Chatbot is a shopping assistant for customers in offline stores. When in the store it suggests outfits with the product range in the store, collects customer's purchases and later sends push-notifications with recommendations of outfits with previous purchases and new collection of the brand.
Current Status
By current moment we've finished 3 pilots with international fashion brands, the 4th is in process, released beta version of the product and ready to go on the market with one of the partners.
Market
Our customers: international apparel retail chains, worldwide. Our users: men and women of 16-45; active users of smartphones and other gadgets with the Internet connection. We plan to spread our product in Russian (Volume - $US 36,68 bn), Europe ( Volume - $US 435 bn) and USA (Volume - $US 334 bn). CAGR of our market is 7,8%.
Problem or Opportunity
Problems that we help retailers to solve:
- lower sales in brick-and-mortar stores;
- decrease of customers’ traffic;
- obsolescence of brand’s audience;
- need for enriching customer experience;
- poorly developed omni-channel.
There is a dramatic decrease of customers' traffic in break-and-mortar stores. According to PwC report in 2017, 58% asked retailers said that there is a real threat of shutting down of their stores. During 2017 2000 stores in the USA were shut down.
According to vc.ru, 50% of russian fashion retailers have a goal to activate omni-channel and to enrich customer experience in their stores by the end of 2019.
Solution (product or service)
Our solution is AI-powered chatbot Amby designed for apparel retailers and their customers.
Amby gives an opportunity for fashion retailers to:
Boost sales in offline;
Attract younger generation to the stores with free innovative customers service;
Activate sleeping clients;
Support brand image as innovative, digitalized company with high standards of customer care;
Merge online and offline channels - activate omni-channel;
Enrich customer experience;
Hyper-personolize marketing communications with clients suggesting shopping recommendations based on their preferences;
Increase CLTV.
Competitors
Alternatives that people use now: - Offline stylists (shopping with stylist, audit of wardrobe) - Online stylist apps (online consultations with stylists, selected looks are delivered by mail): GetOutfit, StitchFix B2B: - e-com solution: Wide-eyes, UpMeStyle - in-store solution: Mercaux B2C: - Mob apps to operate wardrobes (Cladwell, Finary) - AI services that recommend outfits you may like (Inspora chatbot)
Advantages or differentiators
As a B2B solution: - our service does not require extra equipment or extra work for the store stuff - activate omni-channel.
As a B2C solution: - our chatbot saves customer's purchases and creates his/her virtual wardrobe; - suggested outfits include the clothes that customer bought before and a new collection of the brand
Finance
Current burn rate is 2000 USD per month. Monthly fee for each store - US$120-200 (depends on the particular store) + 8-15% CPA (commission of m-commerce).
Business model
Business model with offline retailers: Subscription per month per store. Fixed fee + set up fee. And commission on m-commerce.
- FashionTech is growing intensively and there always appear new and new in store services, so we must grow fast enough in order not to miss the moment
Incubation/Acceleration programs accomplishment
- completed the acceleration programme in NUMA Moscow - startup school Y Combinator
Won the competition and other awards
became №3 start-up among 50 woman-led Russian startups in the 1/2 of Women Startup Competition Europe