In attesa di pubblicare le case history di Choralia, che compariranno anche su Harvard Business Review, proponiamo questo articolo, riportato per esteso anche in calce, in cui McKinsey presenta un caso di successo dell’utilizzo di people analytics. Si tratta di una catena di ristorazione, con fast food sparsi per il mondo, che sta usando le tecniche di people analytics per prendere decisioni organizzative e di sviluppo delle risorse, con l’obiettivo di incrementare la soddisfazione dei clienti e le entrate, tramite investimenti mirati. Raccogliere ed analizzare i dati di comportamento, incrociandoli con i risultati e, successivamente, costruire metodi di ricerca del dato là dove manca sono fattori chiave di successo di progetti di questo tipo.

People Analytics: costruire la catena di valore del talento

Definire cosa conta

Innanzitutto è necessario concordare e definire quali dati ci interessa prendere in considerazione nell’analisi: quale dato influenza maggiormente il successo di una performance da raggiungere? In altre parole, quali dati impattano maggiormente i risultati di business?

Riempire i gap di dati

Non sempre però abbiamo dati a sufficienza rispetto a determinate aree di indagine, per cui è necessario trovare delle modalità per colmare queste lacune. L’azienda ha individuato in particolare tre aree di indagine sulle quali investire per coprire i gap di dati.

1. Tratti caratteriali: Le caratteristiche individuali possono avere un impatto sui risultati? L’azienda non aveva dati sulla personalità dei dipendenti al momento dell’assunzione. E’ stata dunque necessaria la collaborazione con uno specialista in assessment psico-metrici con il quale costruire dei giochi online che hanno permesso di avere un quadro della personalità e delle abilità cognitive dei dipendenti.
2. Capacità di management: La qualità del management è un’altra area nella quale è stato necessario coprire delle lacune di dati. Attraverso uno strumento di indagine proposta da Mckinsey, l’Organizational Health Index, è stato possibile evidenziare le best practices in termini di capacità di management che contribuiscono alla salute dell’organizzazione e alla sua performance sul lungo periodo.
3. Collaborazione e interazione fra colleghi e con i clienti: Una terza area da monitorare è stata la collaborazione fra colleghi. Sono stati così introdotti dei sensori per tracciare l’intensità dell’interazione fisica fra colleghi, catturando i movimenti dei dipendenti, il tono di voce e il tempo speso a parlare oppure ascoltare ascoltare un collega o cliente.

People Analytics: Insight emersi

La personalità conta, ma i tratti correlati con la best performance non sono quelli che ci si aspetta: non è emersa infatti una correlazione con coloro che avevano una personalità più amichevole, ma con coloro che avevano un elevato orientamento al risultato e la capacità di portare il lavoro a termine minimizzando le distrazioni. Di conseguenza, investire nella socievolezza non produrrebbe i risultati attesi.

La carriera vale di più dello stipendio: le analisi mostravano che rispetto agli incentivi e allo stipendio in sé, ciò che risultava maggiormente correlato alla performance era lo sviluppo di carriera.

Non sono emerse correlazioni significative rispetto alla qualità del management: questo ha spinto la compagnia a identificare in modo più preciso quali fossero i comportamenti messi in atto dai loro migliori store manager, per trovare dei modelli da utilizzare poi come esempi in sede di formazione.

I turni fanno la differenza: la performance risultava marcatamente più debole durante i cambi turno più prolungati (da otto/dieci ore). In molti ristoranti si era passati a policy che prevedevano meno giornate di lavoro con turni più lunghi in modo da semplificare l’organizzazione del lavoro per il management. Ma questi turni prolungati non erano allineati né ai pattern di domanda dei clienti, né alle necessità dei dipendenti stessi che dopo sei ore avevano inevitabilmente un calo di energia. L’analisi dei dati ha potuto dimostrare che se queste policy semplificavano le responsabilità manageriali, nello stesso tempo danneggiavano la produttività.

People Analytics: Risultati

Queste evidenze sono state utilizzate per avviare un progetto pilota di intervento mirato solo ai comportamenti davvero utili a produrre risultati. L’effetto è stato incoraggiante: Customer satisfaction aumentata più del 100 per cento, velocità di servizio (misurata dal tempo fra l’ordine e la transazione completata) migliorata di 30 secondi, attriti legati all’ingresso di nuovi dipendenti diminuiti sostanzialmente e vendite incrementate del 5 percento. Il tutto con un investimento formativo inferiore a quello che sarebbe stato necessario, senza identificare le competenze target.

Gli strumenti di People Analytics non cercano di soppiantare l’istinto e la conoscenza del business degli HR o della linea; forniscono però uno strumento in più da appiare ad essi, determinando così una maggiore probabilità di riuscire a capire quali competenze o condizioni influenzano maggiormente i risultati di business.

Di seguito, Riportiamo l’articolo originale:

Using people analytics to drive business performance: A case study

A quick-service restaurant chain with thousands of outlets around the world is using data to drive successful turnaround, increase customer satisfaction, and grow revenues.

People analytics—the application of advanced analytics and large data sets to talent management—is going mainstream. Five years ago, it was the provenance of a few leading companies, such as Google (whose former senior vice president of people operations wrote a book about it). Now a growing number of businesses are applying analytics to processes such as recruiting and retention, uncovering surprising sources of talent and counterintuitive insights about what drives employee performance.

Much of the work to date has focused on specialized talent (a natural by-product of the types of companies that pioneered people analytics) and on individual HR processes. That makes the recent experience of a global quick-service restaurant chain instructive. The company focused the power of people analytics on its frontline staff—with an eye toward improving overall business performance—and achieved dramatic improvements in customer satisfaction, service performance, and overall business results, including a 5 percent increase in group sales in its pilot market. Here is its story.

