Using CLV Concept for Marketing Budgets Allocation Olga Oyner and Elena Panteleeva (National Research University Higher School of Economics) Ljubljana, 2011 EMAC 40 th Conference The Day After: Innovation, Inspiration, Implementation

Theoretical background – To make marketing measurable: More transparent from financial point of view (Kotler 2002; Kumar2004; Ambler 2006 ) More efficient in terms of output/input (Shet, Sisodia 2002) To consider marketing results from long time perspective: from costs to investment and from profit to equity (Rust 2004; Ambler 2006; Strivastava 2001; Doyle 2001) To estimate marketing impact on financial results and firm value (Rust et al 2004; Ambler 2006; Shet, Sisodia 2002; Srivastava et al 2001) 2

Theoretical background (cont.) Marketing productivity chain – the marketing impact on the firms value (Rust et al 2004; Ambler 2006; Shet, Sisodia 2002; Srivastava et al 2001) Customer equity and CLV (Pfeifer, Haskins, and Conroy 2004; Rust and Lemon 2001; Rust, Lemon, and Zeithaml 2004; Kumar and Reinartz 2006) 3

Research object Large multinational business-to-business (B2B) PC hardware manufacturer Business task: how to generate more value from every client? 4

Model: chain of marketing productivity 5

Aims The proposed model is aimed at increasing the efficiency of the intrafirm budget allocation because it will help determine which marketing instruments have the greatest impact on CLV and sales Sales targets are set on individual level and dont take into account marketing budgets spent on each single customer in order to achieve these results. So we need to find out if there are any differences in how marketing instruments contribute to the sales volumes and CLV 6

Data characteristics Data on purchase volume and marketing budgets spent on various marketing activities for companys clients 451 clients from one of the sub regions of Russia and CIS countries 1471 datum 7

Hypotheses H1: discount amount positively influences the sales volume, but doesnt significantly add to the CLV H2: account manager (communication level) has positive and the greatest impact on sales H3: retail promotion has positive impact on sales but this impact is less then account manager has H4: core product promotion has positive impact on sales but less then all above has H5: other product promotions has positive impact on sales but less then all above has 8

Variables and CLV Independent variables for the models were: – Account manager – investments in relationship marketing – Retail promotions – budgets for co-marketing with retailers – Discount – price reductions given to specific customers – EPSD – budgets for co-marketing activities with clients for server products (seminars for end-users, trade-shows) – UPSD – budgets for co-marketing activities with clients for motherboards (seminars for end-users, trade-shows) – DT - budgets for co-marketing activities with clients for core product (seminars for end-users, trade-shows) Sum of SO Amt Disti CostCLVDiscounts Retail promotions Account ManagerUPSDEPSDDT

Regression equation: sales Sales = -2080, ,468*Account manager +594,232*EPSD promo -163,746*UPSD promo +55,565*DT promo - 115,572*retail promo+1,548*discount 10 Model Unstandardized Coefficients Standardized CoefficientstSig. 95% Confidence Interval for B BStd. ErrorBeta Lower Bound Upper Bound 6(Constant) -2080, ,633 -,732, , ,016 Account Manager 215,4688,273,76426,045,000199,205231,731 EPSD 594,23219,225,71930,910,000556,440632,023 UPSD -163,74618,840-,226-8,691, , ,709 DT 55,5658,672,1176,408,00038,51872,612 Retail promotions -115,57226,309-,141-4,393, ,290-63,854 Discounts 1,548,493,0623,142,002,5802,517

Regression equation: CLV CLV (for 4 periods) = , ,753* Account manager +2261,849**EPSD promo -775,462* UPSD promo -767,601* retail promo + 246,695 DT promo +0*Discount 11 Model Unstandardized Coefficients Standardized CoefficientstSig. 95% Confidence Interval for B BStd. ErrorBeta Lower BoundUpper Bound 5(Constant) , ,892 -2,599, , ,170 Account Manager 1204,75348,275,90424,956, , ,651 EPSD 2261,849111,820,57920,228, , ,662 UPSD -775,462109,376-,227-7,090, , ,453 Retail promotions -767,601144,021-,199-5,330, , ,487 DT 246,69550,856,1104,851,000146,722346,667

Regression equation: sales (2) In order to rationalize the budget allocations we need to determine the equation of the non-linear multiple regression: Sales = Account Manager*EPSD Account Manager2+0* Discounts Account Manager*UPSD DT Account Manager Retail promotions*DT Discounts*EPSD DT 12 Unstandardized Coefficients Standardized CoefficientstSig. BStd. ErrorBeta (Constant) Account Manager*EPSD Account Manager Discounts E Account Manager*UPSD DT Account Manager Retail promotions*DT Discounts*EPSD DT

Budget allocation optimization Current budget allocation Optimized budget allocationDifference Discounts Retail promotions Account Manager UPSD90000 EPSD DT Company local budget Company overall budget (incl discounts) CLV

Framework limitations and future research directions The study is based on one B2B hardware PC company. Similar studies in identical companies are needed to outline general correlations It would be useful to calculate CLV and customers profitability not only on an aggregated level, but on individual ones in order to tailor marketing mix tactics for each customer specifically 14

Framework limitations and future research directions (cont.) We evaluated only those budgets, that can be attributed to each customer specifically. We didnt take into account corporate marketing activities, such as PR, ATL, etc. – how will the proportion between those customer-specific and general budgets influence the CLV? – what if general budgets are more efficient on the company level? 15

Framework limitations and future research directions (cont.) We didnt take into account competitors response to the marketing actions of the company. But for the oligopoly in which the company in study is in, it is vitally important 16

Q&A THANK YOU! 17