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Segmentation for Targeting

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Presentation given as part of a class to FSU. Deals in (1) Segmentation and clustering and (2) How to pitch an idea successfully

Presentation given as part of a class to FSU. Deals in (1) Segmentation and clustering and (2) How to pitch an idea successfully

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  • 1. April 2, 2012 Segmentation 1
  • 2. Today• Advertising• Targetting The building blocks of our business• Media + Messaging• Segmentation• Clustering• Decision making based on segmentation 2
  • 3. The only purpose of advertising isto persuade someone of something  Idea  Action  Choice  Opinion  Try  Continue  Return 3
  • 4. Two things need towork in unison MessageChannel 4
  • 5. Define the universe and give three examples5
  • 6. So let’s talk about the Universe 22 year-old womenBlonde 40 year-old who go to collegewomen who own a and live in a dorm red Bentley Continental GTC Women who had a 28 year-old, Single baby less than 6 White females, who months ago own a chocolate- covered Labrador 6
  • 7. Blonde 40 year-old women who own a red Bentley Continental GTC 22 year-old women who go to college and live in a dorm 28 year-old, Single White females, who own a chocolate- covered Labrador Women who had a baby less than 6 months ago7
  • 8. SEGMENTATION & CLUSTERING 8
  • 9. Why go through the whole trouble?• Eliminate waste• Customize messages to increase response 9
  • 10. What is segmentation?• Dividing a heterogeneous population into groups where the members are similar to each other and different from all others – Behavioral and attitudinal factors are the key factors in determining clusters; demographic information is too general• Not category-specific – Should really take into account the entire population or clusters might not be real – Can be a sub-cluster• Ideally tied back to the database that was used to define the clusters in the same place10
  • 11. Building Blocks• Database (Simmons, MRI, TGI…) – Syndicated databases – Robust samples – Four key areas: • Demographic information • Attitudinal Batteries • Product consumption (Behavioral) • Media consumption (channel)• Regression analysis11
  • 12. Databases• Simmons, PRIZM, MRI, TGI… are all syndicated research: companies subscribe and may add their own questions to what is commonly known as an “omnibus” research• Typically have thousands of respondents and thousands of variables – TGI for Latin America has 55,000 respondents and 4,300 variables: 236,500,000 data points that are analyzed via regression analysis• Variables describe a behavior not a response – Drinking 24+ beers per month is a variable (heavy beer drinker) – Drinking Presidente beer is not a variable, it is a response12
  • 13. Database StructuresVariables go in columns… Respondents go in rows…  Responses: Each Respondent will be defined by the answer to all the variables13
  • 14. Respondents can belong to many segments & clusters … be an avid photographer… … enjoy Golf… … be a CEO… … read magazines…A woman can… … and be a mom This is why it’s important to segment an entire population in order to get the full picture 14
  • 15. Regression Analysis• Regression analysis is a pure statistical term which includes the techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.• In layman’s term, it lets you see how value of one variable(also called dependent variable) changes when any one of the independent variables varies.• You ask the software for a number of solutions containing a number of predefined groups (26, 28, 30…)• The software forms the groups. Typically, you can’t remove one of the groups without affecting the total answer, which is why you need to have several solutions15
  • 16. Regression Analysis• At its simplest, regression analysis does the following• Say it is forming a solution with 28 groups.• It takes respondent #1 and assigns it Group #1• Then it takes respondent #2 and checks whether it is so similar to Respondent #1 that they should be grouped together – If they are… Respondent #2 goes into group #1 – If they are not… Respondent #2 forms Group #2• And so on and so forth until it has grouped the thousands of respondents from the database16
