Making Sales Technology, CRM, And Sales Force Automation Effective

8/14/2019 Making Sales Technology, CRM, And Sales Force Automation Effective

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Making Sales Technology EffectiveMaking Sales Technology Effective

. . , .

Journal of Marketing71 1 Januar 2007 . 16-34.

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TraditionalTraditional

ContemporaryContemporary

ont nuum

Nature of IO interfaceCom etitiveCom etitive Coo erativeCoo erative

--

Distribution of relationshi

outcomes (Clopton 1984):

ShortShort--termtermmaximizingmaximizing

LongLong--termtermoptimizingoptimizing

Temporal outlook (Dwyer,Schurr, & Oh, 1987;

Ganesan, 1994):

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TraditionalTraditional ContemporaryContemporary

DimensionSalespersons Role Gatekeeper /Gatekeeper / Manage key accountManage key account

on t onson t ons on onson ons

Channel Power

(Moon & Armstrong, 1994) Linking PinLinking Pin / functional experts/ functional experts

ManufacturerManufacturer RetailerRetailer(Messinger & Narasimhan,1995)

HorizontalHorizontalVerticalVertical

(network economy)(network economy)

Sellers CompetitiveOrientation

(Achrol & Kotler, 1999)

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5Introduction


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1)Generating revenues

2) Improving sales force effectiveness

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SalespersonSalesperson--DependentDependent(this and other research)(this and other research)

SalespersonSalesperson--IndependentIndependent(e.g. Online reverse auctions, Jap 2003)(e.g. Online reverse auctions, Jap 2003)

Replaces or reduces sales forcesReplaces or reduces sales forcesstrategic significance bystrategic significance by

Alter sales force strategyAlter sales force strategy

(People, skills, processes,(People, skills, processes,

--a participant in firms goa participant in firms go--toto--marketmarketstrategystrategy

,,architecture, size, etc.)architecture, size, etc.)

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The strategic importance of driving the top line (Rust, Moorman, and Dickson 2002)


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refers to information technologies that canfacilitate or enable the performance of sales.

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SalesTechnology

CRM astechnology,

CRM

SFA

includes ITs thatbridge w/ salesfunctions but

also some that

Primarily efficiency-

.

Sales-based CRM

technologiesdeveloped to

automate sales tasks.

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e cons er oo s as e en re gamu o n orma on ec no og es a salespeople use to perform their rolesnot just the subset designed as

sales-CRM or SFA tools.


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B2B sales ersons different uses of information

technologies to build better relationships withbusiness buyers.

Exam le: A P&G customer business develo ment

rep using scanner data to make marketing mixrecommendations to a major grocery chains

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.

Introduction


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Sales technology is,

expensive, andits returns are ambiguous.

Opportunities to build better relationships between

organizations through sales technology are oftenunrealized.

,understanding concerning technologys role

towards improving relationship building efforts.

11Introduction


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, ,

associated with forging buyer-sellerrelationshi s.

Investigate the role sales technology plays inac tat ng or ena ng sa espeop e to per orm

those new tasks.

Develop and test a behavioral process modelusin structural e uation modelin .

12

Introduction


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Ubiquitous relationshipRoachs roductivit aradox circa 1980s- Some support (cf. Powell & Dent Micallef, SMJ, 1997)

- Others contradict (cf. Sharda, Barr, & McDonnell, MS, 1998)Spectrum of frameworks (Hitt & Brynjolfson, MISQ, 1996)

Innovative debate over appropriate dependentvariableTypically use accounting measures like ROE, ROI, etc.

erna ve examp e: or n s q ara wa , ara wa , onsyns ,MS, 1999)

Black box approachpThis stream of research usually employs traditional econometricmodeling measuring relationship between firm spending andproductivity (inputs/outputs)

13Relevant literature


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Descriptive studiesInvesti ate a re ate usa e of technolo ies e. . Widmier ackson & McCabe PSSM 2002

Forward-looking studies that focus on newinnovations . ., ,

Early classification schemes for sales automationHelped lay foundation for conceptualization (e.g., Wedell & Hempeck,JPSSM, 1987)

Causes of sales automation failures(e.g., Speier & Venkatesh,JM, 2002)

Motivating sales and information technology adoption(e.g., Venkatesh and Davis, Mgt. Sc, 2000)



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How different uses of sales technologyinfluence behaviors that ma hel re s buildstronger relationships with customers while

simultaneously considering influences ona m n strat ve per ormance.

