In this article, we'll explore the importance of personalization in customer retention strategies and highlight some of the key tools and techniques businesses can use to create customized experiences for their customers.
In today's crowded marketplace, it goes without saying that customers have more choices than ever before. To stand out and build lasting relationships with customers, businesses need to go above and beyond to personalize their interactions. From tailored recommendations to personalized emails and offers, personalization can help your hotel, restaurant, or retail store to create meaningful connections with your customers and keep them coming back for more.
For more than 10 years, there's been a saying that “data is the new oil”. On a topic like personalization, data is definitely the key to unlock that so-called “new oil”. And still, it’s often a challenge for business leaders to start thinking data-driven which then often leads to poor results. Moreover, to collect and centralize data requires a vision and understanding of customer’s data, in which channels it can be collected, where not and ultimately, what can be done with it. Some business teams lack inspiration and creativity to come up with powerful cases to collect that precious data. Therefore one of the key elements in this article will be to highlight some examples where data is used in the context of the relationship with the customer, and where these examples also expose the need for relevant customer data.
Kicking off with an example in retail, through targeted email campaigns. For instance, a clothing retailer may segment their email list based on past purchases and browsing history to send targeted promotions and product recommendations to each segment. Customers who have purchased dresses in the past may receive an email with new dress styles, while customers who have purchased shoes may receive recommendations for new shoe arrivals. This targeted approach can increase the likelihood that customers will engage with the brand and make a purchase.
The Sayl platform provides multiple integrations with different channels to collect data in a central place and then make it available to start such recommendation process.
When we look to the hospitality industry, and hotels in particular, we can easily come up with other examples of personalisation which data unlocks.
A hotel can personalize the guest experience to provide customized recommendations for activities and amenities during the guest's stay. For example, a hotel may use information about a guest's past stays, their interests, and their demographics to suggest nearby attractions, restaurants, and events that align with their preferences. Another idea in terms of 'going the next level': the hotel may provide customized amenities in the guest's room, such as a particular type of pillow or a complimentary bottle of wine based on the guest's preferences. By creating a personalized experience for each guest, the hotel can build a stronger relationship with the customer and increase the likelihood that they will return for future stays.
For a restaurant that wants to work with customer data, instead of the past stays of a guest, they must collect and analyze customers' past orders, and then provide a personalized recommendation based on detected food preferences. For example, if a customer has ordered vegetarian dishes in the past, the restaurant chain could suggest new vegetarian items on the menu that they might enjoy.
Getting back to hotels, another way hotels can personalize the guest experience is by offering a loyalty program that rewards customers for their stays. For example, a hotel may offer personalized discounts and special offers to members of their loyalty program based on their past bookings, preferences, and purchase history. By providing exclusive benefits and discounts to loyal customers, the hotel can create a sense of exclusivity and personalization that incentivizes guests to keep coming back.
There are many different ways businesses can personalize their interactions with customers. One of the most common is through targeted recommendations based on a customer's past purchases or browsing history. By analyzing customer data and even by using machine learning algorithms, businesses can identify patterns and preferences that can inform their recommendations and increase the likelihood that a customer will make a purchase. We expect a lot of evolutions in tools, certainly when we see what solutions such as ChatGPT brings to the table.