ABOUT PERFORMANCE IN RETAIL STORES

BRIEF

In a world of increasing complexity, the concept of performance seems to be a big challenge for business people, experts, consultants, managers etc. Moreover, in the realm of retail stores, understanding performance remains challenging. The root of the problem is that most of the retailers cannot define the optimal amount of staffing for their stores. Moreover, the staffing itself is often associated to bad jobs. In the US for instance, head count per store has fallen by more than 10% over the past decade, while wages per employee have dropped by 4%. And payroll isn’t the only thing being cut; training budgets have decreased as well. Axonify, a training provider, recently carried a survey which found that nearly one-third of retail store employees receive no formal training; this is the highest deficit in any of the industries surveyed.[1] Thus, an article published in Harvard Business Review (HBR) concludes that understaffing stores and undertraining workers were never a good idea, but it’s especially bad now, because it takes away the biggest advantage traditional stores have over e-commerce incumbents: a live person a customer can talk with face-to-face.

The conclusion is plausible, as long as brick-and-mortar retailers treat labor as a variable cost. As a matter of fact, it is the second-largest expense for most, and stores can cut it quickly just by giving their many part-time associates fewer hours. The trouble with this approach is that it ignores the simple fact that salespeople drive sales. For every dollar a retailer saves on staffing costs, it may be losing several dollars in revenues and gross margin if customers leave a store empty-handed because they can’t find a knowledgeable employee to address their needs. This can create a downward spiral in which fewer associates lead to poor customer service; this, in turn, causes a further decline in revenues, and another round of workforce cuts. And the beat goes on until stores close for good, as 7,795 retail locations in the US did in 2017, the highest number ever.

For sure, treating core labor needs is a big challenge for understanding the performance concept. The decimation of traditional retailing is widely expected to continue over the next decade. A recent UBS study predicted that by 2025, another 30,000 to 80,000 US stores will have closed their doors. Many more chains will die. Unless retailers change the way they hire, schedule, and train labor, they risk being among the casualties. The approach we propose isn’t complicated, and it yields almost immediate results. It is high time for retailers to abandon old, ineffective ways of operating and recognize that store employees are one of their best weapons in the battle for consumers’ business.

WHY A PERFORMANCE APPROACH

In the past few years, performance has become a controversial concept. It has evolved from a dry financial case to the ability to adapt the business to new challenges of the environment, to the relationship with the ecosystem and so forth. Today, performance indicators are more qualitative and nuanced; they do not represent mere black or white approaches.

The problem of bad and good jobs. It is well-known that the traditional retailers are large employers. There are millions of jobs in retail but most of them are lousy. They offer low pay, few benefits, and no career paths. Conventional wisdom holds that bad jobs are the unavoidable price of low-cost service. Today, a radical shift seems necessary in treating the labor force in retail. Why? Largely because of a new competitive landscape. Companies need more growth from their existing units. Those facing increased competition from brick-and-mortar and online rivals need to give customers a compelling reason to buy from them. And companies have realized that engaged workers are more productive, provide better service, and are less likely to jump ship; this is an especially big deal in retail, where personnel turnover in 2016 averaged 65%.

The personnel shortage should be overcome by increasing training budgets and by a better operational usage that might be provided through software solutions.

From many observations, at multiple retailers, the studies developed by HBR estimates that about 25% of employee time is often wasted, and even more for managers. Using different assumptions about the amount of improvement or uplift achievable, executives can run scenarios on the bottom-line impact of a good jobs system. In addition, quantifying these benefits can help them understand what would have to be true specific improvements in metrics like turnover, shrink, and basket size in order to justify a given investment in creating a good jobs system.

Just treating workers better, however, will not boost a company’s competitiveness. A radically different operating system is needed. This would be designed to better serve customers’ needs and increase workers’ productivity, motivation, and overall contributions. At many retail companies, functions at headquarters make decisions in silos and rarely consider the effect on employee productivity and customer service. They see stores largely as places that execute headquarters’ decisions. Here are some elements that are common in headquarters’ decision-making and that affect stores’ performances (besides many other exceptionalities):

• Six-hour (or more) delivery windows. 
• Big swings in the number of promotions. 
• Frequent display changes. 
• Mistakes in the prices sent to stores. 
• Problems arising from coupons. 
• Last-minute changes. 
• Inadequate staffing levels. 

The financial case. To make a performance case for increasing the company’s investment in its people and improving their work, retail executives can quantify three types of benefits:[2]

1. Cost reductions from improving employee turnover, operational execution, overtime and unplanned labor, and legal and compliance fees;
2. Higher revenues from better operational execution and increased basket size and higher number of transactions (from higher traffic) that come from a better customer experience;
3. Labor productivity gains from better workload management and from less time wasted by poor task allocation, poor logistical systems, and frequent- including last-minute- changes.

