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Motivating the Sales Force
Moderator: Greg Marshall, Charles Harwood Professor, Rollins College
PAPER PRESENTATIONS
What Really Matters to Sales Executives? - Paola Guenzi Career Strategies in Sales Organizations - Alireza Keshavarz, Francis Kramarz, Dominique Rouziès, Michael Segalla, and Bertrand Quelin When Managers Misjudge the Abilities of their Salespeople: Devaluing Mr. Brilliant and Overvaluing Mr. Mediocre - Harish Sujan and David Silvera
What Really Matters to Sales Executives?
Presenter: Paolo Guenzi, Associate Professor, Bocconi University
Problem
• Increasing concerns about academic research ability to focus on managerially relevant topics
• Jaworski (2011): managerial relevance 1. is a perceptual measure that must be judged by managers, not “organizaEons” in general → we focus on individual percepEons of managers
2. should be related to “routes to impact” → we focus on key success factors for managers’ professional life
3. calls for research focusing on concepts, since they have a longer-‐lasEng impact than other knowledge types → we aim at idenEfying key concepts
• Research goal: idenEfying concepts summarizing sales managers’ percepEons about the key success factors in their professional life
4
Is relevance an issue?
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Approach: research summary (1/2)
• RQ: what are the key success factors in the professional life of sales managers?
• Sample: 159 respondents • Text analysis on statements wriSen by informants (i.e. answer to
the quesEon: based on your experience, what is/are the key factor/s to be successful in your profession?)
• 7020 words, summarized into 400 “chunks of meaning”/concepts. Informants’ own language used as the source of concept labels whenever possible (Strauss & Corbin, 1990). To ensure the trustworthiness of the coding scheme (Lincoln & Guba, 1985), three coders assessed it independently. Disagreements between the researchers were discussed unEl consensus was achieved.
6
Approach: research summary (2/2)
• Concepts coded and then assembled into aggregate dimensions using the big eight competencies model (Bartram, Robertson, & Callinan, 2002;
Kurz & Bartram, 2002): 20 competency dimensions and 112 competency component Etles/concepts (SHL Universal Competency Framework)
• Meta-‐analyEcal research on this model (Bartram, 2005, p.1199) suggests that “The contribuEon of the Great Eight model for understanding of job performance is clear. Each of the eight predictors was shown to predict a different area of job performance consistently across jobs, measurement instruments, and cultural contexts.”
• “Member check”: final results discussed in a meeEng with a sample of informants to increase trustworthiness of the research process (Storbacka, Polsa & Sääksjärvi, 2011)
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Big eight competencies (1/2)
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Leading and deciding
Deciding & IniEaEng acEon Leading & SupporEng
SupporEng & CooperaEng
Working with people Adhering to principles and values
InteracEng & PresenEng
RelaEng & Networking Persuading & Influencing PresenEng & CommunicaEng
Analyzing & InterpreEng
WriEng & ReporEng Applying experEse Analyzing
Big eight competencies (2/2)
9
Enterprising & Performing
Achieving personal work goals Entrepreneurial & commercial thinking
AdapEng & Coping
AdapEng & Responding to change Coping with pressure and setbacks
Organizing & ExecuEng
Planning & Organizing Delivering results & MeeEng Customer
expectaEons Following procedures
CreaEng & Conceptualizing
Learning & researching CreaEng & InnovaEng FormulaEng strategies and concepts
0
10
20
30
40
50
60
70
80
90
100
Courage Listening Curiosity Learning
Crea/vity
Flexibility
Op/mism
Goal orienta/on Passion
Ques/oning yourself
Ac/on Rela/onships
Knowledge Vision
Growing
Results: key concepts by competency dimension
Results: key concepts (size of words corresponds to frequency of cita/on)
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Passion Curiosity
Courage
Listening
Learning
Goal orientation
Adaptiveness
Challenging yourself
Optimism Knowledge
Action Relationship Communication
Creativity
Vision
Growing
Enterprising & Performing Achieving personal work goals
Leading-Deciding
Creating-Conceptualizing Adapting-Coping
Supporting-Cooperating Working with others
Implications for research
• Academics should focus on key concepts for execuEves. Unfortunately, some of the key concepts iden/fied in this study are largely underinves/gated in the sales literature. Titles in EBSCO:
– Passion AND Sales: 0 ar/cles Adap/veness AND Sales: 33 ar/cles – Curiosity AND Sales: 0 ar/cles Knowledge AND Sales: 26 ar/cles – Courage AND Sales: 0 ar/cles Learning AND Sales: 42 ar/cles – Op/mism AND sales: 6 ar/cles Listening AND Sales: 13 ar/cles – Crea/vity AND Sales: 9 ar/cles Rela/onship AND Sales: 282 ar/cles
• Some of the constructs highlighted by our study have been so far neglected in organiza/onal psychology studies in general. This is true, in parEcular, for Curiosity (Mussel, 2013) and Passion (Liu et al., 2011).
