Utility Week

40 | MAY 2022 | UTILITY WEEK Comment Making sure your frontline is on the front foot Kaluza's Melissa Gander runs a slide-rule over some modern customer support operating models to help guide energy firms towards improved decision making, innovation and service. N ever before has it been so critical to get support for energy custom- ers right. Customer helpline Citizens Advice has seen calls double since the energy crisis began in August 2021 and with further price rises ahead, delivering excellent service is high on the C-suite's agenda. So how can energy companies organise their support centres to deliver outstand- ing service, now and in the future? What are their options as they look to evolve their operating models? The 'traditional' model The typical customer support operat- ing model has evolved to become heavily segmented. First are frontline teams split by core skill set, then sits a second line of more experi- enced advisers who are further segmented into specialisms to manage more complex cases, and ultimately back office teams who fix even more complex issues. Altogether, issues are mainly handled reactively following customer contact although there is some proactive resolution across the segmented teams. Why is the model so heavily segmented? Most probably due to the level of system- driven complexity faced by advisers in com- pleting key processes. Frontline advisers are trained to handle a relatively small range of short, transactional tasks with limited system access, while sec- ond line support have deeper training on specific specialisms and enhanced access enabling them to fix underlying issues. Segmenting teams in this way reduces the volume and diversity of tasks an individual adviser needs to learn and be proficient at on the system. It's based on traditional retailers' focus on output over outcome, where speed is the priority. The theory is that advisers will always be faster, and more accurate, when repeating one process rather than switching between 10 or more processes. However, this operational approach only accommodates the fundamental challenge of high system complexity. This complexity is the result of a number of different factors. For example, user interfaces and processes have been so tightly coupled with the "back end" system that process flows mirror the data architecture beneath the surface – advisers practically see the architecture of data tables and have to navigate multiple screens at multiple levels to complete simple tasks. Additionally, numerous workaround pro- cesses, bolt-on widgets and shadow IT are added over time to compensate for deficien- cies within core systems. The end result? Multiple hand-offs per query – from frontline to second line to back office teams. Each step puts a customer issue back into another queue for another adviser to pick up and attempt to solve. This leads to work- flow backlogs, long customer wait times and non-customer-facing teams handling issues, culminating in a dissatisfying and disjointed customer experience. Advisers' morale also takes a hit, with many becoming frustrated by their inability to provide better service due to their limited training and access. Team and individual goals are o"en unaligned, resulting in siloed ways of working and a lack of accountability, causing further anguish and disengagement. Suddenly, the benefits in individual effi- ciency are reversed as resources are pooled (think Erlang C) and bigger issues arise in the form of decreased resource resiliency and flexibility. In summary, poor, patched-up tech has necessitated moving to increasingly complex operating models – negatively impacting efficiency, flexibility, adviser happiness and, most importantly, customer experience. The new 'squad model' Spotify claims the title of creator of the "squad model" whereby product people are divided into very small teams, known as squads, each owning a certain part of func- tionality end to end, such as "search" or "recommended artists". A squad will have a dedicated product manager who will work with other skilled members in their team to solve problems in the best way possible for a given customer journey. These squads sit together and have one long-term mission. They are an autono- mous, self-organising unit possessing all the skills, tools and expertise needed to design, develop, test and release to production – comparable to mini start-ups. So how does this default model for so"- ware development translate into an opera- tional environment? The emerging approach is to set up a cross-functional team made up of multiple skill sets, for example, frontline care advis- ers, back office experts with individual spe- cialisms, complaint management experts etc, and allocate to them a cohort of custom- ers to manage. The team is headed by a squad leader whose role is to oversee the management of their customer base to drive key business metrics. The squad leader can choose where to focus their team – from proactive work to reactive work; from focusing on commercial metrics to service metrics – they make the trade-offs. An example could be choosing to allocate more team members to outbound customer loss prevention activity rather than inbound call answering – leading to higher wait times and theoretically lower customer satisfaction but increasing customer retention. Team members still report into their func- tional "homes" – care advisers into care leadership, back office specialists into their respective specialist leaders. Is there an alternative approach? A"er evaluating these two models, energy companies face a dilemma. Sticking with a segmented traditional

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