  • 17. Application of segmentation in real lifeSEGMENTATION PROCESS 17
  • 18. Step 1 – Database to segments Begin with a robust database. All respondents in rows, one row per respondent, all variables in columns. This database is exported, typically in a CSV format, which the software doing regression analysis can process18
  • 19. Step 1 – Database to segments Run the regression analysis. For really small groups you can use Excel –and there are several great tutorials in YouTube For a large database, where you might process millions of datapoints and have thousands of columns and rows, you have to use professional software and hire specialists19
  • 20. Step 1 – Database to segments Solutions with pre-formed groups. The output from the regression analysis software contains a number of groups (which you pre- determined). This could be 24, 26, 28… etc. groups. There is a limit to the number of groups we can really understand At this stage, you only have a mathematical construct which might or might not make “sense”20
  • 21. Clustering21
  • 22. Clustering (1)• Once you have the set of solutions, you have to analyze all of them to determine which one will fit your “model of the world”• Parameters: – Number of groups (not too many, not too few) – How different are the groups from each other – Do they “make sense” (the smell test) – Are the groups significant either in numbers, purchasing power or other metrics?• By now, the segments have also been inserted back into the original database and you are ready to go22
  • 23. Clustering (2)• Once you have a final solution that everyone is happy with:• Name the groups so that understanding them is instinctive – Knee Deep in Toys – Salt of the Earth – Blueblood Estates• Personal peeve: “Cutesy” names, though. I am worried that they become shortcuts• Recommendation: Pictorial Profile – As a group choose a group of 3-5 pictures that you think represents each segment to establish a visual identity – Focuses efforts23
  • 24. Clustering (3)• Analyzing the segments/clusters – What they look like Group Variable Index 25 Have had a child in the past 12 months 450 25 Have had a child in the past 24 months 395 25 Have purchased toys in the past 1 month 360 25 Bought a digital camera 275 25 Watch daytime television 220 25 Rent/download movies 5+/week 215 25 "I would rather spend a quiet evening at home" 209 25 "We often entertain at home" 205 25 F 25-34 120 25 F 35-44 11524
  • 25. Is everyone familiar with an “index”? Item Value Index An index relates a value toItem 1 130 66.7 the average for the seriesItem 2 120 61.6Item 3 200 102.6Item 4 400 205.2Item 5 315 161.6Item 6 221 113.4Item 7 76 39.0Item 8 95 48.7Item 9 55 28.2Item 10 32 16.4Item 11 500 256.5Average 195 100.025
  • 26. Clustering (3)• Analyzing the segments/clusters 1. Size – Ideally, there should be some sort of size continuum so that we have large groups and small groups. But: • No group should be huge compared to the others • A situation where there are two or three large segments and many small ones is also flawed (the large groups carry the weight) 2. Homogeneous – ideally, the members of each segment should all index high against the main components of the group (e.g., have travelled to a foreign country 4+ times in the past 12 months) and this will not be shared by all the other groups. 1. This is why gender and age are NOT good discriminators26
  • 27. Clustering (3)• Analyzing the segments/clusters 1. Heterogeneous – While a few groups can share some traits (e.g., purchased a new car in the past 12 months) if a trait or variable is shared by a large number of groups, then it fails to really segment 2. Attitudinal Similarity – Since the end goal is to be able to customize messages for each group, groups that share some variables (e.g., purchased new clothing in the past month) but differ in attitudes (e.g., some are very conservative, some are very liberal) probably will not work 3. Extremes are good: high indices in the top 15% or so of the variables is great27
  • 28. Clustering (3)• Analyzing the segments/clusters – Look for media habits… are there any media vehicles that index high in this group but not on others? (e.g., visit radio/music sites on the web more often) – Bears repeating: does the group make sense? Sometimes you will read through the variables and the group that emerges just doesn’t make sense. For example, have taken 6+ foreign business trips in the past 12 months AND index high to knitting but are predominantly males. Discard it. Leave it in the attic.28