• Passion refers to the autonomous internalizaEon of an acEvity, making it part of one’s idenEty and thus creaEng a sense of personal enjoyment and free choice about pursuing the acEvity (Vallerand et al., 2003; Vallerand & Miquelon, 2007; Liu et al., 2011).
• Curiosity affects work-‐related behavior and performance beyond other 12 predictors (Mussel, 2013) due to its relevance for learning and impact on the ability to deal with uncertainty and change
12
Implications for executives
• How can companies (through their culture, systems and processes) and sales managers develop and exploit the key success factors idenEfied here?
• Recrui/ng and hiring: use a competency model based on the key success factors. This may mean radically re-‐thinking needed traits, aptudes, skills
• Training: same as above • Performance management and Rewards: behavioral performance KPIs on many
key success factors idenEfied in this study should be measured and sEmulated through appropriate incenEves
• Leadership: the ability to spread the key success factors throughout the team and the organizaEon will ulEmately affect the leader’s success
• Socializa/on and climate • Etc.
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Socializing individual passion: the case of Finmeccanica
Stimulating curiosity: the case of Henkel
Career Strategies in Sales Organizations
Presenter: Alireza Keshavarz, Doctoral Student, HEC Paris
Co-authors: Francis Kramarz, Professor, Ecole Polytechnique ENSAE Dominique Rouzies, EDF Chair Professor, HEC Paris Michael Segalla, Professor, HEC Paris Bertrand Quelin, Professor, HEC Paris
THE SALES CAREER
Sales career strategies?
EVIDENCE OF INCREASING MOBILITY
13.5% Mobility among sales forces
0%
2%
4%
6%
8%
10%
12%
14%
16%
2004 2005 2006 2007 2008 2009 2010
Turnover Rate in Sales Jobs -‐ France
Inter Industry Mobility
INTER AND INTRA INDUSTRY MOBILITY
0%
2%
4%
6%
8%
10%
12%
14%
16%
2004 2005 2006 2007 2008 2009 2010
Turnover Rate in Sales Jobs -‐ France
Inter Industry Mobility Intra Industry Mobility
9.5 % inter-‐industry and 4 % intra-‐industry mobility
CONSEQUENCES OF MOBILITY
0%
2%
4%
6%
8%
10%
12%
14%
16%
2004 2005 2006 2007 2008 2009 2010
Turnover Rate in Sales Jobs -‐ France
Inter Industry Mobility Intra Industry Mobility
Diversity of profiles enter and exit sales jobs
OCCUPATION, COMPANY, AND INDUSTRY EXPERIENCE
Sales Manager Pfizer
Pharma.
Sales Manager J&J Pharma.
1 Sales Manager Boston ScienEfic Medical Devices
2 General Manager Small X Medical Devices
3
How much of the value of her experience has changed? How does the new employer value her experience?
OUR DATA
• Data source: – Large-‐scale administraEve database (1993-‐ 2010) – NaEonal insEtute of staEsEcs and economics studies
• Number of: Workers/ ObservaEons: – Salespeople (n=76,401/214,101) – Sales managers (n= 13,003/32,663)
We “follow” the person through a period from 1993-‐2010 !
