Introduction to Optimization Group

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Introduction to Optimization Group

  1. 1. A brief introduction Prepared for: 1
  2. 2. Who we are … Optimization Group is a marketing analytics firm offering the following solutions:  Traditional and on-line focus groups  Traditional survey services (CATI and on-line)  Text mining analytics  Conjoint (trade-off) analysis  Data mining and modeling  Dashboard analytics 2
  3. 3. Our People Optimization Group consists of people from two worlds: – Technology “Automate and systematize complex data sets” • Systems analysts • Programmers • Database designers • Process engineers – Marketing “Make data and analyses work in the real world” • Marketing research & consulting • Corporate brand management • Agency account service • Marketing & media database (applications focus) 3
  4. 4. Our Global Experience  US  Canada  Brazil  Mexico  UK  France  Spain  Poland  Italy  Germany  India  China  Australia  South Korea  Japan 4
  5. 5. Some of our Clients 5
  6. 6. Proprietary tools  Unique Solutions IdeaLoopz®  Generating and optimizing ideas Model Incite  Finding the “marketing signal” in “noisy” data Search Incite™  Context based text search SiteCRM™  Brand website effectiveness 6
  7. 7. IdeaLoopz  Components: – brandDelphi™ online ideation system, based on Rand Corporation geo-political (Delphi) research technique – IdeaMap® online concept and messaging optimization, rooted in conjoint analysis – Brand Impact Analysis identifies how brand linkage “turbocharges” specific features and benefits 7
  8. 8. IdeaLoopz: “The Diamond Principle” Idea Expansion Optimized Idea Reduction 8
  9. 9. Case Study: Blades Servers 9
  10. 10. Sample Definitions:  Company size segments were defined as follows: – Medium business = 250-999 employees – Enterprise = 1,000+ employees – Public sector = federal/state/local government, education, medical  IT Decision Maker: – Work in a IT function AND check at least one of the following as it relates to their job: – Managing and maintaining the servers and storage environment at your site – Helping to set overall company/site strategy regarding servers and/or storage – Evaluating and recommending new servers and storage products – Recommending or selecting the specific brand of servers and storage – Recommending or selecting the specific configuration of servers and storage  Business Decision Maker: – Do not work directly in an IT function AND have influence over the server and storage purchases at their company 10
  11. 11. 11
  12. 12. Check the ideas you like (the basis for the relevance score) Then add your own idea or build on one input by someone else 12
  13. 13. Next rate the ideas you just checked (the basis for the importance score) 13
  14. 14. Ideal Blade Server  Overall, respondents defined the ideal Blade server as… Q1 Key Phrase(s) % of Ideas with Word/Phrase Price/affordable/cost 15.7% Large/capacity/room/space 11.8% Service/support/warranty 9.8% Reliability/quality 9.8% Fast/quick/time 3.9% Easy/friendly/simple 3.9% 14
  15. 15. Filter on “Best Ideas” Idea Innovation Map Niche Stars IMPORTANCE Static Question Marks RELEVANCE 15
  16. 16. Q1: Stars - Potential Differentiators 16
  17. 17. IdeaLoopz: “The Diamond Principle” Idea Expansion Optimized Idea Reduction 17
  18. 18. The Principles of IdeaMap 1. Rooted in conjoint…determines cause and effect 2. Based on fundamental communications theory (stimulus response) 18
  19. 19. Methodology Overview  Based on customer input from 1st Phase, team generated 9 “tight” attribute/benefit statements – Four categories of elements included: • Brand/Price • Servers • Storage • Better Together  Elements mixed and matched in an experimental design to form holistic concepts  Respondents evaluate concepts  we analyze impact of each element 19
  20. 20. Key Learning  Consistent with work in the PC space among the B2B target, language that communicates the ability to keep things running rose to the top… – Upgrade/add/replace without taking down infrastructure – Lower operational expenses – setup time drops from 12 hours to less than 30 minutes – 24x7 support before, during and after – Work is transferred to a spare if blade fails 20
