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Empowering businesses with Data

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Businesses are increasingly wanting to become data-driven, improving productivity or reducing waste through exploring existing data the business is collecting. Machine learning and Artificial Intelligence are promising a “Golden Age” for the data-driven business.

I aim to show:
How data analytics can discover inefficiencies in past practices and how this data can be used to streamline business processes.
That machine learning can predict the needs of new customers, and how this can lead to both improved customer retention and spending.
Adam Fletcher – Data Analyst,Equal Experts

Veröffentlicht in: Daten & Analysen
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Empowering businesses with Data

  1. 1. Empowering Businesses with Data Lightning Talk Adam Fletcher
  2. 2. $whoami PhD in Molecular and Structural Biology 10 years experience in biological research Almost 1 year at Equal Experts on EvolvE Research Areas Detected DNA abnormalities from blood Predicting resistance to therapy in cancer patients Working with clients to understand and better use their data afletcher@equalexperts.com LinkedIn: adamfletcheruk
  3. 3. Introduction What is Machine Learning? A system which can perform tasks without being explicitly programmed. Instead it relies on patterns of data and inference. Why Should Businesses Care? Understanding why certain patterns are present to take specific actions Personalising experiences for users by predicting their needs Understand public perception on Social Media
  4. 4. What Can Data Analytics Reveal? How Can Machine Learning Help? Talk Aims Using statistics, understanding variations in a process that impact productivity. Lead to restructuring processes based on results. Understanding of how users interact with a site. Tailoring their experience leading to greater user retention and satisfaction.
  5. 5. Data Analytics in Business The Cost of Variation ● UK-based events planning company ● Franchised, operate through standard pricing ● Organisation and staffing coordinated in branch Aims and Objectives ● Different areas coordinate the events differently ● What is the impact of this variation ● What happens if we reduce this variation
  6. 6. Data Analytics: Variation in Consultations Initial Consultations 2+ Appointments: ● East Lancs, W.Yorks ● Hamp and W. Sussex ● Manchester North ● S Wales ● Greater Glasgow ● Merseyside ● W. Lancs
  7. 7. Data Analytics: Variation in Consultations Length of Appointments Which are Good /Bad Results? * * ** * * * * ** H1: Detailed planning means team can proceed quickly H2: Length of Time in appointment has no impact on delivering event
  8. 8. Data Analytics: Variation in Consultations Hypothesis Testing Time between 1st Consultation and Event (Days) Region Expected Observed Correct Central and SW Scotland SLOW FAST N Manchester North SLOW NEUTRAL N Essex SLOW SLOW Y Northern Ireland SLOW FAST N West London SLOW NEUTRAL N Cumbria FAST FAST Y Durham FAST NEUTRAL N South Yorkshire FAST SLOW N Central Yorkshire FAST SLOW N East Yorkshire FAST NEUTRAL N
  9. 9. Data Analytics: Variation in Consultations Impact of Variation As-Is Model: Total Time = 111,897 hrs Sample Size = 58,161 events 100,000 Events/Year = 192,391 hrs New Model: One 90-min Consultation Total Meeting Time = 100,000 x 1.5 Hrs = 150,000 Hrs Difference = 42,391 hrs per year Assume 50% Model Uptake: 21,955 hrs to reclaim Approx 14,000 additional consultations
  10. 10. Data Analytics: Variation in Event Staffing Requirements at an Event Capabilities Requested ● 2 Drivers ● 1 Event Conductor ● 2 Event Setup Staff Members Needed ● 2 Drivers ● 1 Event Conductor ● 2 Event Setup Do you need 5 staff members to fulfil these 5 capabilities?
  11. 11. Data Analytics: Variation in Event Staffing Impact of Variation As-Is Model: Proposed Model: Task Conductor Staff 1 Staff 2 Staff 3 Staff 4 Pre-Use Check - 0.5 0.5 - - Pick up Clients - 1 1 - - Travel to Event 1 1 1 1 1 Event 2.5 2.5 2.5 2.5 2.5 Return Trip 1 1 1 1 1 Clean Vehicle - 1 1 - - Sum 4.5 7 7 4.5 4.5 Task Conductor Staff 1 Staff 2 Pre-Use Check - 0.5 0.5 Pick up Clients - 1 - Travel to Event 1 1 1 Event 2.5 2.5 2.5 Return Trip 1 1 1 Clean Vehicle - 1 1 Sum 4.5 7 6
  12. 12. Data Analytics: Variation in Event Staffing Impact of Variation As-Is Model: Total Staffing Time: 27.5 Hrs 95,000 Events a Year = 2.6M hrs New Model: Total Staffing Time: 17.5 Hrs 95,000 Events a Year = 1.6M Hrs Assuming 40% uptake = 400,000 Hours gained Time for an additional 23,000 Events Conclusion: Simple Data Analytics and Statistics can make a difference
  13. 13. Machine Learning in Business Understanding Users ● EU-based job application website ● Want to personalise experience for users Aims and Objectives ● Understand how different people use the site ● Personalise their jobs based on their needs
  14. 14. Machine Learning: Understanding Users Unsupervised Machine Learning ● Applicants asked a series of True/False questions ● Approach used: k-means clustering ● Responses were grouped into 5 clusters (k=5) ● Common trends were seen in the clusters ● Personas were designed to explain the series of needs applicants will face
  15. 15. Machine Learning: Understanding Users Data Analysis ● Blue cells are low values for each question ● Red cells are high values for each question ● Clusters have trends of similarly answered questions Raw Clustering Data
  16. 16. Machine Learning: Understanding Users Personas
  17. 17. Machine Learning: Understanding Users Impact of the Personas ● Characterising users allows customised job search order ● Alter the information required to list a job ● Detailed understanding of how people use the site Results ● Higher user retention and engagement in platform ● More companies paying to promote jobs ● More new users through recommendations
  18. 18. What Can Data Analytics Reveal? How Can Machine Learning Help? Conclusions ● You can examine inefficiencies in your business ● Measure the impact of changing business processes ● Better understanding of users ● Increased satisfaction promotes confidence for paid users
  19. 19. Any Questions? and thanks for listening

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