People Counter

How visitor flow analytics helped us identify – and remedy – a leaking sales funnel


Written by Tony Loxton
Oct 19

Identifying the causes of a leaking sales funnel can be tricky – not least in the property sector, where the stakes are high, purchase decisions are a long and convoluted process, and any number of unanticipated factors could stop a purchase in its tracks. And, because the bulk of the purchasing decision is made on-site when potential buyers come to view the property, a significant part of the customer’s journey down the sales funnel is effectively invisible to marketers.

One of our clients a global property development group with a market capitalisation of A$5 billion – was establishing a number of new housing developments and needed greater insight into visitor flow and the customer sales experience within each display home village. Their main concerns were that some homes were driving significantly lower sales than others in the village, and that sales opportunities were being lost along the way. Builders claimed that lack of foot traffic to houses furthest from the sales centre was to blame for the shortfall in sales, and our client sought to validate these claims and optimise the customer experience.

In this blog, we’ll take a look at what our data revealed about the visitor flow problems our client was facing, and how Blix used these insights to patch up the holes in their leaking sales funnel.

What our data told us about the problem

To understand the visitor flow within the display village, Blix installed foot-traffic sensors in the sales centre as well as each of the homes. Builders pay the property developer to display their houses in the village, and there is competition for the ‘best spots’ – the plots closest to the sales centre and the entrance to the village as they are believed to receive the highest amount of foot traffic. It wasn’t long before our data proved the builders’ suspicions correct: homes further from the entrance did indeed get fewer visitors through their doors, but the average length of visit was also lowest. This indicated that the low sales might be the result of a problem with customer engagement.

We also helped the developer discover how people moved through the village: for example, in one display village, instead of walking straight down the main street, most visitors entered the village and took their immediate right before following a parallel road. It turned out this was due to a house on the right which had the best landscaping, pool and entertainment area in the village, and was pulling customers away from the main road as a result. Our next step was to optimise the visitor flow around the village.

From insight to action: How our data helped patch up a leaking sales funnel.

When it became clear that houses further from the sales centre were experiencing less visitor flow, the developer placed signage around the village that directed customers to homes located further away from the entrance. This also revealed that some builders were better at referring potential leads back to the sales office – a crucial step in closing the sales loop. This information enabled the developer to invest more heavily in top-performing builders, and encouraged underperforming builders to up their game.

We also identified two bursts of peak traffic on Saturdays (which account for 40% of total foot traffic themselves): one around 11:00 AM and another around 1:00 PM. While the first was handled properly, the second burst had an extremely high bounce rate. We soon realised that this corresponded with a significant dip in persistent devices – our measure of staff on-site – which meant that this burst in traffic happened when the majority of the staff were on their lunch break. By simply asking staff to eat lunch on-site, the developer managed to reduce the bounce rate dramatically.

If you’d like to find out more about the incredible insights you can get with Blix’s people counter tools,  download our free guide, ‘Going Beyond Door Counts to Deliver Unprecedented Retail Analytics Data, here

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