Algospark projects span a wide range of challenges and use cases across various industries. Our prototypes cover a mix of techniques that include forecasting, recommending, prioritising, anomaly detection, matching, clustering and allocating. We use these prototypes to fast track delivery of new service offerings and increase process efficiency. Quantification and maximizing value are always at the heart of our solutions.
We are a collective of partners with skills that span data science, customer experience, service design, finance and technology.
Algospark is led by Darren Wilkinson who has over 15 years experience leading and managing service innovation and analytics projects across numerous industries. Darren's expertise includes strategy, data science, corporate finance, programme management and technology.
We use Algospark as a innovation space to explore and fast track new ideas.
Organisations we have worked with:
A classification and feedback tool to streamline adult social care needs assessments. Ensures consistent needs classification, ongoing wellbeing feedback and predictions of future needs. Target 40% process time savings, and cost savings from reducing future hospital visits.
A word prediction tool trained on tweets, blogs and news articles. Built as a foundation for Natural Language Processing (NLP) projects such as document classification and automated responses.
A prototype care supplier selection algorithm for care commissioners. Ensures suppliers are prioritized and selected faster to reduce care brokerage requirements. Expected savings of 25% brokerage time.
A product portfolio dashboard to align product investment decisions across multiple projects, businesses and geographies. Target 25% reduction in analyst time and faster project approval processes.
Cluster analysis tool to determine new store demand patterns using information from across a retail network. Target better store portfolio alignment, reduction in forecast error and 5%+ operational savings.
A machine learning solution using open data sets and forecast model feature engineering to reduce demand forecast error by 15-30%.
A scheduling and team resource optimisation tool for private tutoring, home care delivery and other time slot based services delivered to homes. Target 10% efficiency savings.
Use of artificial intelligence to recommend actions to customer service specialists. Target 20% increase in response productivity. Can be integrated with service "bot" strategy.
Market basket and cluster analysis tool to determine new product demand and impact on existing products. Target 10-30% reduction in forecast error and pull through efficiency savings.
Machine learning algorithms to predict opportunities and challenges across business operations. Focused on process metrics and supply chain dependencies to highlight opportunities and performance challenges.