WNS. Bad Debts in Utilities. How a Predictive Analytics-based Framework Helps Reduce. Extending Your Enterprise

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1 WNS Extedig Your Eterprise How a Predictive Aalytics-based Framework Helps Reduce Bad Debts i Utilities

2 WNS Extedig Your Eterprise Bad Debt Write-offs Busiess Trade-off or Survival Tactic? For the past few years, utilities have reliquished hudreds of thousads of dollars i cosumer bad debts. Customer defaults cotiue to rise i a eviromet speckled with risig levels of uemploymet, ecoomic ucertaity ad dippig cosumer speds. A spate of striget govermet regulatios to protect customer rights, reduce evirometal impact ad improve safety compliace do ot make it ay easier for the utilities busiess to thrive. To make matters worse, uscrupulous cosumers cotiue to exploit loopholes i the utility's busiess processes to default o their paymets. Bad debts force utilities to trade off profits for survival. Whe towig the lie betwee bad debts, failed collectios efforts ad a striget regulatory eviromet, utilities are forced to take the 'write-off' route eve if it meas givig up o the reveue they rightly owed. Uscrupulous Customers Ecoomic Ucertaity Striget Govt. Regulatios Risig Levels of Umemploymet Fig.1: The typical maze of challeges that utilities have to grapple with. Little woder the, that, write-offs have rise from approximately USD 400 Millio i 2008 to about USD 2.8 Billio i 2014, as reported by a leadig strategy cosultacy firm, PA Cosultig, i its recet customer service bechmarkig data. I such a eviromet lade with costraits, how ca your utility compay effectively miimize bad debt write-offs? This whitepaper puts forth the aswers to this critical questio. Utilities ca reduce their bad debts sigificatly by adoptig the 'itegrated three-proged reveue protectio strategy' that focuses o: Idetifyig high-risk customers; Revisig collectios tactics targeted towards the high-risk customer segmet; ad Improvig customer iteractios ad experiece itervetios. 01 ws.com

3 How a Predictive Aalytics-based Framework Helps Reduce Bad Debts i Utilities The customer occupies a cetral positio i this strategy aalyzig, uderstadig ad predictig customer behavior becomes cetral to its success. The level of customer uderstadig, required for this three-proged framework, is eabled oly by predictive aalytics. Predictive aalytics is a advaced form of data aalytics that utilizes a large umber of variables based o both iteral ad exteral data sources ad leverages advaced statistical tools as well as specialized aalytical techiques to predict likely future outcomes. Predictive aalytics lays the foudatio to this strategy by helpig idetify high-risk customer behavior ad i eablig the implemetatio of collectios strategies targeted towards high-risk customer segmets. PREDICTIVE ANALYSIS Targeted Collectio Strategies PREDICTIVE ANALYSIS Idetifyig High-Risk Customers Ehaced Customer Satisfactio Itervetios Fig.2: Predictive aalytics lays the groud for a effective bad debt miimizatio strategy. Predictive Aalytics for Idetifyig High-risk Customer Behavior With the risk of bad debts loomig large, utility compaies caot afford to follow a oe-schemefits-all policy for maagig customer defaults. Most utilities charge their customers o a 'credit' basis, that is, after the use of the service. Reliace o credit paymet is ot ideal for all customer categories, as customers ted to misuse this optio. Utilities should first make efforts to idetify ad classify customers (both existig ad ew) ito high- ad low-risk segmets ad the develop targeted strategies to securitize reveue from high-risk customer segmets. ws.com 02

4 WNS Extedig Your Eterprise Predictive aalytical models that assess risks durig the oboardig of ew customers use profile parameters such as icome levels, demographics, ad credit history. Most utilities have striget SOPs for evaluatig ew customer applicatios; however, they ofte overlook risks lurkig withi existig customer accouts. Risks i existig customer accouts ca be idetified by aalyzig additioal iformatio, such as, customer meter settigs, usage patters, paymet history, ad complaits ad commuicatio. NEW CUSTOMER Icome Levels Demographics Credit History EXISTING CUSTOMER Customer Meter Settigs Usage Patters Paymet History Complaits ad Commuicatio Fig. 3: Profile parameters to idetify risks vary betwee ew ad existig customer accouts. Customers, who fid it difficult to pay their utility dues, usually request for egotiatio of paymet terms ad credit extesios from their utility providers. However, there are istaces where customers default eve after such optios are provided ad may opt for uscrupulous practices to escape paymet. Some may pose as 'ew' customers ad apply to the utility compay for a ew accout, while some may move to ew addresses frequetly, without iformig their utility suppliers. Although most utility compaies ask for iformatio o the accout holder's ame, the Social Security umber, ad / or tax ID, idividuals resortig to the 'ame game' coceal these bits of iformatio that ca prove their liks to other accouts. Utility compaies that fail to idetify customers with prior trailig dues, ru ito the cycle of customer defaults, bad debts ad the resultat losses. Predictive data aalytics helps idetify fraudulet customers that have 'trailig' debts ad may resort to 'ame game' tactics to get away without payig their dues. The segmet of potetial defaulters ca be further expaded by icludig more parameters such as customer profile ad credit ratig data from credit bureaus amely Equifax, Experia, ad TrasUio. Idetifyig a larger umber of customers uder the ambit of potetial defaulters further miimizes the risk of deliquecies. 03 ws.com

