POSTED : July 15, 2022
BY : Lou Powell
Categories: Customer Engagement
Lou Powell
EVP, Solutions & Innovation
Data Personalization: The No Nonsense Roadmap Beyond the Technology
It’s no secret that data personalization has become a key factor of brand success—for marketing, sales, and customer service. Most brands recognize the importance of having a connected data ecosystem for centralized control over the way data is collected, managed, and made available to personalize experiences, consistently, and at scale. However, data personalization demands more than investment in technology to make it work. To increase the chances of success, brands must incorporate the following five steps into their data personalization roadmap.
1. Create a Personalization Capability Beyond the Infrastructure
Personalization is the name of the game in business today, but much can go wrong between the collection of personal data and the execution of a personalization strategy. While you want customers to receive personalized experiences that are unique to them, those experiences still need to be consistent—which is why you want to avoid configuring different levels of personalization using different data sources. We’ve all experienced the results of an inconsistent personalization experience. For example, an ecommerce experience that knows exactly what you want, but a customer service experience that requires you to enter your name and address several times over.
Make sure to focus on creating a personalization capability. Working with a team that understands the issues, data, and processes is a good start. Make this an evolutionary concept at the core of your personalization strategy, so it can evolve based on shifting CX demands.
2. Partner with Data Scientists
Data scientists are your friends. It’s rare for brands to suffer from a lack of data, but what data is most useful for each personalization effort and CX enhancement? This is where the data scientists come in. Data personalization is often something that cannot be fully configured through a singular approach. Data personalization is about geolocation. It’s about demographic. It’s about persona. It’s about the specificity of information an organization has learned about a customer, the behavioral data surmised from each interaction, and the data that outlines the customer’s wishes and preferences. Without this expertise on the team, the project will take longer and may choose the wrong data sets or implement data processes that have little or no value for the customer or your company.
3. Take a Human-Centric Approach
Incorporate human-centric design into every interface development project to ensure your data personalization efforts align with customers’ needs and expectations, including those not always clearly known or expressed. As well as revealing opportunities to enhance CX and service delivery, human-centered design will ensure your AI solutions accurately interpret customer needs, and you effectively meet them, through intuitively designed interfaces and relevant, timely interactions. This approach will help you create your blueprint for personalized experiences and inform your data engineering team on the data needed to deliver these experiences in real-time and at scale—and even trigger apps and IoT devices to automate services and interactions, to provide convenience and additional value.
4. Align Personalization With Outcomes
Getting personalization right requires a holistic approach that often demands a high degree of business and technology-driven transformation. You’re likely to make big decisions on infrastructure design, how processes need to change, and how to prioritize investment. And when building out specific solutions, these will need testing and validating with customers as you transition into an ongoing cycle to optimize results. As you get into more personalization, you get into more complexity. Be cautious about how you use personalization, and make sure that it delivers a positive impact on the customer and drives a positive business outcome. Define your objectives for personalization and how success will be measured. There must be a correlation between data collection and return on investment, not just for brands, but for customers too.
5. Include the Customer in the Personalization Conversation
Customers expect personalization, but at the same time, they are concerned about data privacy. GDPR in Europe and the California Consumer Privacy Act of 2018 (CCPA) require companies to gain explicit permissions from customers before using their data. Build trust by being transparent with customers about why, how, and how long you will be storing their data. Make it easy for them to review your privacy policy. Only collect the data required to provide value to customers and make it clear why you are asking for it.
Don’t just rely on assumed intelligence gleaned from AI-powered analytics. Build trust in your data by inserting steps in your customer journeys to review and confirm what you know about each customer, so you accurately serve their needs—selectively, at points when you are using these insights to inform a decision or make a recommendation. Do it in a way that feels natural—your human-centric designers will advise on the best way to achieve this, in a non-evasive way, that customers will value. By giving customers control over their data, you can organically learn about what matters most to them, as their situations and behaviors change.
An Operational Mindset is Needed
Cloud platforms, analytics, AI, and APIs are the fundamental building blocks that create the foundation for data personalization. However, making it work demands an operational mindset to build a data personalization capability that focuses on the customer, driven by data, led by outcomes, and shaped by human design.
Author
