Predictive Retail Analytics: How to Use Data to Understand Shopper Behavior and Grow Sales

Predictive analytics is one of the biggest retail trends of the year, and it’s here to stay. It is a fundamental branch of business intelligence (BI) that uses past data to predict future actions and generate insights.

Predictive analytics for retail lets you take proactive, data-based steps towards improving the shopping experience and be in sync with the ever-changing customer landscape. Click To Tweet

Regardless if you’re a business owner, innovation manager or insight director, being data-centric is the key to unlocking unbiased shopper insight to drive retail growth. Let’s explore how to obtain such data and how to best put it to use.

Why Collect Retail Data?

Competitive businesses in the retail sector leverage big data to understand their target market in a progressive way. As a key BI tool, big data helps optimize all areas of a company. For retailers, it can be the difference between a leading, profitable store and going out of business.

How?

Retail store analytics provide specific and detailed information about shoppers. As a result, they help remove the guesswork. 

You know who your customers are, what they want, and where, when, and how they buy it. No need for trial and error. A few big data examples are:

Purchase history

Customer demographics

Product preferences

Buyer journey

Engagement patterns

This type of shopper insight enables you to predict and measure purchase intent so you can better manage marketing margins, store operations, and customer experience with true efficiency. With ROPO (Research Online, Purchase Offline) on the rise, multi-channel retailers can use purchase intent to bridge the gap between online and offline patterns.

Thus, predictive analytics for retail help you build sustainable relationships with your customers. 

To do so, most retailers use analytics obtained from their point of sale systems or foot traffic analytics devices (people counters or beacons) – while those are helpful tools that enable you to make data-based decisions, they can only go so far in painting an insightful picture into what customers want to buy. 

Luckily, there’s a more progressive way to obtain much more valuable shopper data by using 3d eye-tracking To measure attention and more – here’s how:

How Eye Tracking Can Help with Predictive Analytics for Retail

In our previous piece about what eye tracking is, we explained how the technology works through a brief definition. 

Eye tracking is a technological process that enables the measurement of eye movements, eye positions, and points of gaze. In other words, eye tracking identifies and monitors a person’s visual attention in terms of location, objects, and duration.

Statistics show that the global eye tracking market is on a continuous growth path, especially owing to the increased interest and adoption of vision capturing technology. 

Healthcare, research, retail, and automotive are among the industries where we can see faster adoption and a particular interest in eye tracking technology. Zooming in on Retail, some of the fields where eye tracking is currently being used is in understanding how people shop and how people interact with the store and products in the store.

In retail, it is a cutting-edge tool for both consumer research and in-store research. So much so, that retail has become one of the top commercial applications for eye tracking.

From Google to Apple or Facebook, the biggest companies in the world have been buying eye tracking startups for years now. At the same time, retail innovation managers have been utilizing eye tracking technology to find out:

#1 What catches shoppers’ attention and what they ignore

#2 Where they instinctively look and in what order

#3 When they shift their gaze from one product to another

#4 How they make their buying decision

Ultimately, eye tracking lets retailers analyze why their customers behave the way they do base on their gaze patterns.

Let’s take a closer look at what specific eye tracking features generate predictive analytics for retail that will improve your marketing and merchandising decisions:

#1 Visual Attention Data

Whether they are aware of it or not, people choose to look at objects that capture their interest.

The brain has a finite amount of resources for processing images. Thus, it has to pick what it considers to be relevant and filter out unnecessary visual information. The term for this mental process is visual attention. With eye tracking, you can measure visual attention and record it for further analysis.  

In the context of retail research, visual attention reveals what customers are naturally interested in. Click To Tweet

Before making a buying decision, a shopper will look at various products to compare their options. Usually, the items they gaze at the longest and most often are the ones they buy.

In this regard, eye tracking shows how long it took the customer to focus on a specific product. It also uncovers the number of times they looked at it. Moreover, eye tracking measures how long they spent gazing at an item for the first time and on average. The formal terms for these metrics are:

#1 Time to the first fixation = how long it took the customer to focus on a specific product

#2 Fixation count = the number of times they looked at it

#3 First fixation duration = how long they spent gazing at an item for the first time

#4 Average fixation duration = how long they spent gazing at an item on average

To present this information, eye tracking software generates heat maps and opacity maps. Depending on the type of visualization, the warmer or lighter an area is, the more visual attention it received.

eye tracking heatmap in retail

#2 Interest, Perception, Intention

Eye tracking also provides retail store analytics by showing the flow of interest, perception, and intention:

Once a product catches a shopper’s interest, they will proceed to view it in a certain way.

