The customer at the center of the retail value chain is a relatively recent phenomenon, and the advent of online retailers is pushing retailers to identify technologies and processes that will bring clarity to the path of purchase in the store. The deluge of buzzwords and the attempts to ‘online’ in-store metrics is creating confusion and chaos. Location Analytics and In-Store Analytics may sound similar but they work to a different drumbeat, and understanding their disparities will help retailers to connect online to offline behaviors.
Customer behaviors are measured and monitored by a range of technologies, including point-of-sale, website metrics, and by video analytics and wireless analytics in the store. The process of discovering meaningful patterns in the data is Analytics. In retail, the outcome of analytics is a range of Metrics. If the retailer has a Customer Service Model that requires a specific action as a result of a trigger (when the metric reaches a specific level such as 60 seconds or 3 people), than the metric is an Actionable Metric.
Behavior Analytics – customer and employee activities measured in the context of Time and Location – as it relates to technology and procedures has three categories of metrics:
- Empiric Metrics: When the data captured from the device – whether from a video or thermal traffic counters, Wi-Fi or BLE smartphones – is structured as a metric. Arrival is an empiric metric because it counts how many people entered the store, per period of time.
- Statistical Metrics: When a metric describes the assumption of behaviors based on a sample of the total population, it is held to the rules of statistics. Score cards for customer satisfaction are typical since retailers make assumptions on their level of service from the compilations of mystery shoppers and customer surveys.
- Calculated Metrics: When a metric is a function of two or more empiric data values, it is considered a calculated metric. The Conversion Rate is an example for a calculated metric, since Visitors can use either Entry or Exit data, Transactions have many variations, and the chosen Period of Time impacts the rules of accuracy.
In addition to category, the value of a metric is judged by the primary target of the analytics. Location Analytics is about the consumer. In-Store Analytics is about the store.
Location Analytics is about the Consumer
Retailers track their customers online, and they want the same access offline; this is the rationale behind customer tracking using their mobile device. Location Analytics is the result of data pinpointing the geophysical location of customers. It includes tracking smartphones location in the retail store, but refers to any situation where the activity happens in the real world, i.e. driving a car or working from home. We focus on tracking consumers in the retail store.
The digital path to purchase in the online store is clear. Retailers such as Amazon and eBay know where their customers entered their site (i.e. home page, product info, or landing pages), how their customers moved from one web page to another, and how they exited the site.
For example, the Bounce Rate measures the ratio of customers entering and existing in the same web page out of total customers; therefore, this metric designates the ratio of customer visits where the retailer ‘failed’ to engage the customer, let alone entice a sale. Retailers want to emulate the Bounce Rate in the store. Such thinking is the source of the chaos.
The Retailer’s customers are the target group for Location Analytics
The objective here is to capture the path to purchase inside the store. The tracking solution has two components: first, the technology to capture (accurately!) the real-time, geophysical location of the customers, and second, the actionable metrics that induce action.
Wi-Fi, Beacons (BLE), NFC, and RFID are based on radio waves technologies, and are distinct by range and the accuracy of the capture. The qualification of data was discussed in a previous post.
Location Analytics, in the context of retail, has two distinct categories of real-time Actionable Metrics: Proximity Metrics and Staying Metrics.
Proximity Metrics relate to actions practiced by Marketing. The concept of proximity relates to a person walking nearby and is drawn into action by a specialized promotion. The technologies, therefore, need to connect between the actual geo-location of the customer and the marketing promotion.
Therefore, in addition to the capture technology, there is a requirement for a marketing application. Since most people object to unsolicited text messages, most retailers prefer using opt-in applications such as Google, Facebook, Shopkick, or their own application to send the marketing enticement.
The Draw Rate, also known as the Capture Rate, relates to the ratio of people entering the store to the people passing by. Inside the store, people can get promotion or information messages as they get closer to a display or exhibit, and therefore the Drew Rate relates to the ratio between people lingering close to the display and total visitors to the store.
The key concept of Location Analytics is that the location creates triggers of action; in other words, the real-time location of the customers offers an opportunity to influence a sale. The triggered actions are typically messages, which delivered by Corporate IT, and are designed and monitored by the retailer’s Marketing Department.
Staying Metrics relate to actions by employees. Staying metrics, such as Occupancy Ratios and Stay Time in a Zone, are commonly used to design a better store layout. Recently, the Stay Time and Wait Time metrics are used for real-time deployment of employees to particular areas inside the store.
In Store Analytics is about Optimizing Store Performance
In Store Analytics contains many behaviors that occur inside the bricks-and-mortar store, including Loss Prevention, Assortment, and Scheduling. In-Store Analytics also contains Location Analytics.
In-Store Analytics is a bit confusing term. The concept of the In-Store emerged as a differentiator from the online store. It is also the evolution of the people counting market, which is primarily door-counting at the entrance to the store.
The Bricks-and-Mortar Store is the focal point for In-Store Analytics
The metrics of Store Analytics can be categorized by of the three phases of the customer behavior – entry, browsing, and exit.