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-

organization performs to help build relationships with externalconstituents.

In a more general sense, relationship-forging tasks refer to activities conductedby boundary spanners to forge or merge their organizational boundaries with an

external organizations boundaries.

Sales technology refers to information technologies that canfacilitate or enable the performance ofsales tasks.

-

Related to CRM (IT product, but ST includes more than computer technologies)

Include new tasks enabled by technology (effectiveness-focused applications).

16Introduction


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This research goes directly to the user to get assessments ony ginformation technologys value.Substantial research on factors that influence the level (and validity) ofuser evaluations and debate over the limitations of this approach.pGoodhues task-technology fit theory and measurement is the mostrelevant to this research

Goodhue (DS, 1995) strongest link between information systems andperformance will be due to a correspondence between tasks needs andinformation system/ technology functionality.

Thi h diff ti t t k f th t h l d i t thhis research differentiates tasks from the technology and incorporates thehuman factor (including voluntary use).



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Draws from six research streams:relationshi marketin

Atuahene-Gima and Li 2002; Doney and Cannon 1997; Dwyer, Schurr,

and Oh 1987; Ganesan 1994

buyer-seller relationships,Cannon and Perreault, 1999

information technology productivityHitt and Brynjolfson 1996; Bharadwaj, Bharadwaj, and Konsynski 1999

organizational learning,Huber 1991; Sinkula 1994; Day 1994

cognitive sellingSujan, Sujan, and Bettman 1988; Szymanski 1988

ne otiationsPruitt 1981; Clopton 1984; Jap 1999; 2003.

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Behavioral modeling approachWidely used to study other aspects of sales roles ininterorganizational markets (c.f. Behrman & Perreault,JM,

1984,JBR1982; Behrman, Bigoness, & Perreault, MS, 1981;Sujan, Weitz, and Kumar,JM, 1994; Singh,JM, 1998).

Relies on primary datawhich contrasts with the IT productivity literature that relieson secondary data

Portfolio of sales technologieswhich contrasts with the IT applications literature

Sales technology effects on tasks are empiricallyestimated

which contrasts with a user rating of the effect.



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What key tasks does the

salesperson perform that canbe influenced by usingn ormat on tec no ogy

Whatmanagerially-controllable

a accounobjectives & key

aspects of salespersonHow much andhow does the

factors motivatesalespeople to

use technology?

influenced by onesuse of information

salesperson usetechnology?

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directly and indirectly

through sales tasks?Conceptualization & Measurement


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salesperson perform that canbe influenced by using

information technology?





use technology?



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,directly and indirectly



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Extensive literature on salesperson performance (almost 100 years old) , -

Adapt measures form inventory proposed by Behrman & Perreault (JBR, 1982;JM, 1984)

and widely used in the marketing and management literatures.

Relationship-building performance with customers (external focus)elationship building performance with customers (external focus) refers to the extent to which the salesperson performs activities that cultivate a

relationship that mutually benefits the selling and buying firms.

Ex: relative to the average salesperson in similar selling situations

working with buyers to develop a partnership thats profitable to both firms. 7-pointLikert SDA to SA These can be viewed as new working smart tasks.

Administrative performance (internal focus) refers to the salespersons ability to complete required, non-selling related activities in a

timel manner.

Ex: relative to the average salesperson in similar selling situationsAddressing my administrative responsibilities in a timely manner. 7-point Likert, SDAto SA

Covariates that predict performance (for completeness) Work experience

Work effort (working harder)

22Conceptualization & Measurement


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use technology?



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are n orma on across oun ar es Hansen (ASQ, 1999): importance of sharing

knowledge across organizational subunits

Eisnenhard & Tabarizi (ASQ, 1995): NPDknowledge sharing

Focus on integrative solutions Clopton (JMR, 1984): critical need in

interorganizational relationships to adopt a win-win (integrative) attitude instead of zero-sumdistributive ame.

