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Measure Social Media

    Presented by Barbara Lewis
      MarQuantAnalytics.com
On-line Business Community Forum
        Sonoma, California
          March 20, 2009
Problem:
Too Many Marketing Activities ~ $400b/year

                         Events


                             1922
                                                 1980s
           1856

                                     1941

                  1889
    1673


                         What’s driving sales?
2
Problem: Too Many
Internet Marketing Activities ~ $25b/year



    Mid ’90s                                               Late ’90s
                                                   Mid 2000s
          Early 2000s
                                  Late
                                 2000s
     Banners
                                                       Clicks
     Key words
                                                       Paid search
                              Social Media
                 Affiliates                  SEO
                                                       Email

                          What’s driving sales?
3
Social Media Makes Measurement Harder
     Podcasts
     RSS Feeds
     YouTube
                  Web 2.0
     LinkedIn
     MySpace                What’s the result
     Wikis                  that impacts the
                            firm?
      Blogs
      Twitter
                    2-way
      Facebook
      Delicious
      Digg
4
Now we can determine what’s working & what’s not
                                     Econometrics
                                                           Results determine the
                                                              effectiveness of
       We gather variables that could
   
                                                              marketing spend –
       affect outcomes and analyze.
                                                              what’s working & what’s
                                                              wasted.


                                         Optimization
                                                          Results provide optimal
       We take results of econometric
  
                                                             budget across marketing
       analysis and build optimization
                                                             activities for maximum
       model.
                                                             performance.



        Econometrics & optimizations have been used for decades with CPG
        companies; in the past 10 years for other companies. MarQuant Analytics
        has been doing econometrics & optimizations since 2003.
Broad range of data inputs can affect outcomes

      TV             Print                 DM   Web
                                                                      Housing starts
                                                                      Unemployment
                                                                      Gas prices



                             Acquisition                    External Factors



  Outcome
  variables can be
  customer equity,                                               Other
  new or total          New Customers                         Controllable
  customers,                                                    Factors
  profitability,
  market share,                                              Sales force #
  unit sales, etc.                                    ATMs/branches per capita   6
Marketing   Other
                                                                 Factors




                            Week 1
                                                                                                                                          National



                                                                                                                            Local




                            2
                                                                                                                            X
                                                                                                                                          X




                                                     Segments
                                                                           Pr
                                                                                                                                                     Brand

                                                                              od
                                                     Marketing   Other           u   ct
                                                                                        s
                                                                 Factors




                            3
                                                                                                                            X
                                                                                                                                          X




                                                     Segments
                                                                           Pr
                                                                              od
                                                                                                                                                     Product




                                                     Marketing   Other




                            4
                                                                                 u   ct
                                                                                        s
                                                                 Factors




     5
                                                     Segments
                                                                           Pr
                                                                              od
                                                     Marketing   Other           u   ct
                                                                                        s
                                                                 Factors




           6
                                                                                            Marketing
                                                                                                                          Other Factors




                                                     Segments
                                                                           Pr
                                                                              od
                                                     Marketing   Other           u   ct
                                                                                        s
                                                                 Factors




                  7….156
                                                                                                                                                               among variables over time




                                                     Segments
                                                                           Pr
                                                                              od
                                                     Marketing   Other           u   ct
                                                                                        s
                                                                 Factors
                                                     Segments
                                                                                            Segments




                                                                           Pr
                                                                              od
                                                     Marketing   Other           u   ct
                                                                                        s
                                                                 Factors
    Weekly data over a three-year period is ideal.
                                                     Segments




                                                                           Pr
                                                                              od
                                                     Marketing   Other           u   ct
                                                                                        s
                                                                 Factors
                                                     Segments




                                                                           Pr
                                                                              od
                                                                                 u   ct
                                                                                        s
7
                                                                                                                                                               Econometric analysis determines relationship




                                                                                                       Variation is key
Econometric analysis produces equation that
explains objective (revenues in this case)




             Equation
    Monthly Revenues=Base Revenues x Television 0.100
    x Radio0.031 x Web Display0.154 x Web Search0.111
    x Print 0.091x Direct 0.122  Internal Factors j=1 External Factors
                                                                            
                              n                      m                       j
                                                   i
                                 i=1




                           R2 indicates % of variance that is explained in prediction compared to actual.
                                                                                                        8
Econometric results include elasticities that
indicate the lift for marketing variables
            Marketing Expense               Elasticity
                                                            The response parameters
                                                            (predictors) are expressed
  1.   Television                             0.100
                                                            as elasticities.
  2.   Radio                                  0.020

                                                            For example, if the “TV
  3.   Web Display                            0.154
                                                            spend” elasticity = 0.1 then
  4.   Web Search                              0.111
                                                            that means that increasing
                                                            TV spend by 10% will yield a
  5.   Print                                  0.091
                                                            1% increase in revenue.
                                                            If Print spend increases 10%,
  6.   Direct Mail                            0.122
                                                            revenue increases 0.9%.

  Elasticities guide reallocation of marketing budget to
  marketing activities that are generating the most lift.
Optimal Marketing Mix Spend
     $100 Million Budget




10
Typical Social Media Metrics

         Content Metrics
        
         Relevance Metrics
        
         Impact Metrics
        




11
Content Social Media Metrics
     Description
       Who is talking about you?
       Who is talking about your industry?
       What are they saying?
        What
     Examples:
       Net Promoter score, Google Alerts, Cymfony, Radian6,
        Buzz Metrics, Techrigy, Blogsearch, Technorati, Social
        Media Platform Provider



12
Relevance Social Media Metrics
           Description – relate your offer with customer interests*

     Fundamental              Metric                          Source                      Reveals
 Influence         Time on site                     Web Analytics                  Value of the opinion &
                   Bounce rate                      Google Analytics               conversations of
                                                    Omniture & Web Trends          interested purchasers
 Engagement        Pass Alongs                      Above+                         Message interest level:
                   Comment to Post Ratio            DoubleClick & Digg             will customers forward
                   Diggs, Stumbles & bookmarks      StumbleUpon                    referral
                   Podcast Listen & Views           Del.icio.us
 Loyalty           Blog Post for Brand              BlogPulse                      Context & intensity of
                   Recommendations                  Google Alerts                  blog posts
                   Tweeters                         Cymfony & Techrigy
                                                    Blogserch & Technorati


                           *Social Media Marketing – An Hour a Day by Dave Evans
13
Impact Social Media Metrics
          Description – measure effectiveness*

     Fundamental              Metric                          Source                      Reveals
 Audience          Referrers                        Web Analytics                  To Whom your social
                   Demographics                     Google Analytics               media campaign is
                                                    Omniture & Web Trends          appealing
                                                    DoubleClick
 Influence         Time on site                     Above +                        Audience involvement
 Loyalty           Bounce rate                      Repeat Customers               with message
 Action            Conversions                      Above +                        Number of desired
                   Reviews                          Reviews & Ratings              outcome (products
                   Recommendations                  Platform                       purchased) e.g. Tweet
                   Tweets, etc.                     Net Promoter                   reference to purchase




                           *Social Media Marketing – An Hour a Day by Dave Evans
14
What are the Social Media Metrics?
          Depends on Business Goal
       Business Goal                $                       Metrics
     Subscription      Customer Lifetime Value   $ Spend
                                                 Retention rate

     Advertising       $                         Demographics
                                                 Number of visitors/members
                                                 Time on site – pay attention
                                                 Pass alongs
     Users             Cost reduction            # problems solved by users
                                                 # problems /time tech support
     Insight                                     Solutions
                                                 Quality of solutions
                                                 Demographics
                                                 # & volume of conversation

                       Predictive analytics
15
Case Study – computer company
                                                   Market Response

      Self Contained Data
 Problem: Can we leverage social
  media to generate $
 Solution: Offer only social media
  special promotion
 Benefit: Generated $1 million in
  revenue

                      Holistic Approach
     Problem: Determine if social media is working
     Solution: Analyze data (all marketing activities, external,
      economic, etc.)
     Benefit: Identify impact of social media on business goals
16
Recommendations

     Tie social media objectives to business goals
     Identify data that measure business goal achievement
     Capture appropriate data
     Analyze data
     Monitor changes in input/output variables to
      determine what’s working and what’s not working
     Test various options




17
What’s Next?
     Social Capital – value of connections among people in
      social networks
     Currently no measure for social capital
     What metrics would define social capital?




          Pilot program to determine social capital
       measurement and relationship to business goals
                 Do you want to participate?

18
For More Information Contact:
Barbara Lewis
Tel: (310) 471-8979
BarbaraLewis@MarQuantAnalytics.com
499 North Canon Drive, 4 th Floor
Beverly Hills, CA 90210
www.marquantanalytics.com

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Measure Social Media Impact

  • 1. Measure Social Media Presented by Barbara Lewis MarQuantAnalytics.com On-line Business Community Forum Sonoma, California March 20, 2009
  • 2. Problem: Too Many Marketing Activities ~ $400b/year Events 1922 1980s 1856 1941 1889 1673 What’s driving sales? 2
  • 3. Problem: Too Many Internet Marketing Activities ~ $25b/year Mid ’90s Late ’90s Mid 2000s Early 2000s Late 2000s Banners Clicks Key words Paid search Social Media Affiliates SEO Email What’s driving sales? 3
  • 4. Social Media Makes Measurement Harder Podcasts RSS Feeds YouTube Web 2.0 LinkedIn MySpace What’s the result Wikis that impacts the firm? Blogs Twitter 2-way Facebook Delicious Digg 4
  • 5. Now we can determine what’s working & what’s not Econometrics Results determine the effectiveness of We gather variables that could  marketing spend – affect outcomes and analyze. what’s working & what’s wasted. Optimization Results provide optimal We take results of econometric  budget across marketing analysis and build optimization activities for maximum model. performance. Econometrics & optimizations have been used for decades with CPG companies; in the past 10 years for other companies. MarQuant Analytics has been doing econometrics & optimizations since 2003.
  • 6. Broad range of data inputs can affect outcomes TV Print DM Web Housing starts Unemployment Gas prices Acquisition External Factors Outcome variables can be customer equity, Other new or total New Customers Controllable customers, Factors profitability, market share, Sales force # unit sales, etc. ATMs/branches per capita 6
  • 7. Marketing Other Factors Week 1 National Local 2 X X Segments Pr Brand od Marketing Other u ct s Factors 3 X X Segments Pr od Product Marketing Other 4 u ct s Factors 5 Segments Pr od Marketing Other u ct s Factors 6 Marketing Other Factors Segments Pr od Marketing Other u ct s Factors 7….156 among variables over time Segments Pr od Marketing Other u ct s Factors Segments Segments Pr od Marketing Other u ct s Factors Weekly data over a three-year period is ideal. Segments Pr od Marketing Other u ct s Factors Segments Pr od u ct s 7 Econometric analysis determines relationship Variation is key
  • 8. Econometric analysis produces equation that explains objective (revenues in this case) Equation Monthly Revenues=Base Revenues x Television 0.100 x Radio0.031 x Web Display0.154 x Web Search0.111 x Print 0.091x Direct 0.122  Internal Factors j=1 External Factors  n m j i i=1 R2 indicates % of variance that is explained in prediction compared to actual. 8
  • 9. Econometric results include elasticities that indicate the lift for marketing variables Marketing Expense Elasticity The response parameters (predictors) are expressed 1. Television 0.100 as elasticities. 2. Radio 0.020 For example, if the “TV 3. Web Display 0.154 spend” elasticity = 0.1 then 4. Web Search 0.111 that means that increasing TV spend by 10% will yield a 5. Print 0.091 1% increase in revenue. If Print spend increases 10%, 6. Direct Mail 0.122 revenue increases 0.9%. Elasticities guide reallocation of marketing budget to marketing activities that are generating the most lift.
  • 10. Optimal Marketing Mix Spend $100 Million Budget 10
  • 11. Typical Social Media Metrics Content Metrics  Relevance Metrics  Impact Metrics  11
  • 12. Content Social Media Metrics Description Who is talking about you? Who is talking about your industry? What are they saying? What Examples: Net Promoter score, Google Alerts, Cymfony, Radian6, Buzz Metrics, Techrigy, Blogsearch, Technorati, Social Media Platform Provider 12
  • 13. Relevance Social Media Metrics Description – relate your offer with customer interests* Fundamental Metric Source Reveals Influence Time on site Web Analytics Value of the opinion & Bounce rate Google Analytics conversations of Omniture & Web Trends interested purchasers Engagement Pass Alongs Above+ Message interest level: Comment to Post Ratio DoubleClick & Digg will customers forward Diggs, Stumbles & bookmarks StumbleUpon referral Podcast Listen & Views Del.icio.us Loyalty Blog Post for Brand BlogPulse Context & intensity of Recommendations Google Alerts blog posts Tweeters Cymfony & Techrigy Blogserch & Technorati *Social Media Marketing – An Hour a Day by Dave Evans 13
  • 14. Impact Social Media Metrics Description – measure effectiveness* Fundamental Metric Source Reveals Audience Referrers Web Analytics To Whom your social Demographics Google Analytics media campaign is Omniture & Web Trends appealing DoubleClick Influence Time on site Above + Audience involvement Loyalty Bounce rate Repeat Customers with message Action Conversions Above + Number of desired Reviews Reviews & Ratings outcome (products Recommendations Platform purchased) e.g. Tweet Tweets, etc. Net Promoter reference to purchase *Social Media Marketing – An Hour a Day by Dave Evans 14
  • 15. What are the Social Media Metrics? Depends on Business Goal Business Goal $ Metrics Subscription Customer Lifetime Value $ Spend Retention rate Advertising $ Demographics Number of visitors/members Time on site – pay attention Pass alongs Users Cost reduction # problems solved by users # problems /time tech support Insight Solutions Quality of solutions Demographics # & volume of conversation Predictive analytics 15
  • 16. Case Study – computer company Market Response Self Contained Data Problem: Can we leverage social media to generate $ Solution: Offer only social media special promotion Benefit: Generated $1 million in revenue Holistic Approach Problem: Determine if social media is working Solution: Analyze data (all marketing activities, external, economic, etc.) Benefit: Identify impact of social media on business goals 16
  • 17. Recommendations Tie social media objectives to business goals Identify data that measure business goal achievement Capture appropriate data Analyze data Monitor changes in input/output variables to determine what’s working and what’s not working Test various options 17
  • 18. What’s Next? Social Capital – value of connections among people in social networks Currently no measure for social capital What metrics would define social capital? Pilot program to determine social capital measurement and relationship to business goals Do you want to participate? 18
  • 19. For More Information Contact: Barbara Lewis Tel: (310) 471-8979 BarbaraLewis@MarQuantAnalytics.com 499 North Canon Drive, 4 th Floor Beverly Hills, CA 90210 www.marquantanalytics.com