France All industrial sectors Public and private firms Varying firm sizes
OUR MODEL
Sales career value is • Measured by
– CompensaEon
• Depends on experience in – Industry – Company – OccupaEon
• Controlling for General experience Size EducaEon Gender Public/Private company Seasonal effects Industry effect Individual effect
Disentangling the effect of each type of experience
2SLS ESTIMATION Compensa/on
Variable Baseline Sales Managers
2SLS Sales Managers
Baseline Salespeople
2SLS Salespeople
Industry Experience 2.2%*** 0.3%*
Industry Experience2 -‐0.17%*** -‐0.03%* Company Experience 2.9%*** 2.5%*** Company Experience 2 -‐0.25%*** -‐0.2%*** Job Experience 3.3%*** 2.6%*** Job Experience2 -‐0.13%*** -‐.16%*** General Work Experience 3.8%*** 2.3%***
Public Firm (vs. Private) 20%** 16.4%** -‐7%* -‐7.2%* Gender 14.5%*** 11.9%*** 8.9%*** 7.9%*** Firm Size 2.7e-‐4%*** -‐2e-‐4%*** 8e-‐5%** 5e-‐5%ᶧ Educa/on Level 3.4%*** 3.4%*** 1.2%*** 0.9%*** # Observa/ons # Workers
32664 13003
32664 13003
214102 76402
214102 76402
R^2 0.322 0.421 0.215 0.309
Notes: 2SLS EsEmaEons; from 1993 through 2010 N observaEons/workers: (32663/13003 and 214101/76401 for sales managers and salespeople respecEvely); ***: P < .001; **: P < .01; *: P < .05; ᶧP <0 .1;
2SLS ESTIMATION Compensa/on
Variable Baseline Sales Managers
2SLS Sales Managers
Baseline Salespeople
2SLS Salespeople
Industry Experience 2.2%*** 0.3%* Industry Experience2 -‐0.17%*** -‐0.03%* Company Experience 2.9%*** 2.5%*** Company Experience 2 -‐0.25%*** -‐0.2%*** Job Experience 3.3%*** 2.6%*** Job Experience2 -‐0.13%*** -‐.16%*** General Work Experience 3.8%*** 2.3%***
Public Firm (vs. Private) 20%** 16.4%** -‐7%* -‐7.2%* Gender 14.5%*** 11.9%*** 8.9%*** 7.9%*** Firm Size 2.7e-‐4%*** -‐2e-‐4%*** 8e-‐5%** 5e-‐5%ᶧ Educa/on Level 3.4%*** 3.4%*** 1.2%*** 0.9%*** # Observa/ons # Workers
32664 13003
32664 13003
214102 76402
214102 76402
R^2 0.322 0.421 0.215 0.309
Notes: 2SLS EsEmaEons; from 1993 through 2010 N observaEons/workers: (32663/13003 and 214101/76401 for sales managers and salespeople respecEvely); ***: P < .001; **: P < .01; *: P < .05; ᶧP <0 .1;
2SLS ESTIMATION Compensa/on
Variable Baseline Sales Managers
2SLS Sales Managers
Baseline Salespeople
2SLS Salespeople
Industry Experience 2.2%*** 0.3%* Industry Experience2 -‐0.17%*** -‐0.03%* Company Experience 2.9%*** 2.5%*** Company Experience 2 -‐0.25%*** -‐0.2%*** Job Experience 3.3%*** 2.6%*** Job Experience2 -‐0.13%*** -‐.16%*** General Work Experience 3.8%*** 2.3%***
Public Firm (vs. Private) 20%** 16.4%** -‐7%* -‐7.2%* Gender 14.5%*** 11.9%*** 8.9%*** 7.9%*** Firm Size 2.7e-‐4%*** -‐2e-‐4%*** 8e-‐5%** 5e-‐5%ᶧ Educa/on Level 3.4%*** 3.4%*** 1.2%*** 0.9%*** # Observa/ons # Workers
32664 13003
32664 13003
214102 76402
214102 76402
R^2 0.322 0.421 0.215 0.309 Notes: 2SLS EsEmaEons; from 1993 through 2010
N observaEons/workers: (32663/13003 and 214101/76401 for sales managers and salespeople respecEvely); ***: P < .001; **: P < .01; *: P < .05; ᶧP <0 .1;
INDUSTRY EXPERIENCE
€ 40,000
€ 40,500
€ 41,000
€ 41,500
€ 42,000
€ 42,500
€ 43,000
0 1 2 3 4 5 6 7
CumulaEve added value for industry experience
S. Manager's industry exp S. Person's industry exp
Very likle added value with industry experience for salespeople
COMPANY EXPERIENCE
€ 40,000
€ 40,500
€ 41,000
€ 41,500
€ 42,000
€ 42,500
€ 43,000
€ 43,500
0 1 2 3 4 5 6 7
CumulaEve added value for company experience
S. Manager's company exp S. Person's company exp
Comparable added value with company experience for both sales managers and salespeople-‐ not in the long run
JOB (SALESPEOPLE/ SALES MANAGER) EXPERIENCE
€ 40,000 € 41,000 € 42,000 € 43,000 € 44,000 € 45,000 € 46,000 € 47,000 € 48,000
0 1 2 3 4 5 6 7
CumulaEve added value for sales job experience
S. Manager's job exp S. Person's job exp
Greater added value with job experience for sales managers
SUMMARY
€ 40,000 € 40,500 € 41,000 € 41,500 € 42,000 € 42,500 € 43,000 € 43,500 € 44,000 € 44,500 € 45,000 € 45,500 € 46,000 € 46,500 € 47,000
0 1 2 3 4 5 6
Salesperson
Industry exp Company exp Job exp
