The development of smartphone and computer technologies, and the internet in general, have influenced customers’ default behavior and expectations when it comes to customer experience. More and more shopping is moving online, which is a very different environment, especially when interacting with users and maintaining relationships.
The pandemic accelerated the move to online shopping, which forced people to stay home. As a result, 2020 was an excellent year for online retailers in terms of growth, yet these companies acknowledge that their platforms need improvement.
One thing is sure: once people get accustomed to online shopping, they prefer to keep doing all their shopping online and rarely go back to physical retailers, except for the quick-shop.
The move to online shopping allows companies with a strong online presence to collect better and analyze data. In addition, it’s easier to track user behavior when the whole process takes place online. Still, companies need to ensure the data collection process stays within ethical boundaries and doesn’t intrude on users’ privacy.
Large and small companies, can get the help of predictive analytics firms to plan the strategy for collecting and analyzing the data. But, most importantly, professionals from these firms can help traditional businesses set up their digital presence and get the most benefits for their efforts.
Insights gained from analyzing the data can be used for many different purposes. For example, companies trying to improve customer satisfaction can use the data to provide their customers with personalized shopping experiences.
This includes valuable product recommendations or suggestions to help them find what they’re looking for.
As we previously mentioned, an increasing number of customers shop online and have products delivered to their door. Sometimes they buy online but actually pick up their purchase at the store.
To keep users engaged, ecommerce stores should provide users with basic features, such as: easy access to information, high-quality pictures, detailed descriptions. However, these features are very basic and automatically expected of any online website.
These days, customers doing business online expect to receive a personalized experience. To provide this, the companies should analyze their past actions and provide them with product or service recommendations, shortcuts, and other tools to help them find what they’re looking for.
In an ideal scenario, every user visiting online retailers should see a different version of the homepage. Based on their past interactions with the website, each user should see the products they are most likely interested in and the information about promotions and offers that might get them to purchase.
Having a solid foundation of data collection and analysis is a good start, but not enough. Companies can use predictive analytics to analyze the tastes and behavior of new and existing customers and give them exactly what they’re looking for.
To provide an excellent customer experience, companies must figure out what the users want and make it easily accessible. Let’s continue with the example of an online store.
Using predictive analytics can help you increase sales by analyzing historical data to anticipate what kind of products the users might like.
If you can successfully understand their needs and satisfy them, the customers will reward you with their business and continued loyalty.
For example, if you know that the customer is interested in reading the reviews of a product, you should provide access to that information and make it easily accessible.
This information might push them to decide and make a purchase or at least gain a better understanding of the product. For example, if you know that a certain specific subset of customers appreciates price reductions, see if you can offer promotions to convert their sales.
Today, most companies provide a personalized experience based on previously collected data about users and their behavior. However, that’s just the first step.
If you want to provide a truly exceptional customer experience, then you need to go one step further and use the existing patterns to predict what other users might want in the future. For instance, imagine the scenario where you don’t have much data collected about their preferences.
However, you will have information, such as what web page, search engine, or ad they are coming from, and what type of products they were looking for before signing up.
Despite not having any information about this user, you can use this small information to predict this user’s behavior and preferences based on the actions of other users that display similar patterns.
Anticipating customers’ needs effectively retains their loyalty because every one of the customers values a different part of the experience. Therefore, knowing their preferences, you should provide them with the most value at minimal cost to you.
If you can guess what the customers want and give it to them even before they realize it, you can ‘Wow’ them and make a memorable impression. This goes a long way and allows you to turn customers into ambassadors of your brand.
User Experience is the term used to describe the overall design and user-friendliness of the website. UX professionals try out many different versions of the website and keep the most intuitive ones for the users. This includes using the appropriate symbols and language to clearly communicate the intended use of every button or other element of the user interface.
Familiarity is one of the strongest emotions that keep users returning to the same websites.
During the last decade, all companies have realized and accepted the importance of UX for keeping loyal customers and are investing more and more resources in developing user interfaces that are easy to figure out.
The developments mean that it has never been more critical for brands and businesses to maintain their online presence during the last few years.
The competition between companies trying to sell their products through digital channels is becoming increasingly intense.
Different ecommerce stores, service providers, and even manufacturers are trying to maximize their revenue by improving the personalization of user experience or establishing themselves in small, specific niche categories. This way, they can effectively serve the customers and deliver maximum value.
