Using Predictive Analytics to Increase Profitability Part III

Transcription

1 Using Predictive Analytics to Increase Profitability Part III Jay Roy Chief Strategy Officer Practical Intelligence for Ensuring Profitability Fall 2011 Dallas, TX

2 Table of Contents A Brief Review of Part II and Takeaways 2 A Business Intelligence Model for Predictive Analytics 6 Executing a Predictive Analytics Roadmap 14 Dashboards that Enhance Profitability 32 Conclusion and Takeaways Predictive Dashboards LLC 1

3 A Brief Review of Part II and Takeaways 2

4 Review & Takeaways Takeaway: Predictive Analytics has a variety of uses in all industries and functional areas and organizations can improve their profitability in a number of ways. The choice for doing so depends on your strategy and tactics as well as the areas needing greatest improvement Takeaway: Using analytics requires more than state-ofthe-art technology; it also requires talented individuals who have the ability to analyze and mine the data to glean the insights to improve profitability Takeaway: If you are not using analytics to improve your organization, be aware that your competition is and that the longer you delay, the bigger the chasm between you and them 2011 Predictive Dashboards LLC 3

5 Do Analytics Really Make a Difference in Performance and Profitability? The New Intelligent Enterprise Study, Fall 2010 Do Analytics make difference to organizational performance? Resounding Yes! 2011 Predictive Dashboards LLC 4

6 Using Analytics and Dashboards for Improving Performance The New Intelligent Enterprise Study, Fall 2010 Traditionally, companies relied on the following tools: 1. Trend analysis and forecasting 2. Standardized Reports 3. Data visualizations Top three changes in usage of analytics in the next 24 months: 1. Data visualizations (i.e. Dashboards) 2. Simulations and scenario development 3. Analytics applied to various business processes 2011 Predictive Dashboards LLC 5

7 A Business Intelligence Model for Predictive Analytics 6

8 A Business Intelligence Model for Predictive Analytics External Environment 1 Your Enterprise Strategic Planning Processes Mission/Vision/Objectives Business Model Business Drivers 2 3 Business Processes Big P Processes* Middle P Processes* Small P Processes* Organizational Resources People Skills Business Practices Innovation Intangible Assets 5 4 Configured BI/IT Systems User Friendly End-User Focus Scalable thru Apps Source: Levels of Processes- Terms used by Thomas C. Redman, Predictive Dashboards LLC 7

9 A Business Intelligence Model for Predictive Analytics External Environment (Inputs) Technological changes Market changes Competitive changes Political changes 1 Organizational Results (Outputs) Information and Insights KPIs/Metrics & Analytics Management Reports Dashboard Updates/Changes Strategic Planning Processes 2 3 Business Processes Organizational Resources 5 4 Configured BI/ IT Systems Your Enterprise 2011 Predictive Dashboards LLC 8

10 A Business Intelligence Model for Predictive Analytics 1 The purpose of business intelligence is to assist in making good business decisions, which start from a healthy understanding of the environment your organization operates in results in and decisions about the future regarding your customers, competitors and markets Good intelligence means having the ability to uncover and seize market opportunities and address business challenges by cost and risk management through business metrics & financial analyses As the competitive environment evolves, those changes impact your strategy, business processes, systems & resources, and ultimately, the metrics on your dashboard 2011 Predictive Dashboards LLC 9

11 A Business Intelligence Model for Predictive Analytics 2 After identifying opportunities and threats, the organization needs to align its planning processes to determine which opportunities can be at a profitably solved Given the environment in which the organization exists, and thus the available opportunities, choice of strategy influences the business drivers impacting profitability Management s strategy framework assists in defining the leading and lagging indicators needed to execute strategy. Technological, financial and human capital resources are the inputs needed to execute the chosen strategy 2011 Predictive Dashboards LLC 10

12 A Business Intelligence Model for Predictive Analytics 3 Data is generated from, flows through, and is captured by your company s automated and manual business processes (3 types: strategic, operational and tactical) The more efficient your business processes, the easier it is to leverage your data to generate the information and build the right models and metrics By embedding predictive analytics into business processes, you improve monitoring and predictive capabilities 2011 Predictive Dashboards LLC 11

13 A Business Intelligence Model for Predictive Analytics 4 Your company needs a configured BI system (easy to use, uncomplicated, etc.) so executives and front-line personnel can easily understand and make decisions Apps are the result of key business drivers and metrics on the dashboard, which move the financial needle The BI system should generate metrics in a visual manner. Use metrics that answer what-if and scenario driven questions 2011 Predictive Dashboards LLC 12

