The understanding of what motivates a customer to pay more for a product or service has al-ways been a fundamental question in business. To the end of answering this question, revenue management is a business practice that revolves around using analytics to predict consumer behavior and willingness-to-pay. It has been a common practice within the commercial airline and hospitality industries for over 30 years, allowing adopters to reach their service capacity with increased profit margins. In this thesis, we investigated the possibility to apply revenue management to the last mile delivery industry, an industry that provides the service of delivering goods from e-commerce companies to the consumer’s front door. To achieve this objective, a revenue management framework was conceived, detailing the interaction between the customer and a dynamic pricing model. The model itself was a product of a machine learning model, intended to segment the customers and predict the willingness-to-pay of each customer segment. The performance of this model was tested through a quantitative study on synthetic buyers, subject to parameters that influence their willingness-to-pay. It was observed that the model was able to distinguish between different types of customers, yielding a pricing policy that increased profits by 7.5% in comparison to fixed price policies. It was concluded that several factors may impact the customer’s willingness-to-pay within the last mile delivery industry. Amongst these, the convenience that the service provides and the disparity between the price of the product and the price of the service were the most notable. However, the magnitude of considering these parameters was never determined. Finally, em-ploying dynamic pricing has the potential to increase the availability of the service, enabling a wider audience to afford the service. / Vad som motiverar en kund att betala mer för en tjänst eller en produkt har länge varit ett centralt koncept inom affärslivet. Intäktsoptimering är en affärspraxis som strävar efter att besvara den frågan, genom att med analytiska verktyg mäta och förutse betalningsviljan hos kunden. Intäktsoptimering har länge varit framträdande inom flyg- och hotellbranschen, där företag som anammat strategin har möjlighets att öka försäljningsvinsten. I detta examensarbete undersöker vi möjligheten att applicera intäktsoptimering på sista milen industrin, en industri som leverar köpta produkten hem till kunden. För att uppnå detta har vi tagit fram ett ramverk för informationsflöden inom intäktsoptimering som beskriver hur kunder interagerar med en dynamisk prissättningsmodell. Denna prissättningsmodell framställs genom maskininlärning med avsikt att segmentera kundbasen, för att sedan förutse betalningsviljan hos varje kundsegment. Modellens prestanda mättes genom en kvantitativ studie på syntetiska kunder som beskrivs av parametrar som påverkar betalningsviljan. Studien påvisade att modellen kunde skilja på betalningsviljan hos olika kunder och resulterade i en genomsnittlig vinstökning på 7.5% i jämförelse med statiska prissättningsmodeller. Det finns mänga olika faktorer som spelar in på kundens betalningsvilja inom sista milen industrin. Bekvämlighet och skillnader i priset på produkten som levereras och tjänsten att leverera produkten är två anmärkningsvärda faktorer. Hur stor inverkan faktorerna som beskrivs i detta examensarbete, har på betalningsviljan, förblev obesvarat. Slutligen uppmärksammades möjligheten att, med hjälp av dynamisk prissättning, öka tillgängligheten av tjänsten då flera kunder kan ha råd med en prissättning som överväger deras betalningsvilja.