  • 29. Groups, segments & clusters• Many times these terms are used interchangeably.• Groups, in general, might have only one thing in common (e.g., play soccer every Saturday morning)• Segments –what we have been talking about– have more things in common and tend to be different from other segments. They are also unique.• Clusters are groups of segments. – For example, you may have four different segments that drink wine often (12+ times/month), but one of the segments might be heavily into cooking, another segment heavily into partying, another might be rich and have wine with their dinner every night, etc. You can conceivably create a cluster of “wine lovers”29
  • 30. Targeting using segments & clusters30
  • 31. This is the fun part31
  • 32. Attitudinal BatteryDemographicInformation Media ConsumptionConsumptionBehavior 32
  • 33. Let’s look at this situation Consumption CPC Size of theGroup per Capita Index Group As a % Consumption As a %Group 1 50.5 218.8 120,000 7.8% 6,060,000 17.0%Group 2 45.3 196.3 75,000 4.9% 3,397,500 9.6%Group 3 30.2 130.8 180,000 11.7% 5,436,000 15.3%Group 4 24.5 106.1 200,000 13.0% 4,900,000 13.8%Group 5 20.0 86.6 150,000 9.7% 3,000,000 8.4%Group 6 18.2 78.8 70,000 4.5% 1,274,000 3.6%Group 7 17.5 75.8 300,000 19.5% 5,250,000 14.8%Group 8 15.9 68.9 250,000 16.2% 3,975,000 11.2%Group 9 12.0 52.0 110,000 7.1% 1,320,000 3.7%Group 10 11.0 47.7 85,000 5.5% 935,000 2.6% 23.1 1,540,000 35,547,500 33
  • 34. Step 1 – Determining target segments Consumption CPC The first step is to run a quick analysisGroup per Capita Index showing per-capita consumption of theGroup 1 50.5 218.8 product in each one of the groups.Group 2 45.3 196.3 The top groups will be more attractive;Group 3 30.2 130.8 the bottom groups we discard unlessGroup 4 24.5 106.1 they are big enough to really merit someGroup 5 20.0 86.6 resource allocationGroup 6 18.2 78.8Group 7 17.5 75.8 This gives us a rough guideline of whatGroup 8 15.9 68.9 groups we should be interested inGroup 9 12.0 52.0Group 10 11.0 47.7 23.1 34
  • 35. Step 2 – We then look at the entire picture of the market Consumption CPC Size of theGroup per Capita Index Group As a % Consumption As a %Group 1 50.5 218.8 120,000 7.8% 6,060,000 17.0%Group 2 45.3 196.3 75,000 4.9% 3,397,500 9.6% 56%Group 3 30.2 130.8 180,000 11.7% 5,436,000 15.3%Group 4 24.5 106.1 200,000 13.0% 4,900,000 13.8%Group 5 20.0 86.6 150,000 9.7% 3,000,000 8.4%Group 6 18.2 78.8 70,000 4.5% 1,274,000 3.6%Group 7 17.5 75.8 300,000 19.5% 5,250,000 14.8%Group 8 15.9 68.9 250,000 16.2% 3,975,000 11.2%Group 9 12.0 52.0 110,000 7.1% 1,320,000 3.7%Group 10 11.0 47.7 85,000 5.5% 935,000 2.6% 23.1 1,540,000 35,547,500Let’s say we have a large brand, so we areinterested in a large base. The top 4 groupsaccumulate over 50% of the consumption. So, rightoff the bat, we would look at those in detail 35
  • 36. Step 3 – In-depth analysis Group Group 1 Group 2 Group 3 Group 4 TotalWomen 18-34 25% 40% 30% 35% 32%Women 35+ 30% 25% 30% 15% 24%Men 18-34 26% 25% 25% 35% 29%Men 35+ 19% 10% 15% 15% 15% 100% 100% 100% 100% 100%Objective: Understand the physical makeup of the target groups.From here we would reach a couple of conclusions:3.Young product (both Men and Women)4.Older men just do not like it 36
  • 37. Group Group 1 Group 2 Group 3 Group 4 So we see that groupsWomen 18-34 25% 40% 30% 35% with a highWomen 35+ 30% 25% 30% 15% concentration of young people tend to indexMen 18-34 26% 25% 25% 35% better in certainMen 35+ 19% 10% 15% 15% purchases Group (Index) Group 1 Group 2 Group 3 Group 4 One conclusion weDivorced 85 65 85 160 could make is thatMarried 115 150 140 95 product “X” is, in somePurchased Flat TV 125 125 110 155 way, a life-transitionPurchased Furniture 105 150 110 170 product:Purchased camera 110 125 105 160Watch TV < 1 hr/day 125 115 95 150 5.Young people moving into their firstWatch TV + 4 hr/day 95 80 115 90 apartment 6.Recently divorced women now livingWe would look at dozens and dozens of statements alonein order to gain insight into the different groups 37
