Since Watchfinder & Co. was first founded in 2002, it has established itself as the premier resource from which to buy and sell premium pre-owned watches. Thousands of watches are available from more than 50 brands, including Rolex, Omega, and TAG Heuer, at boutique retail outlets across the UK and from their website. In the financial year 2015-16, annual turnover was £68 million. Watchfinder is a high growth company; an alumnus of the Sunday Times Fast Track 100, with global ambitions.
Watchfinder engaged with endjin to migrate their existing platform from AWS IaaS to Microsoft Azure PaaS, as their CTO wanted to take advantage of the Advanced Analytics capabilities of the platform, specifically those offered by the Cortana Intelligence Suite. Machine Learning factors heavily in the company's global growth plans; creating more intelligent quotes, recommendations, and demand based pricing. These are all key to scaling current human centric processes, and maintaining margins as the company expands, while keeping the existing "small company" culture that has been so key to their success.
Ambitious global growth plans aren't the only motivation for the migration to Azure PaaS; Watchfinder has started investing in TV advertising campaigns (which led to a 70% increase in enquiries), sponsoring high profile events such as the Tour de France and has produced viral video content generating over 3 million views. All of which means their e-commerce platform needs to scale elastically, on-demand, to deal with sudden surges in site visitors. Dealing with expensive luxury goods means that security is a core business concern; but this isn't just limited to the physical domain, cloud security is vital too.
In addition to designing the high-level architecture that will enable Watchfinder to scale their e-commerce platform globally, endjin also helped improve the existing DevOps tooling and processes to give the development team more confidence about migrating to Azure, and provided a roadmap to becoming a data driven organization. Watchfinder's CTO also engaged with endjin's Brain Trust service to give him access to ad hoc advice on strategic matters.
In 2016, endjin hosted a 2 day Bot Framework hackathon with the Watchfinder development and Microsoft DX teams, to convert the existing "sell your watch experience" into a conversational interface. The hackathon demonstrated the power of the Bot Framework: reimplementing a feature that originally took 4 months to build in 2 days.
— Jonathan Gill, CTO, Watchfinder
Rank Group are a gaming, leisure, and entertainment company, who own Grosvenor Casinos and Mecca Bingo. With new online platforms and physical smart gaming devices, they were experiencing an explosion of data across the organization.
Alongside traditional business intelligence and reporting requirements, Rank assembled a new dedicated data science team tasked with surfacing new types of customer insight using advanced analytics, machine learning and predictive modelling.
Starting with exploratory models in R, endjin helped Rank to develop fully productionized solutions using Cortana Intelligence Suite - orchestrating data production pipelines with Azure Data Factory, processing data with on-demand HD Insight, hosting and executing R models in Azure Machine Learning and displaying interactive data visualisations in Power BI.
With endjin's expertise in productionizing advanced analytics solutions, Rank's investment in data science could be realized across the business.
Anheuser-Busch InBev is the world's largest brewer, with a heritage dating back more than 600 years, spanning continents and generations. Already considered one of the largest fast-moving consumer goods companies in the world, they are committed to driving growth and always looking for new opportunities and ways to improve.
In 2016, AB InBev sought out help from endjin to enable a new capability in their global data team around using advanced analytics tools and processes. Through a combination of hands-on exercises, interactive labs and collaborative workshops, endjin guided the team through exploring a real-world use case to get new insights from publicly available data. From defining testable hypotheses, selecting and preparing data and training and evaluating predictive models, endjin demonstrated a structured process to experimentation, whilst introducing them to new data science tools such as Jupyter Notebooks and Azure Machine Learning to explore data and develop models.
With Azure Data Factory orchestrating the complex data processing and Power BI displaying interactive custom visualizations AB InBev had an end-to-end productionized data science solution that served as an extensible architectural template for future analytical workloads.
AB InBev also used endjin's Data Science Toolkit to kickstart their advanced analytics journey - with notebooks and videos covering example experiments, and templates and scripts for productionizing models, the Toolkit provides valuable resources for teams looking to accelerate their capabilities in data science.
The combination of process, tools and resources allowed AB InBev to be self-sufficient and find answers to difficult questions that they would not be able to do with "traditional" BI tools. Armed with this new capability, they're now able to use data in new ways, exploring new opportunities for improving and driving growth in order to stay ahead in a fast-moving market.
— Marko Trninic, Solutions Africa: Innovation & Analytics Director, AB InBev
Purplebricks are a high growth 24/7 online estate agency offering to sell customer properties for a low cost flat fee. They floated on the stock market with a market valuation of £240m just 19 months after the company launched.
They wanted to explore how machine learning could help the organization scale efficiently, as demand for scarce business resources was rapidly increasing.
By applying our structured approach to experimentation, and using Azure Machine Learning to rapidly develop and train predictive models, endjin were able forecast customer propensity with a high enough level of confidence for the business to invest further.
In doing so, we were also able to disprove a long held business belief about their data. Being able to rapidly prove or disprove business hypotheses enables organizations to zero in on the best solutions, avoiding unnecessary effort and cost of chasing hunches.
Endjin armed Purplebricks with the insight needed to effectively allocate resources, increase business efficiency, and help solve one of Purplebricks' most critical business challenges.
Businesses depend on getting data to and from the cloud securely & reliably: enriching, transforming and manipulating it. Endjin's Modern Data Platform Blueprint is a culmination of the IP, process, and knowledge we have accrued over many years developing enterprise-class data solutions in the cloud, packaged up to help you achieve your data vision.
Built on top of endjin's Modern Data Platform, endjin's Anomaly Detection Platform can be used to identify, classify, and visualize anomalies in device telemetry or customer behaviour, and automatically feed back into your support procedures.