Imagine an apparel retail store launching a new trendy, high-fashion running gear collection. 

High heels, elaborate dresses, three-piece suits, matching cotton waistcoats for men. 

The company is so proud of its new collection. The C-level execs have designed the clothes themselves, and now they’re directing all resources toward promoting the new line. 

No expense is spared, as influencers, external events, and high-budget acquisition campaigns are all financed to spread the word and persuade as many athletes as possible to buy the new line. 

Ridiculous, isn’t it?

Athletic apparel should always value comfort and performance over high fashion. 

The retailers worked for nothing since they didn’t ask for consumer insight or even consider market research. 

They spent money creating and promoting products that failed to resonate with customers, leading to poor sales performance, wasted resources, and excess inventory.

While this situation is a (hopefully) extreme example, it perfectly illustrates what happens if you fail to consider customers’ needs and preferences and incorporate these insights into your retail experience solutions.

Ignoring customer needs results in a misalignment between the products and what customers actually want.

Consequently, this misalignment results in a poor shopping experience, a lack of customer interest, decreased sales, and, ultimately, a loss of market share

How can you avoid all these unfortunate situations and instead benefit from all advantages generated by an intimate understanding of your customers?

By becoming obsessed with customer research and feedback loops – essential practices are obtaining accurate insights into customer preferences, expectations, and pain points.

With customer research, you aren’t just gathering demographic info about people buying your products to create a basic fictional persona. When done right, research gives you the ammunition to orchestrate the most effective acquisition and retention initiatives ever.

People buy results, not products. 

Photographers don’t buy high-resolution cameras because they want to spend money on expensive products. They do it because they desire the recognition and social status that comes after photographing a stunning landscape, creating a masterpiece.

Research helps you uncover those hidden desires, understand your customers’ buying context, and incorporate them into a meaningful customer journey. 

This intelligence is used in the right messaging for your ads, the perfect upsell recommendations, and the cleanest retention strategy. 

Simply put: if you correctly complete your customer research, your customers will actually be the ones who strategize on your behalf.

Feedback loops, on the other hand, are the next natural step after the research stage: the action you take based on the feedback

These loops foster a sense of customer partnership, where customers feel valued and heard. Associating your brand with these fuzzy feelings will increase customer loyalty, advocacy, and trust in your brand.

Retail customers will feel comfortable buying from you, recommending you, and leaving you high praises. They trust you will put your money where your mouth is by implementing their feedback in how you do business.

However, even if you’re already sold on the advantages of research and feedback loops, you might need more help with the practical side.

Where do you find customers’ insights? How can you convince customers to give you an interview? How do you translate your data into actionable steps?

Today’s blog post will show you how to unlock customer insights using a blended qualitative and quantitative data approach while staying out of the research rabbit hole. 

Keep reading to see where to find and how to use customers’ genuine purchasing reasons to position yourselves for long-term success in the face of evolving customer demands.

The Role of Quantitative Data

Quantitative data analysis is one powerful tool in your quest to find your way into customers’ minds

This data analysis means systematically examining numerical data to uncover patterns, relationships, and trends

From before the purchase to after it, and even after product usage, analyzing quantitation ads data provides a roadmap of customer behavior, helping you align your processes with customers’ needs.

How Quantitative Data Helps Understand Customer Behavior

When customers consider a product in the pre-purchase stage, quantitative customer data such as website traffic, click-through rates, and conversion rates reveal how consumers interact with your platforms. 

From this, you can understand preferences (pages viewed), browsing patterns, and even engagement levels. 

In other words, you let the numbers show you what attracts attention and drive conversions on your website. 

Then it’s only a small step to improving the entire website design and user experience to boost sales.

In the post-purchase stage, quantitative data continues to be crucial. 

Customer satisfaction scores, repeat purchase rates, and engagement metrics provide insights into customer satisfaction and loyalty.

(i.e., the two sentiments you want consumers to associate with your brand)

Even after product usage, quantitative data remains valuable. 

