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The Four Machine Learning Models Imperative for Business Transformation

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Machine learning is hot right now and for good reason. We're going to break down what you need to know about what goes into a model and give you four machine learning models your business should have in production right now

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The Four Machine Learning Models Imperative for Business Transformation

  1. 1. The Four Machine Learning Models Imperative for Business Transformation
  2. 2. There isn’t a one-size-fits-all approach to building a machine learning model. The way you code, deploy and assimilate the model into your organization is complex. Putting the proper frameworks in place will help ensure you’re able to organically uncover insights that will move the needle in your business. That starts with laying the groundwork for the production of your models. Building Effective Machine Learning Models. EXPERIMENT Clear Need Hypothesis EXPLORE Research Observe Prioritize Insight Revise Hypothesis Brainstorm Concepts DIRECTIONCONCEPT PAIN POINT RESOLVEPAIN POINT
  3. 3. When it comes to leveraging the assets at your disposal and formulating a strategy centered around advanced analytics, where do you start? With the 3 Ps — people, processes and platforms. Training your team to put new platforms to use and adopt new processes is critical. As you make your people more aware of the possibilities from predictive machine learning, you’re in a better position to deliver a beautiful end-to-end experience. Bringing the 3 Ps Into Machine Learning. Learn & Predict People Explore & Labels Process Collect & Store Platforms MACHINE LEARNING HIERARCHY OF NEEDS
  4. 4. Rules Based (SQL) vs Machine Learning. Standard Query Language (SQL) is a basic, rules-based language used to communicate with databases. Benefits of SQL ● Results are easily explainable and decipherable. ● Disseminating the knowledge from SQL is straightforward. ● Gaining adoption is easier and faster because key players already possess a level of context around the findings. Limitations of SQL ● Dirty data could dramatically skew results and rob the model of accuracy. ● The models do not evolve with time because they are typically manual and fixed. ● Reporting is historical, making it harder to predict future outcomes.
  5. 5. In their simplest form, both statistics and machine learning offer deeper insight around a given data set using pattern recognition, outlier identification, exception-based modeling and more. These approaches differ in that one emphasizes discipline (statistics) and the other emphasizes momentum and dynamic perpetual improvement. Statistics vs. Machine Learning. Databases Statistics Neurocomputing Machine Learning STATISTICAL MODELING VS. MACHINE LEARNING Pattern Recognition AI
  6. 6. DEEP LEARNING Deep learning is required to leverage non-linear tasks. This learning style offers more potential to deepen a code base by allowing the model to separate a data set into various features rather than relying on human input. Deep Learning vs. Machine Learning. MACHINE LEARNING Input Feature ExtractionInput Classification OutputFeature Extraction + Classification Output Cat Not Cat Not Cat Cat VS.
  7. 7. Deep Learning vs. Machine Learning. Accuracy Involvement of Domain Expert Draw Set That Can Be Analyzed Correlations Moderate with a false positive rate at up to 5% Extremely high with false positive rate at nearly zero Not requiredRequired for feature engineering & extraction Only 2.5-5% of available data Only simple linear correlations Non-linear correlations i.e. correlations that exist in complex patterns, rather than simple 1-1 correlations Process 100% of available raw data
  8. 8. Manual workflows using algorithms are no longer efficient. Modern machine learning allows for minimal latency and impressive accuracy on models due to the automated aspects of many of the platforms available. The Vast AI and Machine Learning Landscape.