  21. 21. Example of “Slicing and Dicing” the Data  Most motivating elements are shared regardless of OS  Those with a VMS operating system find several elements significantly more motivating – These elements have a “do more with less” theme 21
  22. 22. Actionable Information for You  What is on your customers’ minds? – What are there problems? Idea – What would they like to Expansion see?  What are the “hot Optimized buttons”? Idea – How to position the idea Reduction – How best to express it – Messaging to target segments 22
  23. 23. Model Incite Optimization Group’s outsourcing solution which uses our proprietary genetic programming based modeling software GMAX and other statistical techniques and tools that your projects require. 23
  24. 24. Classic Regression 50 45 40 R N 35 $10 R $9 $5 30 25 $7R R N $4 $8 20 N$3 15 10 $2 N 5 $1 0 $6 R 0 1 2 3 4 5 6 24
  25. 25. Statistical View Of Data  Tools like SPSS would look at the potential relationship between the likelihood of fraud and:  > income  > filing status  > married status  > SIC Code (if business) (2 digit, four digit)  > Gross Revenue  > Date of filing  > etc.  The available universe of variables is limited to only the ones the modeler has input. The limits the potential for greater insight and predictability. 25
  26. 26.  One day while perusing the stacks at Powell's Technical, I came across an appealing title: Genetic Programming: On the Programming of Computers by Means of Natural Selection by John R. Koza. He posed an intriguing question: How can computers learn to solve problems without being explicitly programmed? In other words, how can computers be made to do what needs to be done, without being told exactly how to do it?  There is a brave, new way for computers to solve problems without being explicitly programmed and it is Genetic Programming (GP).  Koza's innovation represents an extension of the GA involving more complex structures—computer programs, rather than bit strings. Each program, like the bit strings of the GA, is measured for fitness, the most fit reproducing, the least fit dying off. Eventually, a program is found that solves the problem.  In short: One can harness the principles of Genetic Programming to create software that programs itself. 26
  27. 27. Genetic Programming 50 45 40 R N 35 $10 $9 R $5 30 $7 R X1 25 N $4 R $8 20 N$3 15 N $2 10 N R $1 5 $6 0 0 1 2 3 4 5 6 X2 27
  28. 28. How GP works PARENT 1 PARENT 2 + - A + * X B C Y Z 28
  29. 29. How GP works PARENT 1 PARENT 2 + - A + * X B C Y Z OFFSPRING 1 OFFSPRING 2 + - A * + X Y Z B C 29
  30. 30. Mining Key Data Variables Data mining enables you to see the strength of individual variables as well as powerful new combinations that help you better understand your “Key” business drivers. Variable Lift Commissions earned 375 High face amounts on policies 352 Mix of business sold 240 Sales to first time customers 205 Ratio of policies issued to price quotes 200 Rate of underwriting approval 190 Weeks since last activity 188 Multiple product sales to same client 170 High retention rate for policies issued 167 Policies denied in underwriting process 153 Lift is a measure of predictability. 30
  31. 31. Targeting your best Prospects Decile $500K Active Past Decile Total Donors Donors Donors Unknown 1 5,704 148 1,263 2,225 2,068 2 5,704 29 660 1,919 3,096 3 5,704 17 578 1,677 3,433 4 5,704 14 496 1,435 3,759 5 5,704 7 369 1,261 4,068 6 5,704 3 335 921 4,445 7 5,704 0 280 767 4,657 8 5,704 0 125 560 5,020 2,068 “unknown” alums have the same predictive variables as the top4,749 9 5,704 1 160 795 10% of alums who have donated $500,000. 10 5,704 0 98 471 5,136 Total 57,044 219 4,364 12,031 40,431 In the first decile, there are 2,068 “unknown” alums who have the same predictive characteristics as 148 alums who have donated $500K to the organization. 31
