In business, the value of genuinely understanding the consumer is multidimensional.
Through deepened awareness of what our consumers want and how they feel, we are better prepared to make informed decisions on everything from product development to sales pitches, merchandising, marketing, customer experience, and more. Further, when we are able to meet consumer demand and appeal to them in an authentic manner, we increase brand loyalty among our consumers.
For retailers and manufacturers, this is where the analysis of consumer buying behavior comes into play as a critical component in helping them better understand the consumers who buy their products. Read on to learn about the key components of completing a consumer shopping behavior analysis, including the role of consumer data, the metrics that can be tracked, and the various applications that can be implemented to improve business.
What is shopping behavior analysis?
In short, shopping behavior analysis is the act of better understanding consumers through the collection and review of data on consumer shopping habits, preferences, and buyer motivations.
This analysis allows for the identification of patterns in consumer behavior, answering questions around what consumers are purchasing, when they are purchasing, where they shop, and more.
Analyze consumer buying behavior in 3 core steps
While the analysis process may deviate based on elements specific to a given campaign, industry, or audience, the following three steps are fundamental to any effective shopping behavior analysis:
1. Gather and organize necessary shopper data
2. Analyze and interpret data
3. Identify buying behavior trends and patterns
Step 1. Gather and organize necessary shopper data
Shopper data is where we gain insight into consumer attitudes, preferences, motivations, and trends.
Depending on the insights you hope to find, this step could involve collecting survey data, transactional data from point-of-sale systems, social media data, or even website interaction data.
Given the vast variety of data options available, to understand how to gather your data, you will first need to understand the type(s) of data you want to collect.
Understanding the different types of consumer data
From first-party to attitudinal, there are various types of consumer data – and even more ways in which that data can be collected. As technology moves steadily toward a cookieless future and a privacy-first perspective on how we gather information about consumers, it’s important to understand how to distinguish between the nuances in types of consumer data.
What is “____-party” data?
Zero-, first-, second-, or third-party data generally refers to the method by which data was collected. The higher the number, the more privacy-invasive. In other words, zero-party data is direct from the user and voluntarily given, whereas third-party data is collected from an outside data provider, usually using cookies or “stealth” tracking methods, and often purchased.
Standard data types
The four standard data types include basic, interaction, behavioral, and attitudinal.
- Basic (or identity) data is information that has been collected or provided on an individual person using your product. This is the information typically stored with your company’s CRM, the majority of which falls under the Personally Identifiable Information (PII) category. This data will fall under privacy and protection laws.
- Interaction (or transactional) data is derived from how consumers engage with your brand, whether in-store, on your website, in an ecommerce shop, on social media, etc. Common metrics in this category include products purchased, basket analysis, clickthrough rates, time spent on page, social “likes,” and more.
- Behavioral data is specifically about how users experience and engage with your product. Since this data is often stored digitally, it can be found in client usage reports within SaaS platforms or brand apps. Technology industry products gather this data directly from digital user interactions. However, for industries like consumer durables to collect data, user surveys reporting their behavior and usage are often required.
- Attitudinal data includes information on consumer attitudes, preferences, and motivations. This data is often qualitative and gathered from reviews, satisfaction surveys, focus groups etc.
Using consumer data types to understand shopping behavior
Each of these data types will deliver a different level of insight into consumer behavior. Companies with a data lake may even have the ability to combine different data types to generate additional insights. An example of this combination may be combining behavioral data with transactional data to identify purchase patterns and increase total spend. When selecting the types of data you want to gather for your analysis, you will want to ensure the data maps to your desired outcome.
Selecting the right types of consumer data
In understanding buying behavior, identity (or PII) data will be far less important than interaction, behavioral, or attitudinal data, as you will be less concerned with the actions of individual purchasers and more interested in the collective. Similarly, interaction data is important and can be easily extracted from your internal tracking tools or the individual platforms.
Collecting behavioral and attitudinal data
To collect behavioral and attitudinal data that delivers insight into shopping behavior, businesses typically gather information through intentional effort.
Traditionally, organizations have used consumer surveys as a way to ask specific questions about consumer behavior and gather detailed responses, such as why consumers make certain purchasing decisions. That information is then used to inform improvements in product development or business operations. For example, a consumer survey might reveal that a large percentage of consumers are not satisfied with the selection of products offered by a particular store or brand, which could inform the product assortment strategy.
Many successful retailers and manufacturers even conduct routine satisfaction surveys that can regularly provide some of these insights. However, businesses seeking a specific set of insights would need to initiate a new outreach to gather the data needed for the analysis. The planning and execution of which can be time consuming and pricey to get right.
