How Does In-Store Analytics Work
You are most likely aware of how online stores keep an eye on you when you visit their websites. We’re getting quite used to being analyzed when we are shopping online, but what about when we visit physical stores? Is there a way to measure customer behavior in retail shops?
In-store analytics is used for making sure brick-and-mortar shops can also gather data about their customers. This guide will look at what in-store analytics is all about by focusing on its benefits, the core customer behavior it measures and the different ways retailers can use it to their advantage.
WHAT IS IN-STORE ANALYTICS?
In order to understand how in-story analytics work, you must understand the definition of the term. At the time when buzzwords are flying off the shelves in the retail industry, it’s easy to get confused what counts as in-story analytics and what doesn’t.
First, it is a good idea to define what analytics mean in this context. Analytics simply refers to the process of finding meaningful patterns in customer behavior. There are different ways this customer behavior can be measured, as well as monitored.
The process often involves different technologies, such as software, to measure website metrics or video technology to analyze behavior. It is the processing of this data, which is referred to by analytics. As mentioned above, it is mainly used in the context of website analytics, but it can also refer to monitoring customer behavior in physical stores.
The outcome of the analytics is then described through a range of metrics. For example, in behavior analytics there are three separate categories of metrics:
- Empiric metrics
- Statistical metrics
- Calculated metrics
The definition of in-store analytics
When it comes to in-store analytics, the processing of the customer behavior is focused on what happens inside the store. Thus, the analysis focuses on different customer behaviors, which can be measured when the customer is visiting the store.
The target of the analysis is therefore the customer’s behavior in the store. On the other hand, location analytics puts the customer at the target of analysis, for example. In-store analytics is a much broader term and looks at the customer behavior, but only in the context of the store.
In-store analytics is therefore focused on optimizing store performance. It is widely used in the retail sector to enhance both customer experience and drive sales.
How can in-store analytics benefit a business?
There are a number of benefits to applying in-store analytics. The most common three advantages of in-store analytics includes:
- In-depth information for different sectors – In-store analytics is great at providing information for different sectors within the business. You can learn more about customer behavior, which can, not only improve the product and service offered at the store, but also enhance product inventory.
Together the information can help cut down costs at different sectors of the business. For example, you might be able to reduce inventory size or find a more suitable solution for storing heavy energy using items, such as frozen goods at a food store.
- Better understanding of customer needs – Naturally customer needs can be much better dealt with once you understand what customers are looking for at the store and the aspects driving their behavior.
In-store analytics can reveal tips for better product replacement, for example. You can use the metrics to design the store in a way that better fits the customer narrative.
- Offers a way to develop the in-store experience and train staff – All of the above can also develop the way you train staff. This will in turn lead to better customer service, which has the potential to boost sales.
MEASURING THE THREE PHASES OF CUSTOMER BEHAVIOR
In-store analytics essentially focuses on three phases of customer behavior: entry, browsing and exit. In this section, we will look at these three data points separately and discuss the ways they can be measured and used as part of in-store analytics.
The first phase of customer behavior deals with the entry. It is all about the customer entering the store and in-store analytics typically focuses on three different measurements:
- Visitor numbers – You want to look at how many customers enter the shop.
- When customers come – You should also focus on the times when customers visit your shop. You might be able to see a pattern in shopping behavior and it can reveal a lot about when your shop is the quietest, for example.
- Where customers go first – Analyzing the entry also deals with the immediate direction your customers take when they enter your shop. Do they all follow a certain pattern?
Entry analytics shouldn’t focus solely on customers entering the shop. Certain in-store analytics also look at customers who viewed the shop windows, for example. This can reveal a lot about things, such as shop appeal and products that ultimately make customers enter the shop.
The second phase of customer behavior looks at what customers do inside the store. The in-store analytics of the browsing phase focus on measurements such as:
- The conversion rate – How many of the customers that entered the shop actually ended up buying a product.