Jap (JMR, 1999): Pie-expansion



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Sharin market knowled e refers to the extent to which individuals (salespeople)

develop relevant market expertise and share theirknowledge with others (their customers).

Ex: I keep my buyers aware of market changes.7-point Likert, SDA to SA

Proposing integrative solutions refers to the extent to which an individual (salesperson)

applies information and knowledge to construct andpropose recommendations that are mutually beneficial to

.

Ex: Im good at finding opportunities that benefit bothmy firm and my customers (firm).7- oint Likert SDA to SA



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use technology?



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Considered multidimensional instead of uni-dimensional construct(based on learning from previous study:JPSSM2006)

Conceptualizing dimensionsInformation technology is often conceptualized as the collection, analysis, dissemination, and storage of information.

Sinkula (JM, 1994) proposed four stages of market informationprocessing: acquisition, interpretation, istri ution, an storage.

Using sales technology to store information (expands memory) shouldhave temporal effects and would be better addressed through time-

.

Thus, we propose and tested three dimensions: accessing (acquisitioning), , communicating (distributing).



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AccessUsing technology for accessing informationrefers to the extent to whicht i i f tisa espeop e use ec no ogy o retrieve information re evan o eperformance of their sales jobs.

AnalyzeUsing technology to analyze informationrefers to the extent to whichsalespeople use technology to better understand the implications ofinformation relevant to the performance of their sales jobs.CommunicateUsing technology to communicate informationrefers to the extent towhich salespeople use technology to transfer information both toindividuals within and outside their sales organization in theperformance of their sales jobs.Semantic differential (bi-polar adjectives) scales

, , , .



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Factors affecting sales technology usagegy g Personal innovativeness, attitude toward the new ST, and

facilitating conditions influence ST infusion (Jones, Sundaram,and Chin (2002)

ternat ve ramewor s or a opt on o nnovat ons ex st Parsimony dictated constraints on model specification

Social influence theory (Thibaut & Kelley, 1959). .or negatively influence ones behaviors (e.g. sales technology use)

Two key antecedents:wo key antecedents: Customers (buying firm) IT expectations, and Sales technology training effectiveness



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FIGURE 1 Conceptual Model of Antecedents and Consequences of Sales

-

Fundamental

Relationship-building

performance

Key aspects of

performance

Communicating

Analyzing

Type of ST

uses Relationship-forging tasks

Sharing market knowledge

Proposing integrative solutions

Customers IT

expectations

management

inputs

Administrative

performance

Accessing

effectiveness

Work experience

Individual performance factor

covariates

or



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See DeVellis (1991)

Desired host firm characteristics o ulation eneralizations :1. salespeople conducted typical internal and external B2B

sales tasks (e.g. they did not sell to final consumers)2. ST implementation was under way,3. variance in salesperson ST use (voluntary use) and skills,4. management would encourage participation in responses

that would be kept confidential to researchers,5. the sales force was large enough to support statistical

tests of the hypothesized relationships.

-(not P&G, my former employer, and not the same firm used inmy previous sales technology study publishing in JPSSM

32Administration


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Evaluated preliminary questionnaire

and completeness.

na responses ra e was .

We dropped 3 observations for

missing data yielded an effectiverate of 77% and sample size of 151.

33Administration


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Average age was 42 (ranging from 23 to 63)

Annual salaries from $24,000 to $139,000 not including bonus pay whichran ed from 400 to 25 000 er ear

Average years of sales experience was 18--new hire to 38 yr veteran

Average work week: 47 hours 17 were spent doing administrative work,

16 interacting with customers,

7 doing data analyses, and

on ot er act v t es

The average salespeople interacting with 34 people monthly outside the

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Administration


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See Anderson & Gerbing (1988):1. Fit separate CFAs for each of the proposed scales to assess reliability and

2. Fit separate (one vs. two dimensional) CFAs for scales that were mostsusceptible to cross loadings (e.g. two aspects of performance)

3. Tested fit of overall structural model and size of h othesized effects

Relied on following fit statistics:Jreskog and Srboms (1986) goodness of fit index (GFI) and (AGFI)

Bentlers (1980) Comparative Fit Index (CFI)2 statistic with its associated degrees of freedom (and ratios)

Browne and Cudecks (1993) root mean square error of approximation (RMSEA).

