Customer Insight and Prediction for B2B Marketing 22nd November 2013 - - PowerPoint PPT Presentation
Customer Insight and Prediction for B2B Marketing 22nd November 2013 www.sv-europe.com A SELECT INTERNATIONAL COMPANY Agenda Arrival, registration and coffee Introduction: what is predictive analytics and is it realistic for you?
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what results?
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business marketing and customer management
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– Currently resourcing the IBM SPSS public training schedule
– Former UK General Manager – Former SPSS Brand Leader, EMEA – Former UK Head of Professional Services – Senior Professional Services Specialists
– B2B, retail, telecommunications, leisure and hospitality, financial services, publishing, utilities…
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http://www-01.ibm.com/software/data/2012-conference/awards.html
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Projects in the last 12 months….
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Predictive analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events.
— Gareth Herschel, Research Director, Gartner Group
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Core capabilities driven by advanced predictive analytics
grow risk attract retain Behavioural Descriptive Interaction Sentiment
When? How frequent?
Feedback? SIC, Credit Rating, Employees, T/O, EBIT... Offers? Payment history? How often? How long? What value? Feedback/Surveys Social Media
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Propensity/ Classification
Clustering Association / Sequence Time Series
Identify groups within a population displaying homogeneity (based on a wide array of data) Identify repeatable patterns of behaviour or sequence Forecast a future value
time period Predict a particular type
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– What customer account is most likely to respond / convert / cancel based on historical response data and the array of behavioural data we have about them?
– How can I divide my account base into meaningful and usable groups as a framework for marketing communications? By value, by service or product portfolio
– What is the optimal sequence and frequency of events and interventions that lead to a one off purchase from a company becoming a high value account?
– What will product demand be next month / quarter / year?
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– No… – Appropriate skills can be transferred and learnt – Business understanding is just as important as technical skill – Analytical skills can be taught
– You need some data, certainly… – Does not need to be organised into a tidy single supporter view – Data is often [always] fragmented and disparate (occupational hazard)
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– You can start small and develop in phases – Demonstrate initial ROI before releasing further investment – Plan your first projects using a recognised analytical methodology
– Approximate, minimum investment £20 – 30k
– Data access, management and manipulation – exploratory analysis, profiling and modelling capabilities
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John McConnell - Services
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‘Predictive Analytics’ ‘Business Intelligence’
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Acquire customers:
Grow customers:
Retain customers:
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Manage operations:
Maintain infrastructure:
Maximize capital efficiency:
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Predictive threat and fraud analytics: driving mitigation and prevention
Monitor environments:
Detect suspicious activity:
Control outcomes:
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Acquire More Customers Build a Reputable Brand Anticipate Demand Maximise Satisfaction
Lower Cost of Acquisition Cross Sell Up Sell Maximise Lifetime Value Maximise Loyalty Address Poor Satisfaction Lower Churn Rates Reactivate Customers Minimise Defaults Prevent Fraud Prevent Waste Maintain Availability
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– Marketing Response – Customer Acquisition – Cross-Sell/Up-Sell – Customer Retention – Asset Failure – Fraud Detection – Satisfaction Modelling
– Cluster Analysis – Life Time Value – Loyalty – Store Clusters
– Basket Analysis – Forecasting – Sentiment Analysis – Anomaly Detection
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The most effective instigators of predictive analytics are very often data-literate, business-focused people
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service
– IBM/SPSS predictive analytics helped Brammer to manage its inventory more efficiently, significantly reducing the need to carry surplus stock, resulting in a total inventory reduction of £31.1 million in one year – Inventory turnover improved from 3.2 times at the end of 2008, to 3.7 times at the end of the first half of 2009 – Greater understanding of patterns and trends in customer purchasing data helps Brammer forecast marginal stock products more accurately and improve customer satisfaction by making a wider product range available for immediate dispatch – Detailed insight into inventory requirements has helped Brammer develop closer relationships with strategic suppliers leading to further cost benefits
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Combine NPS (KPI) with Customer Feedback (key to actions) and Text Mining Do more of these things Supplementary open ended question:
“what is the single most important thing that makes you likely to recommend us?’’
