Retail Strategy

Here’s What You Need To Know About Consumer Buying Behavior

Retail Strategy

Here’s What You Need To Know About Consumer Buying Behavior

If retailers really knew why consumers buy, then much of the $250-plus billion spent on advertising in the U.S. each year could be saved. Black Friday discounting would be unnecessary and conversion rates in ecommerce would be significantly higher than the current level of around 1% to 3%. Instead, there’s still an inevitable mismatch between product development, planning and customer demand. Even the big, established brands are often taking a shot in the dark. We can’t blame a lack of data anymore. But we do lack the insight and tools to learn more about consumer buying behavior from each transaction. Here’s what we should be tracking and how we can get cracking.

What Factors Influence Consumer Buying Behavior?

By no means is consumer buying behavior restricted to the purchase alone. In fact, the process begins as soon as a potential buyer identifies a need, starts researching the solutions (both online and offline), and considers the options. At each touchpoint, they will be influenced actively and passively by a number of factors on their way from browser to buyer:

  • Cultural - Is the product considered luxury or budget, aspirational or affordable? Does it symbolize or nod to something more significant, such as status or affiliation?
  • Social - Buyers are more likely to be intrigued by a brand or product that they hear about from their network of friends, rather than from the brand directly.
  • Psychological - Fear of missing out, urgency, loss aversion and the balance between risk and reward all influence purchasing behavior.  
  • Contextual - Events and life stages, whether it’s a graduation or new baby, all shape the nature and timeline of our needs. 
  • Personal - A buyer’s baseline knowledge about a brand, or previous experience with it, will inevitably influence how willing they are to purchase. 

Clearly, customer buying behavior is sufficiently nuanced and complex to require a more sophisticated approach than many retailers have right now. Amorphous buyer personas based on broad demographic features (e.g., gender, age) fall short. To understand behavior fully, we need to go granular with the data. 

Why It Matters for Your Retail Operation

According to HubSpot, before a customer says “yes” to your product or service, they will say “no” four times in 60% of cases. There’s an understanding in any retail business that the first sale is the most expensive to convert, but the more you know about the decisions leading up to the purchase, the better you can tailor the customer experience. In other words, listen to what your customers are telling you (explicitly and implicitly) and provide them with the most relevant information to make a decision. 

Tune in to the patterns of your customer buying behavior and your store can achieve the following goals:

  • Increase customer lifetime value - A customer who has already purchased twice is 9 times more likely to convert than a first-time buyer.
  • Increase average order value - Win your customer’s trust with personalized suggestions and they are more inclined to open up more of their wallet.  
  • Reduce churn - When your customers feel that you “get them” with your recommendations and reminders, they will keep coming back. Delight them regularly and they will even subscribe. 
  • Focus on high target buyers - Typically, 80% of sales will come from just 20% of buyers. Prioritizing this cohort based on granular product data is key to accelerating growth for the business. 
  • Inventory management - If you can predict customer demand accurately, there will be less pressure to discount overstocked items or frustrate existing customers with “out of stock” warnings. 

How Do You Predict Future Customer Purchasing Patterns?

Every interaction a visitor has with your online fashion retail store yields value, whether that visitor is already a customer or not. Even if there is no sale, there is still value. Not all ecommerce retailers appreciate this right now — or have the tools to look deeper. Too many focus exclusively on operational data to predict customer behavior, in the form of raw sales figures, inventory, staffing levels and other metrics that can be tied to the bottom line. But profitability today doesn’t offer insight into customer behavior tomorrow. 

To gain visibility into future trends and demand, retailers need to unlock the benefits of experience data. That requires a tool that can synthesize and analyze the granular hints, clues and clear messages that customers leave when they interact with a site. In essence, it’s about uncovering the “why” behind the buy.

Where To Collect Experience Data

The first signs that a product is about to “go hot” or that customers are starting to cool toward an entire brand can be found in social media. Innovators and early adopters will drive demand through sharing and recommendations. Their experience can shape brand perception and even influence the price point. 

Post-sale, customers also provide valuable experience data through their reviews and ratings. Local businesses can expect 87% of consumers to consult online reviews first before visiting. Rather than treating reviews as historical data, the more innovative retailers will treat it as an essential resource for forecasting future demand. 

Synthesizing and analyzing this data requires a powerful tool that capitalizes on the benefits of artificial intelligence and machine learning to model accurate patterns based on current activity. Lily AI processes more than 1 billion data points with 99% accuracy to deliver unparalleled product intelligence, and it is the first and only platform to predict customer buying behavior based on the psychographic profile of each shopper.


Shopify - The Changing Consumer Behaviour and How Fashion and Apparel Brands Can Survive COVID-19

HubSpot - 60 Key Sales Statistics That'll Help You Sell Smarter in 2021

Demand Jump - What Is Consumer Buying Behavior?

University of Delaware - Chapter 6. Consumer Buying Behavior Notes