Metrics like product return rate, feedback ratings, and reviews scores help you understand product performance and customer sentiment toward your offers. 

Going even further, this info can be then put to good use to create better products that exceed customer expectations.

How Quantitative Data Reveals the Reasons Behind Customer Purchases

One critical strength of quantitative data is its ability to uncover the underlying motivations behind customer purchases. 

You can identify factors driving purchases by analyzing quantitative data related to pre-purchase behavior, such as keyword searches, time spent on product pages, and cart abandonment rates. 

Moreover, this data can also reveal patterns and trends that highlight customers’ preferences, pain points, and desires.

This approach offers a more comprehensive understanding of customers, allowing you to make informed decisions, optimize marketing strategies, and enhance the overall customer experience.

Qualitative Research: Unveiling Customer Pain Points and Motivations

While quantitative data provides valuable numerical insights, qualitative research highlights the underlying motivations and pain points that drive customers to make repeat purchases (or break up with a business.)

Delving into the human aspect of consumer behavior reveals the “why” behind the numbers. It writes the customer story, giving more context to your metrics and peeling another layer off the customer behavior mystery. 

Identify Pain Points that Drive Repeat Purchases

With qualitative research, you’re tapping into customer behavior’s emotional and psychological aspects. It reveals the challenges, frustrations, or unmet needs customers seek to address through purchases. 

You can gather rich qualitative data that unveils these pain points through interviews, focus groups, and customer feedback surveys. 

Moving on, this intelligence allows you to develop products that directly address customer needs, enhancing customer satisfaction and loyalty.

People Act to Avoid Pain, not for the Gain

In unlocking customer insights, it is essential to recognize that most people are driven to act to avoid pain rather than for the sake of gain. 

This insight is crucial in shaping marketing strategies and product offerings. 

Qualitative research provides an avenue to explore customers’ fears, frustrations, and challenges, often the driving forces behind their purchasing decisions. 

Uncovering these emotional drivers allows you to create compelling value propositions that resonate with customers on a deeper level. 

Whether addressing a specific problem, providing convenience, or alleviating a pain point, understanding the avoidance of pain can significantly impact the success of marketing campaigns and product development.

The Need for a Blended Approach

Some people are highly logical creatures, needing numbers and complex data to support any decision, no matter how small. 

Others are compassionate humans who get lost in empathy and could talk to you for hours, trying to understand the reasons behind your behaviors. 

When it comes to customer research, you must channel both these people; relying solely on either quantitative or qualitative data is insufficient. 

Since customers are complex creatures, who sometimes can’t explain their behaviors (even to themselves), you will need a blended approach of qualitative and quantitative data. 

That is if your purpose is to genuinely grasp the intricacies of customer needs. 

While quantitative data provides valuable insights into customer preferences and purchasing patterns, it alone may not uncover the deeper motivations and emotions that drive customer decisions. 

That’s where qualitative data steps in, adding context and depth to the numbers and revealing the underlying factors that shape customer behavior.

For example, in eCommerce, quantitative data such as website traffic and conversion rates offer valuable insights about your CRO initiatives. 

Yet, there’s not enough to learn how many people clicked on a website banner. You have to understand why they did it. Qualitative data will capture insights about customers’ emotions and experiences, providing a deeper understanding of motivations and pain points.

Similarly, quantitative metrics such as footfall traffic reveal store performance in physical retail. Yet, they won’t tell you anything about in-store experiences.

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It gets even more complicated when discussing hybrid businesses → operating online and offline.

When you think about it – why do we even need insights?

Because we need to generate behavior change

If you’re an eComm professional, your job isn’t to write an ad, design a button, or solve a ticket. Your job is to make someone who scrolled past your Ads to stop in their tracks, click on it, visit the website, and order something. Then return, again and again, until the natural churn occurs.

If any of these steps is broken, the person won’t walk through the entire journey, and the behavior will stay the same. 

Yet, you can only fix what you know is broken. 