Demand Analytics relates to how many people entered the store, per period of time, and all the related implications of footfall traffic trends, the effectiveness of marketing, and the scheduling to demand.
The checkout phase refers to the where and how the customer pays for a product. This is changing for with mobile payments and self-service kiosks; however, the checkout counter still dominates in certain markets. The main bank is still effective for supermarkets, big boxes, quick service restaurants, and even airports, where there is either a requirement for an orderly exit process or the average basket has more than a handful of products. This is the arena of Queue Management.
In-Store Analytics refers to factors that are directly related to improving the performance of the store. For example, the concept of Employee Engagement has a variety of models, from rates of service measured by customer surveys, to measuring the Average Service Time. Another facet of in-store analytics is measuring Conversation Rates in the store level, per product category, and even down to the individual associate; each metric with its own assumptions and technologies.
In-Store Analytics is used for a variety of scenarios inside the physical store, such as:
- Operations: from the choosing the correct location of the store to the process and procedures of managing inventory, the analytics of Assortment (where to put products) and Fulfillment (the process of stocking the shelves) and others, in order to manage the store more efficiently.
- Workforce: from scheduling to compliance and operations, the analytics of Scheduling, Forecasting, Task Management, and real-time deployment of Predictive Scheduling, analytical solutions for the employees help to improve productivity.
- Security: For surveillance to cash register monitoring, video analytics solutions improve security and loss prevention.
- Marketing: In Store marketing has long-term (i.e. layout design), mid-term (i.e. displays and digital monitors), and real-time (i.e. marketing with proximity metrics) components.
The challenge in designing a Customer Service Model for a retailer is identifying the correct variables. Starbucks, for example, has different challenges than Best Buy, and yet some variables are similar. Both put Customer Service at the core of their model. Both need to address the concept of waiting in queue, but in Best Buy waiting can be ‘finding’ a helpful associate or standing at the checkout counter, while in Starbuck, waiting to order is the first phase of the customer experience.
The first step of in-store analytics, therefore, is to understand the drivers of store performance. The second step is to identify the technology to capture the data. The third step is to analyze the data and build a model, or scorecards, consisting of a triggers and action. The fourth is testing.
The Analytics of Location and Time are at play in the path of purchase inside the store. In this frame, the online store is simply another location.
Connecting Customer Behaviors to the Store
The behavior of the customer is the thread that connects between online and offline metrics. The Path to Purchase, however, can be also defined by the functional groups inside the retail company. The job of Marketing is to bring people into the retailer’s store, whether online or offline. The job of the Online IT is to improve the customer experience on the website. The objective of Operations is to increase sales in the store. While Omnichannel distinction blurs the lines, as it should, we can use the original definitions to seek the difference between online and offline metrics, and bridge the customer experience.
Since comparing offline and online metrics is a big topic, I’ll address just three metrics:
- Visitors: brick and mortar stores have defined entrances and a door-counter provides an empiric metric of how many people entered the store, hence Arrivals. The online store is also defined albeit virtually; therefore, the entry point can be the home page, a landing page, a product page, or any other page where a session begins.
- Conversion Rate: Conversion Rate in the store is calculated as Visitors to Transactions. Sounds simple enough, until a professional analyst tackles the underlying assumptions. In-Store Conversion is more complicated because it is a statistical metric. Online Conversations vary between buying products, sighing up, and other structures. Comparing Conversation, in most cases, is a case of comparing apples to oranges.
- Bounce Rate: In the online world, the Bounce Rate describes visits that start and end in the same webpage. The closest metric in the offline world is the Abandon Rate. The key difference is that the Bounce Rate is an empiric metric, while a sustainable Abandon Rate is a statistical metric. Know the differences, know the customer behaviors.
The confusion in the metrics has a direct impact on technology companies. As Robert Simon wrote in a seminal HBR article, ‘Choosing the Right Customer’, the most important customers are not necessarily those who generate the most revenue but those that can unlock the most value in your business.
We can judge the validity of a specific technology according to the criteria defined by RSR Research in surveying retailers on the state of the store from June 2014. The following are the percentage of retailers who want in-store solutions to:
- 69% Maintain and/or improve the customer experience
- 47% Make our employees “smarter” and better informed
- 46% Increase revenue while holding down operational costs
- 28% Create competitive advantage and new sources of revenue generation
History tells us that sustainable technologies require a direct correlation to improved margins or sales. This survey points that improving the customer experience in the store is, by far, the most important objective for a technology deployed in the store. In a direct reference to mobile technologies, creating additional sources of revenue is not as important for retailers. The confusion in the value of the metrics creates a mishmash in the value of the solution. The weathervane of innovation is riddled with the carcasses of technology companies who tried to do it all.
Ask yourself how your technology improves the customer experience. If the solution improves the connection with a customer in order to directly generate revenue, then the value for the retailer will be related to Location Analytics. If, however, your technology targets the customer experience within the bricks-and-mortar store, than your Actionable Metrics are in the arsenal of In-Store Analytics.
Retailers have many seats to cover, but for technology companies to thrive in a quick and competitive market, focus is the key to success: choose your target – the consumer or the store – and then choose your analytics.