Personalizing products and services is the development tech companies should be paying attention to while leveraging AI to drive growth.
Back in 2008, Spotify’s original value proposition was to provide better content than copyright pirates at a time when the music industry was losing millions to file-sharing sites. It was remarkably successful. Now valued at over $61 billion, the streaming service has since commoditized music arguably better than any previous innovation. Remember cassette tapes? What about CDs? What Spotify has done so well is combine scale and specificity—a difficult feat but one now more accessible than ever thanks to artificial intelligence (AI).
I call the process of turning innovative ideas or improvements into something that can be duplicated and scaled up quickly within a company “commoditizing creativity.” Historically, however, this has meant losing the individuality that so often comes as the price of rapid growth.
Online marketplace Etsy, for instance, built its brand on offering handcrafted items, but as it tried to compete with the bigger e-commerce players, it started allowing manufactured goods. In the process, Etsy alienated its existing suppliers who claimed it had “lost its soul.”
Enter AI. In our business as a B2B optical networking solutions provider, we are training AI agents to monitor industry trends with the capability to answer questions about a specific player. We are also in the process of developing a model to train AI agents with our historical “case management” data. When these capabilities are brought online fully, we expect to shorten the cycle time to design and deliver a customized solution, while increasing the number of customers we can serve.
Personalizing products and services in this fashion is the development tech companies should be paying attention to while leveraging AI to drive growth.
CUTTING COOKIES WITH A DIFFERENCE
To be clear, AI is not reinventing the wheel. We still have to commoditize creativity fast because that is how a company generates value. What has changed is the capacity of the latest iterations of AI to free companies from standardization as they scale up.
Look at it this way: A mom at home can make unique cookies for her kids, but Mrs. Fields has to mass-produce her cookies. Now, imagine an AI-powered cookie cutter that can remold itself to create unique cookies to meet everybody’s taste, just like the mom does, but at Mrs. Fields’ scale.
This principle applies across businesses and functions. In sales and marketing, advertisers traditionally focus on creating buyer personas for mass targeting. But with the latest tech, the persona can be tailored to each individual.
For instance, the clothing styling service Stitch Fix onboards users with a quiz that asks about their sizing, preferences, and budget. Its complex algorithms then analyze this data to create a unique fashion profile for each individual. Later, it adds their browsing and purchase history. In other words, the solution is commoditized yet individual.
BIG DATA, NARROW FOCUS
Tech companies that want to use AI to innovate, scale, and then continue to grow must synergize big data and machine learning in ways no one else in their market is. Spotify was a first mover when it came to curating personalized playlists that draw from its vast library of songs. With over 615 million users, it has an abundance of data to leverage.
But its algorithms go beyond suggesting songs based on listening history or songs listened to by people with similar profiles. It uses convolutional neural networks (CNN) to analyze data such as a song’s beats per minute, key, and loudness to inform its recommendations. That is a lot of granular, nuanced data.
Microsoft’s AI-powered assistant is another example of personalization done well. Integrated into the Microsoft Office applications, its “Copilot” learns from the user’s writing style, preferences, and past projects to make suggestions that capture their unique voice and tone. As these brands have shown, access to big data is only as valuable as what you do with it. Innovations still need to solve a problem for the consumer.
LEVERAGING CONTEXTUAL ANALYTICS
The Stitch Fix business model is based on assigning stylists to curate clothing and accessories for the client after their data has been analyzed. This blend of human creativity and science gives customers an alternative to the frustrating search for items that match their style. “[They] start site-hopping or looking on social media, where you’re just scrolling, scrolling, scrolling,” says chief merchandising officer Loretta Choy. “It feels endless.”
Another tool in Spotify’s analytics arsenal is Latent Semantic Analysis (LSA), a linguistic model that assesses the contextual elements of queries such as mood, genre, artist similarity, and geography. The streamer is constantly scanning all this data to finesse its recommendations of songs and playlists—and it is incredibly popular as a result, dominating rivals such as Apple and Amazon in the music-streaming global market.
The personal touch in the B2B context often translates to greater data granularity for customized solutions. We work with individuals in customer organizations to understand their role and influence. Once we have enough data, that process will be equipped with AI capabilities to become more seamless. Not only are we appealing to the organizations’ needs in our sales process, but also the individual’s personal drive and motivation.
THE SOCIAL VALUE OF PERSONALIZATION
The usual narrative around technology is that it pulls people apart. But in an era where standardization often leads to a loss of personal touch, allowing consumers to express their unique tastes, styles, and identities is delivering a form of social value. It is also giving consumers overwhelmed with too many choices the tools to find what they are looking for—whether an outfit, a song, or just the right phrase—much faster and with much more precision.
Sephora, Netflix, and Amazon are just some of the other brands using personalization as a main feature of their value proposition. As all these examples show, AI can maintain and even enhance individuality while commoditizing innovation. Put another way, the cookie cutter just got infinitely more moldable and the speed of innovation will force brands to adapt fast.
Source: Originally posted on Fast Company