Hyper-Personalization: Technological Advancement in Data Analytics taking Customer Experience to Next Level
Customer Experience is no more dependent on the brand and name, especially in B2B businesses as customers are relying more on user-generated content, reviews, and the face of marketing. Today’s marketplace is constantly changing, and organizations are adopting the power of analytics and AI to make the changes they need to survive and thrive and can create new levels of customer-centric information and actions (like omnichannel bots).
Hyper-Personalization is all about data but sticking to excessive information of customers can harm the customer experience. Hence, sorted, relevant, and timely engagement of data is very significant in defining how companies use this strategy in enhancing customer experience.
According to a study conducted by McKinsey on 1000 North American consumers, 87% of respondents said that they would not engage with the company if it gave away sensitive data without permission. So how companies handle data becomes a point of differentiation and a source of competitive business advantage.
Amazon and Starbucks are using hyper-personalized messages for their customers based on their experience, activities, and past purchases, and its app interface is personalized for each individual user.
Top 5 Untapped Hyper-Personalization Trends for Next Level Digital Interaction
The benefits provided by AI and data analytics in collecting real-time data propel the growth of hyper-personalization. The following technologies will be good choices for improving hyper-personalization:
1.Leveraging Data Technology (Data Platforms): Hyper-Personalization uses AI for collecting data, relevant content, and monitoring their behavior. The optimization of data is a necessary step for personalizing any product that runs up the resource capability for data analytics for effective campaigns. The technology tools and platforms are used to build custom products.
The DMP (Data Management Platforms): Collect, organize and provide data in the first stage of customers’ journey i.e., landing page, websites, or apps.
CRM (Customer Relationship Management) & CDP (Customer Data Platforms): Helps to manage the customer profile.
MO (Marketing Orchestration): Utilizes the AI functions to determine the next best action to be taken by the customer by studying the behavior or browsing habits and history.
2. Conversational AI (Conversational Commerce-bots): The set of all technologies that automate the messaging and speech-enabled applications to provide human-like interaction between computer and human is conversational AI. Conversational AI uses ML and Deep Neural Network to implement corrections and learn from experiences to deliver a better response in future interactions.
Insider Intelligence estimates that up to 73% of healthcare admin tasks could be automated by AI, and the adoption of chatbots could save the healthcare, banking, and retail sectors $11 billion annually by 2023.
3. Continuous Testing: While focusing on demographics, companies must also apply split testing (A/B Testing) for finding out the most effective one, while preparing a marketing strategy. Especially about email marketing, they must find out what combination of headlines, images, subject line, etc. works best for them.
Based on the result they can determine whether they need to stick to the same strategy or need to switch.
Benefits of Hyper-Personalization in B2B
B2C is already a step ahead in terms of personalization tactics and now is the time that B2B businesses can leverage the advantage by incorporating the right technology and with the correct organizational structure. AI and ML have made the task easier not only in terms of data collection and organization but also reduces the time required to make strategy and help in lead generation as well.
- Foresee User Action: By making use of AI algorithms, the best upcoming action or customer decision is easily predictable.
- Connects with Users at Every Stage: Comparing such a huge amount of data helps companies to access these data at different stages differently. This determines how prospects would engage with them at different nodes and how they would react.
- Remarketing: Remarketing is done based on the customers’ action i.e., what they do before closing the window. So judging what kind of actions they take, marketers can change the set of communication they take at different stages.
Hyper-Personalization involves analyzing customer data and behavior and showing potential customers that you care for them personally and recognize their unique needs and pain points. Doing this, the possibility of getting better audience engagement and higher conversion rates.
Technological Leap towards Hyper-Personalization is Imperative
Hyper-Personalization can lead to much better business results through higher conversions and using the correct technical tools will unleash the maximum potential. It is better to start with a good framework that will allow you to customize your online feeds and website content.
To make technological leap forward, marketing and IT must come together and must update the marketing technology roadmap, develop use cases, monitor the effectiveness, and compile a powerful library of standards and lessons learned. Centralized Customer Data (CDP) for combining private and paid data across channels is essential to identify customer behavior patterns and trends, and analytics. ML Automation can cleanse internal and external data, connect a customer to devices, cookies, and ad networks, and enable real-time campaigns to be executed on touchpoints and systems.
Engineering correct technology provides the company with the capabilities they need, capable of keeping pace with the expansion of personalized experiences. to more read the whole blog.