Modern data collection: consumer insights solutions
Rather than conducting dozens of individual research studies, businesses today are turning to insights solutions like TraQline, which are designed to deliver shopper behavior data with ease, right when users need it — saving hundreds of hours. TraQline users specifically gain access to thousands of consumer insights that span 10 years of behavioral and attitudinal data, as well as market share, all at the click of a button.
Step 2. Analyze and interpret data
Once you have collected and organized the data, the next step is to identify trends and patterns within the data. Analyzing the data may involve using a statistical analysis software or other tool to help understand the insights available within the dataset(s) provided.
Key shopping behavior metrics that businesses could track or pull from the data include consideration rate, conversion rate, number of stores shopped, seasonal purchasing habits, reasons for buying a specific product, etc.
With a consumer insights tool, highlighting these metrics is typically an integrated piece of the platform. As an example, TraQline Durable IQ highlights significant changes (positive or negative) in a given metric for all variables, as well as exportable charts that depict the insights visually.
Reasons Purchased (2022 data)
Small Appliance Brand: Cuisinart
Behavioral Metrics such as ‘reason purchased’ are
a key metric in a consumer insights platform
Step 3. Identify buying behavior trends and patterns
The final key step in understanding consumer behavior is to look at the insights over time in order to highlight trends and patterns. This is a crucial step, as YOY or period-over-period can paint a picture of buying behavior that single year or quarter data may not reveal or may misconstrue.
This may involve uncovering patterns in the data that show certain products appearing more popular at certain times each year or that specific demographics tend to make purchases online versus in-store.
For example, in the chart below, which shows 2022 units purchased for select small appliances brands from Durable IQ, there’s a noticeable lift in purchase rates for all these brands from Q3 to Q4.
2022 Share Growth (by units sold)
Select Small Appliance Brands
This Q4 lift may indicate a seasonal trend, led by holiday sales and gift purchases, or it may indicate something specific to that quarter alone. One way to determine which is the reason would be to compare the same selection YOY to see if the trend persists. If it does, it’s a seasonal trend, likely still worth drilling down further for additional insights – such as the growth of KitchenAid, for instance, which jumps from lowest to highest of the selected brands between Q3 and Q4. If it does not, then determining what could have caused the quarter lift would be worth investigating.
As you can see below, in this instance, the Q3 to Q4 lift appears to be a seasonal trend, with the same growth seen over the past three years.
2020-2022 Share Growth (by units sold)
Select Small Appliance Brands
For more great insights like these, fill out the form below to get a demo of the TraQline consumer insights platform or connect with an expert on our team.
Applications of consumer buying behavior insights
Both the short-term insights and long-term trends and patterns can help inform consumer understanding and be used by businesses to inform strategies for purchasing, marketing, sales, and beyond. Let’s take a look at a few specific examples where shopping behavior analysis can improve business performance.
Identify opportunities to enrich the customer experience
The customer experience can easily make or break a sale. Poorly designed planograms, customer service that fails to meet expectations, difficulty with site navigation, and/or confusion in product differences can solicit a response in consumers that has them searching another brand or shopping another store.
As a more nuanced example, if a shopping behavior analysis reveals that a high percentage of online shoppers abandon their cart before completing a purchase, this may indicate a need to improve the checkout process, offer promotions, or integrate more payment options.
Optimize product assortments and pricing strategies
Consumer buying behavior insights can reveal trends around needs and preferences, allowing businesses to optimize their product assortments and pricing strategies to align with their target market. Having the right data at our fingertips helps ensure we understand how our own product lineup compares to that of our competition.
Insights from platforms like TraQline SKU Metrix deliver comprehensive, searchable product libraries. These libraries not only collect daily information about products available at key retailers, but they also allow the user to compare and filter products based on features — all while providing product pricing updated several times per week.
For example, the data might show that a particular feature is becoming more prominent in the marketplace, which could prompt a business to integrate that feature into product development moving forward.
Develop consumer-targeted marketing campaigns
Through consumer shopping trend data, businesses can uncover insights that allow them to create marketing campaigns tailored to the specific needs and interests of their target audience – or even discover new audience segments to target.
The data may show that a particular product, feature, or price is more popular with Gen Z or is purchased heavily within one region, which could inform the decision to target that demographic with marketing efforts.
Better understand your consumer shopping trends
Undeniably, shopping behavior analysis is a valuable tool for understanding consumer trends and identifying opportunities for improvement.
By collecting and analyzing various types of data and tracking key metrics over time, businesses can gain a better understanding of consumer behavior and use this information to inform their marketing and sales strategies.
If you’d like to learn more about how TraQline Durable IQ can help you better understand your target market, inspire loyalty, and uncover trends in consumer durables with ease and efficiency, connect with us below!