Voice of the Customer: Flashback to a Super Bowl Conversation

By 2022, it’s predicted that 333 exabytes of data will be produced on the internet every month. To put that in perspective, some have speculated that all of the words ever spoken by humans equals just 5 exabytes.

This means that the sheer amount of data exchanged and consumed by customers means platforms will have to evolve, especially in the areas of data analysis and storage to further personalization. 

This gap in capacity leaves room for vulnerabilities within the customer-brand relationship, which makes artificial intelligence one of the most important tools a brand has to create personalized experiences for its customers. But, how exactly will data, AI, and personalization interact, both now and in the future? Let’s take a look.


The Importance of Personalization

Customers want to feel engaged with relevant content every time they come into contact with a brand. Ultimately, what sets brands apart from their competitors is an ability to make a customer feel like they’re the only one. In fact, 80% of consumers say they are more likely to purchase from brands who offer personalized experiences.


(Source: Deloitte)

In the long term, providing more personalized experiences will not only lead to more conversions, but increased loyalty and customer retention. So, if a brand wants to build long lasting success, they have to gain a solid understanding of their customers. This is where customer data comes into play.

Read: The State of AI on Market Research (eBook)


Personalization & Data

Data is key. From clicking on a product, to filling in a form, there is important information to be gathered about the customer at every interaction point. 

To get a complete picture of each customer’s preferences, a brand needs to collect a range of data sets – what is known as a customer profile – to decide which messages will resonate the best.


Data Sets for Customer Profiles 

All behavioral, demographic, and personal details (like name and location) are useful to know in order to tailor future communications and messaging. For example, it’s now possible to use data from internet browsing histories to push recommendations of similar products and services. If your website is part of a larger network, you can use data from multiple domains to build a more thorough profile, or even to create a community of domains that serve separate purposes along the purchasing journey. 

It’s also possible to send out purchasing stage-specific messaging. This is particularly useful for bigger ticket items like kitchen appliances, where buyers are likely to go through several rounds of research before deciding to purchase. When this is the case, data pertaining how many times they’ve visited your site and what they’ve looked at each time will help determine where they are in the sales funnel and what it will take to move a customer forward.

Read: 2020 Trends in Digital Media & Advertising (Report)


Turning Data Into Actionable Solutions 

Brands can utilize a number of different data sets to inform their marketing messages. However, the challenge then becomes gathering the data, processing it, and turning it into actionable solutions. Luckily, we live in an age where technology can make complicated processes much simpler, including platforms and tools that streamline the process of data-driven personalization. 


Customer Data Sets

Analytics tools for brand websites like Google Analytics provide a general overview of visitor behavior. Although marketing teams can create their own data sets, public datasets are also available for competitive research and benchmarking. Additionally, Google Trends can provide high-level search intent to expand upon customer profiles. What’s most exciting about Google Analytics is the Analytics Intelligence feature, which uses machine learning to improve website personalization, creates Smart goals and lists, and even predicts conversion probability. 


Personalization Automation

The Adobe Experience Cloud includes a number of tools, but Adobe Target and Sensei are particularly advantageous for marketing and brand messaging. Target uses AI and machine learning to run automated tests at scale (including A/B test data comparisons over-time) to build a wider picture of customer preferences.


(Source: Adobe Target)

Adobe Sensei is dedicated to automating the creation of personalized content on an even larger scale by predicting customer behavior based on attributes, differences, and conversion factors.


Data Storage & Management

The evolution of specialized platforms made to contain and manage customer data have also improved personalization capabilities. So-called Customer Data Platforms (CDP) like BlueShift and QuickPivot offer a centralized location for storing and accessing customer data (normally first-party data) gathered from across channels. Data Management Platforms (DMP) like Adobe Analytics allow marketers to see overall trends as well as collect third-party data and use it to inform targeted messages. 


Customer Feedback Management 

For customer feedback management, consider changing the collection process from the beginning, and build a foundation for interpreting a large amount of qualitative data in an easy-to-digest way. Remesh is one tool with the power to do that, and updates the way groups give and receive feedback for modern workplaces by using artificial intelligence to eliminate traditional barriers to research, especially types that occur in-person. The platform allows brands to have a live conversation with consumers at scale, using AI to analyze and organize responses in real-time. 


(Source: Remesh)

Despite these examples, however, AI-powered tools are still only used by 1 in 3 marketers. That being said, using tools like these to personalize the customer journey can give brands a competitive edge – even in a seemingly saturated, tech-first industry.


Other Uses of AI for Personalization

Before we wrap up, here are a couple of extra uses of AI for personalization. 

  • Complex Re-targeting. Right now, customer data is gathered and used in what is known as an if/then process. Meaning, if a certain condition is met, an ad or message is run. Applying AI to this scenario allows personalisation to become much more specific. Instead of the ad running or not running for customers, AI can segment customers even further and create more conditions for interaction, with which an algorithm can be used to predict the best approach for each group. The end result could be something like LightningAI, which helps form special customer interest groups on Facebook and suggests high-performing creative campaigns.
  • Innovative Chatbots. Gathering accurate data isn’t always easy if there are only strict and designated forms for this to happen. Plus, customers won’t always want to share information within these parameters. Integrating AI-powered chatbots will build more trust than a static form, and allow you to fill in the qualitative blanks between your quantitative website analytics. While Drift is the most obvious choice here, chatbots are not limited to customer service. For example, NBC uses its chatbot to offer-up customized headlines to its readers, MedWhat uses a chatbot to provide a faster medical diagnose, and Disney uses its chatbot to engage kids in a choose-your-own adventure story.
ai and chatbots for customer data

(Source: MedWhat)

The application of AI are actually endless, but it's up to the brand in order to innovate with what tools are currently available.

Read: 3 Customer Insights That AI Will Change in 2020


Final Thoughts on Personalization

If brands want to continue to create personalized experiences, they need to adopt smarter solutions that can handle huge amounts of data, and to invest in educating employees on the application of artificial intelligence. For marketers and brands at large, AI is no longer the shiny and distant buzzword of the future. It’s the tool of the time.

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