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One-to-one targeting with affinity-based personalization
2026-02-28 18:59 Diff
https://www.dynamicyield.com/lesson/exposing-the-complexity-of-user-affinities/
Summarize this articleHere’s what you need to know:
One-to-one targeting with affinity-based personalization uses product metadata to predict what a user is most likely to respond to, increasing engagement and conversion rates.
Affinity modeling is based on the idea that the more a user interacts with a particular product or feature, the more likely they are to be interested in similar ones in the future, making product recommendations more relevant and effective.
Affinity scores are calculated by considering the type of interaction (view, add to cart, purchase) and its recency, allowing for nuanced personalization based on a user’s specific actions.
Affinity scores can be used to personalize the user experience in various ways, including recommending products, showing different content, and tailoring offers, leading to a more satisfying customer journey.
The future of affinity-based personalization lies in leveraging machine learning to create even more sophisticated models that incorporate a wider range of factors, like site type, product variety, and time of day, for even more effective and dynamic personalization.
As marketers, our job is to analyze the behaviors of visitors as well as the products they interact with to gain a better understanding of how everything is connected. With this information, we can then build experiences that will truly resonate on an individual level.
However, despite our best efforts, as visitors become exposed to a wider range of brands, colors, styles, prices, and more, it gets harder to assign meaning to particular interactions. And oftentimes, the true nature of an engagement ends up lost in a web of complex data.
Replaced by a guessing game in which different combinations of targeting conditions are tested in hopes of matching the right experience to the right audience, this challenge is the reason affinity profiling came to be. Adding a score to a user based on the metadata connected to a product they’ve interacted with meant teams could better predict the most relevant items to them in the future.
But even in all of its glory, the industry is still naive in its application and implementation. In this post, I’ll explain where affinity is today and touch on the enormous untapped potential.
Calculating affinity

User affinity profiling starts by capturing each engagement with a digital property and then collecting all of the product attribute values associated with the interaction.
Quick Reference: Example User Affinity Signals
Engagement type: – Tthe interaction the user has with the product
Product attribute values: – Tthe characteristics describing the product
Product view
Add to cart
Purchase
Filter product grid
Add to wishlist
Custom interaction Color: red
Gender: men
Style: T
Category: running
For example, let’s take a look at a sample journey:
In the context of a single visitor, we might see four pageviews generated in which the product color was green, six in which the product was red, and two blue. Additionally, all of the items viewed consisted of Men’s t-shirt under $50. The visitor then places two items in their cart, a red and a green t-shirt, but ultimately purchases the red one.
AttributeColorStyleGenderPrice RangeValue:GreenRedBlueTTankCamiMenWomenBoysGirls<$50>$50Product view462120012000120Add to cart110200200020Purchase010100100010Attribute: ColorValueGreenRedBlueProduct view462Add to cart110Purchase010Attribute: StyleValueTTankCamiProduct view1200Add to cart200Purchase100Attribute: GenderValueMenWomenBoysGirlsProduct view12000Add to cart0000Purchase0000Attribute: Price RangeValue<$50>$50Product view120Add to cart20Purchase10
The entire session might look something like this, with all of the attribute values and engagement types to be considered marked for scoring.
Affinity score weights

Every time a user interacts with a product, the score of each attribute value is updated by the type of interaction with it as well as when it occurred.
Engagement

In order for the data above to become meaningful, a correlation between the user interaction and an attribute value must first be determined. Today, this is done by assigning a weight to each engagement based on its assumed level of intent and then summing up the total number of engagements per each value.
Depending on the affinity algorithm, defaults weights might be quantified as follows.
Purchase: 4X
Add to cart: 3X
Add to wishlist: 2X
Product view: 1X
And more
Understandably, the higher level of intent associated, the greater the weight given to the interaction. For example, making a purchase would receive more weight than adding an item to the cart, which itself, outweighs a simple product view. From there, the occurrence of each interaction is multiplied by the weight.
Engagement score = interaction type weight x attribute value count
So if purchase weight is 4, add to cart is 2, and product view is 1, the total score for the value “color:red” is (1×6) + (2×1) + (4×1) = 12.
WeightColorGreenRedBlueProduct view1462Add to cart2110Purchase4010Score000
These scores can now be used to build an affinity profile, which essentially ranks a list of preferred product colors, categories, brands, and so forth based on the customer’s activity.
