sales-force performance analytics and optimization

Sales-force performanceanalytics and optimization

M. BaierJ. E. CarballoA. J. Chang

Y. LuA. MojsilovicM. J. Richard

M. SinghM. S. SquillanteK. R. Varshney

We describe a quantitative analytics and optimization methodologydesigned to improve the efficiency and productivity of the IBM globalsales force. This methodology is implemented and deployed viathree company-wide initiatives, namely the Growth and Performance(GAP) program, the Territory Optimization Program (TOP), andthe Coverage Optimization with Profitability (COP) initiative. GAPprovides a set of analytical models to measure and optimize salescapacity and profitable sales growth. TOP develops a set ofanalytical models and methods for the analysis and optimizationof assigning customers to sellers and other sales channels.COP provides additional recommendations on sales-coverageadjustment on the basis of an improved estimation of customerprofit. We discuss these three programs in detail and describe howthey work together to provide an analytics-driven transformationof the IBM global sales force to improve various sales metrics,such as revenue and cost.

IntroductionTransformation into a more effective and efficient enterpriseoften primarily concerns fundamental change to the businessoperations of an organization. The goal of such businesstransformation is to achieve broad alignment of activitiesrelated to people, processes, and technology with a moreintelligent strategy and vision in order to satisfy long-termbusiness objectives. As organizations transform and evolveover time, they must continually identify new ways toimprove overall performance and deliver additional valueto their customers. Advanced analytics, embedded intothe core processes of an organization, are critical toachieving these transformational goals and providing theorganization with deep insights and significant competitiveadvantage in the marketplace. Studies show that highlyeffective and efficient organizations use analytics forbusiness transformation far more often than thoseexhibiting lower effectiveness and efficiencies (forexample, see [1]).IBM has committed to the use of analytics to enable

enterprise-wide transformations in pursuit of ever improvingperformance, which in the context of this paper generallyrefers to profitable sales growth. With particular focuson increasing global sales-force productivity, threecompany-wide initiativesVGrowth and Performance (GAP),

Territory Optimization Program (TOP), and CoverageOptimization with Profitability (COP)Vwere created. Themodels and methods of each initiative are built upon aset of advanced analytics, some of which significantlyexpand upon previous mathematical techniques. In thispaper, we present these initiatives and their impact onbusiness transformation at IBM.Sales performance improvement is an essential contributor

to profitable revenue growth and a key enabler of the IBMbusiness strategy [2]. To this end, the GAP initiative hasdeveloped models to comprehensively analyze and managesales performance. GAP models can generate businessinsights to identify root causes for performance challenges(e.g., a distribution of skills that is less effective), compareperformance across geographical regions, and project futureperformance on the basis of sales resource assumptions,where sales resources include alternative sales channels (e.g.,inside sales) in addition to individual sellers. These modelshave been integrated into the Business Units, such as theHardware, Software, and Services Units of IBM, to transformthe way sales productivity is managed. Based on the insightsdriven from the GAP models, one current focus area forsales-force performance improvement is around optimizingthe distribution of sales resources by Business Unit andgeography. The ability to optimize the distribution ofsales resources for a given set of clients and availableopportunity can lead to sales performance gains and

�Copyright 2012 by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done withoutalteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied by any means or distributed

royalty free without further permission by computer-based and other information-service systems. Permission to republish any other portion of this paper must be obtained from the Editor.

Digital Object Identifier: 10.1147/JRD.2012.2216314

M. BAIER ET AL. 8 : 1IBM J. RES. & DEV. VOL. 56 NO. 6 PAPER 8 NOVEMBER/DECEMBER 2012

0018-8646/12/$5.00 B 2012 IBM

profitable revenue growth, where a client refers to anybusiness entity to which IBM sells its products typically notincluding an individual consumer.Another key factor in increasing revenue and improving

sales performance concerns the sets of clients assignedto sales resources, where a collection of clients assigned to asales resource is defined to be a territory. Extensive researchindicates that creating higher-quality sales territories, basedon quantitative metrics for scoring territories according tovarious sales evaluation criteria, is one established path toincreased sales-force productivity (e.g., see [3]). To addressthe challenges of composing and allocating such high-qualityterritories, the TOP initiative has developed models andmethods for the comprehensive analysis and optimization ofthe quality of sales territories. TOP models render businessinsights that facilitate better client assignments. Increasedproductivity is achieved by providing the means toquantitatively assess the quality of current and alternativeterritories. These models then form the basis of anoptimization capability to determine the highest-qualityterritories that maximize revenue growth and salesperformance across Business Units. To address thecomplexities and interdependencies of territory optimizationin a large corporation with correlated Business Units,the TOP analytics are deployed as part of an integratedprocess that reoccurs on a semiannual schedule to provideinformation and support for the corporate sales planningcycle.Yet another important input to consider for improving

sales-force productivity is an estimate of future revenueopportunity at each of the IBM client accounts, such as thatprovided by the Market Alignment Program (MAP) [4].COP builds on MAP and other existing IBM programs. The

existence of such estimated revenue opportunity, however,does not guarantee actual future revenue, and furthermore,the historic levels of sales-effort investment for capturingsuch estimated opportunities have not always been optimal.In addition to the use of historical revenue data and futurerevenue opportunity forecasts for deciding on sales-forcedeployment, COP provides a complementary view based onprofit, also at each of the IBM clients, in order to proposesales-force coverage adjustments by the identification ofclients for which the investment in sales effort has notbeen justified, or where an increase in investment could bejustified, on the basis of observing relative levels of revenue,opportunity, and profit.Figure 1 depicts the primary focus of GAP, TOP, and

COP and the interrelationships among them. In addition tothe insights directly offered by each, GAP and COP provideimportant inputs to TOP with respect to seller and clientcharacteristics, respectively, as indicated by the arrows in thefigure. The small circles, triangles, and squares in Figure 1represent various clients and their respective attributes,details of which are provided to TOP by COP. On thebasis of these client attributes and the attributes of salesresources provided by GAP, TOP composes three territoriesconsisting of the small circles, triangles, and squares andassigns each to one of the three available sellers.To achieve the overall goal of increasing global sales-force

productivity and improving profitable sales growth, advancedanalytics have been developed to provide the foundationfor the solution of each of the three company-wide initiatives.In analyzing and managing sales performance in GAP, wefirst model the sales capacity of the sales force using not justsimple headcount, but also using individual seller ramp-upand ramp-down profiles (i.e., profiles involving information

Figure 1

Integration of GAP, TOP, and COP within the IBM Sales Model to improve profitability and revenue growth.