From that point, their perception can determine whether they intend to buy it or not.

Simultaneously, how the store presents that product influences how the customer perceives it.

Let’s say you’re running an in-store promotion. Eye tracking studies show that shoppers are more likely to perceive it as a better value if the initial price appears too. To make the buying decision with ease, they need visual clues along the way.

Overall, the intention to purchase a product of interest increases if the customer views it in a positive light.

But how do you measure purchase intention? One way is by analyzing consumer behavior.

#3 In-Store Customer Behavior

Eye tracking enables you to record and measure shopper behavior in a natural environment. The technology lets you observe your customers’ intuitive actions in real-time as they explore your store.

By implementing consumer insight and retail store analytics from eye tracking, you can:

Make stores easier to navigate

Design targeted offers

Organize shelves and displays strategically

Evaluate the impact of packaging designs

Craft effective ad campaigns

Improve customer service

Create a tailored in-store experience

Modern eye tracking methods are all non-intrusive. But currently, standard eye tracking technology requires the customer to wear glasses or a headset. As a result, they can adjust their behavior, involuntarily or otherwise. Researchers know the phenomenon as the Hawthorne effect.

To address the observation bias problem, we now have 3D eye tracking solutions like GazeSense.

With 3D eye tracking, you don’t need glasses to gain shopper insight. Our software works with consumer-grade depth-sensing cameras to track eye activity from afar.

Real-time 3D eye tracking allows retail owners and shopper insight managers to:

#1 Work with real shoppers instead of focus groups

#2 Track the visual attention of more than one customer at the same time

#3 Generate unbiased shelf attention analytics

Want to learn how 3D eye tracking can help your company get predictive analytics for retail? Drop us a line and our Customer Success Specialist will be happy to tell you more about our software.

Use Cases for Predictive Big Data Retail Store Analytics

Companies use predictive analytics for retail to improve all aspects of their business. But above all, retail store analytics enable you to create a satisfying experience for every customer. From preferences to buying habits, you will gain actionable insights into every facet of their visit.

#1 Studying Customer Journey

Predictive analytics for retail let you map your customer’s journey beyond transactions. You can view your products, displays, store, and brand the way your shopper does. When using eye tracking, you can quite literally look at them through your customer’s eyes.

predictive analytics for retail eye tracking demonstration

Customer journey analytics use big data to show how a prospect becomes a paying customer. You can identify and analyze all the touchpoints in their interaction with your store, including.

#1 When and how they discovered your store

#2 Communication with employees (positive or negative experience)

#3 How they interacted with products

#4 Follow-up actions (e.g. newsletter sign-up, reviews, etc.)

Once you have this shopper insight, you can personalize the customer experience.

#2 Customizing Shopping Experience

The more personalized a customer’s experience is, the greater their satisfaction will be. By assessing your shopper’s previous in-store behavior, you can tailor their future experiences.

As a result, you can increase not only customer loyalty but also brand awareness and sales. Additionally, you will know how and where to focus your merchandising efforts.

Using predictive analytics for retail, you can customize shopper experience by:

#1 Recommending products of interest (cross-selling and up-selling)

#2 Implementing effective loyalty programs and benefits

#3 Applying online behavior insights to in-store services

#4 Easing the checkout process

#5 Training sales associates for personal interaction

#6 Extending the personalization to all channels

#3 Managing Shelf Space

The way a retailer allocates shelf space influences both shopper experience and direct sales. Out of all areas in a store, the shelf is where a customer makes their buying decision. Thus, you need to ensure that they will find the products they want with ease.

Retail store analytics help you determine which items to place on eye-level shelves. Gaze tracking data enables you to assign shelf space according to how eye-catching products are. By analyzing heat maps, you will better understand how to:

#1 Distribute items on shelves to meet your shoppers’ expectations

#2 Communicate prices and promotions

#3 Increase visibility for key products

#4 Streamline the process of replenishing shelves

#4 Prevent best-sellers from going out of stock

#4 Configuring Store Layout & Design

Besides shelf space management, eye tracking heat maps also provide store design insights. Instead of testing different floor plans, the data points you in a precise direction.

You see what sections, aisles, and displays get the most visual attention. Then, you can adjust your store layout for customer convenience.

Moreover, this retail insight shows the effectiveness of your current:

#1 Signs

#2 Banners

#3 Merchandise displays

You understand where to position them for the best shopper visibility.  