Recency

As user behavior and preferences change over time, affinity profiles must be refined with every new pageview, action, and event in real time. Therefore, the recency of each interaction is also taken into account, ensuring the algorithm favors newer activity over older data.
Three time periods are typically used in scoring:
Real-time or current browsing session: 8X
Recent history or in the last month: 2X
All-time or the last six months 1X
When layering this information into the attribute value score, in addition to multiplying the interaction weight, the recency weight is also multiplied.
Recency score = recency weight (interaction weight x attribute value count)
Assuming the same user referenced above comes back to the site two weeks later and purchases the green t-shirt previously abandoned in their cart, the total score for “color: green” would be 2[(1×4) + (2×1) + (4×0)] + 8(4×1) = 44.
AttributeColorRecencyValue:Weight:GreenRedBlue Recent History
(weight = 2)Product view1462Add to cart2110Purchase4010Score6x2=1212x2=242x2=4 Real Time
(weight = 8)Purchase4100Score4x8=3200Total Score44244Recency: Recent History (weight = 2)Attribute:Color:GreenRedBlue Product View
Weight: 1462 Add to Cart
Weight: 1110 Purchase
Weight: 1010Score6x2=1212x2=242x2=4Recency: Real Time (weight = 4)Attribute:ColorGreenRedBlue Purchase
Weight: 4010Score4x8=3200Total Score44244
Now the affinity profile reflects two strong scores (engagement and recency), enabling us to showcase products the user is more likely to engage with at any given moment in time. And through the normalization of site data, can even uncover “long tail” attributes correlated with a purchase, ensuring results aren’t skewed by demoting frequently-seen or always-popular attributes.
Releasing affinity into the wild

The straightforward method of affinity calculation described above is simple and effective – it leverages available product metadata to better uncover relationships between attributes from different product fields based on the two light formulas above. This makes it easy to communicate to stakeholders and thus emphasize the importance of good-quality attributes in the product feed – it also incorporates newly released products naturally, without the need for any additional setup or configuration. But the future of affinities is a lot more flexible, thanks to the help of machine learning, which introduces opportunities for increased ranking power and more complex correlations to be uncovered.
These smart affinities can adjust the variables used in calculating scores, as opposed to leveraging the same formula with a static weighting system for all visitors. For example, by adjusting the weight of one attribute to focus on long-term behavior while another is geared toward the current session. Trained using past customer sessions which culminated in conversion, Deep Learning models can assign weights to different interactions and time scopes for each attribute as well as surface correlations between attributes. Then, based on a real-time user session, it can rank an attribute value, say color, by the probability of being added to the cart or purchased, even for colors or brands the individual hasn’t been exposed to before. And through a greater degree of data normalization, critical contextual nuances such as the type of site and product offering, date, and time, will all be taken into consideration during calculations.
You could say, affinity modeling is becoming significantly more personalized. And with such much variability, teams need a lot more visibility into how affinities are generated, analyzed, and actioned. Today, most affinity data exists in a black box, meaning those leveraging it to optimize their personalization efforts have very little insight into how the algorithm at work calculated an affinity score. But “this is your affinity score, take it or leave it” isn’t good enough anymore. All of the data around affinities and attributes should be exposed, customizable, and personalized depending on the unique needs of the business
The beauty of user affinity

The only way to unleash the full power of user affinities for predicting what an individual wants is through exposing and providing as much control over that information, as well as how it’s used, as possible. Therefore, it’s important to partner with a technology provider that will allow you to not only play with all of user affinity’s variables and leverage them in relative verbose ways, but also understand the output of those efforts per campaign.