8 : 2 M. BAIER ET AL. IBM J. RES. & DEV. VOL. 56 NO. 6 PAPER 8 NOVEMBER/DECEMBER 2012

on the time required to reach or drop from full capacity)that incorporate human resource transitions (i.e., sellersmoving in and out of job roles) [5, 6]. With sales productivitydefined on the basis of the sales capacity model, wedevelop regression models to identify factors that affectperformance. The regression models thus suggest avenuesinto the problem of sales-force optimization, which requirescareful consideration of several variables. First, the propermetrics for optimization are selected, depending on thedesired outcome. Second, the appropriate segmentationof analysis is determined, aligning to the level in theorganization for which sales-resource decisions andassignments are made. Finally, since all geographies andclients have idiosyncrasies with different performanceexpectations and sales-resource requirements, we performanalysis for different groups of clients separately.From an advanced analytics perspective, sales territories

consist of disjoint partitions of the set of clients that areassigned to sales resources. The TOP problem, then, consistsof determining the best allocation of clients to sales resourcesaccording to a set of evaluation criteria, where the latteris a formal representation of a comprehensive set of businessprinciples, sales coverage strategies, and Business Unitgrowth initiatives to evaluate territory quality. This includesan iterative feedback loop that supports the continualrefinement of evaluation criteria on the basis of businessoutcomes and growth opportunities. Previous solutions tothe territory optimization problem have been limited to veryfew criteria and deterministic formulations such that theresulting linear integer program can be efficiently computedwith standard optimization packages [3]. In contrast, TOPincludes a more detailed and rigorous evaluation criteria,together with associated uncertainties and risks. The resultingstochastic nonlinear dynamic program is far too large andcomplex to be solved in an efficient manner with existingmethods. Hence, on the basis of probabilistic propertiesof the underlying multidimensional stochastic process, wederive an efficient approximation algorithm whose solutionsare proven to be nearly optimal.One of the main technical challenges of advanced analytics

for COP is establishing a reliable estimate of sales expense(i.e., the cost of the sales force directly linked to each ofthe IBM clients) and, with this, an estimate of profit foreach client. We develop a simple analytical approach toestimate such expense on the basis of observed sales activityrecorded in so-called client relationship management (CRM)tools. Subsequently, a simplified framework for combininghistoric profitability with revenue, historic revenue growth,and revenue opportunity is presented, which permits theidentification of clients where qualitative adjustments ofseller coverage can be proposed. We also outline a moreadvanced quantitative approach that seeks to estimate theoptimal level of seller deployment adjustment with respectto maximizing profit on the basis of the estimated revenue

opportunity by learning the appropriate functional forms thatbest describe the relationship between revenue and salesexpense for various segments of clients.Over the past few years, all three company-wide initiatives

have been deployed to provide a set of integrated solutionsfor analytics-driven transformation within IBM. Startingin 2007, GAP analytics have been deployed across thevarious IBM Business Units and geographies, influencingkey decisions from optimizing sales-resource investments todetermining sales improvement strategies. Since the initialintroduction of territory optimization at the end of 2008,TOP analytics have been similarly deployed throughoutIBM to increase the sales-force productivity and improvethe sales performance of more than 17,000 resources(i.e., sales representatives and other IBM channels), withterritory optimization benefits expected to yield significantimprovements in sales revenue annually. Starting as apilot for a single Business Unit (IBM Software Group)and region (Italy) in late 2010, COP analytics have beenused to produce worldwide results for IBM Software Groupthroughout 2011 with usage expected to continue to growas COP becomes fully embedded in the sales process.Each of the GAP, TOP, and COP initiatives has directly

provided key insights and significant benefits to theirrespective sponsors and stakeholders. In addition, all threeinitiatives are integral to the IBM Sales ModelVthecorporate framework for embedding analytics into thesales-related strategic decision-making process, fromopportunity identification to efficient sales resourcedeployment and incentive provisions to increase productivityand improve performance. Its main components occur ona semiannual schedule to provide information for the twomajor sales planning phases of the second and fourth quarter.An example is the tripartite process of TOP that takesinto consideration both client account and sales-resourceattributes. First, current territory qualities are assessed andactions for improvement are identified. Next, deducedmodifications for optimization are processed. Finally, thereview of outcomes concludes the process. GAP similarlycontributes to the overall process by providing improvementsthrough better sales-force development and attributes(e.g., capacity insights), while COP provides analogousimprovements through better client account attributes(e.g., profitability insights). Alongside this globallystandardized process, GAP, TOP, and COP further enablecustomized projects by request, such as localinvestigations into specific sales productivity-related issues.The remainder of this paper is organized as follows.

We first present a more detailed description of the technicalproblems and mathematical solutions for each of the threeinitiatives. Next, we discuss some of our experienceswith the analytics-driven transformation of IBM based onthese initiatives. Finally, we close with some concludingremarks.