14 A Business Intelligence Model for Predictive Analytics 5 The profitability and performance of your organization is only as good as your human capital. A well trained, skilled, workforce is adept at seeing relationships and generating insights for making wise decisions Intangible assets including leadership, culture, continuous improvement and an entrepreneurial mindset all come together and underlie an organization that makes informed, timely decisions Without the right mix of tangible and intangible resources, most organizations will simply never realize the highest potential ROI from their BI initiatives 2011 Predictive Dashboards LLC 13

15 Executing a Predictive Analytics Roadmap 14

16 How To Implement Predictive Analytics into a Roadmap 1. Solve high value business problems 2. Identify the key business driver (s) of the problem 3. Engage in smart data collection 4. Build a practical predictive model that business people understand 5. Test the predictive model with limited data 6. Ensure the business people understand the results 7. Evaluate results and search for improvements 8. Automate the steps into a sustainable process 2011 Predictive Dashboards LLC 15

17 Step 1 Executing a Predictive Analytics Roadmap 1. Solve high value business problems with predictive analytics that provide a high ROI compared with the project investment (costs and time) Compile an inventory of business problems, their success criteria, desired results or insights to be gained and potential metric if determinable Focus on metrics that are leading indicators and that provide value in predicting potential risks or exploiting market opportunities for the enterprise Determine the business benefits for each stakeholder and when this information will be used by management during periodic strategic reviews, monthly financial results Determine how the results (reports, charts, dashboards, etc.) will be presented 2011 Predictive Dashboards LLC 16

18 Example Step 1 Your company has business opportunities for improving either: Customer Loyalty or Employee Turnover The company chooses to tackle the customer loyalty problem first because: Your company s loyalty rates are 9% lower than your competition, and Your marketing department estimates that each percentage improvement in customer churn will add 3% to operating profits High Strategic Value 2011 Predictive Dashboards LLC 17

19 Step 2 Executing a Predictive Analytics Roadmap 2. Identify the key business driver(s) that move the needle for achieving profits Analytics result from understanding how many & which variables impact financial/operational results By understanding the degree of impact or correlation between variables, you can make a determination of the amount of resources to expend and when to focus those resources Business drivers are the basis of What-If scenarios 2011 Predictive Dashboards LLC 18

20 Example Step 2 Identify the drivers of customer loyalty that impact revenue and/or profit Customer Loyalty Rate = 31% for Q (Target 55% for Q ) Customer Loyalty Rates = fn (increased purchases + repurchase + referrals + feedback) To predict loyalty improvement, the company identifies the predictive measure business driver and then identifies the behaviors which drive customer loyalty 2011 Predictive Dashboards LLC 19

21 Step 3. Executing a Predictive Analytics Roadmap 3. Engage in smart data collection do not spend valuable resources on complex master data management strategy; instead focus on lean data collection methods to solve the specific business problem at hand Smart data collection activities will require identification of: Types of data (metrics, numeric, alpha, etc.) Sources and locations of data (internal/external, databases, file types, etc.) Levels of data (aggregated or detailed) Quality of data (formatted, cleansed and standardized) Availability of data (end-of-month, mid-month, special request, etc.) Lean data collection focuses on capturing only the data elements required to complete the predictive data model thus saving time and resources 2011 Predictive Dashboards LLC 20

22 Example Step Three Business Driver Attribute Data Elements/Metrics Customer Loyalty Percentage Customers Data Sources: CRM SQL Database - Location: Corporate HQ Servers - Data Level: Summary & Detailed - Data Type: Structured - Field: Alpha Customer Name Field: Numeric, Dollar Sales Field: Field: Field: Numeric - Dollar, Percentage Numeric - Dollar, Percentage Numeric - Month or Quarter Discounts Margin Number of Purchases 2011 Predictive Dashboards LLC 21

23 Step 4. Executing a Predictive Analytics Roadmap 4. Build a practical predictive model that business people understand explain the value, not the model Identify the insights to be achieved; quantify the ROI to the organization, to executives and to other key personnel Explain how the results of predictive analytics increase profits and value (insights) Ascertain management s understanding of the analytics that underlie the predictive model 2011 Predictive Dashboards LLC 22

24 Example Step Four =#?% Business Analyst Front-line Personnel ROI = Business Analyst Front-line Personnel 2011 Predictive Dashboards LLC 23

25 Step 5. Executing a Predictive Analytics Roadmap 5. Test the predictive model with limited data to endure over time use one reporting period to identify any gaps in data or missed variables Determine if the organization has all the pieces and the right level of data to complete the model. If not, more research and data collection may be required Typically, the model will have to be adjusted to fit the industry type and business cycle Test the results to make sure they makes sense both intuitively & mathematically Benchmark the results against industry standards or historical norms 2011 Predictive Dashboards LLC 24