GroceryCo: Harvard Business School experiment/simulation

As an example of how executives may quantify the upside, we created GroceryCo, a fictional 500- store grocery chain. It has $9 billion in annual revenues and $200 million in profits. It has industry- average performance in employee turnover (60%) and in operational execution (3.6% shrink, 4.0% stockouts, 0.5% abandoned transactions). (The abandoned transaction estimate comes from our work with companies. We did not calculate overtime and legal costs for GroceryCo because we do not have a good estimate and we expect these costs to be small at many retailers).

For GroceryCo, a 25% improvement in employee turnover, shrink, stockouts, and abandoned transactions would amount to nearly $120 million in operating income impact — nearly 60% of current total profits. This does not include higher sales due to increased customer satisfaction and loyalty from better service (e.g., helpful, knowledgeable employees, clean stores with fully stocked shelves, and shorter checkout lines), which is often the biggest upside of a good jobs system. For example, a 1% increase in daily transactions and in transaction size would add $45.2 million in gross profit per year. For context, a $165 million bottom-line gain would be sufficient to offset a 20% wage increase for all hourly employees — raising the average hourly wage from $13.50 to $16.20. In addition, we estimate that with a good jobs system, GroceryCo could free up 7 million hours in re-deployable labor each year.

The Financial Case for Good Retail Jobs by Katie Bach, Sarah Kalloch and Zeynep Ton. HBR Article June 26, 2019

Some operational choices. In order to create a shift, the realities of some operational mechanisms should be thought out, designed, and executed. According to the Axiobit approach, all of these generic tasks can be fulfilled by a strong digital transformation project. The project should be understood as an innovative venture and the convergence methodology should be put in place. The operational excellence should be focused around the following management activities (even though they are truisms, they are still untouched bases by retailers): focus and simplification, standardization and empowering, cross training, acceptance of exceptionalities and their proper management.

MEASURING PERFORMANCE: THE PROBLEM OF METRICS

The approach presented in this section is a Deloitte vision on performance and metrics.

In the past decade, traditional retail has faced unprecedented disruption. Consumers have more choices, competition is increasing, and convergence across industries has changed business dynamics. To remain competitive, many retailers have shifted investment strategies and are looking for new ways to drive growth and profitability (Deloitte).

Analysis of a recent consumer shopping survey showed that as of November 2017, 75 percent of all in-store retail spending was digitally influenced during the shopping journey. The impact of digital is amplified by the sheer number and diversity of companies competing for the same wallet share, shifting the focus from where transactions take place to issues such as capital deployment, investments, and cash management. Taking these advances into account, industry stakeholders should develop new ways to measure performance and define marketplace success.[3]

Deloitte considers that metrics should have the following functions:

• Holistic means channel-agnostic metrics, which allow for a more complete view of the organization.
• Inclusive contains metrics that address all business models, retail formats, channel approaches, fulfillment methods.
• Value driving includes metrics that incorporate the parts of the business that drive value for an organization.
• Operational refers to metrics that reflect the operations of the business, not simply financial ratios.
• Balanced means metrics that are balanced between (1) growth and profitability and (2) attention on recent performance.

The Deloitte performance model include two main aspects of performance. The performance function model includes:

• Creating value (customer lifetime value)
  • Profit per transaction
  • Sales per unique customer.
• Capturing value
  • Revenue growth
  • Return on invested capital
  • Free cash flow.

Even though the Deloitte-based understanding performance is driven by today’s investment model, the variables remain abstract and difficult for store and operational management to understand.

MEASURING PERFORMANCE: THE PROBLEM OF SCOPING

Performance has been perceived for a long time as a financial/economic concept. Recently, the experts concluded that there are some similarities or overlaying, but for sure performance is an operational management concept. Performance should be extracted from the abstract zone; it should be defined and calculated in order to be understood by the operational management stakeholders.

Defining performance. Today performance is a management concept; it refers more than any time as a description of the operational status. Performance should be treated as an internal convention and a communication standard. 

• Performance as a convention. Measuring performance is (and should be) an internal convention. The formula should be controllable and understood by all the stakeholders. Moreover, for stores performance should be organically linked by the operational metrics, which can be entirely controlled and understood by store management. 
• Performance as a communication standard. Comparing performance to a benchmark in real time is an internal communication standard. Managers may better understand failures and can get relevant experience to correct them.

Metrics. This designed performance model by Axiobit requires metrics exclusively related to the operational activities. They are completely understood by the management and are rationally produced by the people. They are qualified traffic, employees and operational managers (capacity), wasted time and additional time banks. Different models may include many other metrics that are completely under operational management control and they completely understood by the entire stakeholders’ staff. In our view there are some experiential ponders that may adapt the numbers associated performance to the context (type of stores, location, etc.). The only problem remains data capturing that opens the discussion to an operation digital project that should design, plan and monitor the relationship between managers, employees, plans, activities, events and real time allocations.  