- Average shopping cart size – What is the typical shopping cart size your customers have? You can look at the items your customers buy and see how long the customer looked at the specific items before making the decision to buy, for example.
- How customers move in the shop – The browsing phase also deals with the hot and cold zones inside the store. This is all about finding the areas where your customers spend the most time, the areas they avoid and the areas they are most likely to buy from instead of just browsing.
The section also looks in more detail whether the customers have favorite products in the shop and if certain promotions work better than others.
Finally, in-store analytics also focuses on dealing with the exit phase of customer behavior. Exit is often the phase stores tend to focus on the least. Although the way and reasons customers leave your shop can reveal a lot more than why the entered the store. The exit phase focuses on measurements such as:
- Bounce rate – Bounce rate measures the ratio of customers entering the store and failing to engage in any activity within the shop – the so-called immediate leavers. It can reveal how many of your shoppers simply enter the shop and leave without making any sort of engagement, such as pausing to view a product, let alone to buy one.
Make sure you don’t mix the in-store bounce rate with online bounce rate.
- How long customers stayed – You can look at the average time customers spent in the store. You should also compare the time shoppers who ended up buying a product spent at the store with the time shoppers who didn’t buy spent looking at the items.
- Did queues affect the decision to leave? – Exit analytics should also pay attention to the payment and queuing process at the store. You want to look at the average queue times and whether certain shoppers simply gave up because making the payment took too long.
All of the above three phases are measured by focusing on a variety of different scenarios. You can then analyze the data and use it to improve four different aspects of the business. By focusing on these four different aspects inside the store, you can further test the ways customer behavior and engagement can be improved.
The four different sections in-store analytics can improve include:
- Operations – The above three phases can help with operations, as you can choose the correct location in store for your products, as well as improve your inventory selection. You can use the products customers like the most to improve visitor numbers and adjust the shop layout to prevent cold zones from forming.
- Workforce – The three phases can also help with workforce management. As you’ll know the busiest times, as well as the bounce rates, you can guarantee the staff is free at the right times. Analyzing the phases can lead to improvements in productivity and enhance employee satisfaction, as you can adjust better to busy times and customer requests.
- Security – You’ll be able to improve in-store security, as you can see where possible thefts occur and how thieves moved around your store prior to the robbery.
- Marketing – Finally, in-story analytics of the three phases will enhance your marketing efforts. You’ll be able to focus on the promotions that lead to better conversion rates, as well as find the products and layouts that fit customer needs the best.
QUESTIONS IN-STORE ANALYTICS SHOULD ANSWER
When considering the use of in-store analytics it is important to ensure you focus on the essentials. There are certain things in-store analytics should be able to tell you. When choosing the products and platforms you want to use for analyzing customer behavior, you need to be able to answer the following questions.
Does the store attract shoppers or buyers?
There’s a difference between shoppers and buyers. The first is a group of people who visit the store to look around, but don’t necessarily buy anything. The second group consists of people who enter the store with the mindset that they are going to spend money at the shop. You should be especially interested in the first group of people because you want to know how this group behaves at the store and what drives their decision to leave.
In-store analytics is especially focused on this group. Whilst your cash flow can reveal a lot about your customers, you want different in-store analytics to tell you the story of the people who don’t end up buying.
What is the optimal product mix?
In-store analytics should also focus on revealing information about the products at the store, especially in terms of the optimal product mix. As anyone in the retail industry knows, merchandising is largely based on intuition. But with proper analysis of customer behavior, you can learn more about what works and what doesn’t.
You can also get better information on product displays. If customers keep spending a lot of time in front of a certain product line without buying it, you can start focusing on why the product isn’t selling even though it is interesting to your customers.
What is the ROI of the store?
The return on investment or ROI is a crucial number in the retail industry. By collecting customer behavior data you can better understand the store’s ROI.
Are customers shopping other things elsewhere? Why?
