What We Talk About When We Talk About Retail Footfall

The soft side of cold, hard data

We’ve released content on the ‘hard’ aspects of using data to drive better operations.  These include noble objectives: to drive return on investment, a sales uplift, quantify exogenous variables and A/B test your options.  These can drive an underperforming business to perform, or a well-performing business to excel.

But businesses are complex organisms – they struggle to operate in uniform, controlled conditions.  Retailers for example - by their nature- are often spread thin, managed into a hierarchy of departments, stores, regions, countries, and functions.  Furthermore, people are emotional; often they don’t respond well to strange new metrics.  All this means it may require a herculean effort to execute an A/B test on a business.  

It's tempting to think this means data won’t be actioned, so a project to collect business data – any data – falls down the priority list.  

But the truth is really the opposite.  From hundreds of conversations with businesses about how they operate and manage teams- we’ve learned that the complex, non-uniform nature of their businesses is exactly why the data is so useful in the first place.

The insights are consistent– a shared dataset trusted by all around the table helps teams communicate, agree objectives, and lets the most talented of us – whether on the shop floor or in head office – to achieve their potential.

We set out three learnings from these conversations below.  Whilst relating to people count, conversion and retail analytics, they are observations that hold for data in general.

• 1)    Data quality is table stakes – if it’s questionable, it gets questioned.  We have heard dozens of anecdotes about bad data being taken seriously, influencing a decision to unfortunate – and avoidable - outcomes.  
  
  But we have heard hundreds more anecdotes about data being dismissed – and never making it to influencing anything - because one person thought it was wrong.
  
  
• 2)    Less is more.  It’s better to have one dataset that is generally understood and agreed upon by most people in the business, than a thousand that few people understand.  
  
  I’ve been in the room when a portfolio manager, two retail managers and a merchandiser tried to draw insight on strategy from two heatmap animations.  They ultimately ignored them made all the calls based on sales data alone.
  
  
• 3)    You don’t have to have all the answers, especially if you have a great team.  Some of the best examples of driving performance with analytics comes when someone other than the boss has gotten hold of the data.  
  
  The data savvy manager in a café/retailer that used street-to-store data to test out A-board messages.  The bar manager that hired staff based on historical occupancy trends.  

It may be the case that a software-style, data-driven management process is a distant ambition for many.  But in our experience, the opportunity presented by business analytics is every bit as massive in complex and non-uniform businesses.  There are huge opportunities to use analytics as a shared resource across the business: compel teams to commune around the data, benchmark and discuss differences, and get the best out of your team.