Black box thinking[4]. According to Syed, black box thinking is about the willingness and tenacity to investigate the lessons that often exist when we fail, but which we rarely exploit. Furthermore, it is about creating systems and cultures that enable organizations to learn from errors, rather than being threatened by them. In order to create this culture, managers should have a permanent image on where are they by variable easy to be understood. This can give them the psychological change for correcting situations they know that are failures. Marginal gains are not about making small changes and hoping they fly. Rather, it is about breaking down a big problem into small parts in order to rigorously establish what works and what doesn’t. Therefore, in our view, black box thinking is an important asset in designing any performance model. Recent researches show that the only way to correct the mistakes is to connect the producer to the punctual situation. Understanding and analyze failure in a punctual manner is the only way to correct it. Moreover, communicating the results to the ecosystem can create success (performance).

THE AXIOBIT MODEL

Year after year, traditional retail performance metrics include growth, profitability, capital invested, or abstract concepts of profitability and competition; however, these become obsolete in today’s digitally enabled economy. The digital world has changed how business is done from the operational point of view, and data and information can be moved to the real users of the systems: the store management.  

Today, performance is evolving from a number or a percentage to a convention; this is especially true for a company with many income centers as the brick-and-mortar retailers are. This trend allows for a simplification of performance function that should include understandable indicators for stores managers and operational staff. Axiobit’s team considers that the convention can be generally defined according to operational functions that link the concept performance to stores’ management and other operational stackeholders. The performance function is as following:

P= ƒ (traffic, capacity, waste time, additional & extra hours)

Performance does not exist (or cannot be measured) without a real-time and automated capturing data model. The complex model in which the store functions does not allow an accurate capturing of the data and an appropriate interpretation. Moreover, the data that might be captured are static. The data required should essentially be more dynamic in relation to business processes and real-time monitoring.

Without a clear and implemented operational model, the performance cannot be defined.   

Therefore, there are two main aspects to be considered: (1) the building of a measurable operation model; (2) the store performance measuring.  

Take the below performance indicators:

• Revenues: R
• Average size basket (income/traffic): B
• Qualified traffic: Q
• Personnel number and structure: C+M
• Idle times & useless time (by HQ decisions): T+HQ
• Additional hour paid: AP
• Additional hours compensated: AC

P= [R(bAC)]/[(cC+dM)e(T+HQ)BfAP]= Q[bAC]/[g(cC+dM)(T+HQ)AP]

Axiobit considers that performance is directly proportional to the qualified traffic and additional compensated hours, and inversely proportional with personnel number, idle and useless time, additional hours paid. The idea is that a set of data should be captured based on an operational module. Also, [b,g,c,d] is a set of numbers (experiential ponders), subject of research that differ according to the store types. 

The model was imagined by Axiobit, driven by the black box effect management theory that is oriented to a good understanding of where we are NOW, more than where we will be in the FUTURE. It is an outstanding call for engagement for middle management and retail stores managers. Based on this knowledge, managers can react and improve their activity and decision-making but, even more so, they can improve the HQ decisions related to logistics management and other interventions in the operational framework of their stores.

Based on previous research and team experiences, Axiobit designed a set of benchmarks that allows retailers to position their performance curves.

CONCLUSION

Understanding the concept of performance leads to many operational implications in the retail industry. As such, Axiobit performance management system implemented in retail stores can expose:

1. The real time rational operational performance;
2. The store capacity metrics;
3. The weaknesses of the logistics systems and management (HQ) decisions.

GroceryCo, an HBR simulation, shows that the outstanding impact may have a small change in the bad jobs’ indicators.

Axiobit, by its original convergence methodology, shapes the new digital world organically, linked to businesspeople. The experts’ team is opening a new thinking method (based on knowledge funnel) for aligning the operational management to the performance of each unit.   

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[1] Fisher, Marshal; Santiago Gallino, Serguei Netessine. :Retailers Are Squandering Their Most Potent Weapons,”Harvard Business Review, January-February 2019 Issue. Accessed at https://hbr.org/2019/01/retailers-are-squandering-their-most-potent-weapons

[2] Katie Bach, Sarah Kalloch and Zeynep Ton, “The Financial Case for Good Retail Jobs,” Harvard Business Review,  26 June 2019. Accessed at https://hbr.org/2019/06/the-financial-case-for-good-retail-jobs

[3] Rodney R. Sides, Dean Hobbs, Matt Marsh, “The future of retail metrics | Measuring success in a shifting marketplace.” Accessed at https://www2.deloitte.com/us/en/pages/consumer-business/articles/future-of-retail-metrics.html

[4] Syed, Matthew (2015). Black Box Thinking. Marginal gains and the secret of high performance. John Murray Publisher.