Technical problems and analytical solutions

Growth and performanceGAP is associated with three main goals: 1) to developmodels to understand historical sales performance, 2) tomodel and identify actionable factors that affect performance;and 3) to develop techniques for improving performanceby affecting such factors. These goals are in line with themarketing literature on improving sales performance andproductivity [7].One measure of sales performance used is sales

productivity, defined as the revenue per unit of sales capacity,where the sales capacity is an adjusted headcount. In anytypical organization, there is considerable movement withinthe sales force, with sellers being hired, transferring, orleaving. Moreover, sellers have different ramp-up andramp-down profiles after such moves. For example, a newuniversity hire has an initial period of little or no salescapacity. During this period, the seller learns how to do aparticular job or activity, which is followed by a period ofgradually increasing capacity until reaching an average,steady-state capacity level. Although the headcount change isimmediate, the capacity changes gradually. Therefore, toobtain an accurate measure of sales performance, it isessential to measure productivity as a function of actual salescapacity, rather than headcount, at any given time.To understand historical sales productivity and revenue

performance, GAP uses regression-based sales capacityand productivity models created as follows. First, salescapacity with respect to time is calculated using historicalheadcount, headcount dynamics (i.e., movement of sellersin and out of job roles), and ramp-up profiles. Ramp-upprofiles quantify the post-event capacity of sellers andwere developed via interviews with sales leaders. We arecurrently exploring the learning of such profiles fromhistorical data by adopting a regularized linear programmingformulation [8]. In addition, intrinsic, historical salesproductivity and productivity growth are calculated byfitting an exponential curve to the sales productivity datawith respect to time, after smoothing the seasonality ofbuying behavior in observed productivity using rollingaverages.Adjusting for seasonal trends and taking a longer time

period view allows a truer picture of the underlying intrinsicproductivity, and correspondingly sales-force performance,to emerge. Moreover, it allows for the development ofregression models to predict future performance on thebasis of historical and projected sales capacity (adjustedfor productivity growth) and revenue. This, in turn, enablessales leaders to make accurate revenue projections todetermine gaps for reaching targets, run scenarios to assessoptimal actions needed to meet targets, and test viabilityof plans/targets relative to different assumptions(e.g., productivity improvements).

While productivity is a commonly used performancemeasure, it is not always appropriate. For example, it mayonly reflect the effects of a few large deals and mask asubstantial number of lost opportunities. Similarly, revenuemay be a less appropriate performance indicator in agrowth market compared with number of clients acquired.Therefore, GAP includes other performance metrics, suchas number of opportunities resulting in actual sales, averageduration of opportunities, and number of opportunitiesidentified. These performance metrics are at the seller,transactional, and aggregate (geography and Business Unit)levels. Regression models are also developed to determinethe effect of various factors on these performance metrics,details of which are provided in reference [6].The capacity and productivity models, along with the

other performance metrics and models, are used by salesexecutives to identify geographies and Business Units forwhich performance challenges exist and provide insightinto which factors have the largest effect. One factor that hasemerged from such analysis and that has also been suggestedindependently by sales executives as a major determinerof sales performance is the composition of the sales force.Consequently, a current focus of GAP is to determine theoptimal team composition within each sales geographyand Business Unit. Team composition refers to the mixof sales resources (along with their characteristics andattributes) that together interact with a client.Because only a small number of geographies and Business

Units exist, there is insufficient data at this level for a richand sound analysis. Hence, we try to answer this question atthe client level and aggregate results to the higher leveldesired. The team composition analysis is performedstatistically and does not take ramp-up dynamics intoaccount because of data limitations; the available interactionand revenue data at the client level is over half-year timeperiods, not at a finer time scale that would permit dynamicanalysis.Two broad categories of sales job roles are considered,

technical and nontechnical, to determine the idealcomposition of a sales team that leads to high salesperformance for a particular client. The answer may bedifferent in different classifications of clients, includingclassifications based on geography, Business Unit,segment, and sector.The first step is to define the sales team composition.

The effort expended on a client is measured using full-timeequivalents (FTEs) of sellers. For example, if seller A spenta quarter of her total effort on client i (with the remainderspent servicing other clients), seller B spent a fifth of his totaleffort on client i, seller C spent half of her total effort onclient i, and no other sellers serviced client i, then thetotal effort expended by the sales force on client i is0:25þ 0:2þ 0:5 ¼ 0:95 FTEs. Furthermore, if sellers A andB have technical job roles and seller C has a nontechnical


job role, then the sales team composition at client i measuredusing the ratio of technical-to-nontechnical effort isð0:25þ 0:2Þ=0:5 ¼ 0:9. Denote this team composition asti 2 ½0;1Þ, for all clients i ¼ 1; . . . ; n.The second step is to define a sales performance metric.

One way to examine sales performance is throughtransactional revenue. However, this obscures the effect ofthe sales team because clients would produce revenue in acertain range with any sales team. The effect of the salesteam is in modulating the point in the revenue range thatis actually achieved. This general range of a client iscaptured through its aspirational revenue, an expected valueof actual transactional revenue that may be achieved.Therefore, sales team performance is better revealed throughthe ratio of actual transactional revenue to aspirationalrevenue. Let this ratio for client i be yi 2 R.Thus, we have composition-performance pairs ðti; yiÞ,

i ¼ 1; . . . ; n. We would like to understand the functionalrelationship, mapping t to y in order to develop guidelinesfor the sales organizations of various Business Units andgeographies. We estimate this functional relationshipusing the Nadaraya-Watson estimator, a form of kernelregression [9, 10]. Other estimators from the applied datasmoothing literature may also be used. Through this analysis,the team composition t that maximizes the estimate yðtÞ isa suggested team composition to achieve the best positivemodulation in transactional revenue. Of course, thisrecommended team composition is subject to constraintsinduced by the actual job role staffing of the sales force.Importantly, not all clients respond to team composition inthe same way.Different groups of clients have different behavior in

response to sales team composition. For example, a salesteam with composition t ¼ 1:4 may have better performancewith clients in the industrial sector than clients in the publicsector. Therefore, composition-performance functions yðtÞare estimated for different groups of clients separately. Thisis easily achieved by using only the data points of theappropriate subset of clients in the estimate. To obtain acomposition-performance function for North Americanclients in a specific segment, the ðti; yiÞ pairs only for thoseclients are utilized. Furthermore, rather than pre-specifyingthe groupings to consider, we can automatically discoverthe specification (based on features such as geography,sector, and coverage segment) of client groups with similarcomposition-performance behavior [11].