“ what is the single most important thing we could do to make you more likely to recommend us?’’
“ what is the single most important thing that currently makes you unlikely to recommend us?” Fix these things
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–Information from supplementary question, categorised, reveals areas for improvement (product owners, market managers, operations managers)
–Customer issues drive 1:1 interactions and resolutions
–Satisfaction improved in > 50% of cases –23% of detractors converted to promoters
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– Improved customer response rate to marketing initiatives by 15-20 percent. – Improved customer loyalty by 7 percent. – Supports continuous improvement of dealerships and repair facilities. – Centralized analytical reporting and modeling system enhances productivity and lowers costs. – Efficiently works with large Oracle database
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Major helicopter manufacturer
helicopter based on history and operations
Manufacturer of mining equipment
before failure
UK water companies
asset failure risk
action
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‘deployable’
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Company/Individual Company size Business type Job function Age Association membership Gender Location
Lifetime Lifetime value Annualised value Back issue claims Payment method Time taken to pay Amount paid last time
Frequency of contact Acquisition Channel Renewal channel Subscription term Preferred response method
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– Created proxy measures for annualised value – Refined range of date used through EDA
– Decision trees used because…
– The winning model was deployed as a test/control 50:50 split
Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment
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– We are working collaboratively to further automate the model development and deployment process
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Challenge: In a highly competitive environment, Banco Itau Argentina wanted to find a more effective way to increase customer satisfaction and lifetime value while maximizing the bank’s profitability. Solution: The bank chose IBM Business Analytics software and used it to create predictive models that identified clients with a high probability of accepting marketing offers. It also used sales optimization technologies to maximize the effectiveness of its marketing campaigns. Impact:
targeting and coordination of campaigns which decreased cost and increased revenue
Banco Itaú Argentina
Optimizing customer cross-selling and acquisition strategies
“We wanted to increase customer satisfaction and life- time value while maximizing bank profitability through the analysis and execution of
acquisition strategies. That’s why we chose solutions from IBM.“ Mauricio González Botto, COO, Banco Itaú Argentina
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Used predictive analytics to boost student retention and academic excellence
“For the first time, we have a statistical model that can show, based upon data, which students are most susceptible to
SPSS predictive analytics, we were just guessing from among hundreds of variables and trying to put them together by hand.”
for APUS.
The need: APUC wanted to boost student retention and academic excellence by better predicting the likelihood of students staying on course or dropping out. The solution: Data-driven analysis of student behavior provided the university administrators with an understanding of the factors contributing to retention. The predictive analytical model made connections across datasets, including those that were not readily apparent. The university could spot, in real-time, anomalies in students’ behavior indicating whether they are at risk of dropping out. Administrators can then quickly build intervention strategies, including better course designs, to keep those students in the program and
Real business results:
drop out.
flow.
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Forecasting medical demand
“Using IBM SPSS Modeler, the IBM SPSS data mining workbench, we now plan to forecast the medical activity levels of selected hospital departments over the period 2008 to 2012.” — Delphine Yzèbe, of the Niort DIM
The need: To measure annual medical activity and trends. To produce projections of medical activity for selected departments. The solution: The Centre Hospitalier de Niort uses IBM/SPSS Statistics to centralize all medical practice data and cross-reference data items on the basis of time parameters. IBM SPSS Modeler, the IBM SPSS data mining workbench, will enable the hospital to run activity forecasts for future years Real business benefits:
programming
Attentiveness and availability of local support from IBM/SPSS
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Break
Break
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– E.g. CRISP-DM
– Tests may take days, sometimes weeks, not months
– Have we done this before? – Has anyone else?
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Understanding and Deployment
impact for the least effort.
response rate lift, increased cross-sell, revenue/profit impact
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– We’re agile, responsive and generally easier to deal with
– deliver classroom training courses –
–
– run booster and refresher sessions to get more from your SPSS licences – Give no strings attached advice
support directly from us (identical costs to IBM)
– We offer telephone support with real people as well as web tickets / email queries – We offer “how to” support to help you get moving on your project quickly
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