26 Example Step 5 Customer Loyalty Rate = 31% for Q (Target 55% ) Customer Loyalty Rate = fn (increased purchases + repurchase + referrals + feedback) What If we reallocated 50% of our marketing budget, directed it towards our premium market segment and offered more value pricing through targeted up-selling promotions, would that increase revenue and loyalty for the next 4 quarters? Results: Sales Q1 Q2 Q3 Q4 Q1 Booyah! Loyalty Rate 31% 32% 33% 33% 34% 2011 Predictive Dashboards LLC 25

27 Step 6. Executing a Predictive Analytics Roadmap 6. Ensure people understand the results and the effect on company goals and customer expectations Provide the necessary training for end-users to be able to correctly interpret the results Provide context of how and when to utilize the analytics in order to incorporate them into daily business decisions and customer interactions Analytics are a means of empowering your personnel to better serve customers by delivering innovative products and services 2011 Predictive Dashboards LLC 26

28 Example Step Six Customer Loyalty Rate = 31% for Q (Target 55%) Customer Loyalty Rate = 34% for Q (Target 55%) What does the organization need to do to close the loyalty gap? More marketing and loyalty training Better understanding of the premium market segments Better customer service systems Value pricing Better products Gap How do we fix this gap? Hmm...? Marketing Manager 2011 Predictive Dashboards LLC 27

29 Step 7. Executing a Predictive Analytics Roadmap 7. Evaluate the results and search for improvements After a number of reporting periods, monitor the results for appropriateness relative to the insights to be gleaned and the goals of the enterprise The results will often determine the number of iterations required to tweak the precision of the predictive model Gather input from both users and management to validate the results of the analytics in order to ensure value and ROI to the organization 2011 Predictive Dashboards LLC 28

30 Example Step Seven Results: Loyalty Rate 34% Target Good start, but we need more loyalty! Ok, let s change promotion budget to 80% & tweak the model 55% Results: CEO Marketing Manager Loyalty Rate 56% Target ROI = ChaChing! 55% CEO Marketing Manager 2011 Predictive Dashboards LLC 29

31 Step 8. Executing a Predictive Analytics Roadmap 8. Automate steps into a sustainable business process and repeat these steps by tackling other high-value business problems As the environment evolves, changes from competitors, customers, and technologies impact the number and type of analytics used by the organization Having efficient and effective business processes will allow analytics to be embedded into the key business processes thus automating the generation and reporting of analytics as part of the way we do business here Sharing positive wins from analytic activities with other C-level executives may encourage trials and experimentation from hesitant executives 2011 Predictive Dashboards LLC 30

32 Example Step Eight External Environment (Inputs) Technological changes Market changes Competitive changes Political changes 1 Organizational Results (Outputs) New Information & Insights New KPIs/Metrics & Analytics New Management Reports New Dashboard Updates/Changes Strategic Planning Processes 2 3 Business Processes Organizational Resources 5 4 Configured BI/IT Systems 2011 Predictive Dashboards LLC 31

33 Dashboards that Enhance Profitability 32

34 A Dashboard Identifying Business Drivers Notice the various business drivers under Company Metrics and the predictive graphs below Predictive Dashboards LLC 33

35 A Predictive Dashboard to Increase Profitability We predict a net income change of $ 326,832 from sales increase in July, August and Sept Predictive Dashboards LLC 34

36 A Predictive Dashboard to Increase Cash flow If we use a what-if scenario, we can estimate the effects of improved cash flow by lowering Days Sales Outstanding ( DSO ) from 47 to 30 days in Predictive Dashboards LLC 35

37 A Predictive Dashboard Enhancing Cash flow We predict the cash effect of a DSO reduction from 47 to 30 days in 2011 will be $ 4,354, Predictive Dashboards LLC 36

38 Conclusion & Takeaway 37

39 Conclusion & Takeaways Conclusion: The BI Model is an iterative and continuous process of generating insights and analytics so that further decisions can be improved for satisfying customers Takeaway: A BI Model can be developed either from a top down or bottom up approach: Both approaches still require a number of key components including strategy and business processes, organizational resources and the right BI/IT systems - all which evolve, as external changes occur Takeaway: Developing predictive analytics with the goal of a comprehensive system (e.g. dashboard) can be challenging if you don t have the right road map 2011 Predictive Dashboards LLC 38

40 Sources, References, and Trademarks MIT Sloan Management Review, The New Intelligent Enterprise Study, Fall 2010 The Lego Minifigure is a trade mark of The Lego Group Data Driven, Profiting from Your Most Important Business Asset, 2008, Thomas C. Redman Clipart provided by OCAL and Dashboard examples from Predictive Dashboards LLC 39

41 Using Predictive Analytics to Increase Profitability Part III Jay Roy, Chief Strategy Officer T: