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Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
Research on Sabermetrics and Sales Performance
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Research on Sabermetrics and Sales Performance

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This presentation explains how baseball's Sabermetrics may be applied to sales using OnCorps and a mobile sales platform.

This presentation explains how baseball's Sabermetrics may be applied to sales using OnCorps and a mobile sales platform.

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  • Note:
    The data comes from Standard & Poor‘s Compustat, a database containing fundamental financial and price data for both active and inactive publicly traded companies and going back annually to 1950. This database can be assessed from Wharton Research Data Services (WRDS). The name of the original online dataset is Compustat Monthly Updates – Fundamentals Annuals.
    Certain criteria – companies, time, and variables - have been put into the query to obtain a desired dataset.
    Using this dataset, average annual revenue growth rates from 2007 to 2012 for S&P 500 companies are calculated. Since some companies’ revenues are missing, there are 475 growth rates.
    Outliers (companies with extreme growth rates) are removed.
    Thus, growth rates of 408 companies are used to make this chart.
  • Note:
    The data comes from Standard & Poor‘s Compustat, a database containing fundamental financial and price data for both active and inactive publicly traded companies and going back annually to 1950. This database can be assessed from Wharton Research Data Services (WRDS). The name of the original online dataset is Compustat Monthly Updates – Fundamentals Annuals.
    Certain criteria – companies, time, and variables - have been put into the query to obtain a desired dataset.
    Using this dataset, average annual revenue growth rates from 2007 to 2012 for S&P 500 companies are calculated. Since some companies’ revenues are missing, there are 475 growth rates.
    Outliers (companies with extreme growth rates) are removed.
    Thus, growth rates of 408 companies are used to make this chart.
  • Note:
    Company, revenue, and growth rate come from the dataset from Standard & Poor‘s Compustat.
    Direct sales cost is mainly from annual reports.
    There are 47 companies in total.
    Top performers are those having high revenue growth rates. Companies in top quartile group are those having a growth rate of 11.91% or higher. 11.91% is the top quartile calculated using all S&P companies with missing values and outliers removed, calculated in previous slide.
    There are 16 top performing companies among these 47 companies.
    The red column represents all companies and the green column represents contains top quartile companies.
    The height of the bar represents the median ratio of sales cost to revenue.
    The black line represents the spread between top quartile and bottom quartile.
  • Note:
    Company, industry, revenue, and growth rate come from the dataset from Standard & Poor‘s Compustat. Direct sales cost is mainly from annual reports.
    There are 47 companies in total. There are 10 software companies in “Software” group. Top performers are those having high revenue growth rates. There are 16 companies in “Top quartile” group.
    This graph shows the result from statistical modeling. The Gaussian curves represent the percent increase in revenue (not the absolute increase in revenue).
    Transformed data is used. The sales and costs are highly skewed on raw scale. After log-transformation, the transformed data is used for model fittings.
    Linear regression is used and the model equation is: log(sale) = a + b*log(cost). Fitted regression lines:
    All: log(sale) = 5.72789 + 0.52555*log(cost)
    Software: log(sale) = 5.8920 + 0.4734*log(cost)
    Top quartile: log(sale) = 0.3303 + 1.1363*log(cost)
    From the fitted regression line, one will be able to derive the expected percent increase in revenue. Using slope “b” from log(sale) = a + b*log(cost), we have: increase in percentage = [ exp( b * log(1.1) ) - 1 ] * 100%.
    Simulation is used to obtain the distribution of expected percent increase in revenue (i.e. Gaussian curve). 1000 data points are generated.
    Means and standard deviations used to plot Gaussian curves are calculated from the simulated data points.
  • Transcript

    • 1. Sabermetrics for Sales Research TM © OnCorps 2014. All Rights Reserved 1
    • 2. 2 TM Sabermetrics Introduction OnCorps S&P 500 Research Analytic Modules © OnCorps 2014. All Rights Reserved
    • 3. Two fundamental approaches to managing The star system The system is the star Source: http://www.sportingcharts.com/mlb/stats/mlb-cost-per-win-by-season/2013/ 3 2013 wins: Cost per win: 82 $2,790,677 94 $645,367 85 $2,354,321 92 $629,296 teams changes the cost-to-win © OnCorps 2014. All Rights Reserved
    • 4. 4 Studies show talent is less important than Talent 24% Randomness 76% managing predictability How much does paying more for talent count for winning in baseball? Source: http://www.econ.tcu.edu/harvey/blog/Salary_Performance_Causality.pdf © OnCorps 2014. All Rights Reserved
    • 5. 5 Rewarding past winners in highly random events does not payoff If he told you he had a special technique for winning the slots, © OnCorps 2014. All Rights Reserved would you pay him to gamble for you?
    • 6. Resource allocation by scenarios or years 6 1. Break outcomes down into stages 2. Measure stages by success rates 3. Compare competing methods 3 1 4. Compare teams and players by stage 5. Identify the most predictable combinations 6. Change resources and investment 2 H 5% 2.8 % 32% 0.4 % Buyer A Buyer B Team B Team A Those who win more with less adhere to a disciplined and analytic system Team B + Buyer A Baseline 1 2 3 4 © OnCorps 2014. All Rights Reserved
    • 7. 7 Our method lets teams match the highest payoff prospects with the best methods 1. Payoff 2. Odds All Prospects All Methods Highest value prospects Most predictable methods 1. Increase per customer revenue and profit by targeting the highest payoff prospects 2. Reduce cycle times and costs-to-win by executing the most predictable sales methods 3. Identify the offerings and methods best matching the highest payoff customers © OnCorps 2014. All Rights Reserved
    • 8. 8 TM Sabermetrics Introduction OnCorps S&P 500 Research Analytic Modules © OnCorps 2014. All Rights Reserved
    • 9. 9 There is a huge spread in the cost-to-win ratios in baseball and in business 130% 10 year average spread between the highest an lowest team payroll to win ratio for all major league teams 100% of teams track statistics 195% 10 year average spread between the top and bottom quartiles in incremental sales cost to sales growth in S&P 500 55% of companies use CRM systems © OnCorps 2014. All Rights Reserved
    • 10. 10 The average annual growth rates in the S&P 500 are quite distributed Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved Note: 486 data points are used: • Mean: 7.47% • Standard deviation: 16.57% • Bottom quartile: 0.96% • Median: 4.98% • Top quartile: 10.40% Extreme values (smaller than -25% or larger than 65%) among these 486 data points not shown on histogram.
    • 11. 11 S&P 500 sector growth leaders and laggards Sector Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved Overall (percent of companies in S&P 500 in each industry) Top performers (how many top performers are in each industry) Bottom performers (how many bottom performers are in each industry) Energy 8.44% 18.03% 3.28% Materials 6.17% 6.56% 4.92% Industrials 12.55% 9.02% 9.02% Consumer discretionary 16.67% 13.93% 19.67% Consumer staples 8.02% 4.92% 6.56% Health care 10.70% 12.30% 4.10% Financials 16.67% 11.48% 32.79% Information technology 13.17% 18.85% 4.92% Telecommunication services 1.23% 3.28% 0% Utilities 6.38% 1.64% 14.75% Total 100% 100% 100% Note: 486 data points are used. Green: higher than overall percent in the same industry Yellow: similar than overall percent in the same industry Red: lower than overall percent in the same industry
    • 12. 12 The ratio of direct sales cost by industry varies considerably Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved
    • 13. 13 The top 25% outliers in growth spend 36% more on sales and marketing Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved Note: Height of bar represents median value. Black line shows interquartile range (i.e. spread between bottom and top quartile). All companies: • Bottom quartile ratio: 4.15% • Top quartile ratio: 20.82% Top quartile companies: • Bottom quartile ratio: 4.16% • Top quartile ratio: 25.45%. 47 data points are used.