Territory optimization programTo address the question of territory quality, an initial elementof any territory alignment process usually consists ofdetermining the criteria for the evaluation of sales territories.This often involves four classes of objectives: balancingworkload, balancing sales potential, minimizing disruptionof relationships, and minimizing travel time and costs [3].

The TOP evaluation criteria include all four objectiveswith the appropriate levels of detail to capture the trade-offswithin and among these objectives. In addition, TOPincorporates client attributes (e.g., industry, historicalrevenue, forecasts of revenue opportunities, coveragesegments) and sales resource attributes (e.g., individualproductivity, served channels, skills, areas of expertise)into the evaluation criteria. Interdependencies among pairedclient and resource attributes manifest further criteria, suchas geographical considerations to reflect travel time andcosts to cover a sales territory. Taking into consideration theunique requirements of each IBM Business Unit, the TOPcriteria are fine-tuned for each of the different Business Units.Finally, criteria that consider the cooperation of multipleBusiness Units reflect measurements of efforts towards acoherent unified sales approach employed throughout IBM.Each of the evaluation criteria represents a dimension

of the territory alignment process. An initial set ofdimensional attributes was developed based on pre-existingand independently defined sales coverage strategies. Forexample, the attempt of a Business Unit to increase clientvalue through industry expertise can be measured by thenumber of distinct industries covered within the territory ofa sales resource. The effectiveness of implementing thisinitial set of quantitative translations from business principlesto dimensional attributes of territory quality was evaluatedwith respect to strategy alignment, which in turn has led tofurther refinement and augmentation of the dimensions ofthe territory alignment process over time. This evaluation andrefinement of attributes includes periodic correlation analysesof individual resource productivity metrics (e.g., quotaachievement) with territory quality (i.e., evaluation criteriarepresenting dimensional attributes, such as revenueforecasts). One important aspect of this process has beenan iterative feedback loop that provides information tofacilitate the development of coverage strategies based onproductivity findings at an individual resource granularity.The next step in the TOP process is to optimize the

composition and assignment of sales territories. Moreformally, the objective of territory optimization is to allocatemulti-attribute clients among multi-attribute sales resourcesin order to optimize the evaluation scores taken overeach dimension, d ¼ 1; 2; . . . ;D, of the overall problem.The scoring of each dimension d is modeled by a utilityfunction, fd , based on Business Unit-specific criteria (e.g.,revenue, cost, or profit). The utility function fd severelypenalizes any significant deviations from a range ofreasonable values to reflect business preferences, and, inpractice, fd is a piecewise linear and quadratic function.The result is nonlinear functional forms for each dimensionthat are combined together through weights, wd , assignedto each dimension. Then, the goal of the mathematicalformulation is to determine an allocation that maximizesthe weighted combination of utility functions,

PDd¼1 wdfd ,


which renders an optimization problem falling within therealm of nonlinear mixed integer programs (becauseallocation of clients cannot be fractional). Solving evenmodest-size instances of such problems requires tremendouscomputing resources, which makes an exact solution ofthe IBM problem computationally prohibitive.Our approach is consistent with Skiera and Albers [12] in

that the objective function is designed to simultaneouslyinclude multiple considerations, such as profit maximizationand fairness. However, Skiera and Albers consider a muchsimpler problem by modeling profit as a concave function ofselling times that are allocated to sales resources, whichresults in a formulation solely of profit maximization as aconvex program with integer variables. In stark contrast, ourformulation includes simultaneous consideration of manymore dimensions, each of which has more complicatedstructures than selling times. Hence, our objective functionis not equipped with second-order monotonicity propertiessuch as convexity or modularity. This in turn allowsgreat flexibility in modeling the complex structures of eachdimensional attribute and the multidimensional relationshipsamong these evaluation criteria that arise within theIBM Sales Model. Moreover, our solution is much moreefficient, which was a design requirement to support aninteractive tool.Furthermore, there are uncertainties and risks associated

with many of the problem dimensions, something notconsidered in reference [12]. For example, the revenueforecast has considerable uncertainties related to clientbehavior, market conditions, and so on. Similarly, the traveltime and cost of a sales resource has considerableuncertainties related to client characteristics, geographicaldistance, and the like. Risks are also associated withdimensions where investments are made, and the possibilitiesof perceived benefits may not be realized. In such situationswhere the uncertainties and risks are non-negligible,considering only average values for each dimension willrender poor representations of the actual utility functionvalues, large discrepancies among different territories, andpoor alignment of sales resources. To cope with thesedifficulties, the utility functions fd include characterizationsof the uncertainties and risks associated with the dimensions d.In addition, the objective function of our optimization problemcan be adjusted to obtain solutions that hedge againstassociated risks (see, for example, [13] for basic details onhedging).Given the complexities related to both computation and

uncertainty/risk, we use an approach that maps the originalstochastic nonlinear mixed integer programming problemto a corresponding multiple, multi-dimensional stochasticknapsacks problem. Here, each knapsack corresponds to asales territory, the multi-dimensional aspects correspond tothe multiple dimensions of territory evaluation scoring, andthe stochastic aspects correspond to the uncertainties and

risks associated with each dimension. Different forms ofrelated stochastic scheduling problems have receivedconsiderable attention in the research literature (e.g., [14]),as have several different instances of the stochastic knapsackproblem (e.g., [15–17]).To the best of our knowledge, virtually all of the previous

