In eCommerce, customers call the shots. They can swiftly compare prices across multiple stores and choose to vote with their wallets.
This is exactly why the world's most successful businesses place such a high premium on customer satisfaction. And, there is only one thing that will assist you in doing it right – customer data.
In the increasingly cutthroat world of e-retail, the key driver of success is centered on building a deeper understanding of consumer behavior or measuring and analyzing customer interactions. In essence, this is what data analytics is all about.
“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – Angela Ahrendts, Former Senior Vice President of Retail, Apple Inc
Still skeptical that data analytics is the best arsenal since the inception of online shopping?
In this article, we will walk you through 4 ways your customer support team can leverage data to take customer satisfaction — and sales — up a notch.
- Hyper personalized Marketing
- Personalized Shopping Experience
- Data-Driven Product Development
- Sophisticated Customer Support
- Streamlined Delivery
Customer Satisfaction Analytics: Technology Is Just An Enabler
Companies are building data analytics capabilities to sift through the goldmine of consumer data that will help them cater better to customers.
But what exactly does Data Analytics entail? Is it just about the fancy tools and dashboards?
The answer is a no.
Before the advent of the Internet, businesses would take resort to surveys to determine what products customers would be interested in buying.
Fast forward to today, businesses are now able to predict consumer behavior by analyzing past purchases, search history, or even social media profiles, thanks to data analytics. For example, if a person searches for animal-related items such as fish or birds, it may lead to the purchase of pet supplies.
But, here’s the thing: Simply understanding customer preferences or shifts in their behavior is not enough. You have to act on it.
In other words, you have to make data-driven decisions. The goal is to turn data into information and information into insight.
“Data is what you need to do Analytics. Information is what you need to do Business”- John Owen, a theologian.
However, businesses often get overwhelmed by the sheer volume of data they must collect.
To put it in perspective, modern-day customers come in various shapes and sizes - everyone has a unique set of desires and demands. A group of consumers may be interested in the product features, while another group may be drawn to the business because of the excellent customer service.
And as a business owner, you cannot trade off one for the other.
In the next section, we highlight the key metrics that will help you depict the bigger picture.
Data (And Metrics) Is Only As Good As The Questions You Ask
Asking the right questions is the precondition of selecting your metrics.
Regardless of how sophisticated your IT is, your data will not deliver meaningful insights unless you ask the relevant questions.
Speaking of customer satisfaction, there is tons of information available online that only serves to complicate the task of evaluating customer satisfaction. But, all you need is to focus on three primary questions.
- Are customer expectations being met?
- Was the shopping experience convenient?
- Will customers recommend you?
If the problem lies outside the questions you ask, then you will have incomplete data that will count for nothing. Keep your measurement parameters open and you’ll have a much greater variety and depth of information to understand your consumer base.
Now, let’s take a look at the metrics that are essential to gauge customer satisfaction in certain stages of the customer lifecycle.
Gaining all the necessary information and being delighted with your brand's customer experience, the prospect makes a purchase, officially converting into your customer.
However, customers may ditch their cart halfway through. So the metric to look out for in this stage would be cart abandonment rate.
There is a slew of reasons why your shopping cart abandonment rate can be high - and customers can be dissatisfied. However, determining the actual reason is not a piece of cake.
Fortunately, data analytics can help you battle shopping cart abandonment. You will know exactly where users are dropping off on the checkout funnel. For example, if a checkout page takes too long to load and shopping is dropped as a result, it will be identified straight away.
A satisfied customer who you have wowed often turns into a loyal customer And what does a loyal customer mean?
A returning one, that adds to higher customer retention and higher customer lifetime value (LTV). There you have it – the impact of satisfied customers on customer retention.
That being said, how can you evaluate your odds of keeping customers - in other words, measure your retention rate?
Well, simply pay attention to how your customer feels about your product and brand as a whole. Carry out customer service surveys and measure your Customer Satisfaction Score (CSAT). The best way to get a CSAT measurement is to ask the customer how happy they are with your product right after an interaction.
