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Unlock data possibilities Turning Big Data into Big Revenue Oliver Halter Principal, Management Consulting
#1 Why this is important
PwC's Global Data & Analytics Survey: Big Decisions™ >$1bn • Big decisions have a big impact on future profitability; however, more big decisions are made opportunistically than deliberately • Highly data‐driven companies are three times more likely to report significant improvement in decision making, but only 1 in 3 executives say their organization is highly data‐driven. 62% 3X • The majority of executives rely more on experience and advice than data to make business‐defining choices. • Many executives are skeptical or frustrated by the practical application of data and analytics for big decisions, especially in emerging markets. Source: PwC’s Global Data & Analytics Survey: Big Decisions™ Data Quality Usefulness 1,135 senior executives interviewed from across the world representing a total of 18 industries where majority (74%) of companies reported annual revenues last year of at least $1bn
Making strategic decisions Company leaders often rely on gut instinct to guide them—what we think of as the ‘art’ of strategic decision making. But what about the ‘science’ side of the equation: data and analytics? 85% 85% of CEOs told us that data and analytics creates value for their organizations. The question becomes— where and how are they realizing that value? 94% 38% 59% 16% While 94% of respondents said that senior management believe they are prepared to make their next big decision… … just 38% relied on data and analytics to do so. The majority of respondents (59%) in our survey pegged their next big decision at a value of $100 million or more And 16% said its impact to the business was in the $1 billion to $5 billion range. How you approach these pivotal decisions matters Source: PwC’s Global Data & Analytics Survey: Big Decisions™
Big impact on future profitability Big decisions have a big impact on future profitability; nevertheless, more big decisions are made 'in the moment' (either reactively or opportunistically) than deliberately. Impact on Profitability Opportunistic NA 1 in 3 >$5bn Motivators of Big Decisions < $10m 30% Delayed 25% Deliberate $1bn to $5bn $10m to $100m $100m to $1bn Source: PwC’s Global Data & Analytics Survey: Big Decisions™ 18% Experimental Reactive Mandatory 15% 9% 4% 33%
Big improvement on decision making Highly data‐driven companies are three times more likely to report significant improvement in decision making, but only 1 in 3 executives say their organization is highly data‐driven. How Data Driven? Significant Improvement? Other Partly data‐ driven 0% Highly data‐ driven 1 in 3 10% 20% 30% 40% Highly data-driven 43% 3X Somewhat data-driven 14% Somewhat data‐driven Partly data-driven Source: PwC’s Global Data & Analytics Survey: Big Decisions™ 15%
Organizations that delay starting the Big Data journey risk being leapfrogged by more datasavvy competitors 58% PwC’s Digital IQ Survey 2014 respondents who indicated transitioning from data to insight is a major challenge
#2 How can your organization adapt and execute?
Many organizations face challenges in adapting to the recent trends in the Big Data landscape Information explosion due to Digitization, Internet of Things and external data have increased the number of data sources, volumes and complexity available for analytics to achieve competitive advantage Enterprises have to balance near‐term and long‐term goals while enabling data and analytics capabilities in an agile manner, to realize iterative business value before committing to long‐term investments Proliferation of commoditized technologies to enable speed and sophistication of high volume data processing and analytics have contributed to a complex technology landscape Data monetization strategies are increasingly adopted among competitors across many industries to develop innovative products/services and generate new revenue streams
How can companies adapt and execute? The ‘DIAO’ mindset Big Data into Big Revenue – Journey Building Blocks SHAREHOLDER VALUE CREATION ACTIONS DISCOVERY INSIGHTS “Observations to Information” “Information to Insights” Discover value in your internal and external data Apply analytic techniques on internal and external data for tailored, value creating insights “Insights to Actions” Make decisions; deliver quick wins; build operational capabilities to enhance products and services OPERATING MODEL OUTCOMES “Actions to Outcomes” Test and learn; link insights and actions to financial and operational metrics; enhance shareholder value
Discovery: Observations to Information D I A D1. Idea Intake • Develop a process to intake and build a pipeline of ideas on improving business decisions with data and analytics, both from internal organization resources and external partners D2. Idea Qualification • D3. Identify Data Assets • Identify internal/external data sets required to unlock the value out of the idea; e.g., data sets may cover a broad spectrum of domains, namely customers, products, services, sensors, demographics, social media O Qualify ideas based on potential business value (financial, operational, risk or quality metrics) D4. Platform, Tools and Infra. • Develop ‘data lake’ architecture; make technology decisions and operationalize infrastructure to capture and store data assets from internal and external sources
Insights: Information to Insights D I A I1. Analytics Techniques • Categorize the type of analytics techniques (forecasting, clustering, regression, time series, machine learning, etc.) required for the ideas and map analytics tools to purpose I2. Analytics Architecture • I3. Ideation Sandboxes • Develop a holistic ideation sandbox strategy and tool environment to empower practitioners in their data discovery process. Consider cloud models and tools available as an enabler O Develop the ‘right fit’ architecture with tools to enable a rapid prototyping environment. Consider scalable in‐memory analytics and visualization tools as core components I4. Process Agility • Develop efficient processes in the discovery lifecycle which promotes agility and eliminates administrative bottlenecks; e.g., a self‐service sandbox provisioning model
Actions: Insights to Actions D I A A1. Decision Model • Define decision models and rights that categorize and specify the decisions that get made, insights, options, subsequent actions and potential for automation A3. Embed results • Embedding decision results into new products and services design could be a game changer and avenue for many organizations to add shareholder value O A2. Automation • Integrate and automate decisions made from models with company’s existing business processes, operations and technology in real‐ time; e.g., Are your sales processes ready to handle the predicted cross‐sell / up‐sell scenarios?