  • 38. Group Group 1 Group 2 Group 3 Group 4A/MA - A job should be more than work, itshould be a career 110 115 115 140A/MA - There are still many opportunities foradvancements if one works hard 115 120 120 110A/MA - I am religious 80 120 140 80A/MA - It is important to take care of theenvironment 90 130 150 75A/MA - Speaking a second language is agreat advantage 140 125 90 125A/MA - I like to celebrate traditional holidaysat home surrounded by my family 80 140 130 100A/MA - My friends seek my advice beforebuying electronic products 140 95 80 140 Liberal Traditional/Conservative LiberalIn looking at the attitudinalbattery, we then see the What emerges is two big groups, one more religiouspsychological makeup of and conservative and the other more liberal. Ideally,the groups we would want to craft focused messages that match their attitudes and beliefs 38
  • 39. Group (Index) Group 1 Group 2 Group 3 Group 4Watch TV < 1 hr/day 125 115 95 150Watch TV + 4 hr/day 95 80 115 90Listen to talk radio 3+ times/week 60 50 115 30Listen to web-radio 3+ times/week 140 90 40 120Read daily newsp 3+ times/week 80 110 130 70Web - 30+ hours/month 140 105 90 130Web - 60+ hours/month 220 90 80 105Finally, we look at media For this group, for example, For this group – which washabits to determine which we might consider more more conservative– we liberaly messaging and a might consider a channelchannels we will be using media strategy that leans strategy that used talk radio heavily on the web and newspapers.to reach each group (websites, web-radio) We would then choose the Given the size of the group, messaging form, whichOn a first pass, we would it might be that, a priori, we could include live reads, won’t be recommending advertorials and regularlook at each group television advertisingindividually 39
  • 40. Wrapping it up Group Group 1 Group 2 Group 3 Group 4Cons/Capita 50.5 45.3 30.2 24.5CPC Index 218.8 196.3 130.8 106.1Size of the Group 120,000 75,000 180,000 200,000As a % of the Universe 7.8% 4.9% 11.7% 13.0%As a % of the Sub-groups 20.9% 13.0% 31.3% 34.8%Consumption 6,060,000 3,397,500 5,436,000 4,900,000As a % of the Universe 17.0% 9.6% 15.3% 13.8%As a % of the Sub-groups 30.6% 17.2% 27.5% 24.8%Watch TV < 1 hr/day 125 115 95 150Watch TV + 4 hr/day 95 80 115 90Listen to talk radio 3+ times/week 60 50 115 30Listen to web-radio 3+ times/week 140 90 40 120Read daily newsp 3+ times/week 80 110 130 70Web - 30+ hours/month 140 105 90 130Web - 60+ hours/month 220 90 80 105 40
  • 41. As a reminder…THERE’S NO POINT IN SEGMENTATION ORCLUSTERING IF YOU ARE NOT GOING TOCREATE SPECIFIC MESSAGES 41
  • 42. Some conclusions• We might consider television (e.g., cable) for broad, more generic messaging.• We identified 2 liberal groups with a high web indices: – Web radio – Regular websites• One of the groups also had a high divorced index• Conclusion: – We might consider a mix of ad networks (for quick reach and cheap CPM) and depending on our product, some premium sites – We might also want to consider “dating” sites specialized on divorced women  offer a promotion of some sort to test response42
  • 43. Some conclusions• We also identified 2 conservative groups that account for half of our sub-groups consumption (44%) with high indices for – AM Talk radio – Newspaper readership• They will also be exposed to our TV campaign• Conclusion: – We might consider some live reads on AM radio – Program sponsorship and, depending on the product itself, long- form programming (e.g., 30 minute shows with live talent) – We might also consider a newspaper campaign including advertorials and coupons43
  • 44. The Millward Brown BrandDynamics© PyramidWHY IS THIS IMPORTANT? 44
  • 45. Loyalty & ConsumptionConsumers “bonded” with a brand spend more on that brand.The lowest level for any brand is awareness. Awareness doesn’t translate intobuying. The main purpose of going through a segmentation exercise is tocreate a true bond with the brand (often described as “this brand fits me”) 45