Using their inputs, you can continuously make improvements to your products and services, as well as the customer experience. This also raises the likelihood of a consumer returning and making repeat purchases.
At this stage, customers not only love your products, but they also rave about them to their friends and family. Being aware of the metrics and acting accordingly is what makes the difference between an average eCommerce business and a successful one.
The central metric of Advocacy is the Net Promoter Score - how willing your customers are to recommend your products or services to others. Based on their ratings, you can categorize customers into three groups:
- Promoters: Most loyal customers
- Passives: Customers that are satisfied with your brand but not happy enough to be your brand ambassadors
- Detractors: Dissatisfied customers
To convert passives and detractors into promoters, ask your promoters to name one reason they are so satisfied. Additionally, passives can provide you with excellent feedback on what you can improve as well.
Following that, using text analytics, determine the most frequently used keywords in your NPS survey. Once you've identified the events that contribute to customer satisfaction, focus your efforts on recreating those experiences to increase promoters and growth.
Now that we’ve covered the eCommerce satisfaction indicators, it’s time to move on to the application side of things.
Improving Customer Satisfaction With Data Analytics
Hyper personalized Marketing
It only takes customers a couple of seconds to decide whether they like your marketing message. Provide something relevant and you’ve got a satisfied customer. Miss the mark, however, and they’re gone.
Previously limited to targeted offerings, personalization now encompasses the entire consumer experience. This means that customers expect personalization throughout their interactions with a store.
And hyper-personalized marketing is a big part of it — serving customers the right messages and offers based on their actual behavior at the right time on the right channel.
Over time, Amazon has shaped its customization efforts through the use of advanced analytics. The brand now shows customers related products when they are shopping for an item, suggests bundles of related products when the customer is checking out, and so on.
Naked Wines welcome new visitors with an entirely different home page and navigation menu. This level of personalization is geared to onboard them as customers and members of their wine club.
Personalized social media campaigns are also a great way to boost customer satisfaction. KLM, the Dutch Airline is popular for its campaign, in which they surprised passengers with personalized gifts by analyzing the data on their social media.
According to McKinsey & Company, successful personalization programs yield 20% higher customer-satisfaction rates, a 10-15% boost in sales conversion rates, and an increase in employee engagement of 20-30%.
Here are 2 steps that will guide your way to successful digital personalization.
- Use behavioral data to find where the value lies
The core of personalization is acting on behavioral data. The first step is to group clients with similar habits and needs. For example, mothers who solely shop a brand for their children.
The next task is to understand the customer journey for each segment. Marketers can do this by gathering data such as frequency of site visits, most visited category pages, the channels customers are most active on, their purchase history, their location, and so on.
With platforms like MyAlice, you can effortlessly manage all your customers' data - contact details, demographics data, and purchase history, from one dashboard. And the best part is you can try MyAlice for free. Try signing up and see what it has to offer!
- Plan in advance to react quickly to customer signals
Personalized marketing is a two-way street in which the customer sends signals—information about his or her needs and intentions—via behaviors such as purchases, web browsing, and social media posts. The organization responds to the signal by sending a timely message - a trigger - to each consumer.
You can create a library of trigger messages- images, offers, and titles that correspond to specific signals. These sorts of personalized triggers are 3x more effective than blast messages.
A study by MarketingDive revealed that consumers are 40% more likely to consider products that are suggested based on data they've shared with a brand. Thus, providing a personalized experience is a big part of satisfying customers.
Personalized shopping experience
Amazon continues to raise the personalization bar with innovative offerings to individual customers. For example, Amazon Prime Wardrobe has recently launched a personal shopping service exclusively for Prime members.
Customers complete a survey about their styles and fit preferences, and a team of stylists provides personalized recommendations from more than half a million items. Small and medium e-retailers are also attempting to keep up with the personalization trend.
Shopify surveyed buyers leading up to Black Friday and found that 59% welcomed personalized product recommendations while 78% of millennials have an even greater appetite for it.
The most effective recommendations allow shoppers to jump to associated complementary categories. For example, when a shopper on Bandier’s site views a tank top, they’re also shown recommendations of matching shoes and pants.