work on stochastic knapsack problems has considered asingle or one-dimensional knapsack. However, since territoryoptimization requires the allocation of multiple clients amongmultiple sales resources, both with multiple attributes, weinvestigate a significantly more general class of stochasticknapsack problems than has been previously considered.Each of the items available for allocation and each of theknapsacks into which the items can be placed consist ofmultiple dimensions. Here, the size of an item for eachdimension is drawn from a general probability distribution,and the size of each dimension is fixed for every knapsack.The item size is realized only after an attempt has beenmade to allocate the item into one of the knapsacks. Avalue is associated with each item, which is realized uponsuccessfully allocating the item into one of the knapsackswithout capacity overflow. The value of the knapsack is afunction of the summation of the values of all the items thathave been allocated into the knapsack multiplied by a weightthat is unique to the knapsack. The overall value of anallocation is the summation of the values of all knapsacksunder the allocation. We consider a combination of strategiesfor sequentially allocating a set of items among a collectionof knapsacks with respect to maximizing a functional ofthe overall value of the allocation, and establish fundamentalproperties of these strategies.The optimization algorithm we implemented in TOP

is a careful combination of both non-adaptive and adaptivestrategies. In the non-adaptive (greedy search) step, eachclient is examined sequentially with respect to its impacton the score of existing territory assignments based on thechange of the overall utility function for each possibleallocation, and then the client is assigned to the territory thatrealizes the greatest overall improvement. Once the set ofclients is allocated through this non-adaptive step, thesubsequent adaptive (local search) step selects a small groupof clients across different territories and explores whether thecurrent assignment can be improved by solely adjustingthe allocation of this selected group. This adaptive step isrepeated until a tolerance constraint is satisfied. Based onnumerous numerical experiments, we observe that ouralgorithm returns near-optimal results within a second, usinga standard laptop computer, for typical IBM instances ofthe problem consisting of many thousands of variables andconstraints. More importantly, we use optimal stopping ofmartingales and other probabilistic analysis to explorethe structures of the underlying stochastic processes. Wethen demonstrate that the algorithm is a polynomialapproximation for the original stochastic nonlinear mixed


integer programming problem, and the solution provided byour algorithm is within a constant ratio of the optimalallocation. Additional details can be found in [18].

Coverage optimization with profitabilityAt the heart of COP is the estimation of client-level profit.In the context of COP, profit is defined as the revenue that theclient generates, minus costs (defined here as costs of goodssold, including installation and other direct costs that areaccounted for and linked to client-specific revenue) andminus expense (defined here as the expense of salesactivities, including seller salary, commissions, and otherpayments that are part of direct BSales, General, andAdministrative[ expenses), for a given time period, typicallyone year. Expense is not currently allocated at the client levelin IBM financial systems, since there is no standardizedcompany-wide formal tracking of the time a seller has spentwith clients. Many tracking tools do exist and are in usethroughout the company, but their main purpose is not toallocate and track the time a seller has spent with clients, butrather to keep records of seller-client interactions for thepurpose of managing the relationship.In order to estimate sales expense, we have re-purposed the

data from a CRM tool used widely in IBM, which providesclient overviews of each stage of the sales process. Bymaintaining information for each of these stages, the salesopportunities are tracked as they evolve from being noticed,to being validated, and ultimately to being converted into aBwon[ sale (and subsequently considered Bfulfilled[) orBlost[ sale. Only time spent by sellers in true sales stages(even for those resulting in Blost[ sales) are considered inestimating expense; their time spent fulfilling wonopportunities are recorded as costs and these are allocatedto clients.The allocation of expense is based on distributing the

time a seller has spent on any and all clients that the sellerparticipated with during any of the sales stages. In summary,the time of a seller is distributed to clients based on the salesopportunities that they are linked with, as shown in theCRM records (for any client, there may be multiple salesopportunities, and one or more sellers linked to these). Theentirety of the time of the seller (equal to one FTE) isallocated to opportunities on a fractional basis, such that herFTE allocation to all opportunities for all clients sums to one.The allocation of fractional FTEs is directly proportionalto the value and the duration of the opportunity, but inverselyproportional to the size of the sales team linked to it. Foreach client, the sum of all allocated FTEs represents theinvested sales effort in the period being analyzed. To finalizethe profit calculation, the client-level expense is estimatedfrom the average expense per seller multiplied by the sumof its allocated FTEs. Subsequently, a value of clientprofitability is calculated. In order to account for normalfluctuations in revenue, for example, stemming from cyclical

patterns in information technology expenditure, we use amulti-year profit estimate to fairly compare profit levelsacross clients.The goal of COP is to further improve resource allocation

decisions by complementing the current IBM usage ofhistoric revenue and estimated future revenue opportunitydata [4] with a new dimension, namely, client-level profitestimation. A very pragmatic framework was developed foridentifying clients that can be considered as candidatesfor sales coverage adjustment, based on the relative levelsalong variations of these three dimensions. For each client,values are calculated for profit margin (defined as clientprofit normalized by revenue), as well as for historic revenueand opportunity growth. Here, historic revenue growth isdefined by an estimate of a representative growth ratefor a given period. Opportunity growth refers to the requiredgrowth rate for achieving the estimated revenue opportunityfrom current revenue levels.Depending on the combination of the relative levels of

each dimension value for any client, a qualitative resourceadjustment action is proposed. For example, the frameworkidentifies clients as candidates for adding resources if alldimension values are Bhigh[ or as candidates for reducingresources if all dimension values are Blow.[ The settingof appropriate thresholds is done by trial and error in order toobtain reasonable sets of clients for adjustment of coverage.Note that there may be other factors to consider, whichwere not modeled in this approach, that might override adecision to increase or reduce resource levels assigned to aclient, such as strategic coverage decisions for Bprotecting[a client from a competitive presence. Also, note that thisapproach is qualitative in nature, since the recommendedlevel for adding or reducing resources is not provided.In addition, in its current form, the framework does notdistinguish between resource type (for example, technicalversus nontechnical sellers), and treats all sellers the same.The qualitative approach for leveraging profitability data in

