We all love Sephora, don’t we? But its not just because of the amazing quality product that it has. It is also because of the love for the brand and how it treats its customers. So, what does Sephora do to keep its users engaged?
One of its most interesting assets is the app that keeps users involved with the brand. Using the app, you cannot only get mini makeovers, but also personalized recommendations, location-based notifications and on top of its beauty guides. So, what is the secret behind Sephora’s mind-blowing marketing efforts? Well, you guessed it right. Its data!
Data plays an important role today in marketing and branding as marketing in this era is a lot more than just giving your customers the right product; it is about creating customer experiences.
Let’s see how data today helps marketers build a relationship with their customers.
Psychology says that calling someone by their name shows affection as names are very personal and makes people feel important. This is probably why you may have seen various emails in your inbox that start with ‘Hey X, check out your freebie.’ This is just a small example of personalization.
However, personalization today is a lot more than just sending an email with the customers’ name; it is about meeting the customer at every touchpoint and showing that the brand knows what the customer wants. If you visit some fashion website for example, and browse through the products, you will notice that you will start seeing ads for the products you liked on different social media channels. This is how personalization or targeted marketing works.
Personalization is extremely important today given the digital clutter and short attention spans. According to research, a mere sixty seconds on the internet translates to astounding figures: around $283,000 spent on Amazon purchases, a staggering 167 million videos consumed on TikTok, and an impressive 65,000 images exchanged on Instagram. Now imagine getting attention of your customers through this noise. Seem impossible, which is why it is important to build a relationship with customers.
The Role of Data in Personalization
To understand how data helps, just try to remember the number of apps you interact with each day. It is probably, Facebook, Insta, Snapchat, Tiktok, Twitter and maybe Threads too to name a few. These apps gather your data every day; every reel you like, engage or comment on, every story you post. You could say these apps know you better than anyone perhaps.
What marketers do is collect all of this data, combine it using sophisticated ETL tools and then analyze it to find out patterns and your likes and dislikes. Some of the data that marketers collect includes:
- Demographic Data: Demographics include characteristics such as age, gender, location, income, and education level. These data points provide a basic understanding of a customer’s background and can help tailor marketing messages to specific groups.
- Behavioral Data: Behavioral data tracks customer actions and interactions with a brand. This can include browsing history, purchase behavior, click-through rates, and website engagement. This data allows marketers to predict future actions and recommend products or services that align with your interests.
- Psychographic Data: Psychographic data delves into the psychological aspects of consumer behavior, including their values, beliefs, hobbies, interests, and lifestyle choices. Understanding psychographics helps marketers create content and campaigns that resonate on a deeper level with their target audience.
Once they collect all data, then begins the magic of targeted marketing.
Crafting Tailored Campaigns
One of the great examples of targeted marketing or personalization is that of Netflix. It is amazing how Netflix knows what shows we will like. When Netflix says it’s a 98% match it usually is. Similarly, Amazon knows what kind of products you would like and recommends accordingly, and we can go on. But goes on behind the scenes? These companies analyze your data at a dizzying rate.
Netflix, for example, uses collaborative filtering to find patterns and relationships in user behaviour. If users with similar viewing histories have watched a particular show or movie, the system might recommend it to you as well.
It also uses uses a machine learning model that predicts how much you will enjoy a particular title. This machine learning model considers factors like your viewing history, ratings, and how much time you spend watching certain types of content. Interesting isn’t it.
Is there something as too much Personalization?
With the introduction of AI, personalization is becoming more exciting and only time will tell how these brands will leverage new technologies. However, one thing that they need to be wary of is consumer privacy and over personalization.
Marketers may often use an excessive amount of personal data to create highly customized content or recommendations, often without considering consumers’ privacy or comfort, which can result in a negative user experience, where individual feel their privacy is invaded.
So, it is important to draw a line that separates personalization with intruding.
Personalization has become a strategic imperative for brands that want to cut through the digital noise. Customers are more demanding than ever and personalization helps foster loyalty. However, it is important to strike a balance where it doesn’t feel too creepy.
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