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Targeting</a></div><h1 class="article-title">One-to-one targeting with affinity-based personalization</h1><h2 class="article-excerpt">Affinity modeling as we know it is simple and effective, but product metadata can be better leveraged to predict the most relevant experience to users.</h2><div class="article-authors lc-flex"><div class="article-author-box lc-col lc-flex"><div class="article-author-image lc-col"><div class="entry_author_image"><img src="https://www.dynamicyield.com/wp-content/authors/shana-126.jpg"  alt="Shana Pilewski" width="76" height="76" /></div></div><div class="article-author-title lc-col"><div class="article-author-name"> <a href="https://www.dynamicyield.com/blog/author/shana/" title="Posts by Shana Pilewski" rel="author">Shana Pilewski</a></div><div class="article-author-title-text"> Senior Director of Marketing, Dynamic Yield</div></div></div></div><div class="entry-content article-entry-content"><p></p><div id="WYNTK-Sec" class="what-you-need-to-know-sec"><p><img decoding="async" src="/wp-content/uploads/2020/09/star-icon.svg" style="width: 20px; margin-right: 5px; vertical-align: -2px;"><strong class="first">Summarize this article</strong><strong class="hidden">Here&#8217;s what you need to know:</strong></p><ul style="margin-left: 4px; font-size: 16px;"><li style="margin: 10px 0;">One-to-one targeting with affinity-based personalization uses product metadata to predict what a user is most likely to respond to, increasing engagement and conversion rates.</li><li style="margin: 10px 0;">Affinity modeling is based on the idea that the more a user interacts with a particular product or feature, the more likely they are to be interested in similar ones in the future, making product recommendations more relevant and effective.</li><li style="margin: 10px 0;">Affinity scores are calculated by considering the type of interaction (view, add to cart, purchase) and its recency, allowing for nuanced personalization based on a user&#8217;s specific actions.</li><li style="margin: 10px 0;">Affinity scores can be used to personalize the user experience in various ways, including recommending products, showing different content, and tailoring offers, leading to a more satisfying customer journey.</li><li style="margin: 10px 0;">The future of affinity-based personalization lies in leveraging machine learning to create even more sophisticated models that incorporate a wider range of factors, like site type, product variety, and time of day, for even more effective and dynamic personalization.</li></ul></div><p></p><p>As marketers, our job is to analyze the behaviors of visitors as well as the products they interact with to gain a better understanding of how everything is connected. With this information, we can then build experiences that will truly resonate on an individual level.</p><p>However, despite our best efforts, as visitors become exposed to a wider range of brands, colors, styles, prices, and more, it gets harder to assign meaning to particular interactions. And oftentimes, the true nature of an engagement ends up lost in a web of complex data.</p><p>Replaced by a guessing game in which different combinations of targeting conditions are tested in hopes of matching the right experience to the right audience, this challenge is the reason affinity profiling came to be. Adding a score to a user based on the metadata connected to a product they’ve interacted with meant teams could better predict the most relevant items to them in the future.</p><p>But even in all of its glory, the industry is still naive in its application and implementation. In this post, I’ll explain where affinity is today and touch on the enormous untapped potential.</p><h2>Calculating affinity&nbsp;</h2><p>User affinity profiling starts by capturing each engagement with a digital property and then collecting all of the product attribute values associated with the interaction.