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B Qu Transforming Data Into Competitive Advantage


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B Qu Transforming Data Into Competitive Advantage

  1. 1. Page 1<br />Transforming Data into Competitive Advantage Easily and (relatively) Cheaply<br />A Presentation by<br />Jacques de Cock Anton Dominique<br />CEO BQu Director LSM<br />[Date]<br />1<br />
  2. 2. Page 2<br />Agenda<br />Introduction<br />Clear Information is Key<br />Problems with Traditional Market Research and Data Management<br />Changes in Information and Environment making Change in Management Inevitable<br />Good Market(ing) Information Practice<br />Benefits<br />Key Success Factors<br />
  3. 3. Good Information and Clear Analysis – the starting point for Strategic Thinking<br /><ul><li>Strategic Thinking is structuring and distilling from the maelstrom of information and opinion what the key facts and actions are that will create Competitive Advantage
  4. 4. Identifying key trends
  5. 5. Creating simple causal models
  6. 6. Understanding positioning and key strengths
  7. 7. Analysis is the critical starting point of Strategic Thinking.
  8. 8. Clear analysis key to evaluating situation
  9. 9. Dynamic analysis more valuable than single point analysis
  10. 10. Opinion cheap, factual analysis creates possible consensus
  11. 11. Good Analysis is only possible with Good Data
  12. 12. All information valuable, need to understand it
  13. 13. Analysis based on misleading data creates misleading information and wrong decision
  14. 14. All information has weaknesses, need to understand them to make sure the conclusion still stands</li></li></ul><li>Page 4<br />Five attitudes stand in the way of good usage of Data and Clear Analysis in management<br /><ul><li>Better blame someone else
  15. 15. Fear of doing own analysis
  16. 16. Argue about data rather than action
  17. 17. Lack of time, so need to use what already exists</li></ul>Only rely on published information<br /><ul><li>Data gathering and analysis is a learned skill, not innat!
  18. 18. Data is plentiful but takes time to understand and analyse
  19. 19. Good research requires knowledge of sources
  20. 20. Everyone finds different data that conflicts </li></ul>The myth of the easily available data<br /><ul><li>Want to cover all options
  21. 21. Use all possible known analytical models
  22. 22. Result is confusion and lack of clear understanding and direction </li></ul>“Kitchen Sink” approach<br /><ul><li>Everyone has their own data
  23. 23. Not sharing it is keeping power
  24. 24. Only analysed and distilled information is communicated
  25. 25. Gut Feel is used unchecked, except maybe in brainstorms
  26. 26. Only facts that confirm views are used and acknowledged as right
  27. 27. If a fact challenges the perceived view the facts are challenged</li></ul>Information is Power<br />Mistaking Hypothesis for Fact<br />
  28. 28. Page 5<br />Changes in the Information Environment make changes in management vital <br /><ul><li>There is no need to be selective anymore
  29. 29. The whole of the detailed operational data of the company can be stored on the cheapest computer</li></ul>Data Storage is virtually free<br /><ul><li>Primary data is available and often free
  30. 30. Often ill defined and difficult to find
  31. 31. Incomplete and confusing</li></ul>Base data available and plentiful<br /><ul><li>Gathering own primary data easy and cheap
  32. 32. Market research and customer satisfaction easy to gather
  33. 33. Every event creates valuable information</li></ul>Gathering Own Data Much Easier<br /><ul><li>Multiple sources prevalent
  34. 34. Definitions different
  35. 35. Confusion created and maintained</li></ul>Data Confusion Prevalent<br />
  36. 36. Page 6<br />Good Practice can be summarized in three elements<br />Focus<br /><ul><li>Focus analysis on the information needed rather than available
  37. 37. Ensure all analysis supports decision making requirements
  38. 38. Consolidate primary
  39. 39. Both internal and external
  40. 40. Make available to allno complex software
  41. 41. No gate keepers
  42. 42. No specialist to access data, maybe specialist to ensure data is easy to access and analyse </li></ul>Easy to Access Raw Data<br /><ul><li>All data clearly explained
