3 top data trends and analysts must watch

3 top data trends and analysts must watch

3 top data trends and analysts must watch

This year, personalization and first-party data will collide, deep learning will evolve, and privacy regulations will help platforms survive.

Like many startups, we’ve been spending more time lately looking back at 2022 and planning ahead for 2023. Before my co-founder and I started Miso, when we were graduate students at the small computer lab at CornellTech in 2014, the idea of ​​privacy-first personalization felt like an intellectual challenge we couldn’t resist.

At times, even though the National Science Foundation and others noticed, it felt like we were building a solution for a technology problem that didn’t yet fully exist in the world. Data scientists did not take into account personal data, privacy and especially personalization as we are now. That is changing, and 2023 is going to be the year when personalization and first-party data collide. As such, here are three trends that data and analytics professionals need to watch in 2023.

Trend #1: As app tracking and third-party cookies continue to crumble, businesses need to rethink their first-party playbook

A simple truth from 2022 is this: When an app or website is offered a choice to track them across the web, a vast majority of users simply say no. Consumers will instinctively choose essential cookies if given the choice, or will decline app tracking when iOS prompts them, and remember – these are just consumers. Safari and Firefox (and soon Google Chrome) are proving to be hostile terrain for third-party cookies as well. Apple’s iOS continues to upgrade with privacy, and with GDPR and CCPA fully in place, the days of widespread cookies and tracking are coming to an end.

While this is a move in the right direction, it will not come without its challenges, as third-party ads continue to decrease in effectiveness and increase in cost. As a result, companies will need to rethink their third-party strategies. The key is living in the pools and lakes of data we’ve probably been ignoring for a while now — first-party clickstream data. This approach enables practitioners to put to work the data they already have about what users are interested in and engaged with.

Thanks to advances in artificial intelligence (AI) and natural language processing (NLP), we can now operationalize individual user insights into hyper-engaging search, recommendation and discovery experiences that keep customers coming back. These insights can be applied to email, SMS, push notification strategies – even loyalty and referral programs. It’s a win-win for businesses and consumers.

Trend #2: Deep learning will continue to grow, with further advances in neural search and deep learning recommender systems

Investing in machine learning to process said first-party data is going to be critical, and the timing has never been better. Given what deep learning and transformer models have unlocked across neural search, recommender systems and predictive modeling, the possibilities are exciting. Not long ago, first-party clickstream logs were the kind of data that only a project manager or a BI analyst could love. For years, value was derived from collecting and analyzing them in commercial products.

Now we finally have deep learning-driven tools and systems to understand these clicks and the story they tell about users and visitors. For the first time, we are seeing transformer models and coding systems come online that can analyze and semantically interpret product catalogs, content and advertisements beyond what they is and more for what they are About.

When you combine this product or content insight with a user’s clickstream, you suddenly have an interpretive lens to see what interests a user. One that you can spin off into personalized semantic search or recommendation systems, multi-objective rankings for affiliate marketing or promoted listings. You can even extend this to target marketing and decide what offers to make. This can lead to improved user experiences and revenue growth, and businesses will be able to do it quickly and at scale.

Trend #3: New privacy rules and practices will help more platforms survive and thrive

Changing the way we do business to deal with ethical and privacy issues will be painful in the short term. It forces a marked change in how companies, publishers and platforms view their internal data and what they do with it. From this we will see a major shift in the skillset of how websites and apps personalize the user experience and put their insights to work in better direct marketing, loyalty programs and advertising.

In the long run, this can only be good. The teams that fully commit to this strategy will see higher engagement, retention and organic growth. They want to unlock new revenue streams without exposing users’ data or abusing their trust. This ultimately means that more platforms, marketplaces, brands and publishers will have the agency and the opportunity to succeed. In essence, stronger data regulations may actually be the rising tide that lifts all boats.

A final word

For a long time we have had a “winner takes all” view of the internet. It can only be one large online store or ride-sharing service. It is not particularly good or healthy for our online economy. What we really want is a vibrant ecosystem – one where consumers have fair choices about where they can shop, be entertained and be informed. We have a chance to unlock a whole new phase of the web.

It’s an exciting time in the world of data and analytics. Although some of the changes ahead may seem daunting, we are at a turning point. Companies that find smarter, more ethical and privacy-first ways to engage with their customers will be the clear winners, while those that don’t adapt will crumble like third-party cookies that will soon be a thing of the past.

About the author

Lucky Gunasekara is the founder and CEO of Miso. He has more than ten years of experience assembling multidisciplinary teams of researchers, designers and engineers to build products rapidly, based on the latest advances in NLP, machine learning, data visualization and RecSys. You can contact him via LinkedIn.

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