In-store analytics should also provide you information about the products customers don’t buy at your store. For example, if you were a supermarket owner, it would be useful to know why customers buy all of their other basic goods except vegetable and fruit from your store. Information on the items your customers choose to shop elsewhere can help you direct your promotions to the sectors lacking behind or even improve the quality of the products you stock.
What is the security in the store?
Finally, you should ensure the platforms you use for in-store analytics help you improve the security at the store. You want to use the data to improve security, as this can have an important impact on your revenue stream as well. If there is a certain product thieves are stealing or a specific part of the store where products are stolen regularly, you can use the data to enhance security in these areas or place the products differently.
It can also be a helpful idea to create a database of the thieves. Are there certain characteristics the robbers seem to share? Do they strike at specific times? You can also use things, such as video, to see whether certain things have prevented a potential thief from stealing.
HOW IS IN-STORE ANALYTICS ACHIEVED?
The above sections have introduced you to the concept of in-store analytics and the metrics you can learn by utilizing this strategy at your store. You’ve also been able to better understand what makes in-store analytics successful.
But how can stores use in-store analytics and what methods are available for collecting in-store data?
The different methods of collecting data
When it comes to in-store data analysis, retailers have a selection of ways to measure the above behaviors and find out more about the way customers shop at the store. Whilst technology certainly offers the best options for gathering and analyzing data, there are certain old-fashioned methods to use as well.
For example, you could opt for simple in-store questionnaires that measure customer experience. These could be conducted during all three phases: at the point of entry, while browsing and at the point the customer exits the store.
But of course, technology has provided retailers a range of other alternatives to simply asking questions. The most common example is a simple sensory system at the store, which can count the customers entering the story. Furthermore, by utilizing surveillance video, you can follow the route customers take inside the store and how they behave in front of specific products.
Furthermore, smartphones are also being utilized for in-store analytics. Through wireless Internet, you can gather different data sets on customers, such as the time-spent in-store or the areas they spent the most time in.
Check out the below video for more information on WiFi in-store analytics and the possible dangers of using it without customers’ knowledge:
Finally, you are able to harness information from your sale and transaction data. This can help you in calculating conversion rates, as well as conducting research on which promotions work the best.
Examples of enablers for in-store analytics
Finally, it is a good idea to look at some of the enablers of in-store analytics. The following examples highlight the different options available for retailers and the wealth of information you can learn from to data the software systems allow you to collect.
Apple has developed its own in-store analytics program to track customer behaviour. The iBeacon system uses the iPhones Bluetooth to track the customer’s movements in store.
The customer can choose to opt-in to use the system, which essentially focuses on tracking your movements at the Apple store, for example. It’ll show the retailer the amount of time the person spent at the store and the areas the person spent the most time in. The system can even allow Apple to contact the customer with different promotional messages, which can boost sales.
Index is another system, which is looking at in-store behavior in a more open setting. The system, developed by former Google employees, has already raised $7 million in funds.
The app is similar to iBeacon in the sense that the customer can enable its use. Customers can create unique identification to use the app and allow it to view their credit card transactions. When the customers enter stores, which have enabled Index, the system will see if the person has shopped there before and notifies the customer over specific deals or favorite items available for purchase. The idea is to help customer experience become more personalized.
Finally, there’s another in-store analytics platform called Nomi. The system combines new data gathered through smartphones with the more general information obtained from video surveillance, for instance.
The company provides retailer-led data analysis and works together with the stores to ensure the information they gather is the most suited for the business needs. Unlike iBeacon and Index, Nomi’s attention is driven by the store and how the store can directly benefit from data collection.
THE BOTTOM LINE
In-store analytics can connect the dots between the consumer, the store and the buying decision. It is a great way to learn more about how the store is able to attract customers and help retailers to optimize customer experience.
With the help of technology, you are able to understand why customers visit your store, how they behave inside the store and what are the reasons behind them leaving empty handed. Brick-and-mortar stores have a number of different methods available for gathering this data and new technology is constantly being invented to further in-store analytics. In-store analytics is a strategy that can drive sales and ensure customers enjoy shopping at your store.
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