  43. 43. Limitation explicit
  44. 44. Inferred data differentiated from primary data
  45. 45. Sources explicit
  46. 46. Shared experiences</li></ul>Clear Data Validation<br />
  47. 47. Information gathering should focus on the information that is uniquely valuable to you<br />Market<br />Company<br />Company<br />Market<br /><ul><li>Segmentation and penetration
  48. 48. Financial Performance
  49. 49. Product/Customer Contribution
  50. 50. Profit drivers metrics and trends
  51. 51. Margins and trends
  52. 52. Causal Drivers of Good & Bad performance
  53. 53. Employee satisfaction
  54. 54. Marketing events and metrics
  55. 55. Overall Trends
  56. 56. Profitability
  57. 57. Distribution Channels
  58. 58. Technology Trends
  59. 59. Regulatory Trends
  60. 60. Constraints
  61. 61. Competing Markets
  62. 62. Overall Economic Conditions</li></ul>Clients<br />Competitors<br />Clients<br />Competitors<br /><ul><li>Who are they
  63. 63. Offering
  64. 64. Pricing policy
  65. 65. Business Model
  66. 66. Growth & Profitability
  67. 67. Successes and Failures
  68. 68. Financial Performance
  69. 69. Relative Competitive position
  70. 70. Who are they
  71. 71. Decision making
  72. 72. Value perception
  73. 73. Buying Pattern
  74. 74. Profit Drivers
  75. 75. Other relationships
  76. 76. Perception of suppliers and yourself</li></li></ul><li>Company data shared enables all parts of the company to gain better insights<br />Market<br />Company<br />Example: Operational Data Helps Marketing<br />Airline Regional Marketing Planning Support<br />Making Information Accessible<br />Regional Distribution<br />Clients<br />Competitors<br />Enables Tailored Insights<br />Down to Local Authority<br />
  77. 77. Having a simple causal model creates a simple way of providing insightful information<br />Market<br />Company<br />Example: Causal Demand Model<br />- Market Build Up Method CD Demand -<br />Total Population<br />61m<br />CD Buyers<br />10m<br />CD Bought<br />6 per Year<br />Avg Price<br />£15<br />Market <br />£900m<br />Clients<br />Competitors<br /><ul><li>Segmentation
  78. 78. Demographic trends
  79. 79. Segmentation
  80. 80. Trends
  81. 81. Trend & Forecast Result of Drivers
  82. 82. Type by segment
  83. 83. Buying Events
  84. 84. Alternatives
  85. 85. Demand Elasticity
  86. 86. Purchase types
  87. 87. Price Trend
  88. 88. Overall
  89. 89. By Type</li></li></ul><li>Competitor Information needs to be captured when discovered and made easy to share<br />Market<br />Company<br />Example: Competitor Database<br />- Education Market; Database focussing on Key Information-<br />Courses<br />Articles/ Documents<br />About<br />Provides<br />About<br />Institution<br />Gives<br />About<br />At<br />Clients<br />Competitors<br />Works at<br />About/By<br />Has<br />Lecturer/ Staff<br />Locations<br />
  90. 90. Focus Your Analysis on what is required to make The Decision<br />The Scientific Model<br />Objective – Issue – Hypothesis- Analysis – Findings – Conclusions<br />Objective<br /><ul><li>Reason for analysis
  91. 91. Problem to be solved</li></li></ul><li>Focus Your Analysis on what is required to make The Decision<br />The Scientific Model<br />Objective – Issue – Hypothesis- Analysis – Findings – Conclusions<br />Issues<br />Objective<br /><ul><li>Reason for analysis
  92. 92. Problem to be solved
  93. 93. Key Questions
  94. 94. What we need to know the answer to</li></li></ul><li>Focus Your Analysis on what is required to make The Decision<br />The Scientific Model<br />Objective – Issue – Hypothesis- Analysis – Findings – Conclusions<br />Hypothesis<br />Issues<br />Objective<br /><ul><li>Key Questions
  95. 95. What we need to know the answer to
  96. 96. Reason for analysis
  97. 97. Problem to be solved
  98. 98. What we think the answer is
  99. 99. Our Gut Feel</li></li></ul><li>Focus Your Analysis on what is required to make The Decision<br />The Scientific Model<br />Objective – Issue – Hypothesis- Analysis – Findings – Conclusions <br />Analysis<br />Hypothesis<br />Issues<br />Objective<br /><ul><li>What we think the answer is
  100. 100. Our Gut Feel
  101. 101. Key Questions
  102. 102. What we need to know the answer to
  103. 103. Reason for analysis
