TL;DR: User cold start is a common challenge that businesses face when launching new products or services. It refers to the difficulty of gathering user data and making informed decisions without a pre-existing user base. This blog explores the effects of user cold start on business decisions, such as marketing strategies and product development.

Let’s start with an example:When a new subscriber joins Netflix- Netflix wants to recommend the relevant content. Yet, the recommendation system does not have access to the user’s historical data as none exists. To solve this problem, Netflix asks a series of questions through an initial survey to help determine a user's set of preferences. Then, Netflix recommends titles based on users with similar tastes. Once the user starts engaging with the content - Netflix leverages this data and gradually improves the user’s experience through better personalization.

That’s how Netflix handles with the cold start problem, but for most cases, and recommendation service, this solution is not feasible - as we don’t have the ability to survey the user and collect the crucial X amount of data points, to transition from a popularity based recommender into a personalized based recommender.

What is a cold start?

A cold start refers to a situation where a system or process has little to no information or historical data to work with when starting up or launching. In the context of business and user data, a cold start can occur when a new product or service is launched, and there is no pre-existing user base to gather data from.

There are three types of cold starts:

1. New User: When the system encounters new visitors to a website, with no browsing history or known preferences, creating a personalized experience for them becomes a challenge because the data normally used for generating recommendations is missing
2. New product: When a new item is added to the system there is almost no information about the product and no interactions are present, the product cold-start problem arises.
3. New community: When, although a catalog of items might exist, almost no users are present and the lack of user interaction makes it very hard to provide reliable recommendations

Is it worth worrying about cold-start problems?

The answer is a definitive yes, particularly in the context of user data and product launches. Cold starts can make it challenging to gather user data and make informed decisions about marketing strategies, product development, and user engagement. This can lead to costly mistakes, inherent biases and missed opportunities for growth. However, with proper planning and strategies, businesses can overcome cold-start problems and successfully launch new products and services, for new and existing users, as one.

For example, the cold-start problem can make the promotion of new products a challenging issue. According to the collaborative filtering concept, the recommendation engine will always rate popular products higher than new products, regardless of a user’s interaction or preferences. Typically, products with higher visibility sell better than products that are hardly ever recommended. That leads to the situation of falling into a loop where the recommendation system promotes products that are already popular, and often those that do not suit the user.

How will a cold start look in a cookieless world?

Over the past two decades, businesses have been trained to believe that the third-party cookies (i.e., external data sources) — is the easiest and fastest way to gain access to customers’ personal information, and is the best way to get around the long-standing cold start problem. Unfortunately, this strategy has also opened up a new can of worms, resulting in customers who either feel spied on, due to hyper-personalized recommendations or who become frustrated by poor recommendations based only on very simple set of rules. In those cases, allowing third-party cookies to pull personal information is a sacrifice consumers have made for years, without fully understanding the repercussions.But things are changing. The power of the third-party cookies and mobile identities is in a rapid decline, thanks to constantly tightening consumer privacy laws. Facebook made headlines for its privacy protections that led to the Cambridge Analytica scandal, and Google has announced plans to phase out its own use of third-party cookies in Chrome by the end of 2024, joining Apple and others, who already opted out their third party cookies and identities.

A world without third-party cookies could exacerbate the cold start problem for businesses. With the phasing out of third-party cookies, businesses will need to find alternative ways to gather user data. This lack of meaningful personalization can have dire consequences when it comes to conversion rates and consequently to revenues. In fact, 91% of today’s consumers say they’re more likely to shop with brands that provide offers and recommendations that are relevant to them — but on average, 68% of visitors on a site are new users.

How to tackle the problem?

1. Collect more data through zero and first-party cookies: Instead of focusing on who customers are, companies should focus on what they’re doing once on a website. With increased access to live data through on-platform interactions like short quizzes or clever content filtering and product indexing, businesses can understand what customers really want and immediately deliver compelling experiences.
2. Recommending trending products: is another efficient way to “break the ice” with a cold start problem. Further into the user journey, on product detail pages, item-to-item recommendations like “Frequently bought together” help to overcome the cold start problem. This is because the item-to-item logic relies on data from the interactions of previously, identified users, not the specific visitor to whom the recommendation is being served.
3. Build an incentivized user base: Businesses can offer incentives such as discounts, promotions, or free trials to encourage users to sign up and provide feedback. This can help to build an initial user base and gather data about their behavior and preferences.
4. Leverage lookalike modeling: Lookalike modeling is a technique where businesses use existing data to identify users with similar characteristics and preferences. This helps build a new user base and create targeted marketing campaigns.

How does Kahoona tap in?‍

Kahoona is solving the increasing CAC and data scalability issues (a.k.a user cold-start problem), today and especially in a soon-to-be cookieless world.

Kahoona provides segmentation and activation on unauthenticated and unknown users, with deep-behavioral based interoperable 1st party audience enrichment.Kahoona’s proprietary data solution is unveiling the gaps, covering nearly every user with 15X more data points. It is analyzing every step of the funnel, allowing activating personalization engines on every user, even anonymous and one-time visitors, from the first page.It also recommends the best campaigns from endless variations, and launches optimized campaigns to all acquisition channels - focusing the resources only on relevant customers.

We stand at the beginning of an inflection point. The demise of the third-party cookie is poised to transform how companies interact with their customers, and it all begins with a cold start — once a problem, now an opportunity.

If you want to tackle your cold start problem — Book a demo!