Top 10 Predictive Use Cases and Customer Case Studies

Transcription

1 Top 10 Predictive Use Cases and Customer Case Studies Confidently anticipate and drive better business outcomes Pierre Leroux, Director Predictive Analytics 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 1

2 Data Is the New God! 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 2

3 Data revolution FROM Collecting Information One version of the truth Structured Relational Centralized processing Terabytes Analytics to a few Analyzing TO Connecting Insight Multiple sources and perspectives Unstructured Non-relational Distributed, parallel processing Petabytes and exabytes Analytics for everyone Predicting 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 3

4 Getting More Value From Data: 3 Drivers A B C Decision Making Understanding Customer Improve Business Performance Source: TDWI Research; Next-Generation Analytics and Platforms 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 4

5 How Do Executives Make Decisions? Aspect Consulting, 1997 Economist Intelligence Unit, % Hard Facts 10% Hard Facts 88% Gut Feel 90% Gut Feel 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 5

6 The Opportunity: Taking Analytics to the Next Level Performance (business value) Advanced Analytics Traditional Analytics Volume / Variety / Velocity of Data 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 6

7 The Other Opportunity: Broaden Access to Predictive Data Scientist I m building churn models for every region MS Statistics, Berkeley Data Analyst I need explain to the CEO why sales are down in EMEA MBA, U of Pennsylvania Business User The app needs to tell me what offer to make in real time BS French Literature, UC Davis.01% ~3% 97% 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 7

8 Wherever You Are in Your Company Value Chain, You Can Beneficiate from Predictive Start Customer Material / Suppliers Innovation and Development Planning Manufacturing Distribution Sales and Marketing Customer Service Value Chain 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 8

9 Increase Cross-sell / Up-Sell Opportunities Current Problem Benefits Irrelevant/impersonal marketing campaigns don t draw expected leads and revenue Reduced market basket deals due to online competitiveness, price matching strategies, No insight in to product associations, therefore unable to make smart recommendations Lack of insight in to social linkages between spending groups Using behavioral segmentations to predict future behavior (like churn, propensity to purchase) Create cross-sell and up-sell opportunities using intelligent, predictive models based on the top customer behaviors/purchases Get 360-degree view of customer and reuse to create new targets Expand social communities profiles to understand linkages and paths to greater attach rates Make real-time product recommendations to increase basket size Measures 380% higher campaign response rates 14% more products per customer household 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 9

10 Cross-Selling to Active People in the Winter Mobile Business with prepay and afterpay plans +5M Subscribers Mature market with +128% penetration rate Challenges Offer relevant products / services Don t overcontact customers Help customers avoid bill shock ie exceeding their data allowance 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 10

11 Skiing Indoors Cross-Selling to Active People in the Winter How Understand winter roaming patterns Develop a predictive model to identify people who go skiing Offer a product to enjoy mobile phone aboard with no surprise! fixed costs 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 11

12 Cross-Selling to Active People in the Winter 1000 variables looked at for model 2/day Wintersport roaming package Build and Offer Roaming Package for Scalable solution to support both short and long-term needs 2 days to build model Low cost Don t need Data Scientists 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 12

13 Reduce Customer Churn Current Problem High customer churn rates dictated by competitor offers and social influence Lack of customer loyalty programs Marketing campaigns not targeted toward existing (top) customers No visibility in to social sentiment and how it affects churn reduction efforts Benefits Reduce customer churn, increasing revenue Design targeted retention programs and improve performance of existing retention programs Create improved viral marketing campaigns to increase adoption of products or services Score a multimillion-member+ customer base in minutes or hours Minimize the spread of negative word of mouth via social interaction analysis Measures 8x improvement in uptake of customer retention offers Decrease churn by as much as 12% annually 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 13

14 Reduce Customer Churn Company Mobilink Headquarters Islamabad, Pakistan Industry Telecommunications Products and Services Fixed line and wireless; data services Employees 7,000 Revenue $1.1 billion Objectives Outpace the competition with a next-best-activity approach - right incentive to the right customers at the right time Use Big Data and predictive analytics to build customer trust, improve loyalty, and decrease churn The Resolution Quickly deployed SAP solution for its excellent capabilities, user-friendly interface, and compatibility with other solutions Predictive models combined with clustering techniques and social network analysis Key Benefits Increased usage of value-added services ringtones, text messages, and music Lower attrition by predicting as well as preventing churn Better insight into communities of interest - enabling viral marketing in new segments 8X Increase in uptake of customer retention offers, from 0.5% to about 4%, 380% Boost in campaign response rates Analysis <1 Day To deploy new predictive models 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 14

15 Improve Fraud Detection Current Problem Current investigation process is a manual, time consuming process Need to increase ability to investigate a greater number of suspected fraud cases Investigative process is not efficient little to no delineation between cases suspected of low and high fraud probability Too much time spent on fraud identification, where the time should be spent on resolution Benefits Reduction in the time, effort, and resources it takes to identify fraudulent behavior Fraud scoring of referrals lifts the most likely fraud cases to the top of the list for investigation before less likely cases. Fraud costs the insurance industry more than $100 Billion annually reduce fraud, increase revenue Fraud represents up to 10% of every claim dollar and adds ~ 15%, or an estimated $ to the total insurance premiums for the average household Measures Reduce e-fraud over $1B annual transactions 92% of fraud cases identified Leading Turkish Bank 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 15