€ 40,000 € 40,500 € 41,000 € 41,500 € 42,000 € 42,500 € 43,000 € 43,500 € 44,000 € 44,500 € 45,000 € 45,500 € 46,000 € 46,500 € 47,000
0 1 2 3 4 5 6
Sales managers
Industry exp Company exp Job exp
KEY RESULTS
Sales managers Salespeople
All type of experiences are valued more for sales managers than salespeople
Job experience is the most important component
Diminishing returns to company experience
Diminishing returns to industry experience
Very likle return to industry experience
POINTS OF DISCUSSION
• HR policies in sales organizaEons – Salesperson vs. sales manager jobs – Disentangling the effect of different sets of experiences
• Future Studies – Sales careers as boundaryless careers? – PromoEng salespeople to sales managers?
When Managers Misjudge the Abilities of their Salespeople: Devaluing Mr. Brilliant
and Overvaluing Mr. Mediocre
Presenter: Harish Sujan, A.B. Freeman Chair Professor, Tulane University Co-author: David Silvera, Associate Professor, University of Texas at San Antonio
• A de-valued Mr. Brilliant is likely to under perform
• P[r
• An over-valued Mr. Mediocre is likely to develop less
INACCURATE ABILITY APPRAISALS DE-MOTIVATE
Pride < -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ > Disappointment
Smug < -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ > Interested
THE (ENORMOUS) BENEFIT OF ACCURATE APPRAISALS
• It is so hard to recruit a Mr. Brilliant – Losing him, de-motivating him is tragic.
• T. J. Rodgers, Cypress Semi-Conductor, HBR, 1990
– More important than anything else you may be doing is attempting to retain, re-motivate a high performer.
• It is very important to develop talent, Mr. Mediocre
• Peter Senge, The 5th Discipline, Learning Organizations
– There are no winners or losers, just learners – Before him, Edward Deming,…
WHAT CAUSES SUPERVISORS TO JUDGE THEIR SALESPEOPLE INACCURATELY?
• Judge and Ferris, AMJ, 1993. – Social and situational factors shape raters’
evaluations • Supervisor’s affect towards the subordinate
– If the supervisor likes the subordinate more lenient ratings • Demographic Similarity • Work relationship
– Clarified by Duarte et al 1994, high quality LME relationships, more lenient ratings
• Huffman and Cain, Psy and Mktg, 2000. – Sales managers make adjustments for territory
difficulty while evaluating their salespeople • Performance (ability) evaluations tend to be
subjective, biased, Landy and Farr 1980. – Blend objective, self and supervisor evaluations
COULD A REQUEST FOR TRAINING BIAS EVALUATIONS
• Training improves ability – When a mediocre sales performer asks you
(the supervisor) for more training will this cause you to evaluate him higher than the ability change?
– When a superior sales performer ask you for more training will this cause you to evaluate him lower than had he not asked?
THE PSYCHOLOGY OF HELPING
• Dan Gilbert (of Stumbling and Prudential insurance advertising) and David Silvera researched this. – They argued that helping others has behavioral benefits, they
enact better strategies, signaling improved ability. – It also has attributional costs. The evaluator believes that their
improved strategies, in part, were from the help they received—not translated into improved ability. In the future, without the help, the strategies used may be less smart.
– So, depending on the focus, behavior change or reason why, ability evaluations change.
– Considering the case of the average performer first, because of the human tendency to see behavior as primarily driven by dispositions (Gilbert and Jones 1996), ability is likely to be judged as greater than it really is.
– For the excellent performer, because they are no behavioral benefits of the help (he is already at the peak), there will only be attributional costs. His ability is likely to be judged less than it really is.
GILBERT AND SILVERA (1996) DEMONSTRATED THIS
• With artificial tasks, anagrams • With non-expert observers • But, the fundamental attributional error has
been demonstrated widely. • The findings should generalize to sales
supervisors’ evaluations of their salespeople.
