restaurants on board Continue refining our algorithm that recommends food choices.