35Methods


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Construct reliability estimates Fornell and Larcker (1981) computations

agozz an cr ter on: over . s es ra e

All of the constructs here exceed .71 Worst fittin CFA fit had GFI = .94; AGFI = .90; CFI =.90.

Item reliability estimates . . , ,

squared multiple correlation coefficients greater than .38.45) indicate internally consistency with other items

compr s ng a sca e 36 items for composite measures:

33 have item reliabilities .30, and

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all items are greater than .20 (lowest is R-scored).

Results


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Followed conventional approach for large item surveys Scale scores for constructs

model constraints Set error construct effects to the square root of the reliability

Set the error variances to the products of the scale variance and (1 scale reliability)

Block-recursive model required algebraic identification(Bollen, 1989; Bekker & Pollock, JE, 1986)

Overall fit statistics indicate an excellent fit(2= 37.1 (p= .17), df= 30;

= = = =

37

. , . , . , . .

Results


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Variance explained for endogenous constructs

Communicating: 24%Analyzing: 20%Accessin : 15%

Relationship-forging tasks

Sharing market knowledge: 30%Proposing integrative solutions: 21%

Aspects of salesperson performance

Relationship-building performance: 57%Administrative performance: 12%

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ou ypo es ze pa s are s gn can a p.


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FIGURE 2FIGURE 2 ResultsResults

Types of SalesTypes of SalesTechnology UsesTechnology Uses

RelationshipRelationship--ForgingForgingTasksTasks

ManagerialManagerial

inputsinputs

PerformancePerformanceAspectsAspects

Sharing

Customer ITexpectations

. p. .

.33

(p.< .001)

CommunicatingSMC = .23

. p. .

Rel.-buildingperformance

SMC = .50

marketknowledge

SMC = .26

.18 (p.< .05)

Analyzing andbetter

understandingSMC = .21

Proposingintegrativesolutions

SMC = .21

-.04

(p.= .56)

Trainingeffectiveness

.23

(p.< .01)

-.08

Experience Effort

.22 (p.< .01) AccessingSMC = .15

Administrativeperformance

SMC = .12.30 (p.< .05)

.=.

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a Standardized maximum likelihood parameter estimates.b P-values for one-tailed significant tests of path coefficients. Solid lines indicate significant effects.

c Model was specified as a block-recursive which produced correlations estimates of .34 between buyer IT expectations and training effectiveness,.31 between communicating and analyzing, .37 between communicating and accessing, .68 between analyzing and accessing, .71 between the relationship-forging tasks, and .48 between the two aspects of performance.

* Overall model fit statistics indicate an excellent fit: (2 = 37.1 (p = .17), df= 30, CFI= .98 RMSEA =.04, GFI = .96, AGFI = .91).


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Specification alternatives and discriminant validity Comparisons between:

proposed block-recursive model and one-dimensional alternatives(non-nested model comparisons via CFI and AIC),

proposed block-recursive model and higher-order constructs

(nested models comparisons via 2 difference test)

proposed block-recursive model and unrelated constructs (nestedmo e s compar sons v a erence test

All tests supported block (see paper Appendix Table A1)

Item to scale composite and other scale comparisons- . .

Common method bias test

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Same source bias test with equivalent effect constraint

No evidence of bias in estimates.Results


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RFTs represents an important new theoretical perspectiveor u ure researc n n erorgan za ona con ex s

RFTs as new smart-selling behaviors in a relationalcontext

Salespeople can be viewed as value-creators inimplementing a sales-service differentiation strategy.

different aspects of performance.

Sales managers may increase sales technology costs yet

Training and buyer encouragement represent relevant socialinfluences on technology use that may be in conflict witheach other.

41Discussion/Conclusion


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Standard concerns with self-reports

Potential for common method bias despite absence ofstatistical evidence

Behavioral performance measures where ideal wouldbe truly objective measures (if such measures exist)

Sellers views on relationship-building performancewould benefit from supplements from buyersperspectives

Consumer packaged goods industry versus othercontexts

42Discussion/Conclusion

Making Sales Technology, CRM, And Sales Force Automation Effective

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