assisting sales coverage optimization described above lacksthe numerical completeness and rigor for truly optimizingresource allocation. We are currently exploring severalalternatives. One approach that has shown promising resultsis based on two steps. First, the data are clustered intonumerically homogeneous groups, in order to find the bestlocal fit of an appropriate functional form of expense vs.revenue. Second, for each client in each data cluster, we findthe optimal level of expense that maximizes future profit(capping predicted revenue as a function of expense withestimated revenue opportunity) and identify the properresource adjustment necessary (i.e., either increasing ordecreasing seller expense) for such level of maximum profit.An exponential relationship between revenue and expense

approximates the Bsaturating[ behavior of diminishingreturns for increasing levels of expense. As cost tends toincrease linearly with revenue, the corresponding profit


function for the cluster will likely have a convex maximumin the region of interest, which represents the optimal levelof expense. This optimal level may be lower than thepredicted level required for achieving an expected revenuevalue.Many refinements to this approach need to be explored,

including, but not limited to, identifying appropriateclustering algorithms, automatically identifyingcluster-specific improved functional forms, distinguishingbetween seller types, and subsequently proposing optimalseller team composition and size. Of particular interest isto apply/extend the GAP approach to team compositionto optimize with respect to client profitability and profitmargin, rather than just transactional revenue.

ExperienceThe combination of GAP, TOP, and COP analytics is beingused extensively to help guide strategic decisions pertainingto the sales force across IBM. Specific areas that directlybenefit from the use of these three aspects of the IBM SalesModel include sales resource planning, sales performanceinvestigation, sales territory optimization, client profitabilityinvestigation, and client coverage optimization. Duringdesignated planning cycles, insights from GAP, TOP, andCOP models and methods help guide planning decisionsregarding sales resource allocation. The ability to projectrevenue based on sales productivity trends, expectedBusiness Unit sales capacities, sales territory quality trendsand expected client profitability allows for the modeling,analysis, and optimization of various sales resource plans.Taking into account these and other related factors, such asmarket opportunity, sales leaders use GAP, TOP, and COPanalytics to make informed resource allocation decisions.In particular, GAP models enable deep-dive investigations

of business challenges by providing holistic analysisand management of sales performance. These deep-diveinvestigations occur across Business Units and geographies,each with a specific focus. One such deep-dive investigationfocused on examining sales performance challenges in aparticular Business Unit by using analytics to betterunderstand root causes across several dimensions. GAPmodels were used to fairly measure sales-force productivityand examine the effects of various sales-force attributes onsales performance. Applying the aforementioned GAPmodels, it was determined that the sales force for this specificBusiness Unit had both a current sales productivity leveland a long-term historical productivity trend lower thanits peers. This challenge validated the need for furtherinvestigation of root causes. The impact of sales-forceattributes such as seller skills, sales-force composition, andstrategy for covering clients were examined in detail byutilizing GAP analytics. Preliminary findings indicated thatthe distribution of sales resources across key skill sets for thisBusiness Unit yielded a higher proportion of sales resources

with lower skill levels when compared to its peers. Inaddition, the analysis showed that adjustments to thesales-force composition along with an enhanced strategyfor covering clients could result in improved salesperformance for this Business Unit. Ultimately, the analysisled to a set of actionable recommendations to be implemented,measured, and managed over the coming months.Since late 2009, the TOP initiative and optimization

capabilities have been provided to operating units acrossIBM. After overcoming initial barriers and challengesof adaptation to some new concepts of optimization,stakeholders found the program to be essential inunderstanding and improving the territory assignments ofa global sales force. Individual adjustments and localcustomization have proven to be key components of thesuccess of the program, as diverse business organizationswith heterogeneous priorities select dimensional attributesand weight their importance accordingly.IBM-internal studies found that the described

analytics-driven territory optimization increases sellerproductivity by an average of 7%, affecting the transactionalrevenue Bin scope[ (i.e., excluding non-transactional revenuestreams such as software licenses or maintenance cost).Business Units have improved their average territory qualityby 16% every optimization cycle during the first two years(measured on a qualitative scale, with 0 being the worstand 10 being the best score). Diminishing returns areexperienced during repeated iterations of optimization, butthe benefit still does not fall below a third of its initiallevel. This threshold is the result of ongoing external(e.g., clients and markets) and internal (e.g., reorganization)changes, which create a continuous flux in the attributesto be optimized.Moreover, certain dimensional attributes exhibit a stronger

correlation with sales performance than others; thus, certainattributes contribute less to the success of sales resources.Purity in the client segmentation of a territory (e.g., when asales resource exclusively serves clients identified forcompetitive selling) as well as a low number of differentindustries (to drive industry expertise and, thus, client value)were continually identified to have the highest impacton the productivity of a sales resource. Attributes that reflectfuture states of analyzed dimensions (e.g., field-validatedrevenue forecasts instead of financial revenues) also are morestrongly supported by statistical analyses, as planning happensfor upcoming sales cycles and not current sales cycles.As of late 2011, global COP results based on a three-year

sales cycle (2008 to 2010) have been finalized for one ofthe Business Units of IBM and have been provided asadditional input for 2012 coverage decisions for Europeanas well as for growth market regions. So far, only thequantitative approach (based on resource adjustment actionclustering of clients) is in use. Although the re-purposingof CRM data for allocation at the client level of sales expense


has some precision problems, our results have providedsales teams with very useful additional information forresource deployment decision making, and have receivedpositive feedback. Immediate practical uses of COP datainclude reprioritization of sales efforts and overall support ofthe sales territory design. The acquisition of results foradditional Business Units is underway, and the additionaldata will allow strengthening of the quantitative approachoutlined earlier.In the future, the full realization of benefits around

sales-force optimization requires that COP be fullyembedded in the sales process, complementing theestimated client-level revenue history and future opportunityprovided by mature IBM programs like MAP [4] with theestimated client-level historic profit as an important inputfor optimizing seller territories within TOP and furtherimprovement of other resource deployment decisions.It is expected that COP will become fully embedded aspart of the input of TOP via MAP, reaching maturitywithin several years.