&nbsp;</p><div class="affinity-table affinity-table-1"><table><thead><tr><td colspan="2"><p><strong>Quick Reference: Example User Affinity Signals</strong></p></td></tr></thead><tbody><tr><td><p><strong>Engagement type<span class="colon">: </span></strong><span class="hyphen"> &#8211; </span><span class="colon">T</span><span class="hyphen">t</span>he interaction the user has with the product</p></td><td><p><strong>Product attribute values<span class="colon">: </span></strong> <span class="hyphen"> &#8211; </span> <span class="colon">T</span><span class="hyphen">t</span>he characteristics describing the product</p></td></tr><tr><td> Product view<br /> Add to cart<br /> Purchase<br /> Filter product grid<br /> Add to wishlist<br /> Custom interaction&nbsp;</td><td> Color: red<br /> Gender: men<br /> Style: T<br /> Category: running</td></tr></tbody></table></div><p>For example, let&rsquo;s take a look at a sample journey:</p><div class="bg-f5f8f9 affinity-image"><img fetchpriority="high" decoding="async" src="/wp-content/uploads/2019/07/Timeline.png" alt="Affinity Timeline" class="blog-1000-width no-image-margin-bottom retina_image" width="825" height="1338"></div><p>In the context of a single visitor, we might see four pageviews generated in which the product color was green, six in which the product was red, and two blue. Additionally, all of the items viewed consisted of Men&rsquo;s t-shirt under $50. The visitor then places two items in their cart, a red and a green t-shirt, but ultimately purchases the red one.&nbsp;</p><div class="blog-1000-width affinity-table affinity-table-3 desktop"><table><thead><tr><td>Attribute</td><td colspan="3">Color</td><td colspan="3">Style</td><td colspan="4">Gender</td><td colspan="2">Price Range</td></tr></thead><tbody><tr><td><strong>Value:</strong></td><td><strong>Green</strong></td><td><strong>Red</strong></td><td><strong>Blue</strong></td><td><strong>T</strong></td><td><strong>Tank</strong></td><td><strong>Cami</strong></td><td><strong>Men</strong></td><td><strong>Women</strong></td><td><strong>Boys</strong></td><td><strong>Girls</strong></td><td><strong><$50</strong></td><td><strong>>$50</strong></td></tr><tr><td>Product view</td><td>4</td><td>6</td><td>2</td><td>12</td><td>0</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>12</td><td>0</td></tr><tr><td>Add to cart</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>Purchase</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr></tbody></table></div><div class="blog-1000-width affinity-table affinity-table-3 mobile"><table><tr><td><table class="attr-color"><tr><th colspan="4">Attribute: Color</th></tr><tr><th>Value</th><td>Green</td><td>Red</td><td>Blue</td></tr><tr><th>Product view</th><td>4</td><td>6</td><td>2</td></tr><tr><th>Add to cart</th><td>1</td><td>1</td><td>0</td></tr><tr><th>Purchase</th><td>0</td><td>1</td><td>0</td></tr></table></td></tr><tr><td><table class="attr-style"><tr><th colspan="4">Attribute: Style</th></tr><tr><th>Value</th><td>T</td><td>Tank</td><td>Cami</td></tr><tr><th>Product view</th><td>12</td><td>0</td><td>0</td></tr><tr><th>Add to cart</th><td>2</td><td>0</td><td>0</td></tr><tr><th>Purchase</th><td>1</td><td>0</td><td>0</td></tr></table></td></tr><tr><td><table class="attr-gender"><tr><th colspan="5">Attribute: Gender</th></tr><tr><th>Value</th><td>Men</td><td>Women</td><td>Boys</td><td>Girls</td></tr><tr><th>Product view</th><td>12</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Add to cart</th><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Purchase</th><td>0</td><td>0</td><td>0</td><td>0</td></tr></table></td></tr><tr><td><table class="attr-pricerange"><tr><th colspan="3">Attribute: Price Range</th></tr><tr><th>Value</th><td><$50</td><td>>$50</td></tr><tr><th>Product view</th><td>12</td><td>0</td></tr><tr><th>Add to cart</th><td>2</td><td>0</td></tr><tr><th>Purchase</th><td>1</td><td>0</td></tr></table></td></tr><tr><td></table></div><p>The entire session might look something like this, with all of the attribute values and engagement types to be considered marked for scoring.&nbsp;</p><h3>Affinity score weights</h3><p>Every time a user interacts with a product, the score of each attribute value is updated by the type of interaction with it as well as when it occurred.</p><h4>Engagement</h4><p>In order for the data above to become meaningful, a correlation between the user interaction and an attribute value must first be determined. Today, this is done by assigning a weight to each engagement based on its assumed level of intent and then summing up the total number of engagements per each value.</p><p>Depending on the affinity algorithm, defaults weights might be quantified as follows.&nbsp;</p><ul><li>Purchase: 4X</li><li>Add to cart: 3X</li><li>Add to wishlist: 2X</li><li>Product view: 1X</li><li>And more</li></ul><p>Understandably, the higher level of intent associated, the greater the weight given to the interaction. For example, making a purchase would receive more weight than adding an item to the cart, which itself, outweighs a simple product view. From there, the occurrence of each interaction is multiplied by the weight.&nbsp;</p><p><em>Engagement score = </em><em>interaction type weight</em><em> x attribute value count</em></p><p>So if purchase weight is 4, add to cart is 2, and product view is 1, the total score for the value &ldquo;color:red&rdquo; is (1&#215;6) + (2&#215;1) + (4&#215;1) = 12.