Personalization
What it is and why it matters
Personalization uses data and analytics to tailor customer experiences to the individual. Using shopping history, demographic data and pattern recognition data, an individual’s experience can be modified to fit their unique preferences. Offering options that best fit their needs helps the customer to find what they want quickly, giving them more personalized experiences, ideally leading to happier customers and more sales.
History of Personalization
While you may think personalization is driven solely by technology, that’s far from true. Personalization has been around since the first shop owners.
For example, in the 1800s a man might walk into a cobbler and ask for a pair of shoes. The cobbler could look at his customer card to see what size the man wears, how much he usually spends, how much time he spends on his feet – and then make a new pair of shoes based on his previous customer data. Especially in smaller towns and villages, early retailers were likely to recognize their customers and could create exactly what the customer needed. This level of personalization became scarce after the Industrial Revolution when mass production largely replaced handmade items. As the early stages of the internet eventually came around, personalization was still not much of a concern and most marketing teams still handled each customer in virtually the same way.
The customer experience shifted when web-based companies like Amazon appeared. Web personalization first took off with Amazon due to its “customers who bought this item also purchased …” feature. And the age of the recommendation engine was born. In this more modern era, companies began grouping customer segments based on merchandise preferences that could be shown to other buyers in the same segment. Today, segmentation is not considered the purest form of personalization – but it allowed for early personalized experiences and sparked the continued growth of the concept.
Personalized customer experiences help generate growth and loyalty
Norway's largest telecom, media and tech provider has increased revenue during a period when most providers are struggling to capture customer value. See how Telenor uses data and AI in a hybrid cloud approach to succeed in staying relevant with its customers.
Personalization in Today’s World
Find out how personalization is used today
Balancing personalization and privacy
As technology advances and starts to collect more customer data, people have become increasingly wary of how it affects their privacy. Security breaches, government use of personal information and marketing communications that are a little too personal are making people more on edge about their personal information being shared. This makes it difficult for marketers to know the balance between how much personalization to use versus what would make someone feel their privacy was violated. As technology expands, companies need to show that they understand the customer and that they can protect their personal information.
Who’s using personalization?
We often think about personalization in retail, but it’s useful across practically every industry. The reason is that people are unique. There is no single mold for everyone and that's what makes personalization offerings so successful. Using customer data in conjunction with machine learning, marketing teams can consider peoples' differences and present them with what they want or need individually.
When I look at hype and innovation, I think of companies that are able to stand out by putting a smile on people's faces and really by providing what I call a frictionless customer journey. These are companies that are actually combining convenience, personalization and timing to really deliver and excel at bringing customer satisfaction. Steven Hofmans Customer Experience, Analytics and Marketing Adviser SAS
CNM Helps Nonprofits
Nonprofits were one of the many organizations significantly affected by COVID-19. However, personalization offerings allowed them to keep up with demand and further their efforts during the crisis. Their use of analytics allowed them to build back safer and more healthy communities. Watch this video to take a closer look into how nonprofits fought back during COVID.
How Personalization Works
Personalization is mainly accomplished today by using algorithms and machine learning. Algorithmic complexity varies from basic to advanced, but they all offer a degree of differentiation. Basic algorithms might present new products or bestsellers to a buyer. More advanced algorithms for personalization will be able to identify specific things about a customer and recommend similar items. For example, Netflix uses an algorithm that looks at the shows you watch in real time, and then recommends shows to you based on your viewing data. Decision trees are created to direct you to different paths to find more products related to your known interests.
In many ways, personalization is the modern equivalent of excellent customer service. Customers expect it and may even become annoyed when the sites they visit do not include personalization features. When looking at car insurance for a new driver, for example, an insurer who knows the ages of your children and what types of cars you drive can personalize an offer more quickly.
There are multiple approaches to personalization. Here are four ways personalization may be used in companies either separately or in conjunction with each other:
Contextualization
This form of personalization focuses on using factors (such as location or education level) to know more about a person’s perspective, and therefore, the context. This allows for content that not only the viewer would like to see, but also relates to their situation. For example, by knowing someone’s location, a company suggests the nearest store that has the shirt in stock that a viewer wants to buy. Contextualization allows for an easier way for customers to navigate through the valleys of information on the internet.
Hyperpersonalization
Hyperpersonalization is exactly what it sounds like. Machine learning is used to consider more customer data than personalization to achieve more helpful personalization offerings. It allows your website to act in real time and adapt its content as the customer moves through a web page.
Real-time interactions for the customer journey
Personalization can also extend beyond marketing and be an asset to customer-facing functions like sales, service and support. Sophisticated analytical decision engines can orchestrate two-way, interactive engagements between consumers and brands, allowing the customer needs to be met immediately. These next-best experiences in real time require customer data platforms, advanced analytics, machine learning, automated processes and integrations with operational systems to create an engaging customer experience.
Customer recognition programs
Customer recognition programs are rather popular nowadays. Brands have found that analyzing consumer behavior is one of the top five drivers for customer loyalty. As a result, many businesses implement features such as reward systems and loyalty programs. This serves as a benefit to both the customers and companies as the recognition programs make the customer feel excited and motivated to keep coming back and claim their rewards.
Next Steps
Check out the Reimagine Marketing podcast and learn more about how customer experience is advancing technologically.
Personalization Solutions
Customer experience is a key part of any business. With SAS, you can propel your business to the top by using the latest technology, machine learning and tactics to get to know your customer better. This allows for a high amount of personalization options for individuals and can better predict what a customer may want or need.