How in store WiFi analytics can help retailers track metrics that matter

4th Sep 2017

Online retailers know virtually everything about their customers. They know which individuals have shown an interest in which products, when are they most likely to buy, which websites their traffic comes from, how long they spend on each page – every click is recorded automatically.

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In the world of physical retail, the only way to accomplish something similar used to be the “Mark I eyeball” method, whereby a manager spends hours personally observing shopper behavior. Apart from the time lost in this activity, its accuracy leaves a great deal to be desired. Someone might assume the woman in the $500 dress is a valuable client, but the same may be true of the teenager wearing mismatched socks. Are people lingering in a central area to look at a promotional display, or are they simply first-time visitors trying to get their bearings? That person who looks vaguely familiar may be a regular customer, or the manager might recognize him from some completely different context.

Unless extensive notes are taken and later collated, simple observation yields no real quantitative data, meaning that the average retail manager will simply see what he already believes is true. With customers becoming more discerning as far as their individual shopping experiences go, and margins being as tight as ever, using technology as a force multiplier that improves both customer service and management is increasingly being considered in all kinds of retail establishments.

Drawbacks of Legacy Solutions

The retail industry has not been unaware of the lack of concrete data hampering their operational efficiency, but until recently there were few economical options available to address this.

At the most basic level, an optical gate or beam break sensor can be installed at each entrance, which counts the number of people either entering or leaving a store. This is unsatisfactory in a number of ways: a large group may accompany a single shopper, or the same person may enter and leave more than once while considering a purchase. It does, however, give a rough estimate of the store’s conversion rate, or in other words the ratio of people visiting to the number of transactions performed.

The most important drawback of this system is that it offers no information whatsoever about the in-store behavior of shoppers. How long people stay, how they move around the premises and where they spend the most time are all of obvious importance. CCTV footage can be analyzed to produce heat maps of where cameras are spotting people most frequently, but this is still very unsatisfactory. Intersections, aisles drawing traffic that simply passes through and choke points cannot be distinguished from points that actually attract customers’ attention.

WiFi Location Technology: A Smart New Way to Identity Customers and their Behaviors

With the increasing ubiquity of wi-fi enabled smartphones, this situation can be improved significantly through the use of location based technologies. A WiFi location analytics platform, for example, which monitors the real-time location of the mobile devices in shoppers’ pockets, allows a large number of useful, quantifiable user metrics to be derived. Some of these metrics are discussed below:

Footfall Metrics: For most brick-and-mortar retail businesses, the first step to making a sale is to actually get a potential customer through the door. The key to this is often to gain a realistic perspective on how visitors are currently behaving, as well as the effect of any changes store management might make – storefront decoration, publicity campaigns, etc. This also allows for improved demand prediction and supply chain planning by integrating historical sales information with other variables: expected weather, nearby store events, promotions and so forth.

The main footfall metrics are defined by analogy to website activity tracking and are self-explanatory:

  • Visits (per day, hour, etc.)
  • Unique Visitors (determined by mobile phones’ individually assigned network identifiers)
  • New versus Repeat Visitors

Engagement Metrics: Once a visitor has entered a store, they might browse at random, they might head directly for what they wish to purchase, or they might spend a great deal of time hovering around a certain section comparing their options. Understanding this kind of behavior – especially when combined with other sources of data, such as determining the number of serious browsers who make a large purchase at a future date – can make the difference between retail success and failure.

  • Dwell Time may be measured either for a store as a whole, or as divided into zones reflecting different types of merchandise. Due to its power when making deductions about the real customer experience, this is one of the most important metrics WiFi analytics provide.
  • Conversion Rate may be calculated as the number of invoices issued divided by the number of visitors, but this is not the end of its usefulness. It can also be used to refer to the proportion of coupons redeemed in some campaign. With WiFi analytics, this kind of measurement can be made both automatic and highly accurate, as only unique visitors are counted (correcting for repeat browsers) and online and offline sales are consolidated for each visitor.

Zone Interdependence Metrics: The principle of cross-selling is well understood in the retail environment: a customer who buys a hammer will probably be looking for nails as well, hot dogs and buns are strategically positioned in relation to each other, and so on. What is less well-known is that layouts can often be optimized in unexpected ways when customer behavior is objectively analyzed.

These metrics are usually based on zone dwell time, but also take into account the order in which zones or sections are visited, how this affects buying behavior and in several other ways.

Loyalty Metrics: Loyalty metrics take advantage of the underlying technology to uniquely identify each networked mobile device. This means, for instance, that a sales lead can be pushed to a staff member as soon as a solid prospect walks through the door, be integrated with loyalty programs and also provide the following information:

  • Repeat Visits for a given individual are an indicator of brand loyalty within a customer pool as well as on an individual level. A fall-off in repeat visits over time is often a sign that a customer is about to churn, which may then be addressed.
  • Cross-Store Visits are the basic metric as to how mobile customers are between different stores in the same chain. Loyalty, behavioral and other data can also be shared across the entire organization, maximizing its effectiveness.

Data is the Currency of the Future

While the basic principles of retail management are unlikely to change any time soon, it is now possible to apply them more scientifically than ever. The most important facets are undoubtedly location and the customer –WiFi analytics finally bring these two together, yielding a great deal of actionable information.

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Proximity MX helps retailers gain actionable insights on customer behavior in store using existing WiFi or Beacon infrastructure. Request for a free demo by clicking the button below to learn how Proximity MX can help you gain customer insights and engage with them real-time.

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