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Top 5 values of customer counting

Analyzing the flux between the number of shoppers and transactions provides possible explanations as to why people enter stores with an intent, or openness, to purchase but never complete their journey.

June 2, 2016

By Tim Denison, Director, Retail Intelligence, Ipsos Retail Performance

Managing a retail business solely through a transactional lens falls short of what is needed in today's competitive environment. Although sales data is key, what it doesn't track are those shoppers who have visited but not purchased. In many ways these are the shoppers that need more attention if retailers are to optimise their performance. At the most basic level, quantifying the number of these 'non-customers' exposes the scale of opportunity that retailers have to grow their sales from the shoppers that already grace their stores. Analyzing the flux between the number of shoppers and transactions goes on to  provide possible explanations as to why people enter stores with an intent — or openness — to purchase but never complete their journey. Customer counting is now a "must have" management tool for retailers that can be used in both a strategic and operational context to measure performance and analyse the efficacy of decision-taking.

The most valuable applications for customer counting include:

Operational

• Staff scheduling and resourcing

In any U.S. retail operation, staff is the most critical cost in the business. With staff costs burgeoning, efficient management of resources and effective scheduling is a crucial factor in keeping budgets under control. All too often we find that decisions on store resourcing levels and staff scheduling across the week are based on historic sales patterns. However, those customers who make it to the tills don't represent the true volume of consumers passing through the store that might require service and management. Some retailers are turning to footfall data instead to establish a full picture of in-store activity and service demand levels. The result? Improved resourcing, less wasteful downtime and consistently improved customer service scores. The equation of lining up demand/traffic with supply/staff is a simple one that works every time.

• Target setting, incentivisation and performance measurement

It's common knowledge measuring in-store sales performance is a building block of incentivising a workforce. In turn, motivated staff play a significant role in driving sales in store. Being able to accurately measure performance makes target setting more accurate, more effective and — ultimately — sees it yielding better results. Sales targets have always been a mainstay in performance setting, but conversion rates — the percentage of shoppers who make purchases — is fast becoming a favourite performance motivator. Why? Because staff understand that it is something in their control to influence. Staff with strong product knowledge and great people skills can readily turn browsers into buyers. We've all been grateful recipients.  

Our advice is not to set blanket conversion rate targets across all stores or even store segments. Every store has its own local factors that impact on its performance. The vicinity of competitor stores, the quality of pitch location, the catchment demographics even the size of store parking lot can impact on store conversion rates. Instead, by setting weekly average conversion rates as daily target, the goal becomes store specific, fair and achievable. 

Strategic

• Store portfolio analysis

In this omnichannel world, there is more need now than ever before for insightful estate analysis and planning. We see reports of store sales falling and online shopping surging ahead. And yet there is now also a compelling case that lack of visible presence downtown can negatively impact digital sales. Out of sight means out of mind, and without passing that visual prompt of a store fascia every day, brand awareness levels can soon fall off a cliff resulting in on-line sales slumping in neighbourhoods where stores used to exist. Store portfolio analysis is increasingly incorporating store footfall and website hit data to address the question of how large a store portfolio should be and how catchment areas should be defined in this brick-and-click world.

• Marketing efficacy

With margins under so much pressure these days, cost control is in every retailer's mind and making marketing more effective and providing a stronger return on investment is very much part of the game. However, measuring the impact of marketing campaigns can be a difficult job, particularly in store without the kind of metrics that might be available to a digital retailer. All marketing campaigns should impact in some way on store traffic, whether short, medium or long term, dependent on their call-to-action messaging. Therefore using footfall to quantify the impact of a marketing campaign or a promotion is a worthy supplement to sales analysis. It also helps track the speed of decline and best time windows for promotions. As a mark of its industry importance, footfall counting is increasingly being used by leading marketing agencies as a method of benchmarking success. 

• Testing new store concepts

In retail, testing is a necessary part of any new investment, whether that is in something as simple as a new product range or entering a new region, revamping a fascia, or as complex as an entirely fresh format. Footfall data is becoming a part of standard investment testing practice. Hothousing is a fast track to working out where the right road lies and in reaching conclusions without excessive spend. It’s something that smart retailers are using to make investments more efficient, conscious and well informed. Luck isn't something top retailers rely on and only accurate, hard data can provide genuine guidance as to potential. Footfall and conversion rate changes from test bed stores compared against data from control stores is helping retailers make better informed strategic moves and decisions that grow the business rather than lead to its demise.  

Tim Denison heads up the Retail Performance analytics team at Ipsos Retail Performance, and has 24 years of research experience including 10 at an international business. His research specialties include consumer purchasing and decision-making: branding, retailing and behaviour.