At the core of Nosto’s segmentation tools are affinities and affinity settings. In this article you’ll learn why you can and should use them, what you can achieve with them, and the basics of the principle how they work. 

Vast majority of buying intent and affinity signals are created by users who simply browse a site’s offering. Unfortunately many systems and segmentation tools rely only on transactional data, thus missing a lot of invaluable information about customer preference, which are learnt only after a purchase is made, hence missing many opportunities to improve your customers' experience. This is especially a problem when new customers without any buying data visit the site the first time, but also when existing clients change their scope and want to shop something else than they did before. 

Affinity based segments help you to achieve both targets as they link products and customers together with the highest degree of relevancy, updating on real-time, while segment sensitivity settings are mechanism to control the degree of personalization and volume of users belonging to a segment.

How Does The Scoring System Work?

The affinity system is based on scoring, indicating the strength of affinity between a customer and a range of products. A product range is based on a product property such as product category, brand, or a custom product attribute such as a colour or a product feature to name two examples. In brief, customer’s strong affinity indicated as high affinity score towards a product range suggests a close relationship, undoubtedly increasing likeliness of a purchase conversion if the on-site experience is personalized by exposing more the preferred product ranges.  

The scoring is individually calculated for each customer and based on all interactions a customer does while visiting the site, re-calculated in real-time, across multiple sessions and taking time into equation. Below an example of a few self-explanatory interactions specific for e-commerce websites which are used for scoring, starting from feeble intent signals towards very strong affinity:

  • Product listing view (weak)
  • Product page view 
  • Adding a product to a shopping cart
  • Buying a product (strong) 

Because website design, product types, buying cycles and in general businesses also affect the number of interactions per visit and interaction types, the scoring is self-learning, not static or limited only to given interaction examples as other interactions weigh in too. To avoid possible machine learning errors caused commonly by anomalies, some global safety rules are in place and regardless of site’s parameters. For example, an interaction of adding a product to a cart is always stronger signal than simply opening up a product page in a browser. As another example, opening the same product three times across three different visits to the site produces a stronger affinity score than eying out the product twice during the same session, while interactions done months ago are less impactful than those made just a few days ago.

An outcome of the automated process could be visualized as a network of links how a customer is connected to product ranges, including the strength of the connection: An affinity score.

How Does Affinity Sensitivity Work And How To Use It?

Affinity sensitivity is simpler and effectively works as a control mechanism and campaign configuration by adjusting the strength of  segments affinity and reach/volume of users. When creating a segment you can choose from three different sensitivity settings: Top, High and Low. 

  • High will include the strongest affinities instead of only the most favorite ones, but by disregarding feeble ones, narrowing down the reach but increase quality of the audience to only include the absolute fans. Following the same example as above, it would take some time for the shopper to be included in an Adidas Brand Affinity segment set with a Top sensitivity rule. To do so, he would need to increase drastically his amount of interactions for Adidas products or give strong signal types such as buying Adidas products for example.
  • Top will include customers who have a high affinity and also include the ones with a strongest preference for a given product range. For example, a customer who is balanced between three different brands - say, Nike, Reebok & Adidas - but who starts interacting noticeably more with Adidas is eligible for an Adidas Brand Affinity segment set with a Top sensivity rule. In other words, he doesn't have only high preference yet for Adidas, but it is his most preferred brand.
  • Low produces the highest reach, but also includes affinities towards ranges that might've been considered, but where the link is not as strong as in Top and High. In the example above, the shopper would be eligible for all three Brand Affinity signals.

Note: if you are using Low as a sensitivity rule, we recommend you to take advantage of our Segment Prioritization tool to prioritize one experience over another.

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