16 Reducing Fraud for Electronic Transactions Company Monext SAS Headquarters Courbevoie, France Industry Banking Products and Services Payment and money card processing solutions and services Employees 480 Revenue 67 million Objectives Reduce e-fraud for some of Europe s largest e-businesses, retailers, and banks Replace an outsourced analytics solution that used a generalized model to predict e-fraud Decrease false alerts to improve the consumer experience and reduce call center costs The Resolution Custom-tailored predictive modeling for each unique card provider and card type, including credit, debit, prepaid, and luxury cards Automated learning for rapid modeling, combined with a powerful and user-friendly interface Effective Results in hours instead of days or weeks Efficient Just one half-time analyst to build customized models for each provider and card type Fast Analysis of hundreds of millions of transactions, and up to 500 native and derived attributes used Economical Hundreds of millions of euros saved in potentially lost revenue annually for card providers 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 16

17 Forecasting Current Problem Based on same day last week, similar event last year or month analyzed to generate forecast Effect of temperature, humidity, rain, seasonal impact, etc. are not incorporated nor considered Current analyses are often not focused on AMI data but instead hourly demand through remote metering Benefits Obtain insights on customer energy demand by understanding typical consumption patterns and seasonal behaviors Overall better decision making and improved prediction of future loads Produce short term demand forecasts at different levels in the network Reduce fuel and operational losses due to running generation when not needed Measures Reduced overall time to forecast by 70% (2 months to 3 days) Achieved ROI of US$6 million over 12 months Achieving 82% accuracy in sales forecasts, a 10% improvement over prior forecasting techniques SAP SE or an SAP affiliate company. All rights reserved. Internal 17

18 Product Recommendations Current Problem Benefits Lack of analysis into single-transaction patterns Inability to market products to consumers based on previous patterns and affinity Marketing and product offers not aligned to regional and local consumer purchasing behaviors Inability to make personal product recommendations Real-time product recommendations to increase basket size and simplify shopping experience Intelligent rules on Next Best Product/Offer Sell larger basket sizes by identifying products that drive drag-along sales Understand importance of individual products for over-all business Estimate brand effect on customers purchase decisions Measures 2x more friend requests and a corresponding increase in the acceptance rate 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 18

19 Cox Communications: Supercharging Customer Relationships 14% More products per customer household 28% Reduction in customer churn rate 80% Reduction in model creation time 42X Greater throughput for central analysts Build predictive models to help create personalized offers more quickly & accurately Why SAP Predictive Analytics? Precise, accurate, and fast polling of 10 million observations and 800 variables Scalable solution to support both short and long-term needs

20 Customer Buying Propensity Current Problem Score customers based on likelihood to buy across purchase, loyalty, social, and other attributes Score and rank leads based on likelihood to become opportunities for defined segment management Automate the lead generation process using repeatable rules and processes Decrease reliance on human effort for sales propensity and lead generation efforts Benefits Reduce costs of marketing and sales by focusing on most likely segment(s) Close opportunities faster Reduce manual marketing and sales operations Measures Reduction in catalogs and purchase probability analysis for 4M customers across 40M attributes in a matter of days 25% reduction in target audience size for any individual campaign, thanks to more-precise analytics 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 20

21 Predictive Maintenance Current Problem Benefits Escalating maintenance costs Increased equipment failures across supply chain Rising operational downtime No real-time monitoring allowing maintenance to be planned before the failure occurs No sufficient warning for maintenance to be planned and executed Decreased time to resolution Increase equipment lifespan Decrease production downtime lost to maintenance Reduce catastrophic breakdowns and equipment failures Minimize the cost of spare parts and supplies. More predictable and efficient manufacturing processes Measures 94% faster runtimes on unsuccessful test-drives from 50 minutes to just a few Sub-second sensor-machine failure prevention 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 21

22 Kaeser Compressor 500 Millions and 4800 employees Sigma Air Manager 2 Combine sensor and ERP data (location, type of usage, last service, etc) to predict future failures of Kaeser s customer equipment Challenges Make maintenance and other services offerings more costefficient and valuable Streamline the supply chain Innovate through new technologies and models 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 22

23 Kaeser Compressor Long-term disk storage all Customer Field Svs Sales R&D User Interfaces Predictive Model (in-memory) sampled CRM ERP DW Event Stream Processing 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 23

24 5 Best Practices to Take Home 1. Realize there is no silver bullet, but don t do nothing Companies that are measuring value with analytics are taking risks, experimenting, and finding success 2. Consider new infrastructure technology Companies succeeding with next-generation analytics are putting an ecosystem of multiple technology types 3. Consider more advanced analytics Companies measuring value are using more advanced analytics. Although this requires skills and training, the upside is clear 4. Start with a proof of concept Start with a metric you are already measuring, so you can demonstrate that you can reliably predict it 5. Utilize disparate data Disparate data types can enrich a data set and provide lift to models 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 24

25 Thank you Contact information: F name MI. L name Title Address Phone number 2015 SAP SE or an SAP affiliate company. All rights reserved.