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Author: Mike Elgan

Personalization is good. It makes people feel good about brands that offer it. Good old-fashioned personalization typically uses data points such as name, title, purchase history, zip code and behavioral data to present relevant information.

The most common example of personalization is mass marketing emails that address each customer by name. Another is when a consumer is browsing for a brown jacket and are then shown online ads for brown jackets on other sites.

Hyper personalization takes it up a notch with artificial intelligence and near-real-time data to provide extremely relevant and timely content to customers. Using AI, customer behavior and preferences can be finely captured, and that data can be turned into specific messaging delivered at the right time and place for maximum effect.

Hyper personalization vs. customer profiling

Hyper personalization isn't the same as customer profiling. Customer profiling sorts customers into broad profiles. Hyper personalization places each customer into a category of one. In other words, customer profiling focuses on market segments. Hyper personalization focuses on each individual. It's about matching facts known about the customer with actions taken by the customer, plus near-real-time data from the context of the interaction.

Because customers are using many channels, AI-based customer personalization calls for an omnichannel strategy. Data needs to be captured throughout the customer journey across many channels.

The hyper personalization strategy road map

Different companies have variable needs, requirements and constraints. But this road map can generally be adapted to suit the needs of customer experience (CX) leaders:

1. Identify the sources of all consumer data

These can include website analytics, a data management platform, sentiment analysis platforms, social listening tools—all the tools available that can acquire and process consumer data.

2. Figure out what kind of data you want to track and process

Include data such as the frequency of brand interaction, how they've responded to various types of messages, what time(s) of day or days of the week they're most active with interaction. Determine their triggers, such as discounts, popularity and more. To the greatest extent possible, engage the customer in choosing what they'd like to share. In addition to what you choose to gather, offer customer personalization options that allow the customer to express themselves in what data you gather.

3. Create hyper personalized messaging

This could include messages with offers based on known customer preferences, sizes and locations that are delivered at a time and in a context known to elicit a positive response by each specific customer. Or it could involve other content, such as serving up custom versions of your website based on each customer's history and preferences. Get experimental and measure which types of content work on which customers using A/B testing. Use a great content management system for creating and distributing the content.

4. Distribute the relevant content using content distribution tools at the right touch points

For example, send a text message when the customer is physically near an opportunity) for driving interest, engagement and sales. Tools or combinations of tools should include customer relationship management platforms, A/B testing applications, customer data platforms, predictive analytics and others can help to construct and automate the full customer journey.

5. Constantly analyze the effectiveness of the messaging program

Use tools that measure traffic, engagement and action. Use A/B testing, monitor customer feedback—or seek it out with surveys or quick feedback.

Customer personalization: Things to keep in mind along the way

Hyper personalization calls for enormous amounts of customer personalization data. So, it's important to safeguard this information and spell out to customers exactly how it's being safeguarded and how it will be used, thereby helping to build trust even further.

It's also very early days. All roads lead to a near-future of applying deep learning methodologies to the data. It's complicated. But the bottom line is that tools are being developed that will enable AI engines to find the patterns in your data that humans cannot find. For example, AI could discover that a combination of the customer's age, location, day of the week and color preferences greatly impacts when that person is most receptive to discount offers. And this will further the cause and expand on the benefits of hyper personalization for both customers and your organization.

The hyper personalization journey is an interactive one, filled with experimentation and learning by the entire team. And there's no time like the present to get started.

Learn how to capitalize on your valuable data with solutions from Verizon.