  32. 32. Customer Satisfaction Model Our data mining revealed the variables that influence satisfaction. New Data Combinations Length of Time for Call resolution Team: Durangos, Thunderbolts Overall satisfaction W/rep Getting through to Cust. Service rep 32
  33. 33. Customer Satisfaction Window The Customer Satisfaction Window contrasts the perception of the company’s delivery rating in an area against that area’s importance to overall satisfaction (GCSI). Here is a list of the areas included in the survey. A Easy to Get Started A F B Sales Person Support C Easy Installation Highest Leverage D Quality soft/training 0.500 A A E Easy Info Access J L A N F Pick-up Reliability A G Helpful Driver B A M A H Professional Driver A G A V Some Potential I Easy Tracking H A A O J Delivery Reliability 0.400 T A A K E K Package Condition A L CSA Helpfulness S M Easy Claims Resolution A A I U N Fair Claims Resolution Lowest Leverage A O Accurate Invoices A P D 0.300 P Timely Invoices Q Easy Acct. Maint. Cost of Entry R Easy Supplies A A A Q C S Easy Website R T Easy Paperwork 1.40 1.60 1.80 2.00 2.20 U Easy Customs clear. V Easy Preparation 33 Delivery Rating
  34. 34. Customer Satisfaction Window The Customer Satisfaction Window contrasts your “ability to deliver” customer satisfaction variables against the “expected value” of those variables. Customer Satisfaction Window 0.200 A Some A Time to Answer potential Highest leverage G Number of Transfers Modeled Expected Value K B G I Overall Rep Quality D 0.100 I H Lowest leverage 0.000 J Cost of entry L F -0.100 E C 20 40 60 80 Ability to Deliver 34
  35. 35. Monetizing Customer Satisfaction 35
  36. 36. Case Study: GMAX™ and ROMI 36
  37. 37. Objective  Develop model and understanding of relationships between marketing expenditures and sales Direct Mail Catalog Print Ads Client Emails Controlled Online Advertising Advertising Total Sales $ Pricing Attitudinal Customer Awareness Outcomes Customer Experience Sales Sales Outcomes Market Share 37
  38. 38. Print Costs  While print costs appear in the GMAX model, the relationship is not clearly seen in graphical analysis of print costs by themselves 400000000 300000000 200000000 ALL Enterprise 100000000 100000 200000 300000 400000 Print Out of Pocket 38
  39. 39. Marketing Communications Variable Tree Share of voice, print, online, and Prod B Print direct mail all Share of voice Out of pocket have an affect on sales Sales Shipments Prod A Share of voice Direct Mail Note how Print has an impact by itself Print AND in combination with Direct Mail Online costs Out of pocket 39
  40. 40. Typical ROMI Output Estimated Sales Impact per $ Invested Type of Data Total Sales (Direct + Indirect) Direct Mail $330 -350 Online Advertising $54 Catalog Out-of-Pocket $ $124 Print Varies by CPM “tier” Overall (SOV) Varies Email $82 Pricing - 1% change $22MM-$26MM 40
  41. 41. ROMI Model Using this model to predict sales does a very good job of matching the actual data 700,000,000 A 600,000,000 A A A 500,000,000 A A A R-Square = 0.62 400,000,000 A A A A A A A A 300,000,000 A 200,000,000 20000000.00 30000000.00 40000000.00 50000000.00 Predicted Sales Using Model 41
  42. 42. ROMI Simulator Linear Effects Value of +1 point change Commercial Education Hospitality Value of +1 pt in Awareness $11,777,724 $4,611,587 $847,189 Share impact 0.22% 0.35% 0.19% Value of +1 pt in Consideration $42,152,400 $ 11,212,500 $ 2,849,408 Share impact 0.78% 0.86% 0.63% Value of +1 pt in ITB $53,394,000 $12,653,368 $4,506,830 Share impact 0.99% 0.97% 1.00% 42
  43. 43. ROMI Benefits  Identify the marketing levers which contribute to sales – And those which don’t  Calibrate the impact to guide marketing investment decisions  Conduct “what if” analyses – How much should I spend to achieve $X sales? 43
  44. 44. Search Incite™ Context based search technology 44
  45. 45. Typical Keyword Search 45
  46. 46. Search Incite Results 46
