The Softer Skills Analysts need to make an impact

1. Paul Laughlin, Managing Director, Laughlin Consultancy The Softer Skills Analysts need to make an impact in their businesses

2. Client-Side Background ❖ Created and lead customer insight teams for all the major insurance brands, products & channels used by Lloyds Banking Group over 13 years ❖ Added over £11m incremental profit to bottom line annually ❖ Developed team of 44 analysts & mentored future leaders

3. Laughlin Consultancy Background “Helping businesses make money from customer analytics” Laughlin Consultancy helps companies maximise sustainable value from their customer insight, for example by growing their bottom line, improving customer retention and demonstrating to their regulator that they treat customers fairly.

4. Customer Analytics can improve Finances © Laughlin Consultancy Ltd, not to be used without permission.

5. Businesses need Analytics that works © Laughlin Consultancy Ltd, not to be used without permission.

6. Focus is often on technical skills © Laughlin Consultancy Ltd, not to be used without permission. An SMA Perspective © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 4 Business intelligence and analytics are becoming increasingly vital to every part of the insurance business. Insurers need capabilities that address a wide variety of questions across marketing, sales, and service as well as enterprise operations. The general types of questions raised are illustrated in Figure 1. At a high level, insurers want to explore questions like the ones above the orange boxes: How do we gain new insights from historical data? What are our What happened? Why is it happening? What can we do about it? There are a variety of technology tools and approaches to address these questions. They generally fall under the categories of business intelligence, advanced analytics, and emerging analytics, and these technologies can be applied to answer the types of questions posed in each of the sections. Figure 1. The BI and Analytics Spectrum for Insurers Source: Strategy Meets Action 2016 As can be seen from the diagram, the questions to be answered range from the traditional, more operational data enables reporting on the state of the business (What happened? What is happening now? Where is the (Why is it happening? What if it continues?). Towards the middle and the right of the diagram, more complex new opportunities, and ultimately, through emerging analytics, move to human augmentation and automated decisioning. It should be noted that big data is an overlay onto this diagram, providing a set of approaches and technologies to answer these questions when the volume, variety, and velocity of the data cannot be addressed in a timely manner by traditional analytics. How do we gain new insights from historical data? BUSINESS INTELLIGENCE What are our new opportunities? How do we capitalize on new opportunities? How do we leverage human intelligence? ADVANCED ANALYTICS EMERGING ANALYTICS Analytic Collab- oration Predictive Modules Predictive Analytics Data & Text Mining Advanced Statistical Analysis ScenariosAnalysis Ad-hoc Queries Dashboards & Scorecards Reporting Cognitive Computing What can we do about it?What is likely to happen? What if it continues? Why is it happening? Where is the problem? What is happening? What happened? EDSF Release 2: Part 1. Data Science Competence Framework (CF-DS) Table 4.2. Identified Data Science skills related to the main Data Science competence groups SDSDA Data Science Analytics SDSENG Data Science Engineering SDSDM Data Management SDSRM Research Methods and Project Management SDSBA Business Analytics SDSDA01 Use Machine Learning technology, algorithms, tools (including supervised, unsupervised, or reinforced learning) SDSENG01 Use systems and software engineering principles to organisations information system design and development, including requirements design SDSDM01 Specify, develop and implement enterprise data management and data governance strategy and architecture, including Data Management Plan (DMP) SDSRM01 Use research methods principles in developing data driven applications and implementing the whole cycle of data handling SDSBA01 and Business Intelligence (BI) methods for data analysis; apply cognitive technologies and relevant