Outcomes: Actions to Outcomes D I A O1. Impact Linkage • Establish tighter link and integration between insights generated, actions taken and impact to financial, operational and risk metrics O2. Monitor and Observe • O3. Test and Learn • Foster ‘test and learn’ culture where people can implement change in decisions and actions in a limited form, observe the results, and change the model to reflect reality O Monitor any deviation from the expected outcome of predicted business impact, filter external factors (e.g., inflation, dynamic market trends) to measure effectiveness of management decisions O4. Data Monetization • Explore monetization strategies with the insights gained as an additional revenue source for the organization; e.g., licensing fee for aggregated data sets as an event indicator
Operating with a DIAO mindset requires rethinking the data and analytics operating model Four Primary Types of Operating Models Information Enabler Functional • Team typically reports to the CIO and provides data delivery, reporting and business intelligence services • Team reports to functional leaders (e.g., Marketing, Sales, etc.) that build targeted data marts and analytic models to improve functional performance • Team reports to a cross‐ functional business role (e.g., CFO, COO) to deliver cross‐ functional analysis to support strategic, financial and operational decisions that span multiple functions Business Unit / P&L Owner Cross Functional • Investment focused on Infrastructure and Tools • Primary focus on acquiring, storing, managing and reporting the information as opposed to developing deep analytic modeling skills • Less focus on innovation and usage of 3rd party data • Relies on the services provided in the “information enabler model” as well as their own specialists to enable data capabilities • Heavy focus on 3rd party data and exploring new analytic techniques and tools • Investments are made in innovation, 3rd party data sets and tools, as well as proprietary analytic models • Skills include data scientists and deep quantitative experience • The group reports to business unit or P&L owners (e.g., chief digital officer, VP of online/mobile) and creates value by embedding data and analytics‐driven offerings into new or existing products and services • Focus is on the impact to revenue, profit and shareholder value growth • Investments are made in innovation and 3rd party data, as well as deep analytic models The Data and Analytics Operating Model Determines Your Speed to New Value
Key takeaways Big decisions have a big impact on future profitability. Organizations which delay embedding data and analytics in their decision making culture will be left far behind their competition. Adopt the DIAO mindset. Start small, validate existing decisions, select the necessary infrastructure, drive new decisions, understand the ROI, invest and scale. A robust operating model is critical. Adopt an operating model which fits the culture of your organization and foster a collaborative and agile ‘test and learn’ culture to enable innovation. For your organization to win … Unleash analytics and empower talent to drive insights to action across your business.
#3 Putting Big Data to work: Case Studies
Make space for profits! Consumer product goods company Business Issues Action Results • Inventory stock out average of 13% vs. 8% industry average • Design and execution of a pilot initiative • Out of stock conditions reduced on average to 6% • Difficulty accurately predicting demand across a distribution network of over 1000 area sales managers • Supply chain challenges: backroom inventory at 24% of volume – and rising • Sought a demand driven inventory and shelf optimization system that provided accurate demand forecasts for use by sales managers on a daily basis — Time series analysis models predict demand at a store SKU level — Forecasting variables include effects of price, promotions, seasonality, product sales velocity, day of the week , delivery constraints and others • Develop business case, design, develop, roll out and implement solution • Measure performance and results • Improved cash flows due to reduced back room inventory • Projected $30m EBITDA contribution a year.