The challenge: Collecting data to map the talent value chain

The company had already exhausted most traditional strategic options and was looking for new opportunities to improve the customer experience. Operating a mix of franchised outlets, as well as corporate-owned restaurants, the company was suffering from annual employee turnover significantly above that of its peers. Business leaders believed closing this turnover gap could be a key to improving the customer experience and increasing revenues, and that their best chance at boosting retention lay in understanding their people better. The starting point was to define the goals for the effort and then translate the full range of frontline employee behavior and experience into data that the company could model against actual outcomes.

Define what matters. Agreeing in advance on the outcomes that matter is a critical step in any people-analytics project—one that’s often overlooked and can involve a significant investment of time. In this case, it required rigorous data exploration and discussion among senior leaders to align on three target metrics: revenue growth per store, average customer satisfaction, and average speed of service (the last two measured by shift to ensure that the people driving those results were tracked). This exercise highlighted a few performance metrics that worked together and others that “pulled” in opposite directions in certain contexts.

Fill data gaps. Internal sources provided some relevant data, and it was possible to derive other variables, such as commute distance. The company needed to supplement its existing data, however, notably in three areas (Exhibit 1):

• First was selection and onboarding (“who gets hired and what their traits are”). There was little data on personality traits, which some leaders thought might be a significant factor in explaining differences in the performance of the various outlets and shifts. In association with a specialist in psychometric assessments, the company ran a series of online games allowing data scientists to build a picture of individual employees’ personalities and cognitive skills.
• Second was day-to-day management (“how we manage our people and their environment”). Measuring management quality is never easy, and the company did not have a culture or engagement survey. To provide insight into management practices, the company deployed McKinsey’s Organizational Health Index (OHI), an instrument through which we’ve pinpointed 37 management practices that contribute most to organizational health and long-term performance. With the OHI, the company sought improved understanding of such practices and the impact that leadership actions were having on the front line.
• Third was behavior and interactions (“what employees do in the restaurants”). Employee behavior and collaboration was monitored over time by sensors that tracked the intensity of physical interactions among colleagues. The sensors captured the extent to which employees physically moved around the restaurant, the tone of their conversations, and the amount of time spent talking versus listening to colleagues and customers.

The insights: Challenging conventional wisdom

Armed with these new and existing data sources—six in all, beyond the traditional HR profile, and comprising more than 10,000 data points spanning individuals, shifts, and restaurants across four US markets, and including the financial and operational performance of each outlet—the company set out to find which variables corresponded most closely to store success. It used the data to build a series of logistic-regression and unsupervised-learning models that could help determine the relationship between drivers and desired outcomes (customer satisfaction and speed of service by shift, and revenue growth by store).

Then it began testing more than 100 hypotheses, many of which had been strongly championed by senior managers based on their observations and instincts from years of experience. This part of the exercise proved to be especially powerful, confronting senior individuals with evidence that in some cases contradicted deeply held and often conflicting instincts about what drives success. Four insights emerged from the analysis that have begun informing how the company manages its people day to day.

Personality counts. In the retail business at least, certain personality traits have higher impact on desired outcomes. Through the analysis, the company identified four clusters or archetypes of frontline employees who were working each day: one group, “potential leaders,” exhibited many characteristics similar to store managers; another group, “socializers,” were friendly and had high emotional intelligence; and there were two different groups of “taskmasters,” who focused on job execution (Exhibit 2). Counterintuitively, though, the hypothesis that socializers—and hiring for friendliness—would maximize performance was not supported by the data. There was a closer correlation between performance and the ability of employees to focus on their work and minimize distractions, in essence getting things done.

Careers are key. The company found that variable compensation, a lever the organization used frequently to motivate store managers and employees, had been largely ineffective: the data suggested that higher and more frequent variable financial incentives (awards that were material to the company but not significant at the individual level) were not strongly correlated with stronger store or individual performance. Conversely, career development and cultural norms had a stronger impact on outcomes.

Management is a contact sport. One group of executives had been convinced that managerial tenure was a key variable, yet the data did not show that. There was no correlation to length of service or personality type. This insight encouraged the company to identify more precisely what its “good” store managers were doing, after which it was able to train their assistants and other local leaders to act and behave in the same way (through, for example, empowering and inspiring staff, recognizing achievement, and creating a stronger team environment).

Shifts differ. Performance was markedly weaker during shifts of eight to ten hours. Such shifts were inconsistent both with demand patterns and with the stamina of employees, whose energy fell significantly after six hours at work. Longer shifts, it seems, had become the norm in many restaurants to ease commutes and simplify scheduling (fewer days of work in the week, with more hours of work each day). Analysis of the data demonstrated to managers that while this policy simplified managerial responsibilities, it was actually hurting productivity.

The results (so far)

Four months into a pilot in the first market in which the findings are being implemented, the results are encouraging. Customer satisfaction scores have increased by more than 100 percent, speed of service (as measured by the time between order and transaction completion) has improved by 30 seconds, attrition of new joiners has decreased substantially, and sales are up by 5 percent.

The CEO’s guide to competing through HR

We’d caution, of course, against concluding that instinct has no role to play in the recruiting, development, management, and retention of employees—or in identifying the combination of people skills that drives great performance. Still, results like these, in an industry like retail—which in the United States alone employs more than 16 million people and, depending on the year and season, may hire three-quarters of a million seasonal employees—point to much broader potential for people analytics. It appears that executives who can complement experience-based wisdom with analytically driven insight stand a much better chance of linking their talent efforts to business value.