services SDSDA02 Use Data Mining techniques SDSENG02 Use Cloud Computing technologies and cloud powered services design for data infrastructure and data handling services SDSDM02 Data storage systems, data archive services, digital libraries, and their operational models SDSRM02 Design experiment, develop and implement data collection process SDSBA02 Apply Business Processes Management (BPM), general business processes and operations for organisational processes analysis/modelling SDSDA03 Use Text Data Mining techniques SDSENG03 Use cloud based Big Data technologies for large datasets processing systems and applications SDSDM03 Define requirements to and supervise implementation of the hybrid data management infrastructure, including enterprise private and public cloud resources and services SDSRM03 Apply data lifecycle management model to data collection and data quality evaluation SDSBA03 Apply Agile Data Driven methodologies, processes and enterprises SDSDA04 Apply Predictive Analytics methods SDSENG04 Use agile development technologies, such as DevOps and continuous improvement cycle, for data driven applications SDSDM04 Develop and implement data architecture, data types and data formats, data modeling and design, including related technologies (ETL, OLAP, SDSRM04 Apply structured approach to use cases analysis SDSBA04 Use Econometrics for data analysis and applications EDSF Release 2: Part 1. Data Science Competence Framework (CF-DS) Table 4.3. Required skills related to analytics languages, tools, platforms and Big Data infrastructure 6 DSDALANG Data Analytics and Statistical languages and tools DSADB Databases and query languages DSVIZ Data/Applicatio ns visualization DSADM Data Management and Curation platform DSBDA Big Data Analytics platforms DSDEV Development and project management frameworks, platforms and tool DSDALANG01 R and data analytics libraries (cran, ggplot2, dplyr, reshap2, etc.) DSADB01 SQL and relational databases (open source: PostgreSQL, mySQL, Nettezza, etc.) DSVIZ01 Data visualization Libraries (mathpoltlib, seaborn, D3.js, FusionCharts, Chart.js, other) DSADM01 Data modelling and related technologies (ETL, OLAP, OLTP, etc.) DSBDA01 Big Data and distributed computing tools (Spark, MapReduce, Hadoop, Mahout, Lucene, NLTK, Pregel, etc.) DSDEV01 Frameworks: Python, Java or C/C++, AJAX (Asynchronous Javascript and XML), D3.js (Data-Driven Documents), jQuery, others DSDALANG02 Python and data analytics libraries (pandas, numpy, mathplotlib, scipy, scikit-learn, seaborn, etc.) DSADB02 SQL and relational databases (proprietary: Oracle, MS SQL Server, others) DSVIZ02 Visualisation software (D3.js, Processing, Tableau, Raphael, Gephi, etc.) DSADM02 Data Warehouse platform and related tools DSBDA02 Big Data Analytics platforms (Hadoop, Spark, Data Lakes, others) DSDEV02 Python, Java or C/C++ Development platforms/IDE (Eclipse, R Studio, Anaconda/Jupyter Notebook, Visual Studio, Jboss, Vmware, others) DSDALANG03 SAS DSADB03 NoSQL Databases (Hbase, MongoDB, Cassandra, Redis, Accumulo, etc.) DSVIZ03 Online visualization tools (Datawrapper, Google Visualisation API, Google Charts, Flare, etc) DSADM03 Data curation platform, metadata management (ETL, Curator's Workbench, DataUp, MIXED, etc) DSBDA03 Real time and streaming analytics systems (Flume, Kafka, Storm) DSDEV03 Git versioning system as a general platform for software developmentSource: EDISON Data Science Framework (2017)

7. But Leaders say otherwise

8. 9 Step Analysis Model Question Data Analysis Insight Planning & Design Presentation & Distribution Solution Buy - in Sign - off “Contracting” translating business questions into actionable, analytical terms “Delivering” expressing analysis & insight in actionable business terms Addressing business need Transparency of activity Engagement with key stakeholders

9. Getting a better quality brief Question

10. The problem with requests

11. Socratic questioning ❖ Help your clients get clarity on what they need, not just what they want: ❖ Concept clarification questions ❖ Probing assumptions ❖ Probing rationale, reasons & evidence ❖ Questioning viewpoints & perspectives ❖ Probe implications & consequences

12. “By failing to prepare, you are preparing to fail” Benjamin Franklin 12 Planning & Design stage Question Planning & Design