Summary and conclusionIn this paper, we have described three businesstransformation initiatives integral to the IBM Sales Model,each created to improve various aspects of strategic salesdecision making through advanced analytics. The IBM SalesModel has clearly benefited from senior executive supportand sponsorship, which is essential for the success oflarge-scale business transformation. Its initiatives havebeen prioritized at the highest levels of the business,including senior sales executives, the chairman, and thechief executive officer.

AcknowledgmentsWe thank our fellow team members associated with theGrowth and Performance, Territory Optimization Program,and Coverage Optimization with Profitability company-wideinitiatives. We also thank the following executives for theirsupport: Chid Apte, Dan Bennewitz, Brenda Dietrich,Martin Fleming, Shiva Kumar, Ross Mauri, and LindaSanford. Finally, we acknowledge the important contributionsof all IBM employees and executives involved in the IBMSales Model, especially the invaluable support from theparticipating Business Units and geographies, Sales ResourceAllocation community, Business Transformation/InformationTechnology organization, and other members of BusinessPerformance Services and Research.

References1. S. LaValle, M. Hopkins, E. Lesser, R. Shockley, and

N. Kruschwitz. (2010). Analytics: The new path to value.MIT Sloan Manage. Rev. [Online]. 52, pp. 1–24, Reprint 52180.Available: http://www-05.ibm.com/es/solutions/sis/download/IBV_Analytics_the_new_path_to_value_Nov_3_GBE03371-USEN-00.pdf

2. D. K. Taft. (2010, Dec.). IBM_s six principles for successfulenterprise IT transformation. Eweek [Online]. 27(21). Available:http://www.eweek.com/c/a/IT-Infrastructure/IBMs-Six-Principles-for-Successful-Enterprise-IT-Transformation-811411/

3. A. Zoltners and S. Lorimer, BSales territory alignment: Anoverlooked productivity tool,[ J. Pers. Selling Sales Manage.,vol. 20, no. 3, pp. 139–150, 2000.

4. R. Lawrence, C. Perlich, S. Rosset, J. Arroyo, M. Callahan,J. M. Collins, A. Ershov, S. Feinzig, I. Khabibrakhmanov,S. Mahatma, M. Niemaszyk, and S. M. Weiss, BAnalytics-drivensolutions for customer targeting and sales-force allocation,[ IBMSyst. J., vol. 46, no. 4, pp. 797–816, Oct. 2007.

5. J. D. Shaw, BTurnover rates and organizational performance,[Organ. Psychol. Rev., vol. 1, no. 3, pp. 187–213, Aug. 2011.

6. M. Singh, D. Bhattacharjya, L. Deleris, D. Katz-Rogozhnikov,M. Squillante, B. Ray, A. Mojsilovic, C. Barrera, D. Kakarania,J. Richard, A. Saha, and J. Fu, BThe growth and performancediagnostics initiative: A multi-dimensional framework for salesperformance analysis and management,[ Service Sci., vol. 3, no. 1,pp. 82–98, 2011.

7. B. Skiera and S. Albers, BPrioritizing sales force decision areasfor productivity improvements using a core sales responsefunction,[ J. Pers. Selling Sales Manage., vol. 28, no. 2,pp. 145–154, 2008.

8. K. R. Varshney, M. Singh, M. Sharma, and A. Mojsilovic,BEstimating post-event seller productivity profiles in dynamicsales organizations,[ in Proc. IEEE Int. Conf. DataMiningVWorkshop Data Mining Service, Vancouver, BC,Canada, Dec. 2011, pp. 1191–1198.

9. E. A. Nadaraya, BOn estimating regression,[ Theory Probab.Appl., vol. 9, no. 1, pp. 141–142, 1964.

10. G. S. Watson, BSmooth regression analysis,[ Sankhya Ser. A,vol. 26, no. 4, pp. 359–372, Dec. 1964.

11. K. R. Varshney, M. Singh, and J. Wang, BDecision trees forheterogeneous dose–response signal analysis,[ in Proc. IEEEStatist. Signal Process. Workshop, Ann Arbor, MI, Aug. 2012,pp. 916–919.

12. B. Skiera and S. Albers, BCOSTA: Contribution optimizing salesterritory alignment,[ Marketing Sci., vol. 17, no. 3, pp. 196–213,1998.

13. M. Baxter and A. Rennie, Financial Calculus: An Introduction toDerivative Pricing. Cambridge, U.K.: Cambridge Univ. Press,1996.

14. M. L. Pinedo, Scheduling: Theory, Algorithms and Systems,3rd ed. Berlin, Germany: Springer-Verlag, 2008.

15. J. Kleinberg, Y. Rabani, and E. Tardos, BAllocating bandwidthfor bursty connections,[ in Proc. ACM Symp. Theory Comput.,El Paso, TX, 1997, pp. 664–673.

16. A. Goel and P. Indyk, BStochastic load balancing and relatedproblems,[ in Proc. IEEE Symp. Found. Comput. Sci., 1999,pp. 579–586.

17. B. C. Dean, M. X. Goemans, and J. Vondrak, BApproximating thestochastic knapsack problem: The benefit of adaptivity,[ Math.Oper. Res., vol. 33, no. 4, pp. 945–964, 2008.

18. Y. Lu and M. S. Squillante, BOn approximations for multiplemultidimensional stochastic knapsacks,[ Perform. Eval. Rev.,vol. 38, no. 2, pp. 45–47, Sep. 2010.