Other sources like CCTV camera footage and in-store sensor tracking also reveal which store areas get the most foot traffic.

#5 Improving Pricing Strategies

Retail store analytics allow you to predict how customers will react and respond to price changes. By analyzing price elasticity, you can see how these adjustments in prices will influence sales.

Predictive analytics for retail let you remain flexible with your prices. Based on the data you collect, you can optimize prices in real-time according to shopper insight. These personal pricing strategies complete the tailored… Click To Tweet

To get the best prices, big data retail tools also detect and process:

#1 Competitor pricing

#2 Product demand

#3 Geodemographic indicators

#4 Shopper buying attitude

#5 Weather and seasonality

#6 Operating expenses

#6 Forecasting Demand

With predictive analytics for retail, you can identify trends and patterns that influence demand. Analyzing sales history and other historical data lets you anticipate what your shoppers will buy in the future. Big data processing systems also consider local and national events, as well as seasonal promotions.

Demand forecasting supports both customer experience and business growth.

Once you can predict what your shopper wants, you can ensure in-store supply. At the same time, you can reduce the risk of overstocking stores with products that are not in demand.

Other benefits of forecasting demand with advanced retail store analytics are:

#1 Reducing costs and increasing efficiency

#2 Maintaining cash flow

#3 Improving return on capital

#7 Optimizing Marketing Messages

Efficient in-store marketing also ties into the personalized customer experience. From retail store analytics data, you already have shopper insight. From there, it’s all about communicating the right messages in an effective way.

predictive analytics for retail 3d eye tracking simulation

For example, take the attention data you get from eye tracking. You know what types of signage attracted your customer. Furthermore, you know what specific elements in the display grabbed their attention.

Combine this insight with product preference data and you can get the best marketing ROI by:

#1 Informing your shopper about the promotions that matter the most to them

#2 Maintaining a consistent message across channels

#3 Driving in-store sales via strategic digital campaigns

#8 Predicting CLV (Customer Lifetime Value)

You can forecast not only demand through predictive analytics but also customer lifetime value (CLV). Based on past transactions, you can generate historical CLV. On the other hand, combining purchase history with behavior analytics results in predictive CLV.

A predictive CLV model provides a detailed picture of your customer’s relationship with your store. Click To Tweet

You know how much they have spent in the past and what products they bought. Put that data together with their behavioral patterns and you get a realistic estimate of how much they will spend in the future.

Identifying your CLV will help you:

#1 Classify your shoppers according to their value

#2 Optimize customer acquisition

#3 Plan marketing budgets with efficiency

#4 Offer custom incentives for different segments

Benefits of Actionable Shopper Insights

In the end, predictive analytics for retail act as a powerful tool for generating shopper insight. You understand how your customer thinks and what they value.

From there on, you can take action and make store improvements to meet their needs, desires, and expectations.

With actionable shopper insight, you are able to:

Make data-driven business decisions

Acquire and retain high-value customers

Increase ROI and store profitability

Develop customer-focused strategies

Gain a competitive edge in the market

Earn customer loyalty

Design compelling promotions

Boost shopper engagement

Uncover opportunities for growth

Provide a rich customer experience

How to Apply Shopper Insight

Observing, aggregating, and interpreting customer data is not enough. Shopper research can prove to be ineffective if you do not apply the insights you gained. Thus, you need to identify the implications and create a plan of action to implement your findings.

Depending on your specific shopper insight, some immediate actions could be:

Reorganizing categories and/or points of purchase

Implementing customer churn prevention strategies

Expanding to new channels, i.e. where your customers are in the digital space

Improving in-store features and facilities

Developing relevant cross-selling techniques

Testing new communication strategies

Revising the allocation of resources

How to Use 3D Eye Tracking for Retail Analytics

Here at Eyeware, we facilitate predictive analytics for retail with 3D eye tracking software.

3d eye tracking heatmap example

As opposed to standard eye tracking, our GazeSense technology remotely tracks visual attention from as far as 1.3m (4.3 ft) away.

No glasses. No VR headsets. No calibration.

All you need is a depth-sensing camera and you can track thousands of customers in a GDPR friendly anonymous way

Additionally, you benefit from a wide range, 3D line of sight, and real-time API. Our software records your customers’ gaze patterns with accuracy, revealing what draws their visual attention.

You can then assess the performance of individual products, brands, displays, and more.

👉 Get in touch with our team to see how real-time, multi-person, remote 3D eye tracking can help you get unbiased shopper insight.