  9. 9. Machine learning platforms empower a larger user base to configure, deploy, adopt and maintain a given model or set of models. There are many platforms out there. Choosing the best one for your organization starts with running a cost- benefit analysis using a critical set of parameters and requirements as it relates to your specific organization. Choosing the Best Platform for You. SELECTING THE PLATFORM FOR YOUR ORGANIZATION’S NEEDS MACHINE LEARNING PROVIDERS SCORE CAPABILITIES Open Source Available? Usability (non technical user) Integrations Scalability Data Exploration and Prep Cost
  10. 10. Finding the Right Learning Structure. ● Supervised: Maps an input variable to an output variable to make predictions based on a given data set. ● Unsupervised: Does not contain an output variable. Learns about the data set via pattern recognition. ● Reinforced (or Semi-Supervised): A combination of unsupervised and supervised learning where an algorithm is used to formulate conclusions. DIFFERENT TYPES OF MACHINE LEARNING
  11. 11. Framing and Problem Definition. Knowing where to start with machine learning can be hard. Asking questions up front can help steer you in the right direction. ● How do you plan to identify the target metric for which you want to predict? ● How do you define the limitations of the target metric? ● What level of context and data breadth are you able to include? ● Is there any third-party data you can leverage to add incremental value to the model? ● How can we assimilate insights (both from the development and deployment) of our model into the appropriate communication lanes, processes and organizational materials/literature so we’re allowing for digital economies of scale?
  12. 12. Every organization has a level of analytical maturity. Knowing where your organization falls can help you start to understand what’s available to you and identify any areas where you can improve in order to drive more business value from your data. Having an understanding of where you are currently is vital for laying out short- and long-term strategies. Analytics Maturity and Self-Awareness. DATA SCIENCE’S ROLE IN BUSINESS TRANSFORMATION REPORTING ANALYSIS MONITORING FORECASTING PREDICTIVE PRESCRIPTIVE PREDICTIVE STATISTICS MACHINE LEARNING DESCRIPTIVE STATISTICS Data Insights Advanced Analytics BI&ANALYTICSCOMPLEXITY BUSINESS VALUE
  13. 13. A framework turns a fuzzy concept into a concrete set of rules that will help your team take the predictions of the machine learning model and turn it into action steps which will drive the business forward. A Framework for Deployment. AdvancementResilient Trusted Prevalent Measurable Advancement PeoplePlatforms Process
  14. 14. ● Validate Use Case ● Data Finalization ● Explore and Diagnose ● Cleanse ● Develop ● Features ● Build ● Infer ● Publish ● Deploy ● Consume Successfully Deploying Machine Learning Models.
  15. 15. To know if the model is giving accurate results, you must analyze your predictions using one or more of the following metrics: ● Confusion or Error Matrix ● Accuracy ● Recall or Sensitivity to TPR (True Positive Rate) ● Precision ● Specificity or TNR (True Negative Rate) ● F1 Score Using Fit Statistics to Validate Machine Learning.
  16. 16. A regression model estimated a function (f) from input variables (x) into continuous output variables (y), as a range of values. Analyze whether regression models are accurate by looking at the baseline output of the model; using a variety of statistics such as: ● Mean Squared Error (MSE) ● Root Mean Squared Error (RMSE) ● Mean Absolute Error (MAE) ● R Squared (R2) ● Adjusted R Squared (R2) Fit Statistics for Regression Models.
  17. 17. Local Interpretable Model- Agnostic Explanations (LIME) is a record explainer mechanism — an important technique to leverage when filtering through the predicted outcomes from any machine learning model. This technique is powerful and fair because it focuses more on the inputs and outputs from the model, rather than the model itself. Using LIME to Understand a Model’s Predictions. Models Model finds a customer has an 89% propensity to churn LIME pulls out the various datasets And predictions... ...and makes small tweaks to the inputs Dataset & Predictions Pick Step Explanations Human Makes a Decision LIME then generates explanations about why a prediction was made and a variable’s impaction the outcome. A human can then make an informed decision about what to do with the model’s findings
  18. 18. Digital economies of scale means the more you leverage data and analytics across an organization, the more complete, robust, accurate, usable and valuable they become. As your informational ecosystem becomes more valuable, the cost of enacting a given (subsequent) digital initiative decreases as previous work is repurposed. Digital Economies of Scale via Model Chaining. SUCCESSIVE MODELING
  19. 19. Requisites for Operationalizing Machine Learning. There’s a lot that goes in the backend of creating a machine learning predictive model, but all of those efforts are for naught if you don’t operationalize your model effectively with proper amount of forethought, scoping, preparation, building, and inferring. MATURITY SCALE OPERATIONALIZING MACHINE LEARNING PREDICTIVE MODELS Intervention Plan Collaborate & Infer Adopt & Apply Pervasiveness & Expansion Attributable Value Change Agent Business Application ML Positioning Concept Drift Controls
  20. 20. Although there’s a lot to the setup for any model, the key to building one that works has less to do with the technicalities, and everything to do with knowing what you want the model to solve. The Four Data Models Businesses Need. ● Lead/Opportunity Conversions ● Attrition and Customer Retention ● Lifetime Value Model ● Employee Retention Model
  21. 21. What Do You Want Out of Machine Learning? Organizations can benefit from machine learning models across all stages of the bow tie funnel. ● At the top, you’re looking at how to convert your customers. ● In the middle, you’re aiming for retention. ● Across the board, you’re hoping to improve the lifetime value (LTV) of your customer, and the internal employee experience to keeps your team on board and engaged.