    • 14. 14 Sales returns flatten for larger companies Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved Note: 47 data points are used in total.
    • 15. 15 Sales returns flatten after about 10,000 people Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved Note: 46 data points are used in total.
    • 16. 16 Most companies would not return a 10% increase in sales investment Note: All: • Mean: 5.16% • Standard deviation: 0.96% Software: • Mean: 11.45% • Standard deviation: 1.35% Top quartile: • Mean: 4.94% • Standard deviation: 1.48% 47 data points are used. Source: OnCorps 2014 © OnCorps 2014. All Rights Reserved
    • 17. 17 TM Sabermetrics Introduction OnCorps S&P 500 Research Analytic Modules © OnCorps 2014. All Rights Reserved
    • 18. 18 1. Modules are sets of questions which may be deployed to groups to track performance in specific areas. 2. Modules may be combined to create a single app. 3. Questions may be combined when more than one modules are selected. 4. Organizations may choose to use modules once, periodically, or continuously. 5. Questions may be customized to refer to specific names and terms of the particular organization. © OnCorps 2014. All Rights Reserved About OnCorps Modules
    • 19. Team Decisions Tracks decisions made by teams and compares to outcomes. Customer Stage Tracks customer success rates by stage of relationship. Sales Methods Compare sales methods to outcomes. Sales Metrics Modules © OnCorps 2014. All Rights Reserved . Customer Share-of-Wallet Tracks share of category spend in customer groups and departments. Competitor Positioning Tracks competition by offering, industry, and region. Product Performance Tracks product performance by customer, industry, region and competitor. Buyer Success Rates Tracks key buyers and influencers by company. Team Skills Tracks team combinations and skills of most successful deals. 19 Customer Decision Profiles Tracks customer decision making profiles by customer.
    • 20. 20 Customer Stage: Description Tracks customer success rates by stage of relationship Why would you use this module? 1. To measure the success rate of closing deals by stage. 2. To compare these success rates with time and capital commitments by stage. 3. To see if costs for talent are lower to achieve more predictable success rates in earlier stages. 4. To see if these success rates by stage differ from traditional pipeline reporting. What might it tell me? 1. Conversion rates from earlier to later stage deals may justify more focus on earlier stages. 2. High payoff deals are more random than we think making too much investment a waste. 3. Talent required to close earlier stage deals may cost significantly less and provide better return on sales. 4. Even large, attractive customers may prefer earlier stage adoption of offerings. © OnCorps 2014. All Rights Reserved
    • 21. 21 Won Customer Stage: Opportunity Tracking What is the deal size? How long did the deal take to win? © OnCorps 2014. All Rights Reserved Won Your deal is small with a short cycle time 10% 40% Cycle time Deal size 20% 30% Short Long Small Large Won What are your top three strengths for this deal? Lower price Brand Team skills Innovative products Existing business relationship Industry expertise Existing personal relationship Won Heat map of top strengths Team skills 70% Industry expertise 55% Existing relationship 40% Innovative products 30% Brand 10% Lower price 5% $100K 7 months
    • 22. 22 Customer Stage: Charts and Findings Comparison of Success Rates by Product type, Time, and Capital. 1. Each bubble represents one product. 2. The larger the circle, the higher the success rate. 3. The location of a bubble corresponds to the time and capital involved. 4. Small circles in the upper right will mean lower success rates requiring a lot of time and capital. 5. Other factors apart from product type may also be used, such as industry, sales method, etc. (when corresponding questions are included) Comparison of Strengths by Deal Size. 1. Each colored bar represents one strength. 2. The height of bar equals the average deal size. 3. Black lines show range between first and fourth quartile. 4. Apart from deal size, cycle time can also be compared by strengths. © OnCorps 2014. All Rights Reserved
    • 23. 23 © OnCorps 2014. All Rights Reserved © www.oncorps.org www.oncorps.io Cambridge, Massachusetts | Bristol, United Kingdom

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