Customers don’t just want products and services anymore – they demand personalized experiences customized to fit their specific wants and needs. This trend has accelerated companies to be personalized on a very large scale using data-driven strategies – as well. Leveraging the insights gained from big data, companies can realize better relationships with their customers that result in more engagement and greater loyalty as well as increased revenue.
The Rise of Personalization
Personalization is a major competitive advantage in modern business. A McKinsey study found that 71% of consumers expect companies to offer a personalized experience and become annoyed if this is not the case (76%). These tailored experiences have been driven by technology and the data at businesses’ disposal.
Big Data: The Foundation of Personalization at Scale
Insight #1 – The Common Denominator Of True Personalization Is Big Data These are fundamentally immense volumes of existing non-structured and structured data business accumulates from diverse sources:
- Customer interactions
- Social media activity
- Purchase history
- Browsing behavior
- Demographic information
- IoT devices
Not only is the data expensive to collate, but it also presents a unique element of complexity in analyzing and using all this information effectively so that each experience remains personalized.
Key Technologies Enabling Personalization at Scale
Businesses are using the following technologies to turn raw data into business insights:
1. AI and MLLightningScripts MALighting Actions MA
These two types of algorithms ensure that massive data amounts can be processed to find patterns, estimate customer machine learning requires a ton of structure and make real-time decisions. Businesses can use these technologies to:
Recommend items purchased in the past or visited before
Tailor the content and offers to fit the preferences of individuals
Optimize your pricing strategy for each segment of customers
2. Data Analytics Platforms
The advanced analytics platforms enable businesses to bring together data from different sources and help businesses gain a 360-degree view of the customer. These platforms may signal:
Data visualization tools
Predictive analytics features
Real-time reporting features
3. 1 Customer Data Platforms (CDPs)
Customer Data Platforms store and transform large, omnichannel data inputs into a structured customer profile. DB Case studies – API Calls from Traffic Tech DB is a master data repository which enable businesses to deliver personalized experiences no matter where those insights occur.
Implementing Personalization at Scale: Best Practices
Technology cannot be separated from personalization at scale but efforts are wasted without a strategic approach. Best Practices to Follow
1. Start with a Clear Strategy
What are your personalization goals and how do they relate to the greater business? Discover what the most prominent areas are where personalization can make a difference in creating emotionally connected/hooked customers and high valuable business results.
2. Evaluate Data Quality and Integration First.
Keep accurate, up-to-date and well-integrated data in all of your systems. Bad data quality can lead to ineffective personalization, and worst yet, hurt your customer relationships.
3. Respect Customer Privacy
More myths Good news is no one can ever take your hard-won strategic advantages/capabilities, the other side of the coin being that they are really easy to ‘fake’, now these BAs amongst you will probably throw out something like process cycle time reduction or culture quicker than saucisse volantes – but proactively giving up such capabilities in a commercially advantageous way is again as difficult for many enterprise (second-order-benefit-destroying) reasons. But hey With significantly increased interest and noise with data about this subject [ahem] some things that should not have been forgotten were lost… minis….with increasing concerns around privacy it’s critical to be transparent about how you collect & use customer data(q’d ). Use appropriate security measures to protect data and enforce the users’ rights on their own personal information.
4. Test and Iterate
Keep iterating on your personalization strategies – and monitor how well they work. Iterate – refine your approach and achieve better results using A/B testing.
5. Balance Automation and Human-element
Automation is necessary to scale personalization efforts, but do not disregard the power of conversation. Leverage your AI-driven personalization and where human empathy and judgement can prescribed its way!
Real-World Examples of Personalization at Scale
Some companies have even achieved impressive results from personalization at scale:
Streaming giants like Netflix use machine learning (ML) algorithms to analyze customer viewing habits and predict what its customers will watch other content in the future, which is proven to boost user engagement and improve retention.
Amazon – Amazon uses its massive amount of customer data to deliver product recommendations in real-time, custom email campaigns like triggered emails, and dynamic pricing strategies.
Spotify Example: Spotify, a music streaming service, uses personalized playlists based on user listening history and inferred preferences to improve both discovery as well as overall experience.
The Future of Personalization
As technology grows, so too will personalization options. Some emerging trends include:
Real-time, hyper-personalization: Ultimately creating tailored experiences on a 1:1 level thanks to actual data.
Personalization based on voice and gesture: Further personalized interactions by voice, through natural language processing and vision-based hand-gesturing more intuitively.
AI empathy: Using emotion recognition to adjust emotional experiences based on what a customer is feeling, reclining or having volunteered.
Conclusion
Personalization at scale paved the way for businesses to form real relationships with clients in an era of growing digital recoil. When used in combination with big data and other advanced technologies, companies can provide deeply personalized experiences that deliver engagement while building loyalty, retention, and growth. These may seem like big ambitions, but to get started (and continue to execute here effectively) what will be needed is a thoughtful plan and some key principles in the use of data quality, privacy-first approach, AND constant product management + iterative focus. It remains to be seen, but going forward those businesses that can effectively deliver hyperscale personalization will excel in the experience economy.