Make space for profits! Big Data, analytics and decisions 1. DATA 1 2. ANALYTICS 2 Classification of products based on average volume sales Classification of high volume items based on formats and volume of sales High Volume Items Complete Sales Data 3 Low Volume Items Low Volume Item Forecast 3. DECISIONS 8 Area Sales Manager Handheld 9 Updated Forecast Make overrides if necessary NEXT DAY DELIVERY Daily Sales Information for past 12 weeks Splitting the weekly forecast Forecasting for low volume items based n the sales of last 8 weeks Correct the sales time series based on discount data to get base demand + 7 High Volume Item Forecast 4 Input sales data in respective time series for every combination 5 + Complete forecast creation 6 for 3 wks Complete Price Information (past 2 years) Forecast calculation for every sales‐item combination based on best time series model
New revenue from where streets have no names B2B specialty pharmaceutical sector Business Issues Action Results • Flat revenues over three years • Big Data pilot using advance analytics • 5%‐7% revenue lift • Recent 16% reduction of sales force • Development of a customer value assessment framework • More efficient sales force (16% leaner) • Inefficient sales force optimization, workloads rewards and compensation • Poor employee morale • Identification of high value customer segments • New targeting strategy • Redesign of sales territories • Reprioritization of sales resources and deployment • Development of a business case for 2012 revenue impact • Improved insight into high potential accounts
New revenue from where streets have no names Customer segmentation and sales targeting 1. DATA 2. ANALYTICS Customer segmentation… Data integration… Consumer Data • Demographic • Insurance • Lifestyle Master Data Who to target? Value based segmentation techniques determine • High potential customers • Best potential customers When to target and where? An independent RFM process was run to segment priority customers by: • Average spend per prescription refill • Average time between prescription orders • Transactions by zip code Patient Data • Office location • Visit frequency • Services used Sales Data • Product revenue by agent / market / territory • Sales agent location by market / territory 3. DECISIONS Redesign sales territories and sales force deployment…. 1 Define Principles Define workload, potential and performance based principles to act as territory balancing criteria 2 Define Constraints Build constraints to meet specifications(e.g. balanced workload) and maintain geographic continuity 3 Perform Optimization Use statistical tools and algorithms to meet design objectives and constraints 4 Calculate Metrics Calculate and forecast key metrics of new territories 5 Target Markets & Customers Generate customer level targeting lists. Develop a visual representation of targeted and omitted customers on potential map
Consumer insights journey Global retailer company Business Issues Action Results • Goal was to enhance how they spend $400m in customer based marketing across multiple channels annually to get the largest return on our investment (higher sales, margins) • Funded an enterprise wide initiative for Customer Data to • Increased gross margin (GM) per customer by capturing 10% more margin for 5% of identified customer across each of our value tiers • Biggest foundational challenges identified was the number of Customer Data silos, quality of data and analytics around the enterprise causing customer disappoints and hurting sales (e.g. thanked 20,000 customers for purchases they never made, misplaced loyalty points in other customer accounts) — better understand the transactions and interactions of all its customers across all of its channels by the usage of analytics (Customer Identification, Segmentation, Clustering) • Company was spending $4‐5m annually in marketing messages and campaign activities with improvement opportunities — Integrate the customer data across multiple channels – stores, online, mobile under one analytics repository — Use the insights generated using analytics to better target customer based on their preferences. Integrated the results into 1‐1 marketing and personalization initiatives like the online recommendation engine • Improved efficiency in the TV/Digital marketing spend, duplicate mail savings and identified cost take outs of ~5m in annual budgets • Increased offer conversion rate by 10% on a quarterly basis • Projecting hard benefits in the range of 50 – 55m this year in Net Operating Profits as a cumulative effect of the customer data program
Consumer insights journey Big Data, analytics and decisions 1. DATA 1 2. ANALYTICS Single view of customer transactions and interactions for products and services across all channels Stores Online Single View of Customer 2 Created multiple rich segments of customers integrated across channels based on a set of key drivers through segmentations and clustering techniques to enable personalized targeting of offers and promotions Customer Engagement Customer Value Customer Behavior Demographics Mobile 3. DECISIONS Best Customers Price Sensitive New Customer Important Quality before Price Most Loyal Opportunistic Product based promotions Retained /Reactivated Uncommitted 3 Presented relevant offers, recommendations. Increased Prefer online shopping Buy online, pickup store conversion rate, profits and customer delight Mobile Shopping Portal Web Passed the insights to the personalization/ decision engine feeding the online and mobile portals Decision /Personalization Engine Filtered a sample of most loyal members who mattered and shopped online
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