5 How a Predictive Aalytics-based Framework Helps Reduce Bad Debts i Utilities Vedors with expertise i data maagemet (data collectio, cleaig, preparatio ad aalysis) ca effectively assist utility compaies prevet reveue leakage by spottig aliases ad customers with high attritio risk. A prove predictive aalytics model is oe that allows utilities to segmet customers based o two parameters the debt value the customer owes, ad the propesity to pay back the debt. By plottig the outstadig dues o the x-axis ad the propesity to pay back the debt o the y-axis, utilities ca create a collectios prioritizatio matrix (as show i fig. 4) to decide o the ext steps i the collectios strategy. Outstadig Dues Low Medium High Propesity to Pay Back Low Medium Do Nothig Miimal Collectios Efforts Do Nothig Steady Follow-up Steady Follow-up Strict Follow-up High Miimal Collectios Efforts Strict Follow-up Accelerated Collectio Efforts Fig.4: Sample collectios prioritizatio matrix prescribig 'collectios' steps to be take. This ovel approach i segmetatio o the basis of the customer's propesity to pay back the debt provides ew isight for the debt collectio strategy. As show i fig. 4, customer accouts with high outstadig ad propesity to pay are prioritized for accelerated i-house collectios. O the other had, a lower outstadig amout ad propesity to pay accouts are writte-off immediately as the effort ad cost-to-collect the debt, exceeds the debt due o the customer amout. Proactive actio upo idetificatio of high-risk customers ca help reduce bad debt write-offs by as much as 40 percet ad securitize reveues for utility providers. ws.com 04

6 WNS Extedig Your Eterprise Predictive Aalytics-drive Techology for Revampig Collectios Tactics A efficiet collectios strategy aims to improve the collectios rate ad miimize the cost- ad effort-to-collect. The collectios process follows a 'duig path' durig which the utility compay follows up the customer to pay up the outstadig amout. If such efforts fail or ed i o recovery after a certai period of time, the customer accout is placed uder a Debt Collectio Agecy (DCA). The average recovery rates for DCAs vary withi percet across primary, secodary ad tertiary placemets, depedig o the regio. The average commissios are i the rage of percet, based o the age of the debt. I scearios where all debt accouts are trasferred to DCAs, collectio costs ted to become exorbitat. Thus, most utility compaies have a iteral debt maagemet ad collectios team focused o early-stage deliquet accouts. Most collectios focused cotact ceters deploy state-of-the-art iformatio techology ad telephoy ifrastructure that help them improve outboud cotact ceter performace. The two most commoly deployed forms of automatio i a collectios cotact ceter are the 'Predictive Dialer' ad 'Computer Telephoy Itegratio' (CTI). Predictive dialers that operate o the priciples of predictive aalytics, measure the umber of available agets, available lies, ad average call hadlig time to improve resource utilizatio. The predictive dialer has the capability to automatically call a list of telephoe umbers i sequece, screeig out o-aswers, busy sigals, aswerig machies ad discoected umbers while predictig possible poits at which a huma caller will be able to hadle the ext call. CTI i tur, liks the dialer with the customer iformatio system to dissemiate customer accout iformatio to collectio agets. It essetially displays the propesity rak (based o aalytical modelig) of the customer alog with the collectios strategy, helpig agets, tailor their coversatio, offer advice ad re-structure paymet methods, as show i fig. 5. Outstadig Dues (i ) High Medium Low Propesity to Pay Back High Medium Low HH HM Collectio MH Tips: MM High Value Customer - Deal Respectfully Recover Full Outstadig - Do ot offer part settlemet util debt > 3000K o the customer accout Maximum LH repaymet teure is 60 moths LM Retai customer HL ML LL Fig.5: A sample CTI screeshot, showig collectio tips geerated for the collectios aget. 05 ws.com