  104. 104. Problem to be solved
  105. 105. The answers
  106. 106. The facts
  107. 107. Validation of our Gut Feel</li></li></ul><li>Focus Your Analysis on what is required to make The Decision<br />The Scientific Model<br />Objective – Issue – Hypothesis- Analysis – Findings – Conclusions<br />Findings<br />Hypothesis<br />Issues<br />Objective<br />Analysis<br /><ul><li>What we think the answer is
  108. 108. Our Gut Feel
  109. 109. Key Questions
  110. 110. What we need to know the answer to
  111. 111. Reason for analysis
  112. 112. Problem to be solved
  113. 113. The answers
  114. 114. The facts
  115. 115. Validation of our Gut Feel
  116. 116. The distillation of the Analysis
  117. 117. Only the information that is required</li></li></ul><li>Focus Your Analysis on what is required to make The Decision<br />The Scientific Model<br />Objective – Issue – Hypothesis- Analysis – Findings – Conclusions<br />Conclusions<br />Findings<br />Hypothesis<br />Issues<br />Objective<br />Analysis<br /><ul><li>The distillation of the Analysis
  118. 118. Only the information that is required
  119. 119. What we think the answer is
  120. 120. Our Gut Feel
  121. 121. Key Questions
  122. 122. What we need to know the answer to
  123. 123. Reason for analysis
  124. 124. Problem to be solved
  125. 125. The answers
  126. 126. The facts
  127. 127. Validation of our Gut Feel
  128. 128. The solution to the objective
  129. 129. The answers to the issues</li></li></ul><li>Using this model enables to quickly reach decisions based on factual information<br />Example: Plan Validation<br />- Army Recruitment Objective-<br />Conclusions<br />Objective<br />Issues<br />Analysis/Findings<br />No<br />Potential only 18,000<br />Even though good conversion track record<br />How big is this segment?<br />1.2 million and declining<br />How many are suitably qualified?<br />Can we recruit 20,000 16-19 year olds this year?<br />Only 50%<br />How many are interested?<br />Very few at 5%<br />Review plan, in particular marketing to increase interest<br />How many do we think we can convert?<br />Very Successful at 60%<br />
  130. 130. Make Sure Everyone Shares the Same Data<br /><ul><li>Single Database (not in the computing sense)
  131. 131. All in one place (virtual)
  132. 132. All accessible by all (within reason)</li></ul>Create Central Repository<br /><ul><li>Do not force people to re-invest the wheel
  133. 133. The sources of information are specific to you
  134. 134. Also enable discussion of the sources to that the knowledge can be enriched</li></ul>Share Sources<br /><ul><li>Use simple web project tools or off the shelf Wiki to start if really no time
  135. 135. Do not go for complex IT driven and highly developed process
  136. 136. The purpose is not to lose information, insights and analysis</li></ul>Keep Tools Simple<br /><ul><li>More computer literate
  137. 137. More time
  138. 138. Good learning experience</li></ul>Use junior & admin staff<br />Most Important : Make it part of Day to Day Operations<br />
  139. 139. Data needs to be clearly understood and validated in particular if shared<br /><ul><li>Period coverage
  140. 140. Date of acquisition
  141. 141. Source of data
  142. 142. Coverage
  143. 143. Sample or total population
  144. 144. All date is valuable
  145. 145. All data is possibly misleading
  146. 146. The limitation and usability of data needs to be understood
  147. 147. Any questions or doubts also need to be specified
  148. 148. The fact that limitations and imperfections are known is good
  149. 149. Most data has these and not knowing them is worse
  150. 150. Show all data as all could be useful at some time in some way</li></ul>Don’t Go for perfection<br />Understand limitation<br />Understand source <br />
  151. 151. Good & Effective Data Management is Simple but requires Discipline and Clarity of Thought and Objective<br /><ul><li>Determine your needs
  152. 152. Fight the Internal Hurdles
  153. 153. Have a Data Gathering – Management and Distribution Process
  154. 154. Focus both Data Gathering on what is “uniquely” beneficial to you
  155. 155. Focus each analysis to the Objective at hand</li></ul>Most Importantly<br /><ul><li>Keep it simple
  156. 156. Make it part of day to day operations</li>