Find the Meaning Behind Your Retail Metrics with Sentiment Analysis

You’ve probably heard the old adage: “If you can’t measure it, you can’t manage it.” That statement rings true for many in retail and customer experience (CX). It’s not uncommon for brands to track dozens of retail metrics to gauge the health of their business.

In truth, the right metrics are critical for every business. Some–like conversion rates, sales turnover, and net revenue–provide a picture of finances. Others–including Net Promoter Score (NPS), Overall Customer Satisfaction (CSAT), and Customer Effort Score–offer perspectives on customer satisfaction. Many retailers find benefit in tracking a mix of these core metrics.

But data and numbers don’t give you a comprehensive insight into customer emotions. Data can help you spot trends in customer preferences and sentiment, but they don’t explain why those trends are occurring. For that reason, many brands are moving beyond focusing on quantitative insights and looking for ways to access voice of the customer (VoC) intelligence. 

VoC is tricky though. The VoC information you collect is open-ended. It can be difficult to know how to make sense of large volumes of unstructured data. Unfortunately, many brands do minimal analysis (if any) and miss opportunities hidden in their VoC feedback.

What’s the answer? Smart brands are applying sentiment analysis techniques to extract meaning from VoC input. And here’s some good news: Conversational platforms can give you access to VoC insights for in-depth analysis. Every 1:1 conversation between a customer and a brand advocate can be part of our sentiment analysis pool. 

Keep in mind, however, that you’ll need human sentiment analysts to unlock all the nuances and emotions contained in your customer conversations. 

If your brand is looking to move from metrics-only analysis to a human-centered approach, you’re in the right place. We’ll be giving you a quick overview of the critical metrics many retailers use to ensure you’re on the right track. Also, we’ll explain the fundamentals of sentiment analysis and tell you how you can extract intelligence from your open-ended VoC feedback.

6 Retail Metrics You Need to Know

There is no one-size-fits-all metrics approach for businesses–but there is a suite of popular metrics that you need to know. We’ve selected six of the most trusted metrics used by retailers today. You’ll likely want to use some or all of these as part of a measurement program. 

But keep in mind that they’re a quantitative snapshot of financial and customer trends. You need authentic, qualitative customer insight to understand how to deliver the best customer experiences. 

Financial Metrics

Tracking financial metrics is a way of life for every successful business. For retailers, specific sales and inventory related are essential to monitor. We’ve identified three of the most important ones that should be on the radar for every retail brand.

Sales Turnover

In business, sales turnover is the number of goods, services, or ideas sold in a specific time frame–typically one year. Most often, companies express sales turnover in monetary terms. For example, if your total goods sold in one year is $1.2 million, your monthly sales turnover is $100,000 ($1,200,000 / 12 = $100,000).

Retailers also need to consider inventory turnover. In short, inventory turnover is a ratio between cost of goods sold (COGS) and average inventory. If you’re COGS was $500,000, and your average inventory was $250,000 in one year, your inventory turnover is two (2). And that’s a good number! It means you turned your inventory over completely two times in one year. 

But if you’re COGS was $200,000, and your average inventory was $800,000, your inventory turnover is 0.4. That’s not-so-good. You’re holding on to too much stock and not making enough sales.

So what’s a good inventory turnover number? Anything between two (2) and four (4) is strong for most retailers. No surprise: Industry giants have higher turnovers.  Walmart and Amazon achieved turnover around eight (8) and Costco reached 11.2 in one year, according to Retail TouchPoints.

Conversion Rate

Conversion rate is the number of visitors to a store or website that make a purchase. For retailers, the conversion rate calculation is relatively straightforward. Simply divide the number of transactions made by the traffic during a specific period, then multiply by 100. So, if you’re website had 50 visitors in an hour, and you made five (5) sales, your conversion rate for that period is 10%.

It’s often helpful to take a deeper dive into your conversion rate analysis. For example, does your conversion rate change during busy periods? If it goes down, you may need to make some adjustments to serve customers better. 

You can also compare different conversion rates across various periods to understand trends. Does your conversion rate go up when you launch promotions or new products? If so, you may want to consider staging your launches to maximize your conversion rates.

Net Revenue

Net revenue is another valuable metric for retailers of all sizes. You calculate it by subtracting returns, sales allowances, or sales discounts from your total sales. 