  22. 22. The lead/opportunity conversions model identifies why a consumer in the engagement stage buys, and which products or services they have a higher propensity to buy. This allows for a lower marketing spend while simultaneously generating higher return on marketing investment. Lead/Opportunity Conversions Model. LEAD/OPPORTUNITY CONVERSIONS MODEL
  23. 23. This model helps you identify the emotional and logical triggers of customers with a higher propensity to buy to fuel a strategy of more personalized marketing through predicting what the buyer needs to hear, and when to accomplish precision messaging. 50% of customers want personalized offers and recommendations specific to their needs. Lead/Opportunity Conversions Model.
  24. 24. The attrition/customer retention model can help you understand which customers are most likely to churn out and when. By understanding the propensity to churn, you’re better able to gauge product/market fit and the health of your overall organization against incoming competitors. Attrition/Customer Retention Model. ATTRITION/CUSTOMER RETENTION MODEL
  25. 25. Old Logo: Attrition/Customer Retention Model. TRIANGLE PEST CONTROL CASE STUDY New Logo: Triangle Pest Control analyzed their customer’s propensity to churn and determined that their buyers had no reason to remain brand loyal to their organization. They took an empathetic stance to improve the customer’s experience and lower attrition rates by: ● Redesigning their logo ● Retraining their employees
  26. 26. As a result of their redesigned strategy, Triangle Pest Control started seeing the benefits in the first year. As a result of their empathetic efforts and fast reaction to their deeper customer understanding, they achieved: Attrition/Customer Retention Model.
  27. 27. Lifetime Value Machine Learning Model. LIFETIME VALUE MACHINE LEARNING MODEL The Lifetime Value model looks across the entire bow tie funnel to identify which customers are likely to have a higher LTV, and should be invested in early on. Pattern recognition can be used to leverage data and analytics to predict where to make strategic changes. You must train your machine learning models to spot patterns against the backdrop of the most important, empathetic set of questions — the ones which stem around your buyer’s WHY.
  28. 28. Lifetime Value Machine Learning Model. When you ask why a customer does something, not what they want, you’re letting your machine learning model focus on insights-centric questions without faults in data or human bias skewing the results. These questions include: ● Why are customers buying from you? ● Why are they leaving? ● Why are they talking about you? ● Why is the market shifting?
  29. 29. Employee Retention Model. The employee retention model predicts which employees have a high propensity to churn, potentially avoiding the typically high costs associated with turnover. Using annual reviews and exit interviews to identify problem areas with engagement is too late. Using predictive machine learning gets you the data you needed to take action before it’s too late. Employee
  30. 30. Employee Retention Model. It’s better to use pattern recognition to find the employees that are at risk of leaving, and target those employees with incentives. Monitor critical factors like: ● Monthly income ● Overtime ● Age ● Distance from home ● Total working years ● Years at the company ● Years with the current manager
  31. 31. To see the biggest results from the insights pulled from the data, organizations need to get the buy-in from teams and executives company wide. Data visualization takes data and uses it to tell the story that will gain that buy-in and get teams to understand how initiatives are impacting their role, department and the company as a whole. Bringing a Modern Approach to Your Organization
  32. 32. Learn more at rocketsource.co/

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