The Problems

The hospitality industry needs a digital transformation and this transformation is going to be driven by context and personalisation. The data-driven models used by hotels and restaurants (Booking.com, Opentable, Just Eat,...) to reach a wider audience have yet to be extended to cafes, bars and pubs.

Users

  • Finding places that fit well with your interests is a hit and miss process and very time consuming.
  • However, the current loyalty solutions for the hospitality industry require lots of effort from the customer side, from app check-ins to scanning QR codes or tapping with physical cards.

“It’s my first time in this neighbourhood. I love my craft beers, where can I get one nearby?” Thousands of people every day

Venues

  • Cafes, bars and clubs want to retain their loyals customers but they’re not able to identify who they are and inspire them to come back.
  • It’s difficult to optimise the customer acquisition investments due to the lack of reliable market data.

“Who are our most most loyal customers and what is their churn rate?” Wetherspoon’s Pubs CMO

The Solution

The recommendations = What you want before you even know that you want it

Nextub lists a selection of the best 1,000 cafes, pubs, bars and clubs in London and recommends to each user the ones that fit well with their lifestyle and interests.

How does nextub know what is relevant? Alohar + Machine Learning. Step by step:

  1. Alohar educates Nextub’s platform about the places that the users visit. This includes their activity while using the app and while carrying on with their regular life schedules.
  2. Nextub takes the geolocation data and using a proprietary machine learning model infers what characteristics of the reported places are the most relevant for each user. Those characteristics are extracted from a purpose built database that includes 600 unique features that can be attached to the venues, from product specialities to the types of atmosphere, dress codes, pricing etc. Each venue is defined with 10 to 30 of those characteristics.
  3. The recommendations become increasingly accurate even while the user is not actively engaging with Nextub.

The art of retaining customers

Facebook likes, app check-ins, physical member cards and bar codes are the most common actions required by most of the current loyalty solutions. What do they have in common? The users have to proactively do something to be accounted.

This is how well they work: Efficiency of action-based loyalty systems = (# ‘check-ins’ on Foursquare/Swarm) / ( # times that you have stepped in a cafe, bar or club).

That formula above tends to 0 because people don’t check-in every single time they get their morning coffee or while they share some pints at the bar across the street from the office.

What Nextub does is the next:

  1. Businesses set up their loyalty milestones and rewards in the Management Dashboard. Example: ‘1 Free Coffee’ after the 5th visit, 50% discount after the 10th visit, etc.
  2. Nextub helps the owners and marketing professionals to gamify the experiences by pushing welcome messages to the customers each time they visit the venue.
  3. Alohar allows Nextub to count the number of times that a person visits each place and triggers the rewards and messages at the right place and time.
  4. The users get rewarded automatically without requiring them to do anything.
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