Obviously, a return occurs when a customer returns a product. But what are the other two factors? Allowances are credits or rebates you provide to customers to compensate for a problem. And sales discounts are discounts you give to customers who use credit but pay their purchase off quickly. For example, you could give a customer 90 days to pay, but offer a 2% discount for paying within 30 days. 

Customer Experience Metrics

In the world of customer experience, three industry-standard metrics are in wide use today. All three of these metrics typically rely on customer input via post-interaction surveys. You may also see them on pop-ups on websites or apps.  

Net Promoter Score

One of the most popular CX metrics is the Net Promoter Score. Nearly two decades ago, Harvard Business Review heralded NPS as “the one number you need to grow.” NPS focuses on how willing a customer would be to refer friends or family to the brand after an interaction. 

Why does this matter? The theory behind NPS suggests that higher satisfaction leads customers to promote brands more–and that causes more satisfaction and new customer growth.

But there’s another reason for NPS’s widespread use. The NPS framework relies on a simple, one-question approach in surveys. In NPS surveys, you ask this one question:

“How likely is it that you will recommend this product to a friend or colleague?”

NPS scores above zero are good, but high-performing organizations score 50 or above. Although NPS has proven value, on its own, the metric doesn’t explain the “why” behind customers’ ratings. 

 

But here’s a bit of positive NPS news for retailers: Recent Forrester benchmarks found that digital retail had some of the highest average NPS scores in a cross-industry study. In fact, digital retailers and luxury automakers were at the top of the pack. Even so, scores averaged in the low 30s. That means many brands have work to do to get to the upper echelon of 50 and above.

Overall Customer Satisfaction (CSAT)

As its name applies, the Customer Satisfaction Score (CSAT) assesses customers’ satisfaction with a product or service.  Like NPS, CSAT uses a single survey question:

“How would you rate your overall satisfaction with the service or product you received?”

Respondents rate their experiences on a five-point scale. One (1) represents “Very Dissatisfied,” while five (5) means “Very Satisfied.” To calculate CSAT scores, companies compute the percentage of satisfied customers who provided a rating of four or five:

 Number of Satisfied Customers (Score of 4 or 5) / Total Number of Survey Respondents X 100 = CSAT Score

Naturally, higher CSAT percentages signal higher customer satisfaction. But while CSAT can gauge customers’ happiness with their last interaction, it doesn’t loyalty or customer’s feelings about the overall relationship.

 

Customer Effort Score

Today, customers prefer the convenience and easy interactions–especially online. And the  Customer Effort Score (CES) addresses those needs. CES also uses one question to collect customer insight:

To calculate CES, brands average all the individual scores they receive. If the average shows that most customers found interactions easy, you’re in a good place. Averages on the other end of the spectrum suggest that improvements are necessary to reduce customer pain points. Although CES directly addresses a hot-button issue for customers, the metric ties to interactions and doesn’t reveal much about customer motivations, needs, or loyalty.

Why Sentiment Analysis Is a Must for Every Retailer

Tracking metrics is critical, but it’s only one step in a comprehensive analytics approach. You need to gather VoC input from customers–and use it to shape decision making. And sentiment analysis is the key.

What is sentiment analysis? In brief, sentiment analysis is a technique that aims to discern the emotions and perceptions of a piece of text. In retail, sentiment analysis can assess a customer’s feelings about a brand. While the words customers use are important, other factors–such as grammar, punctuation, slang terms, abbreviations, and even emojis–play a role in determining sentiment.   

Fundamentals of Sentiment Analysis 

Sentiment analysis starts with a selection of open-ended comments or feedback. When using content from online conversations with customers, you may want to use a sample set of comments from a specific time. For example, in one project we completed for a Fortune 50 electronics brand, we examined 7,000 conversations that occurred in two months. 

At the most basic level, you can group customer comments into three broad categories: Positive, Negative, or Neutral:

This simplified analysis can help you glean some useful starting insights. If your overall sentiment trends positive and your NPS, CSAT, and or CES scores are strong, that’s a good sign. But a mismatch between your CX metrics and sentiment lets you know something may be amiss. Your customers may be inflating scores on closed-ended surveys but expressing their frustrations in comments or conversations.