Received January 16, 2012; accepted for publicationFebruary 13, 2012

Moritz Baier IBM Corporate Headquarters, Armonk, NY 10504USA ([email protected]). Mr. Baier is a Senior StrategyConsultant with the IBM Business Performance Services (BPS)Group, which develops, incubates, and deploys strategic growth andoptimization initiatives across Business Units in IBM. He received aB.S. degree in computer science from Berlin School of Economicsand Law in 2009. He subsequently joined BPS, where he hasfocused on developing and deploying global sales-force optimizationinitiatives, with a particular focus on leading the design andimplementation of business intelligence solutions.


Jorge E. Carballo IBM Corporate Headquarters, EnterpriseOn Demand, Armonk, NY 10504 USA ([email protected]).Dr. Carballo is a Senior Managing Consultant in the BusinessPerformance Services Group, which identifies, develops, and deploysperformance-focused initiatives across Business Units in IBM, basedon innovative application of advanced analytics. He received aPh.D. degree in structural engineering from Stanford University and anM. Eng. degree in structural engineering from the Universidad NacionalAutonoma de Mexico. He joined IBM in 2007 and has led globalstrategic analytics-based initiatives in support of productivityimprovement.

Alice J. Chang IBM Corporate Headquarters, Armonk, NY 10504USA ([email protected]). Ms. Chang is a Senior Strategy Consultantwith the Business Performance Services Group, which identifies,develops, and deploys strategic growth and optimization initiativesacross Business Units in IBM. She received a B.A. degree in cognitivescience with specialization in computational modeling from theUniversity of California, Berkeley, in 2004 and an M.B.A. degree instrategic management and marketing from The Wharton School atthe University of Pennsylvania in 2011. She subsequently joinedBusiness Performance Services, where she has focused on globalsales-force analytics programs.

Yingdong Lu IBM Research Division, Thomas J. WatsonResearch Center, Yorktown Heights, NY 10598 USA ([email protected]). Dr. Lu received a B.S. degree in mathematics from PekingUniversity and Ph.D. degree in operations research from ColumbiaUniversity. He subsequently joined IBM at the Thomas J. WatsonResearch Center, where he has worked on stochastic processes andapplications.

Aleksandra (Saska) Mojsilovic IBM Research Division,Thomas J. Watson Research Center, Yorktown Heights, NY 10598 USA([email protected]). Dr. Mojsilovic manages the PredictiveModeling and Optimization Group in the Business Analytics andMathematical Sciences Department at the Thomas J. Watson ResearchCenter. She received her Ph.D. degree in electrical engineering fromthe University of Belgrade, Serbia, in 1997, and has worked at BellLaboratories (1998 to 2000) and IBM Research (2000 to present). Hermain research interests include multidimensional signal processing,pattern recognition, computer vision, and machine learning. She is anauthor of more than 70 publications, books, and book chapters andholds 14 patents. She received a number of awards for her work,including the IEEE Young Author Best Paper Award, EuropeanConference on Computer Vision Best Paper Award, INFORMSWagner Prize, IBM Market Intelligence Award, and IBM OutstandingTechnical Achievement Awards. Dr. Mojsilovic is a member ofINFORMS and a Senior Member of the IEEE.

M. Jonathan Richard IBM Corporate Headquarters, Armonk,NY 10504 USA ([email protected]). Mr. Richard is a ManagingStrategy Consultant with the Business Performance Services (BPS)Group, which develops, incubates, and deploys strategic growth andoptimization initiatives across Business Units in IBM. He received aB.S. degree in economics and finance from Boston College in 1999 andan M.B.A. degree from MIT Sloan School of Management in 2009.He subsequently joined BPS, where he has focused on developing anddeploying sales improvement and strategic planning initiatives.

Moninder Singh IBM Research Division, Thomas J. WatsonResearch Center, Yorktown Heights, NY 10598 USA ([email protected]). Dr. Singh is a Research Staff Member in the BusinessAnalytics and Mathematical Sciences Department at the Thomas J.Watson Research Center. He joined IBM in 1998 after receiving hisPh.D. degree in computer and information science from the Universityof Pennsylvania. His research interests include the general areas ofartificial intelligence, machine learning, and data mining.

Mark S. Squillante IBM Research Division, Thomas J. WatsonResearch Center, Yorktown Heights, NY 10598 USA ([email protected]). Dr. Squillante is a Research Staff Member in the BusinessAnalytics and Mathematical Sciences Department at the Thomas J.Watson Research Center, where he leads the Stochastic Processes andApplications Group. He received a Ph.D. degree from the Universityof Washington. His research interests concern mathematicalfoundations of the analysis, modeling, and optimization of complexstochastic systems. He is an elected Fellow of ACM and IEEE, andthe author of more than 200 technical papers and more than 30 issuedor filed patents. His work has been recognized by The Daniel H.Wagner Prize (INFORMS), eight best paper awards, nine keynote/plenary presentations , and nine major IBM technical awards. He serveson the editorial boards of Operations Research, Performance Evalua-tion, and Stochastic Models.

Kush R. Varshney IBM Research Division, Thomas J. WatsonResearch Center, Yorktown Heights, NY 10598 USA ([email protected]). Dr. Varshney is a Research Staff Member in the BusinessAnalytics and Mathematical Sciences Department at the IBM T. J.Watson Research Center. He received a B.S. degree in electrical andcomputer engineering from Cornell University in 2004 and S.M. andPh.D. degrees in electrical engineering and computer science fromthe Massachusetts Institute of Technology in 2006 and 2010,respectively. He subsequently joined IBM at the T. J. Watson ResearchCenter, where he has worked on statistical signal processing, machinelearning, and business analytics. In 2011, he received two IBMResearch Technical Accomplishment Awards for business impactof outsourcing analytics and for software sales analytics. He is anauthor or coauthor of eight journal papers and 20 conference papers.Dr. Varshney is a member of Eta Kappa Nu, Tau Beta Pi, theInstitute of Electrical and Electronics Engineers, and the Institutefor Operations Research and the Management Sciences.


sales-force performance analytics and optimization

Documents