7 How a Predictive Aalytics-based Framework Helps Reduce Bad Debts i Utilities Oe very effective way to check paymet deliquecies is to employ a cut-off credit score for oboardig ew customers. May utility compaies set this threshold limit at low levels to esure that margial cosumers cotiue to receive their services without eedig to pay a deposit. By simply raisig this limit, utilities ca icrease the umber of customers that are required to pay a deposit. Though cosumer protectio regulatios vary betwee states i the UK, early all states allow utility compaies to ask for a security deposit from high-risk customers. The deposit amout ca be customized to suit customized specific customer demographics without harmig the reveue-geeratig potetial of the utility compay. For istace, utility compaies ca determie the deposit amout based o the tariff structure, paymet track records, ad the discoectio history of a customer. Pre-paymet meters offer a effective optio to check debt pile-up o a deliquet customer accout. Traditioally, utilities follow the route of covertig customer accouts i debt to prepaymet meters oly after the accout exceeds a certai debt threshold value or if it remais i debt for log. A smarter strategy would be to proactively promote pre-paid meters to the idetified segmet of high risk customers before the first istace of default occurs. This strategy helps protect reveue ad reduces the resources ad efforts spet o debt collectios. A combiatio of these tweaks ad chages leads to improved collectios performace ad reduced bad debt o customer accouts. The first two steps of this reveue protectio framework focus o predictive aalytics tools, techology ad models. The third step cocetrates o improvig customer iteractios ad experiece ad works i tadem with the first two steps. Improvig Customer Iteractios ad Experiece For most cosumers, payig off utility bills, figures as the last of their 'paymet priorities', i compariso with isurace, video-dth retals, ad telephoe bills. Sice, electricity, gas ad water are cosidered essetial life services, utility compaies caot discoect services to customers who have ot paid their bills, for recovery of debts. Despite the pressig eed to recover reveue from defaultig customers, utility providers eed to be midful of customer satisfactio ad experiece. After all, customer satisfactio lays the foudatio to retai market share amidst icreasig competitio. Maeuverig betwee a rigorous collectios strategy ad esurig a high customer satisfactio idex at the same time ca be tricky. However, it is ot impossible. A bled of appropriate advisor kowledge, utility collectios experiece, empathy towards customers i fiacial distress ad compliace to cosumer protectio regulatios ca icrease collectios success ad at the same time create a base of satisfied customers. A kowledgeable customer service advisor has the right orietatio eeded to uderstad the customer ad idustry dyamics. This orietatio comes from rigorous traiigs o evaluatio of the customer's situatio ad the ability to show empathy by offerig fiacial advice or flexible repaymet modes to suit the customer's profile. With such itervetios customers usually ope up to the prospect of discussig differet paymet optios before eterig ito a promise to pay back the debt. This improves customer commitmet to hoor agreed paymet schedules ad reduces effort i the collectios process. ws.com 06

8 WNS Extedig Your Eterprise Further, as utilities operate i a regulated eviromet, they eed to comply with cosumer protectio guidelies of the state. These regulatios pertai to customer iteractio, mode of collectig debt, idetificatio of iability to pay, treatmet of customers o social tariffs, ad protectio of vulerable customers. Nocompliace ivites harsh pealties. Utilities are resposible for the actios of DCAs who collect the debt from customers o their behalf. I recet times, there has a icrease i the umber of complaits to the Office of Gas ad Electricity Markets (OFGEM) about aggressive debt collectio practices. This puts additioal pressure o utilities to re-evaluate their curret debt collectio mechaism ad put i place a system to gover DCA performace. It is advisable to strike a balace betwee proactive collectio steps to address customer deliquecy ad adherece to cosumer protectio regulatios. Optimizig the debt collectio process shows sigificat positive results withi a short period of implemetig a pilot process based o the above discussed three-poit framework. Cosumer deliquecy maagemet methods have the potetial to reduce losses icurred by utility compaies by as much 50 percet. What's more, the payoff is much higher tha ivestmet ad sustaiable i the log ru. 07 ws.com