Abstract in English, Tsonga and Zulu / Mitlumbo ya mifambafambo ya le magondzweni i xin’wana xa miringeto (risks) yo biha ku tlula hinkwayo ya swifambo swa le gondzweni emisaveni hinkwayo, leswi yimelaka xiphiqo lexikulu xa swohanyaswin’we-ikhonomi ngopfu-ngopfu eka matiko lama ya ha hluvukaka tanihi Afrika-Dzonga. Ku va ku nyikiwa masungulo yo tiya ya xiikhonomi eka swiboho swa mbekiso ku tirhana na ntlhontlho lowu, i swa nkoka swinene ku hlela ndhurho wa mitlumbo leyi. Mipimanyeto leyi yi tirha tanihi nxopaxopo wa swinghenisiwa swa mbuyelo wa ndhurheriwo ku endlela ku kuma mphakelo wa switirhisiwa wo tirha kahle eka ku nghenelela eka ku tirhana na mitlhontlho leyi vangiwaka hi mitlumbano ya le magondzweni. Tiko ra Afrika-Dzonga a ri nga ri ku pfuxeteni ka mahungu ya mipimanyeto ya midurho ya mitlumbano ya le magondzweni nkarhi na nkarhi, naswona leyi a yi endliwa a yi tirhisa maendlelo lamo soriwa ngopfu yo languta nkoka wa vanhu (human capital). Hikwalaho, mipimanyeto leyi nga kona a yi nga ta va leyi tshembekaka eka ku kunguhata na ku pimaniseka na mipimanyeto ya matiko man’wana. Hi le ka ku landzelela vundzhaku lebyi laha dyondzo leyi yi nga tumbuluka na ku kombisa matirhiselo ya rimba ra ntirho wo katsa (hybrid) ku kambela ndhurho wa mitlumbo ya swifambo swa le magondzweni eAfrika-Dzonga. Rimba leri ri tirhisa endlelo ro kongomisa eka nkoka wa vanhu na ku pfumela ku hakela (willingness-to-pay), eka dyondzo yi ri yin’we. Mipimanyeto ya midurho ya nkoka wa vanhu ya laveka ku va yi pfuna eka ku kunguhata leswaku yi tlakusa swinenenene swihumesiwa swa rixaka, loko hala tlhelo mipimanyeto yo pfumela ku hakela yona yi ri yona yi fanelaka swinene eka ku pfuneta minghenelelo yo tlakusa nhlayiseko wa vanhu hi ku hunguta ku vaviseka na ku fa. Endlelo ro pfumela ku hakela ri tirhisa maendlelo ya swo ka swi nga ri swa makete (contingent valuation) na ya maendlelo yo langa (preference). Khwexinere yo valanga leyi a yi ri na maendlelo yo ka ya nga ri ya swa makete na swilangiwa leswi a swi boxiwile, yi tirhisiwile hi magoza mambirhi ku sampula vaanguri va 273 eka ntirho wa swo tleketla. Eka mhaka ya endlelo ro kongomisa eka nkoka wa vanhu, mipimanyeto ya ndhurho eka xiviko xa 2016 xa Cost of Crashes in South Africa yi hundzuluxiwile hi ku katsa inifulexini, ku tirhisiwa mpimo wa 2017 wa 5.3% ku kuma mipimanyeto ya ndhurho ya 2017. Dyondzo leyi yi paluxile leswaku endlelo ro kongomisa eka nkoka wa vanhu ri kayiveta ku vona ndhurho wa mitlumbo ya le magondzweni. Dyondzo leyi yi hoxa xandla eka ntsengo wa vutivi hi ku tirhisa endlelo ro languta nkoka wa vanhu na endlelo ro pfumela ku hakela eka dyondzo yi ri yin’we ku kombisa ntirhiseko wa endlelo leri ra nkatso (hybrid)/nhlanganiso eka xiyimo xa Afrika-Dzonga. Ndzavisiso wa nkarhi lowu taka wu fanele ku engeta dyondzo leyi hi sampulu leyi humaka eka swifundzakulu hinkwaswo swa nkaye swa Afrika-Dzonga, leswaku mipimanyeto ya ndhurho yi yimela vanhu va tiko hinkwaro. / Road traffic crashes are one of the worst risks of road mobility worldwide, representing a huge socio-economic problem particularly in developing countries such as South Africa. In order to provide a sound economic basis for investment decisions to address this challenge, it is critical to assess the cost of these crashes. These estimates serve cost-benefit analysis inputs to facilitate a more efficient resources allocation for interventions to address the challenge posed by road crashes. South Africa has not been updating crash cost estimates on a regular basis, and those that were conducted used the much criticised human capital approach. Therefore, the available estimates could not be relied upon for planning purposes and comparison with the estimates of other countries. It is against this background that this study developed and illustrated the application of a hybrid framework for assessing the cost of road traffic