You can also create personalized bestseller lists to drive click-throughs. LeSportsac is a striking example in this regard.
For many years, the company's main selling point was its minimalistic "deluxe" items. However, Google Analytics data revealed that a significant percentage of their traffic comes from Hong Kong, where customers prefer bolder designs.
Resultantly, they introduced Trending In Your Area, exposing their Hong Kong clients to a wholly unique collection of products:
Data-driven product development
Nike has taken personalization all the way to the individual product by allowing customers to configure their own clothes and shoes. The company recently launched a 3-D sneaker-customization platform that allows shoppers to generate real-time, shareable snapshots of finished footwear.
“Today’s consumers do not buy just products or services — more and more, their purchase decisions revolve around buying into an idea and an experience.”- McKinsey
On a similar note, the swimwear brand Kaikini realized that swimwear fit can be as tricky as bras and introduced a fit quiz to help customers select the best size or style.
In fact, the brand took the personalized experience one step further by adding custom-made swimwear to its lineup. On the product page, customers can build suits by plugging in their own measurements and preferences (like more or less coverage).
The Kano Model can really come in handy when you’re trying to develop new product features. The goal is to load up on features that bring excitement, performance, or meet basic expectations.
But, how do you actually know the amount of delight that individual features will bring to customers? This is where you need real-time intent data. Intent data will provide insight into subtle clues about what’s important to your customers right now and in the coming days.
In the case of Kaikini, the fit quiz is the Must-Be feature while custom-made swimwear is an Attractive feature.
Sophisticated Customer Support
One industry that hasn’t typically been at the forefront of the data revolution is customer support. However, with many businesses now realizing that support isn’t always a cost center, the demand for actionable data has been on the rise.
So, how exactly can you leverage support data for improved customer satisfaction?
Well, over the years, AI-powered chatbots have emerged as an efficient way to streamline customer service conversations. AI bots can automatically add to a customer profile by progressively seeking more information during customer interactions.
- Collecting Customer Feedback: Chatbots help boost customer satisfaction by getting feedback through polls and surveys. You may, of course, use email and social media to solicit responses to survey questions.
But chatbots are more likely to succeed in this regard. Because of the chatbot's conversational form, respondent fatigue is less likely to occur.
- Creating personalized experiences: Chatbots are extremely effective in personalization too. You may have the assumption that this is the only type of communication that chatbots are good for:
"Welcome to Swag Kicks. How can we help you?"
But that’s not true. A chatbot is capable of this too:
“Hi Raphael, welcome back! Did you get a chance to wear the shoes you purchased last week? We’re sure they looked great on you. What brings you here today?”
Modern chatbots are thus capable of understanding emotions and other facets of human communication. A well-trained chatbot can learn from previous conversations and purchase data and improve the quality of its responses.
Businesses can use data analytics to improve the customer experience all the way up to delivery day. Predictive analytics helps merchants and their shipping partners assure on-time delivery as more customers seek next-day and same-day deliveries.
In 2013, Amazon came up with ‘Anticipatory Shipping’ - a predictive analytics model that would predict your purchase and send the product to your nearest warehouse. Then, Amazon would wait until the actual purchase. Amazon believed that this would drastically cut the delay from the time of purchase to the actual delivery and boost the overall company sales.
Thanks to Amazon, customers have also grown to expect real-time delivery updates. According to My Customer, 82% of consumers say that it’s important for them to receive updates during every stage of the fulfillment and delivery process.
Order tracking thus helps to alleviate anxiety by giving customers an ongoing touchpoint with the retailer. It helps to boost reassurance and satisfaction.
Customer Satisfaction Will Always Hold The Leash
In the competitive world of eCommerce, exceeding customer expectations is crucial for standing out in the market. And without the use of analytics, this is nothing more than a far-fetched dream.
With the right data and the right strategies, you can learn more about your customers, how they experience your brand and what improvements you can make. Make the required changes and continue monitoring the metrics. As you keep measuring and taking action, your customer satisfaction will continually improve.