13. 13 What do we already know? What am I missing? How could I fill this gap? How am I going to communicate findings? What are the Hypotheses? You can start at any point in the cycle but you should look to address all elements in completing your analysis Design Map

14. Who Use / Involvement / Experience Shopping/Buying Engagement / Influences▪What characterises the customer/ segment −demographics −stage of life −attitudes −behaviours −dissatisfactions −routines −etc ▪What are their chief concerns in life (attitudinal data)? ▪What are their key needs and aspirations? ▪What are their circumstances and what is going on in their lives that impacts how they see the category? ▪What motivates them? ▪What are their preferred channels for researching and purchasing products and how does this compare with other types of products? ▪How do they make a purchase decision and what factors are important? ▪What are their attitudes to advice? What prompts/triggers them to seek advice? ▪ How do they perceive your brand vs other brands? (product category and wider) ▪ How do they become aware of the category? ▪ What is there attitude to planning for the future? When do they think ahead, what triggers this? ▪ When do they reconsider their choices? What prompts change? ▪ What and who influences them and their choices? Who do they turn to for guidance/information/ recommendations? ▪ What competitive product holdings do they have and why? ▪ When are they receptive to messages? ▪What does the segment need and want when buying your products? ▪What products are they most likely to buy and why? ▪What is the current customer experience? ▪What are the key dissatisfactions (irritations, frustrations etc) with the current process? Pricing/Finances ▪How engaged/informed/involved are they with regard to competitive pricing? ▪What products do they hold? How does this contrast with other segments? ▪What triggered their purchase? What stops them buying? ▪Do they go on to buy something else? (us or competitor) ▪What are their goals and to what extent do they plan their spend (budgets)? 14 Insightful questions

15. “Men often oppose a thing merely because they have had no agency in planning it, or because it may have been planned by those whom they dislike.” Alexander Hamilton (American politician) 15 Buy-In stage Question Planning & Design Buy-in

16. 2 stage Stakeholder Mapping

17. Different types of stakeholder Spotting a Pioneer Pioneer motto: Have fun. It’s just work. Spotting a Driver Driver motto: And your point is…? Spotting an Integrator Integrator motto: Consensus Rules! Spotting a Guardian Guardian motto: Changing the World, One Spreadsheet at a Time

18. “A moment's insight is sometimes worth a life's experience” Oliver Wendell Holmes, Sr. (American writer) 18 Insight stage Question Data Analysis Insight Planning & Design Buy-in

19. 1. Cross functional teams review data, research and analysis to answer a set of core questions about their target customer and “map the evidence” 2. Those “Evidence Maps” are reviewed to identify key customer themes 3. Structured questioning techniques are used to dig deeper to develop insights A “Customer Insight” is: A non-obvious understanding about your customers, which if acted upon, has the potential to change their behaviour for mutual benefit 4. Insights are prioritised & converged with opportunity areas. to generate ideas Insight Generation Workshops

20. Further detail on how to run a Customer Insight Generation Workshop

21. “Think like a wise man but communicate in the language of the people” William Butler Yeats (Irish poet) 21 Sign-off stage Question Data Analysis Insight Planning & Design Buy-in Sign-off