Another simple sentiment analysis technique is looking for repetitions of a specific keyword. For example, you could look for every customer comment that uses the word “service.” Then you could assess whether comments about “service” trend positive or negative. This straightforward approach yields some insights but is ultimately limiting.

Why? The human language is complicated–and people can talk about the concept of “service” using many different words. As an article from Destination CRM explains, this reality is one of the pitfalls of machine-only sentiment analysis

Since people use a wide variety of language to express the same ideas, the real challenge is to classify all variations correctly. The use of sarcasm, for instance, can pose a linguistic problem for machine-based analysis. Even positive words can be misleading when one or more has negative connotations; take the typical British “bloody excellent” as an example.

Polite expressions can also be challenging because they can hide a negative impression under neutral words. A phrase such as “not bad” should be treated in context, as it is somewhat positive despite both words conveying negativity.

Performing In-Depth Sentiment Analysis

Several techniques allow brands to take sentiment analysis to a deeper level. These approaches use models to group sentiments into different emotional categories.

We’ve used a model called the Plutchik Wheel of Emotion in our sentiment analysis work. Developed by a psychological researcher, this model includes eight core human emotions–Anticipation, Joy, Trust, Surprise, Fear, Sadness, Disgust, and Anger. Plutchik also identified other emotions that vary in intensity from the eight core emotions. And other emotions are combinations of the eight primary ones. For example, “Love” is a combination of “Joy” and “Trust.”

CX experts have devised their own approaches, based on common emotions expressed in customer feedback. Thought leader Bruce Temkin devised the “Five A” model. Also, he related each emotion to customer behaviors:

 

Another CX guru, Colin Shaw, applies the “Net Emotional Value” model. His model focuses on determining the balance between positive and negative emotions expressed by customers:

 

Using Sentiment Analysis to Make Sense of Metrics

As you can see, traditional metrics and sentiment analysis are a powerful combination. Metrics can give you a snapshot of what happened, but they won’t tell you why. In fact, most CX metrics, such as NPS and CSAT, are known as “directional metrics.” They inform you about something that already happened. Specifically, they let you know if your company is getting better or worse at serving your customers.

Yes, raw CX metrics can give you some awareness of customer sentiment. But the truth is not all detractors bash organizations after bad experiences. And promoters may be happy overall, but can still point out areas for improvement.

With sentiment analysis, you can get a more accurate understanding of customer sentiment. In particular, you can find out what troubles your customers most and take action to fix it. For one of our customers, we uncovered that customers were having a hard time finding information before purchases. A simple restructuring of the company’s FAQ page made a huge difference in the customer experience–and was a quick and easy fix for the brand.

Sometimes, little changes can make big impacts on customer perceptions. Other times, you need to focus on larger and more complicated issues. But with sentiment analysis, you can prioritize activities that have the most value for customers. You won’t be taking steps and hoping for the best. Instead, you’ll have clear insight on the right way forward.

From Conversation to Action

Corporate financial and metrics programs are commonplace. And most companies have taken some initial steps to solicit open-ended insight from customers. This focus on analytics is a step in the right direction, for certain.

Unfortunately, Forrester has found that most VoC programs are “immature.” Brands may be collecting customer commentary and performing some rudimentary analysis. But few are taking strategic action based on the perspectives and feedback customers share.

For your brand, this widespread struggle for VoC excellence can be an opportunity. With a conversational platform, you gain an automatic source of VoC sentiment. Every conversation gives you valuable information on how customers think and feel about your brand. Using sentiment analysis and looking for themes and trends is vital. Instead of relying on raw numbers and guesswork to refine your CX program, you can use genuine customer questions and comments.

The smartest brands use a mix of quantitative and qualitative approaches to understand customers. Yes, metrics–such as the six we outlined here–will never go away. But the new era of customer experience and conversation demands a new way.  Combining metrics with sentiment analysis will unlock new pathways to customer-centricity and service excellence.

Read more: Find out more about iAdvize’s COVID-19 Support Package which helps businesses maintain their activity during the on-going response and market shifts.

Cover Photo by Becca Tapert on Unsplash

Fritz Lauer
US Marketing Director | f.lauer@iadvize.com

Digital Marketer with a passion for MarTec. Finding the human element and the technology to scale it has always been the name of the game.

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