  • 46. An increase among top consumers can have an oversized result for the entire company Consumption Size of Increase NewGroup per Capita the Group Consumption Goal ConsumptionGroup 1 50.5 120,000 6,060,000 15.0% 6,969,000Group 2 45.3 75,000 3,397,500 15.0% 3,907,125Group 3 30.2 180,000 5,436,000 12.0% 6,088,320Group 4 24.5 200,000 4,900,000 12.0% 5,488,000Group 5 20.0 150,000 3,000,000 6.0% 3,180,000Group 6 18.2 70,000 1,274,000 2.0% 1,299,480Group 7 17.5 300,000 5,250,000 2.0% 5,355,000Group 8 15.9 250,000 3,975,000 2.0% 4,054,500Group 9 12.0 110,000 1,320,000 0.0% 1,320,000Group 10 11.0 85,000 935,000 0.0% 935,000 23.1 1,540,000 35,547,500 8.6% 38,596,425 46
  • 47. Is a consumption increase realistic?THE ROLE OF MARKETING 47
  • 48. You need to do the math From 1 Coke per week (50 per capita) to 1 extra Coke every 6 weeks From 6 per year (very loyal consumer at 50% of the category share) to 7/yearWhat does a 15% From 120 transactions/year to 138. At 3.5% or about $84 perincrease in customer/year to $97 if theconsumption really average transaction is only $20mean? 48
  • 49. Conclusion• Segmentation is a powerful tool• Four cornerstones: – Demographic – Attitudinal – Product consumption – Media habits• Purpose: Increase response• Messaging must be customized• You have to do the math• Avoid introducing personal biases into the process• Channel & Messaging must work in unison49
  • 50. Pitching
  • 51. The only purpose of pitching is selling.Not teaching.Not proving a point.Selling. 51
  • 52. The 7 elements of a successful pitch • Definition • Why this makes sense • WIIFM • Business Plan • Why is the BP credible • Ask for the order • Next Steps52
  • 53. A sample pitchTHE NIKE DRESS SHOE 53
  • 54. 1. DefinitionWe propose the creation of a Nike-branded line of formal shoes 54
  • 55. 2. Why this makes sense• Tighter job market forces many young men to adopt a more “business-like” demeanor• Younger segment accepts logos more readily• Nike well-known for manufacturing premium footwear• Profitable niche – Current margin for dress shoes averages 30%; Nike brand can achieve 45% on average sales of $150 per pair – Current margin for athletic shoes average 20% due to discounting on average sales of $75/pair• Minimum marketing costs: – In-store posters – Social, Internet – Handful of business and/or high fashion magazines55
  • 56. 3. A profitable business for Nike Average Division Unit Sales Price Total Sales Margin Gross ProfitAthletic Shoes 120,000,000 $ 75.00 $ 9,000,000,000 20% $ 1,800,000,000Clothing 100,000,000 $ 45.00 $ 4,500,000,000 17% $ 765,000,000Events & Others 30,000,000 $ 50.00 $ 1,500,000,000 30% $ 450,000,000Formal Shoes 15,000,000 $ 150.00 $ 2,250,000,000 45% $ 1,012,500,000 265,000,000 17,250,000,000 4,027,500,000Athletic Shoes 52% 45%Clothing 26% 19%Events & Others 9% 11%Formal Shoes 13% 25% High inherent margins will increase gross profits of the company by $1bn 56
  • 57. 3. A profitable business for Nike Unit Cost % of Division Total Sales for Mktg Marketing Costs SalesAthletic Shoes $ 9,000,000,000 $ 4.50 $ 540,000,000 6.0%Clothing $ 4,500,000,000 $ 3.50 $ 350,000,000 7.8%Events & Others $ 1,500,000,000 $ 4.00 $ 120,000,000 8.0%Formal Shoes $ 2,250,000,000 $ 4.50 $ 67,500,000 3.0% $ 17,250,000,000 $ 1,077,500,000 6.2% Lower advertising costs (due to lower-cost media) also increases contribution of formal shoe line to the bottom line 57
  • 58. 4. Business Plan• There are plenty of business-plan templates on the web• Just do one Note• The business plan should contain the advertising plan which, in turn, contains the targetting considerations including segmentation58
  • 59. 5. Key success factors• Nike brand – Premium quality – Logo is acceptable – Distribution is assured• Distribution – Already built-in  Nike already distributes to 85% of shoe stores – No cannibalization: formal shoes and athletic shoes have different display footprints• Management Team – Successfully transitioned Cole-Haan from 2nd tier to premium – Excellent distribution expertise – Marketing guru transition from Nike to Nike Formal Shoes59
  • 60. 6. Decision & Timing• Timing & Pipeline have an urgency: – 6 months from concept to store – Must be in stores by September – a key psychological period for “back to work”• We should proceed within the next 2-4 weeks60
  • 61. 7. Next Steps• Week 1, 2 – Review business plan in depth with CFO and Chief Procurement officer• Weeks 2, 3 – Timeline and Pipeline with foreign sources for design, manufacturing and shipping• Week 4 – Present to CEO for final approval• Week 5 – Begin design & outsourcing manuf.61