And you can only fix it by knowing why it is broken.

Going down the rabbit hole of quantitative data without understanding the why is a trap.

Giving context to data through qualitative insights? Priceless. 

The 3 key moments to generate customer insights

Moving on, let’s look at three key moments when you can jump in and collect customer insights – and why each is crucial for your research process.

  • Pre-Purchase Behavior

This stage involves understanding the customer’s journey before they make a purchase.

It includes studying their browsing patterns, the time they spend researching the product, their interactions with customer service, and their engagement with the brand on social media. 

Quantitative data such as website analytics, social media metrics, customer service interactions, cohort analysis, sales figures, and customer segments can provide valuable insights into what drives a customer toward a purchase. Consequently, qualitative data obtained through website surveys and pop-ups provide context for the numbers.

This data can help businesses understand what information or reassurances customers need before they feel confident enough to buy.

  • Post-Purchase Behavior

After a customer has purchased, their interactions with the product and the brand continue to provide valuable data. 

This can include their product review, response to post-purchase follow-ups, and the likelihood of recommending the product to others. 

Quantitative and qualitative data such as customer satisfaction scores, Net Promoter Scores (NPS), and product reviews can help you understand how well the product or service met the customer’s expectations and identify areas for improvement.

  • Post-Usage Behavior 

This stage involves understanding the customer’s experience after using or consuming the product. 

It includes studying their repeat purchase patterns, engagement with the brand after consumption, and the likelihood of becoming loyal customers.

Quantitative data such as repeat purchase rates, customer retention rates, and customer lifetime value (CLV) can provide insights into how well the product or service has satisfied the customer’s needs. 

Interviews and surveys will paint the picture even brighter, showing what could be improved to foster continued customer loyalty.

The Interplay between Qualitative and Quantitative Research

Once upon a time, a skincare retailer aimed to launch a new line of skincare products. 

In their qualitative research, they conducted focus groups and interviews with potential consumers to understand their skincare routines, preferences, and concerns. 

The qualitative data revealed that many participants desired natural and organic ingredients, emphasizing the importance of sustainability and avoiding harsh chemicals.

Excited by these qualitative findings, the retailer developed the product line solely based on the insights gathered from the qualitative research. They invested heavily in producing a line prioritizing natural and organic skincare products.

However, the new line didn’t resonate well with the target audience. 

Sales were disappointing, and people seemed disinterested in the products. The retailer realized they overlooked the importance of validating the qualitative data with quantitative insights.

Had they incorporated quantitative data into their decision-making process, they could have validated whether the qualitative findings represented a larger segment or were specific to the participants in the research.

This story aims to illustrate the importance of combining qualitative and quantitative data to make informed decisions and ensure the validity and reliability of your findings.

Qualitative research provides in-depth insights into customer motivations, pain points, and preferences. 

However, to ensure the validity and reliability of these findings, it is crucial to validate them with quantitative data

Achieving Statistical Significance in Research Findings

Statistical significance refers to the degree to which the observed results are likely accurate and not due to random chance. 

In retail, achieving statistical significance is crucial when unlocking customer insights. 

For example, suppose you want to evaluate the effectiveness of two different acquisition campaigns promoting a new product. 

You can conduct surveys or analyze sales data, measure brand awareness, purchase intent, or engagement, and talk to customers. 

However, without achieving statistical significance, you will never be confident enough to declare which campaign was truly more effective in influencing customer behavior.

Think about it. 

The sole purpose of customer research is to inform strategic initiatives, marketing campaigns, product improvements, and customer experience upgrades.

Seeing how all these resources are directed toward these initiatives and how much time, money, and workforce you will need to carry them out – would you want to do everything in your power to ensure your decision is the best?

Statistical significance will give you confidence in the validity and reliability of your research findings, ensuring the insights truly represent your customer base.

Generating Actionable, Prioritized, and Verifiable Insights from Customer Research

Many retailers, CX people, or data analysts are hypnotized by the insights they get from the research stage, forgetting to put them to good use. 