9 How a Predictive Aalytics-based Framework Helps Reduce Bad Debts i Utilities How Oe of the Leadig Utility Compaies Icreased its Debt Collectios by 50 Percet i 3 Moths with Predictive Aalytics The Cliet A Leadig Eergy ad Utilities Compay The Challege The cliet wated to better maage its eergy fial debt portfolio. Its debt recovery rate was at 4 percet, compared to the 14 percet achieved by its competitors. O the other had, the commissios charged by the cliet's debt collectio agecies were as high as 50 percet of the collected amout that kept the operatioal cost-to-collect very high. This made dets i the profit margis of the cliet. The cliet wated to optimize its fial debt collectio processes to improve recovery of receivables. The cliet also wated to formulate focused debt maagemet strategies for differet customer segmets to maage customer write-offs more effectively ad i the process decrease operatioal costs. The WNS Solutio WNS cocetrated o trasformig the cliet's collectios process by embeddig predictive aalytics ad makig chages to the customer iteractio strategy. Key aspects of the WNS solutio were: Propesity-to-Pay predictive data model exclusively for residetial customers. This model predicted the likelihood of customers beig able to pay their dues after their accouts were fialized. The model assiged a propesity-to-pay score to every customer. Customer classificatio ito high, medium, ad low propesity-to-pay segmets based o their scores. Focused deliquecy maagemet strategies for every segmet. Customer segmet prioritizatio for outboud cotact. Customer segmets were prioritized o the basis of the propesity-to-pay scores ad the amout of outstadig debt. Rigorous cost-beefit aalysis to streamlie operatioal, fiacial, ad huma resource activities. This exercise would go o to optimize the debt maagemet process. Iboud ad outboud test strategy implemetatio to egage customers. The customer service executives used the customized call scripts ad pre-determied verbiage to carry out settlemet egotiatios with ad provide debt maagemet advice to customers. Performace moitorig of pilot strategies regularly agaist critical tactical ad quality idicators ad also agaist the parameters set by the champio process. Beefits Delivered By usig predictive aalytics to carry out propesity based customer segmetatio ad eforcig customized egagemet ad advisory policies for differet customer segmets, WNS was able to fulfill the busiess objectives of the cliet. The WNS solutio achieved the followig results for the cliet: Debt collectio icreased by 50 percet withi 3 moths The challeger process recorded a 8 percet rise i coversio rates compared to the champio process Operatioal expeses decreased by 20 percet Thus WNS helped the cliet optimize its collectios process by brigig i actioable isights usig predictive aalytics. ws.com 08

10 WNS Extedig Your Eterprise Impact assessmet Reduce customer effort by streamliig processes ROI projectio Defiig Cotext for Growth Aligig Busiess Objectives Gatherig Busiess Itelligece Defiig Program Statemet Learig best practices from diverse idustry experiece Periodic model treatmet for relevacy Kowledge Maagemet Collectio Reveue Promoter Idex Cotiuous Improvemet through Iovatio Cost to Collect Broke Promises Embeddig Aalytics Buildig Productive models ad Visualizatios Drawig data drive coclusios Real-time model assessmet by champio / challeger strategies Ed-to-ed service architecture to achieve busiess outcomes Operatioalizig Process Excellece Desigig Strategies Aalytics based tailor-made process improvemets (based o customer segmetatio ad risk score) Fig. 6: The framework used by WNS to trasform the cliet's collectios process Fiacial Beefits Reductio i Days Sales Outstadig Icrease i Top-Lie Reveue Improvemet i Credit Ratigs Compliace with Regulatory Guidelies Improvemet i Customer Relatios ad Market Stadig Reputatio Maagemet Figure 7: The Beefits of Reducig Cosumer Bad Debt Write-Offs i the Utility Idustry 09 ws.com

11 How a Predictive Aalytics-based Framework Helps Reduce Bad Debts i Utilities Coclusio Reducig cosumer bad debt write-offs has several beefits i the utility idustry. These beefits have a positive effect o the day-to-day operatios of the compay ad also impact its reputatio i the market alog with stakeholder relatios. Utility compaies eed to reduce istaces ad volumes of cosumer bad debt write-offs to stay competitive i a dyamic ecoomic eviromet. What's more, they eed to achieve this goal i the face of cotiuig challeges shrikig icome levels of cosumers, striget regulatory guidelies, ad pressure from shareholders to optimize performace ad returs o ivestmet. Predictive aalytics has emerged as a key eabler that helps segmet customers ito idetical groups based o their attributes ad formulate customized debt maagemet ad advisory policies targeted to a particular customer segmet. Targeted strategies assure better performace ad acceptace with the customer segmet ad helps reduce cosumer bad debt write-offs ad drive several sigificat busiess beefits, by improvig customer satisfactio levels. ws.com 10

12 Copyright 2015 WNS Global Services I ws.com About WNS WNS (Holdigs) Limited (NYSE: WNS), is a leadig global busiess process maagemet compay. WNS offers busiess value to 200+ global cliets by combiig operatioal excellece with deep domai expertise i key idustry verticals icludig Travel, Isurace, Bakig ad Fiacial Services, Maufacturig, Retail ad Cosumer Packaged Goods, Shippig ad Logistics ad Healthcare ad Utilities. WNS delivers a etire spectrum of busiess process maagemet services such as fiace ad accoutig, customer care, techology solutios, research ad aalytics ad idustry specific back office ad frot office processes. WNS has its global delivery etwork spread across Chia, Costa Rica, Idia, Philippies, Polad, Romaia, South Africa, Sri Laka, Uited Kigdom ad the Uited States. Write to us at marketig@ws.com to kow more WNS Extedig Your Eterprise