*By Joyce Kim
This has been a year of drastic change for companies. Inflation, sweeping changes to privacy and transparency, and intensifying digital competition are creating significant obstacles to the way businesses are growing, innovating, and building customer relationships.
One of the biggest changes has been the emergence of a new era of artificial intelligence, specifically large language models, which can generate substantial amounts of text with astonishing quality and accuracy.
These days, you can't turn to a news website without seeing dozens of headlines about generative AI and its potential to transform seemingly every aspect of business. As CEOs rush to invest in AI, they also need to determine whether their data systems are compatible with the technology.
This isn't just the latest fad. Generative AI brings enormous potential for companies to grow, innovate, interact with customers and expand their competitive differences. There's a good reason it's risen to the top of CEOs' agendas: it's powerful, and companies that can capitalize on this new generation of AI will have a huge competitive advantage.
These CEOs are right about the potential of AI, and investments in the tool will continue to increase. But is the data ready for it?
For AI to be effective, it depends on good data – and not all information has the same value.
True adoption of generative AI requires a fundamental platform shift. It puts everything in a new context and prompts a reevaluation of the entire technology apparatus. If companies are serious about investing in AI, they need to rethink how they collect, manage and use this information.
Not all data has the same value for AI
Where data comes from and how it is collected and stored plays an important role in whether it can be used effectively for AI.
Today, most organizations have an enormous amount of data – more than they realize, in many cases. The problem is that most of them are isolated across dozens or even hundreds of sources: web applications, e-commerce applications, different communication channels used to interact with customers, and even the customer support center.
All of these applications generate good data, but they are often organized in completely different ways, stored in different locations, and managed by different teams.
Bringing all this data together in a form that you can use is a significant challenge. Another consideration is that companies typically have two types of data available. Outsourced data comes from an outside vendor, such as a social network, search engine, or other entity that is “renting” or providing it for the company's use.
The first-party data comes directly from the customer, who provides it directly to the company in the course of doing business with it: website clicks, purchasing behavior, name and email address, and so on.
For AI-powered customer experiences, first-party data is much more valuable. This is because you own and control them, so you are not dependent on others to provide them. They can be structured and stored optimally for your specific uses.
First-party data is also based on a direct relationship with your customer. At a time when consumers are increasingly concerned about privacy, the use of third-party information is under attack. Consumers don't like this data, browsers block it, and it can't be trusted.
Connecting the dots for better customer experiences
Let's take an example of how good data makes a difference: Personalization with AI. The tool can help personalize the customer experience in many ways, but it depends on good data that is well integrated across your company's range of applications.
To be effective, the personalization system needs to know that the 'Jane Doe' in your customer service app is the same 'Jane Doe' in your email marketing platform. If you can't do this, your AI-based efforts will be missing key pieces. This will be immediately obvious to the user when she receives an animated marketing message asking her to buy something she already owns and had a problem with yesterday.
Now imagine that your company's personalization engine is based on a platform that can connect the dots between all the different applications and databases your company is using. The engine is based on first-party data that your company has collected with full transparency and permission from your customers and connects customer identities between them.
You can now send messages to 'Jane Doe' (and everyone like her) that acknowledges the difficulties she has recently had with the product. You won't be sending offers to purchase the same product, but you may be reaching out with specific advice and an offer to help.
And if your customer service app has access to complete data about your customers, including their entire customer journey with you, an AI-powered customer service assistant can provide much more relevant and personalized support.
Personalization with AI gives you the ability to personally know and care for each of your customers: ensure the next marketing campaign is aligned with the customer's needs, help guide the customer service agent on what to say to them and rearrange the site whenever he visits to only show the products he's interested in.
When you can treat 'Jane' as a unique individual, with full awareness of everything she has done with your company, you begin to unlock more deeply personalized and human experiences.
And that's a powerful transformation in the way we do business.
Want to be on the winning side of the biggest platform change in a decade? Put the information in order. Otherwise, AI will just be a waste of money.
Find out if your customer data is AI-ready with Twilio on here.
*Joyce Kim is the director of marketing at Twilio
*This content was adapted from the article “AI Is Just a Money Pit Without Good Data”, published in the WSJ
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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