• Raising the question of how to correct the bias.
LAB INVESTIGATION 1: COMPARING GOOD AND POOR HELP
• Student participants were asked to play the role of supervisors at a calling center in which university student employees call alumni to solicit donations to the university.
• Information about the typical number of calls per hour and typical caller success rates in soliciting donations was provided. o Typically 40 calls are made in an hour and only 10 result in direct contact
with a potential donor. • 4 of 10 potential donors spoken to turn out to be “impossible”;
even the most skilled of student callers cannot persuade them to contribute.
• 2 of 10 are “difficult”; they will contribute if the student caller does an extremely good job of persuading them.
• 2 of 10 are “somewhat difficult”; they will contribute if the student caller does a decent job of persuading them.
• 2 of 10 are “easy”; they will contribute if the student caller is at all persuasive.
• Followed by descriptions of the performance of two student callers (one excellent performer and one average performer).
CONDITIONS
• Target – Average versus Excellent Performer
• Help (Training) – No versus Poor versus Good
• Generic versus Personalized • Part of a refresher course that was not optional
• A 2 x 3 ANOVA was conducted with the target’s calling ability as the dependent measure.
• The key result from this analysis was a significant interaction between the target and help manipulations (F (2, 54) = 9.96, p < .001).
• One-way ANOVAs conducted for each target indicated that help had no effect on ratings of the excellent performer, but that higher quality help significantly increased ratings of the average performer.
GRAPHICALLY: CRITERIA IS CALLING ABILITY
0
1
2
3
4
5
6
7
Excellent Average
No HelpPoor HelpGood Help
PARTIAL REPLICATION
• We replicated only with the average performer Gilbert and Silvera’s (1996) anagram task findings.
• Explanation – An additional analysis showed that quality of help
significantly influenced the degree to which participants viewed the help as a strong explanation for the excellent target’s calling success
– This indicates that help was not seen as “assistance” but as “ability development”.
LAB INVESTIGATION TWO: SOLICITING HELP
• What was the reason why the Gilbert and Silvera finding that suggests that Mr. Brilliant can be undervalued as a result of help did not replicate?
• Perhaps this was because help was given, not asked for.
• What if Mr. Brilliant solicited help? • Study Design
– Target: Average or Excellent – Help (Training): No, Unsolicited, and Solicited.
• Key Finding: A significant interactive influence on caller ability (F (2, 69) = 8.21, p < .01).
PLANNED CONTRASTS
o Excellent performer evaluations: no help = unsolicited help > solicited help
o Average performer evaluations: no help < unsolicited help = solicited help
o The findings for Mr. Brilliant replicate if help is asked for.
GRAPHICALLY: CALLER ABILITY IS THE CRITERION
0
1
2
3
4
5
6
7
Excellent Average
No HelpSolicitedUnsolicited
SUMMARIZING FINDINGS
Mr. Average gains (evaluations of ability) irrespective of whether he asks for more training or is given it, and gains more the better the quality of help.
Mr. Average appears to be over-evaluated prevalently, costing him in terms of genuine ability development.
Mr. Brilliant does not lose unless he asks for help (additional training). Illustratively, if he is pro-social and asks for help to benefit others he is under-valued, and consequently de-motivated.
GOING FURTHER
• Is Mr. Brilliant inoculated against being under-valued, as a result of soliciting extra training, by being known to be a good citizen?
• Do key findings hold if the participants are annual fund sales managers. – With 200 such participants we will test a Mr.
Brilliant vs. Mr. Average (2) x no help vs. solicited good help (2) x good vs. average citizen (2) design.
– With a 100 we will leave out the the third condition.
IMPLICATIONS
• Train Sales Managers to overcome biases, notably the fundamental attributional error. – As result improve motivation, learning and
retention. • Train salespeople to better protect
themselves. – Develop a practical intelligence relating to
ability impression management.
David H. Silvera, Ph.D. University of Texas at San Antonio [email protected] Harish Sujan, Ph.D. Tulane University [email protected]
Charles Hardwood Professor
MOTIVATING THE SALES FORCE PANEL DISCUSSION
Rollins College
Greg Marshall Elenor Mak
Co-Founder
Keaton Row
Steve Young
Founding Director
Sales Executive RoundTable
Mike Turner
VP - Sales
UPS
Moderator Subject Matter Expert Panelists