The most prominent players in the industry, such as Amazon, are already using AI and ML to offer a top-notch personalized experience, so at this point, users have become accustomed to it.
As a business, you can take your customer experience efforts to the next level by using predictive analytics to anticipate the behavior of existing and future customers.
The brilliance and utility of predictive analytics are that customers often don’t know what they want.
Collecting data to analyze their browsing patterns allows the brands to gain insights into their needs and create products, information, content, and services to properly address these needs.
Users sometimes have expectations they are unaware of, so predictive analytics can help you manage these expectations.
Predictive analytics is concerned with more than just analysis of existing data to provide a personalized experience to returning customers. Predictive analytics firms can help you figure out the type of data you need to make predictions about brand new users.
More importantly, companies can use predictive analytics to categorize every new customer into predefined segments. This is achieved using custom algorithms that match what little information is available about new users with the unique characteristics of each category.
In combination with these algorithms, predictive analytics solutions use AI and ML to predict the future behavior of their customers and fill in the blanks in their expected behaviors and preferences.
Advanced predictive analytics solutions can do much more than you might expect. For example, depending on the provider, companies can use predictive analytics solutions to analyze the engagement about their products on social media, including discussions, reactions, and more.
Predictive analytics to improve customer experience can improve your business’s revenues and profitability.
Keeping customers loyal
In the world of business, it’s common knowledge that retaining a loyal customer is cheaper than acquiring a new one. However, many costs are associated with getting new users, such as advertising costs.
User’s every interaction with your platform presents an opportunity to learn more about their habits, preferences, and behavior and to improve their experience after every use. This will keep them coming back as customers and maybe even become promoters of your product to friends and family.
Predict potential churn
Throughout the history of financial markets, many companies’ stocks have crashed due to alarmingly high customer churn. This means billions of lost market cap value, not to mention the losses in recurring revenue.
So keeping customer churn to a minimum is essential for any business to survive and thrive. For steady growth, businesses need to keep all their customers happy and prevent them from leaving.
Companies can use predictive analytics to detect the patterns that lead up to customers leaving and take measures to change their minds. This way, companies could save billions of dollars simply by using predictive analytics.
It’s particularly useful for subscription-based services, which heavily rely on recurring revenues to operate successfully and succeed in the long run.
Improve customer experience offline
Brands can use the insights from predictive analytics to improve customer experience across multiple channels, even if it’s offline.
For instance, a retailer of digital products could gain insight into which features of refrigerators customers find particularly interesting. Most likely, all customers will be curious about the same features, regardless of whether the shopping takes place online or offline.
Businesses can use this information to train their salespeople and emphasize specific features in physical stores.
To implement a system for providing a perfect customer experience, businesses should first understand what their customers want and find a way to provide it.
Grouping customers based on their needs is the first step in this process. Creating broad categories to describe different types of customers with their other priorities allows you to establish connections between certain factors and use them to better understand the needs of brand new customers.
Customer grouping is possible by assigning new users to each of these categories and using general predictions of that sector to offer a personalized experience to new customers.
Before starting to collect data, every business needs to decide what type of data they need. One of the most essential criteria for dividing customers into different segments is the stimuli.
Some customers respond to sales, price reductions, while others are more concerned about limited-time offers and similar.
Once divided, this information allows you to personalize promotions, unique offerings, and similar stimuli to maximize sales based on individual customers’ preferences.
Creating separate segments for every different type of customer may be time-consuming, but luckily the advances in AI allow you to generate them automatically.
Next, you can use the same AI algorithms and existing information about new customers to categorize them and use the same underlying principles to predict their behavior.
With more and more customers are doing their shopping online, companies will have to excel at customer experience to stay competitive.
Using predictive analytics can minimize costs, increase revenue and help companies maintain healthy profit margins.
Predictive analytics firms can help you implement state-of-the-art systems for collecting and analyzing the data to gain actionable insights to improve customer experience.
These solutions heavily rely on other technologies, such as AI and ML. Fortunately, many scientists are working to perfect the underlying algorithms that make it possible to do predictive analysis of customer behavior.
The developments in these technologies will also improve the quality of customer experience.
Image Credit: Tim Douglas; Pexels; Thank you!
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