Hyper-personalization is an extension of traditional personalization techniques used in marketing. For example, using a customer‘s name in an email or sending a coupon on their birthday. While traditional personalization only used limited customer data, hyper-personalization is based on a wide variety of provided and collected data.
This data often includes demographic, geographic, purchase history, search history, and real-time behavioral information. This data is combined to form individual customer profiles. Marketers can use these profiles to customize product recommendations, time engagement attempts, and adapt service to customer expectations.
In this article, you’ll learn how hyper-personalization works, how big data factors in, and how you can leverage big data and hyper-personalization.
When creating a hyper-personalization framework, you should follow these steps:
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Collect your data ”you need to consistently collect a wide variety of data on as many of your users as possible. The more data you have on an individual, the easier it is to create personalized content for them. You can collect this data from a variety of sources, including personal profiles and histories, website analytics, and third-party data collection agencies.
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Segment your customers ”while hyper-personalization doesn’t use segmentation as traditional methods do, it can still be helpful. Segmentation enables you to better visualize your various markets and customer needs. It enables you to quickly narrow down your best options for each customer and to increase the efficiency of your efforts. You can perform segmentation along a variety of axes, including satisfaction ratings, brand interaction history, and average spending.
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Create targeted journeys ”targeted journeys apply your hyper-personalizations to customer interactions. These journeys determine the communication channels you use, the timeliness of your communications, and the content that is presented. The closer your journey aligns with the expectations and needs of your customers, the more effective it is.
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Evaluate and refine ”once you have applied hyper-personalization to your efforts, you need to measure its effectiveness. Analytics tools need to be refined with feedback to produce optimal results. This refinement is done using real-world results and by making changes accordingly. When measuring these results, you need to take into account soft measures, such as reported satisfaction, and hard measures, such as revenue.
The Technology Behind Hyper-Personalization
Leveraging hyper-personalization requires the integration of a variety of different tools and platforms condensed into an analytics pipeline. These pipelines can be built on-premises or in the cloud, where data is typically already stored.
When building these pipelines try to incorporate as much automation as possible to increase efficiency. You should also take care to ensure that your pipelines and environments are secure to reduce the chance of costly data breaches.
Stages of a hyper-personalization pipeline include:
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Audience selection ”your target audience is defined and parameters are set to collect and filter data accordingly. You can choose a variety of parameters in this phase, including purchase history, spend behavior, customer lifetime value, or brand loyalty.
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Event definition and detection ”you need to decide which events or behaviors should trigger your personalizations. These triggers then need to be defined in your monitoring and event listening tools. Trigger events may include contract expiration, negative feedback, product returns, or new sign-ups.
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Messaging decisions ”you need to determine what messaging should be tied to each of your selected triggers. This messaging is built on templates that are then personalized with the appropriate details and content. For example, offering anniversary reminders, reminding about abandoned cart items, or simply thanking customers for their loyalty.
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Channel decisions ”before your message is sent, you also need to define the channels it is sent through. Different trigger and message combinations can use different channels or multiple channels. These decisions should be based on customer contact history, bandwidth or resource restrictions, and click through rates.
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Result analysis ”after hyper-personalized communications are created, you need to monitor and analyze customer response. This feedback is used to improve the accuracy of your machine learning algorithms and increase the effectiveness of your campaigns. These analyses can also provide a benchmark from which to base your budgets and provide insight for overall process changes.
Conclusion: The Importance of Hyper-Personalisation
According to a study by Accenture, 81% of consumers want brands to deliver a timely, personalized experience. When messaging is off-target, poorly timed, or generic, customers are less willing or likely to engage. This often means a poor customer experience and causes damage to customer relationships.
Hyper-personalization enables you to ensure that your messaging is on-target and appropriate. It can help ensure that your efforts are not wasted, reducing costs and increasing revenue and brand loyalty. In fact, according to a study by PWC, 42% of customers are even willing to pay more when they have a positive experience with a brand.