crashes in South Africa. The framework uses the human capital approach and the willingness-to-pay approach in one study. Human capital approach cost estimates are needed to inform planning to maximize the national output, while the willingness-to-pay estimates are more suitable when the main concern is to inform interventions to increase social welfare by reducing injuries and fatalities. The willingness-to-pay approach uses the contingent valuation and the stated preference methods. A survey questionnaire with contingent valuation and stated preference questions was administered in two phases to a sample of 273 respondents within the transport industry. For the human capital approach, the cost estimates in the 2016 Cost of Crashes in South Africa report were adjusted for inflation using the 2017 rate of 5.3% to obtain 2017 cost estimates. This study revealed that the human capital approach underestimates the cost of road crashes. The study contributes to the body of knowledge by using the human capital approach and the willingness-to-pay approach in one study to illustrate the applicability of this hybrid/ combination within the South African context. Future research needs to replicate this study on a sample drawn from all nine provinces of South Africa, so that the cost estimates are representative of the country’s population. / Ukuphazamiseka komgwaqo kungenye yezingozi ezimbi kakhulu zokuhamba komgwaqo emhlabeni jikelele, ezimele inkinga enkulu yenhlalo nezomnotho ikakhulukazi emazweni asathuthuka njengeNingizimu Afrika. Ukuze unikeze isisekelo sezomnotho esizwakalayo ezinqumeni zokutshala izimali ukubhekana nale nselele, kubalulekile ukuhlola izindleko zalezi zingozi. Lezi zilinganiso zisebenza njengeziphakamiso zokuhlaziywa kwezindleko zokuhlomula ukuze kube lula ukunikezwa kwezinsiza ezenzelwe ukuxazulula inselele ebangelwa ukuphazamiseka komgwaqo. INingizimu Afrika ayizange ibuyekeze ukulinganisa izindleko zezingozi njalo, futhi lezo ezenziwa zisebenzise indlela enkulu yokugxeka ukusebenzisa abantu. Ngakho-ke, izilinganiso ezitholakalayo azikwazanga ukuthenjelwa kuzona ngezinjongo zokuhlela nokuqhathaniswa nezilinganiso zamanye amazwe. Lokhu kuphikisana nalesi sigaba ukuthi lolu cwaningo lusungulwe futhi luboniswe ukusetshenziswa kohlaka oluxubile lokuhlola izindleko zokuphazamiseka komgwaqo eNingizimu Afrika. Uhlaka lusebenzisa indlela yokusebenzisa abantu kanye nendlela yokuzimisela-ukukhokha ocwaningweni olulodwa. Ukulinganiselwa kwezindleko zokusebenzisa abantu kuyadingeka ukuze kwaziswe ukuhlela ukwandisa umkhiqizo kazwelonke, kanti ukulinganiselwa kokuzimisela-ukukhokhela kukulungele kakhulu ukwazisa ukungenelela ukwandisa inhlalakahle yomphakathi ngokunciphisa ukulimala nokubulawa kwabantu. Indlela yokuzimisela-ukukhokha isebenzisa ukuhlaziywa kwesilinganiso kanye nezindlela okukhethwa ngazo. Imibuzo yokuhlola ngokuhlaziywa kwesilinganiso kanye nemibuzo ekhethwe ngayo yenziwa ngezigaba ezimbili embonakalisweni yabaphendulile abangama-273 embonini yezokuthutha. Ngokwendlela yokusebenzisa abantu, izindleko ezilinganiselwa ku-2016 Izindleko Zokushayisana eNingizimu Afrika kubikwa ukuthi zalungiselwa ukwenyuka kwamandla emali, kusetshenziswa isilinganiso sango-2017 esingu-5.3% ukuthola izindleko zango-2017. Lolu cwaningo luveze ukuthi indlela yokusebenzisa abantu ithatha kancane izindleko zokuphazamiseka komgwaqo. Ucwaningo lunomthelela emzimbeni wolwazi ngokusebenzisa indlela yokusebenzisa abantu kanye nendlela yokuzimisela-ukukhokha ocwaningweni olulodwa ukukhombisa ukufaneleka kwalesi sivumelwano / inhlanganisela ngaphakathi komongo waseNingizimu Afrika. Ucwaningo lwesikhathi esizayo ludinga ukuphindaphinda lolu cwaningo embonakalisweni othathwe kuzo zonke izifundazwe eziyisishiyagalolunye zaseNingizimu Afrika, ukuze ukulinganiswa kwezindleko kummele abantu bezwe / Business Management / D. Phil. (Management Studies)