But you can do better.

To ensure you’re not paralyzed by fascination, here are the three attributes of your insights: actionability, prioritized, and verifiable

Everything else can be recycled and ignored for the time being. 

Actionability is a vital aspect of valuable insights. 

In essence, actionable insight provides clear guidance on improving products, services, or customer experiences. 

Besides offering guidance, your insights should drive tangible outcomes that bring meaningful change.

Equally important is the notion of prioritization. 

Not all insights hold the same weight or immediate impact. 

Prioritizing insights ensures that you are in the most critical areas. 

To achieve prioritization, you need to find the initiatives with the highest potential for business growth and the lowest level of effort. 

For example, suppose you discover that Product X was so low-quality it caused 15% of your customer churn over the past quarter. At the same time, you learn that a small % of low-value customers would appreciate recyclable packaging. 

Evidently, you’d first deal with the first insights – streamlining efforts and ensuring that you address the most pressing needs.

Lastly, verifiability is another crucial factor to consider. 

Insights should be grounded in evidence and capable of withstanding scrutiny.

Otherwise, you create fake customer profiles that can do more harm than good.

Data, research, and rigorous analysis support verifiable insights. They are not based on assumptions or personal opinions but on objective findings. They also inspire trust and foster a data-driven approach to decision-making.

From Insights to Business Initiatives

You need a clear action plan to turn insights into initiatives – outlining specific steps, allocating resources, and setting measurable goals. 

For example, if an insight reveals that customers value personalized recommendations, you can develop an initiative to implement an AI-powered recommendation engine on the website. Retail customers access this engine to create customized cards, and voila!

This initiative directly addresses the insight and provides a tangible solution to enhance the experience and improve customer loyalty. 

By taking action based on customer insights, you demonstrate your commitment to meeting their needs and fostering a positive customer experience.

As discussed in the previous section, you should prioritize initiatives derived from customer insights based on their potential impact on customer satisfaction, business growth, and overall strategic goals. 

This way, you can allocate resources, time, and effort to the most significant areas that will yield the highest returns. 

At the same time, you can make retail customers aware of your efforts to ensure your care doesn’t go unnoticed.

source: The CLV Revolution Book | Valentin Radu

Avoiding the Data Rabbit Hole

Sometimes, the sheer volume of your data can be overwhelming, leading to analysis paralysis. 

Companies become fixated on gathering more data and insights without taking the necessary steps to translate those insights into action. 

Insights alone are not enough. 

The valid justification for investing time, resources, and effort into unlocking customer insights lies in the actions taken. 

Setting time limits is crucial to avoid analysis paralysis. 

Decide on a specific timeframe for data analysis to ensure you don’t spend excessive time on minor details. Deadlines encourage efficiency, keeping you focused on the most important findings.

Look for insights that have practical implications and can drive positive change in your business. Avoid getting sidetracked by exciting but non-essential details that won’t contribute to actionable outcomes.

You should also seek input from others. 

Collaborate with colleagues or experts to gain different perspectives and challenge your own biases. Their insights can help you make better-informed decisions.

Once you have gathered sufficient insights, prioritize and implement the necessary actions to capitalize on the findings. 

Remember, it’s better to take action based on imperfect data than get stuck in a never-ending analysis cycle.

Wrap Up

Before anything else, your customers are people too – and there is a way to understand what they truly need from you. 

Technically, a well-rounded approach that blends quantitative and qualitative data will validate insights and drive action. This methodology helps you stay ahead of the competition, build loyal customer relationships, and thrive in the ever-evolving retail landscape.

Yet, you will need patience, commitment, and dedication to unveil what drives customer behavior. 

You can’t just skim the surface – you must dive right in, head first, into an ocean of data.

Remember that data and insights are vital but hold little value without taking action. 

The true winners in the market are those who consistently take action based on customer insights, fostering continuous improvement and customer-centric strategies.

Good luck!