  47. 47. How Search Incite Works Search Incite consists of three components: Query Ontology Algorithm Index Data - Developed by a team of - Inference engine experts over 3 ½ years (over 30 man years of work) - Based on Search Incite’s intelligent sort algorithm - Over 50,000 linguistic elements - Combines linguistic analysis with automatic - Up to 500 keywords and pattern matching phrases relevant to each knowledge domain - Customizable, scalable and upgradeable to adapt to your changing needs. 47
  48. 48. Ontology Development 48
  49. 49. Content Selection 49
  50. 50. AMEX Verbatim Comments 50
  51. 51. Isolating Problems 51
  52. 52. Automated Corrective Action Specific words, terms, phrases and issues can be programmed for automatic intervention/handling. 52
  53. 53. Search Incite Hardware Overview PC PC PC Firewall CALEA Police Dept Accreditation Intranet Program Transfer Standard Manual Web Protocol Server Server Intranet Server can be hosted internally or remotely depending on security, IT infrastructure, and response time requirements
  54. 54. SiteCRM™ Measuring Brand Website Effectiveness (In partnership with crmmetrix) 54
  55. 55. Illustration showing the flow of website visit experience of a single visitor Business Impact (Sales) Measurement Lift In Purchase Intent Lift In Brand Health Purchase Impact=Estimated ROI Purchased Probably will Definitely Brand purchase will purchase Media Pull TV Re-contact survey 1 week Packaging after website visit WOM Search SiteCRM™ SiteCRM™ Online Ad Entry Survey Exit Survey Email On Site Entry On Site Exit Typed URL Offline Media Site Exposure ROI (Purchase Tracking) Module Purchased the brand within past 7 days Spent $4 on most recent purchase Media Impact – website visit influenced 50% Estimated Web Influenced Revenue = $2.00 Estimated Web Influenced Revenue (aggregated)– Monthly Total Unique Visitors/Month = 65,000 Estimated ROI = 23.8% Average Estimated Web Influenced Revenue / Visitor = $2.00 Total Estimated Web Influenced Revenue = 65,000 x $2.00 = $130,000 Total Interactive Marketing Spend / Month = $105,000 55 55
  56. 56. 6 Dimensions of Brand Website Effectiveness The Six Dimensions analysis, developed by crmmetrix, aims to help marketers identify what to leverage, in order to turn your website into a powerful marketing engine. VISITOR QUALITY CRM IMPACT Who are you attracting SITE to your website? PERFORMANCE Is the website building customer relationships? Is the site performing to How many of my visitors registered for the VISITOR QUALITY my visitors expectations? What are newsletter? the improvements I Is the content of the need to make to the website building a website? Are the positive brand visitors accomplishing perception? Six their goal for coming to the site? Dimensions BRANDING CAMPAIGN EFFECTIVENESS IMPACT Is the visit to the Which campaign website driving a increases Purchase positive change in Intent? opinion for the brand? Which campaign drives offline purchase? BUSINESS Is the content of the website building a Does the campaign IMPACT strong brand engage visitors? perception? Is the website driving a lift in purchase intent? Is it driving offline purchase? And brand recommendation? 56
  57. 57. What’s keeping you up at night? 57
  58. 58. Contact Information Jeff Ewald T: 248.459.1194 E: jewald@optimizationgroup.com Kenn Devane T: 917.208.4649 E: kdevane@optimizationgroup.com 58
  59. 59. Intersecting marketing, science and technology™ 59
  60. 60. Search Incite Software Overview Search Incite Web 2.0 SAAS/ASP Interface Search Incite Pre- Indexer Background Process User Auth. Filter (role/permissions) User Search Incite's Intelligent Sort Algorithm Organization & Web html via ajax User Manager Browser http DBI DBI Ontology Editor Database Management System Document Manager Ontology Doc 3rd Party External Applications SAAS/ASP Interface Core DB Meta Document Data Automatic Store E-Mail DBI smtp Import imap Custom Views DBI DBI Customer Other DBMS & Reports Specific xml-rpc Filter Logic for Results Filter Algorithm and Triggers Document Document Notification Queue Warehouse Search (Email/XMPP)

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