22. Politically Savvy Segmentation noeuvres differ from those of the player of psychological games is that they are about knowing w the organisation works rather than about an individual predisposition. Knowing the written and written rules requires the ability to read ‘who cares? who knows? who will?' in the manager's ironment. Managers can use that ability and maintain their integrity or they can use it and play chological games. chological game-playing' is taken from the Transactional Analysis model (see Harris, 1970; ne, 1968). Integrity implies the absence of psychological game-playing based on a degree of eptance of yourself and other people for what they are. Game-playing is self-oriented. Harris cribes 'a recurring set of transactions, often repetitious, superficially plausible with a concealed ivation'. The pay-off for playing a psychological game for the player is that the player's feelings ut himself or herself and about the rest of the world are confirmed. In this process somebody s up feeling bad. When people act with integrity in their dealings with others their behaviour s not involve the compulsion to engage in exchanges with this kind of pay-off. ing these two dimensions together gives us further insight into 'clever’ and 'innocent' behaviour. ead of solely being at opposite ends of the two poles, we can see that 'innocent' is both itically unaware' and has 'integrity', whereas ‘clever' is 'politically aware' and a 'game-player’. gure 1. Descriptive Model of Political Behaviour Politically aware Politically unaware Psychological game-playing Acting with integrity CLEVER INEPT WISE INNOCENT CARR YING READING Figure 3. Owl, Fox, Donkey or Sheep Conclusion In a world which requires expertise exercised with integrity there is a predisposition among many managers to steer clear of anything which appears 'political'. This is not a good stance for personal survival. It is not good for one's profession nor for one's dearest projects and enthusiasms, nor for the life of an organisation, nor indeed for Politics. CARR YING READING Politically aware Politically unaware Acting with integrityPsychological game-playing © Management Education and Development. Vol. 18. Pt. 1. 1987. pp. 3-19

23. Communicating your analysis Question Data Analysis Insight Planning & Design Presentation & Distribution Buy-in Sign-off

24. Effective Communication ❖ Complete ❖ Concise ❖ Considerate ❖ Concrete ❖ Clear ❖ Courteous ❖ Correct

25. Hierarchies of communication from tabloid print media ❖ Hierarchies of headlining ❖ Short, eye-catching, wording

26. Effective Storytelling from TV dramas © Laughlin Consultancy Ltd, not to be used without permission. Four elements of TV dramas that create effective stories which engage audiences: 1. Proven narrative structure 2. Characters you care about 3. Good pace (brevity) 4. Visually attractive & easy to follow

27. Data Visualisation design principles, are you misleading your audience? ❖ Graphical integrity ❖ Data-Ink ratio ❖ Chart Junk ❖ Data Density ❖ Small Multiples

28. Make more effective use of Basic Chart types by avoiding pitfalls Well Designed Simplicity chart: 0 5 10 15 20 25 United States West Germany France Japan Britain South Korea Employment Costs for a Steelworker per Hour Average of first 9 months of 1982 in U.S. Dollars $23.99 $13.45 $12.37 $11.08 $9.32 $2.39 Copyright © 2011 Stephen Few, Perceptual Edge Page 8 of 1 -20 0 20 40 60 80 100 120 1976 1977 1978 1979 1980 1981 1982 Over Their Own Barrel OPEC’s Current International Account Balance in Billions of Dollars Year

29. Experiment with a wider range of chart types to learn what works

30. Explore use of Small Multiples, leveraging the power go human eye

31. Keep honing your craft, view Data Visualisation skills as core part of CPD

32. Influencing the Outcome Question Data Analysis Insight Planning & Design Presentation & Distribution Solution Buy-in Sign-off

33. It’s all about taking action ❖ Ensuring request is for action ❖ Designing analysis for action ❖ Including recommended actions ❖ Progress updates on action ❖ Measure effect of actions © Laughlin Consultancy Ltd, not to be used without permission.

34. Influencing at the Top Table ❖ What’s on their agenda? ❖ Bring Customer updates ❖ Bring Commercial updates ❖ Joint updates with Marketing, Sales or Ops (collaborate) © Laughlin Consultancy Ltd, not to be used without permission.

35. 9 Step Analysis Model Question Data Analysis Insight Planning & Design Presentation & Distribution Solution Buy - in Sign - off “Contracting” translating business questions into actionable, analytical terms “Delivering” expressing analysis & insight in actionable business terms Addressing business need Transparency of activity Engagement with key stakeholders

36. Action orientated learning One thing I will do differently as a result of this presentation is…

38. linkedin.com/in/paullaughlin paul@laughlinconsultancy.com +44 (0)7446 958061 Any questions? customerinsightleader.com laughlinconsultancy.com @LaughlinPaul © Laughlin Consultancy Ltd, not to be used without permission.

The Softer Skills Analysts need to make an impact

The Softer Skills Analysts need to make an impact

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