&nbsp;</p><div class="blog-1000-width affinity-table affinity-table-2"><table><thead><tr><td></td><td>Weight</td><td colspan="3">Color</td></tr></thead><tbody><tr><td></td><td></td><td>Green</td><td>Red</td><td>Blue</td></tr><tr><td>Product view</td><td class="red">1</td><td>4</td><td>6</td><td>2</td></tr><tr><td>Add to cart</td><td class="red">2</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Purchase</td><td class="red">4</td><td>0</td><td>1</td><td>0</td></tr><tr class="score module"><td>Score</td><td></td><td data-counter="6">0</td><td data-counter="12">0</td><td data-counter="2">0</td></tr></tbody></table></div><p>These scores can now be used to build an affinity profile, which essentially ranks a list of preferred product colors, categories, brands, and so forth based on the customer&rsquo;s activity.&nbsp;</p><h4>Recency</h4><p>As user behavior and preferences change over time, affinity profiles must be refined with every new pageview, action, and event in real time. Therefore, the recency of each interaction is also taken into account, ensuring the algorithm favors newer activity over older data.&nbsp;</p><p>Three time periods are typically used in scoring:&nbsp;</p><ul><li>Real-time or current browsing session: 8X</li><li>Recent history or in the last month: 2X</li><li>All-time or the last six months 1X</li></ul><p>When layering this information into the attribute value score, in addition to multiplying the interaction weight, the recency weight is also multiplied.&nbsp;</p><p><em>Recency score = recency weight (interaction weight x attribute value count)</em></p><p>Assuming the same user referenced above comes back to the site two weeks later and purchases the green t-shirt previously abandoned in their cart, the total score for &ldquo;color: green&rdquo; would be 2[(1&#215;4) + (2&#215;1) + (4&#215;0)] + 8(4&#215;1) = 44.</p><div class="blog-1000-width affinity-table affinity-table-4 desktop"><table><thead><tr><td></td><td colspan="2">Attribute</td><td colspan="3">Color</td></tr></thead><tbody><tr><td>Recency</td><td>Value:</td><td>Weight:</td><td>Green</td><td>Red</td><td>Blue</td></tr><tr><td rowspan="4"> Recent History<br /> (weight = <span class="purple">2</span>)</td><td>Product view</td><td class="red">1</td><td>4</td><td>6</td><td>2</td></tr><tr><td>Add to cart</td><td class="red">2</td><td>1</td><td>1</td><td>0</td></tr><tr><td>Purchase</td><td class="red">4</td><td>0</td><td>1</td><td>0</td></tr><tr class="semibold"><td><strong>Score</strong></td><td></td><td><strong>6x<span class="purple">2</span>=12</strong></td><td><strong>12x<span class="purple">2</span>=24</strong></td><td><strong>2x<span class="purple">2</span>=4</strong></td></tr><tr><td rowspan="2"> Real Time<br /> (weight = <span class="purple">8</span>)</td><td>Purchase</td><td class="red">4</td><td>1</td><td>0</td><td>0</td></tr><tr class="semibold"><td><strong>Score</strong></td><td></td><td><strong>4x<span class="purple">8</span>=32</strong></td><td><strong>0</strong></td><td><strong>0</strong></td></tr><tr><td><strong>Total Score</strong></td><td></td><td></td><td><strong>44</strong></td><td><strong>24</strong></td><td><strong>4</strong></td></tr></tbody></table></div><div class="blog-1000-width affinity-table affinity-table-4 mobile"><table><tr><th colspan="4">Recency: Recent History (weight = <span class="purple">2</span>)</th></tr><tr><td class="semibold">Attribute:</td><td class="semibold" colspan="3">Color:</td></tr><tr><td></td><td>Green</td><td>Red</td><td>Blue</td></tr><tr><td class="weights"> <span class="semibold">Product View</span><br /> Weight: <span class="red">1</span></td><td>4</td><td>6</td><td>2</td></tr><tr><td class="weights"> <span class="semibold">Add to Cart</span><br /> Weight: <span class="red">1</span></td><td>1</td><td>1</td><td>0</td></tr><tr><td class="weights"> <span class="semibold">Purchase</span><br /> Weight: <span class="red">1</span></td><td>0</td><td>1</td><td>0</td></tr><tr class="score semibold"><td>Score</td><td>6x<span class="purple">2</span>=12</td><td>12x<span class="purple">2</span>=24</td><td>2x<span class="purple">2</span>=4</td></tr><tr><th colspan="4">Recency: Real Time (weight = <span class="purple">4</span>)</th></tr><tr class="semibold"><td>Attribute:</td><td colspan="3">Color</td></tr><tr><td></td><td>Green</td><td>Red</td><td>Blue</td></tr><tr><td class="weights"> <span class="semibold">Purchase</span><br /> Weight: <span class="red">4</span></td><td>0</td><td>1</td><td>0</td></tr><tr class="score semibold"><td>Score</td><td>4x<span class="purple">8</span>=32</td><td>0</td><td>0</td></tr><tr class="totalscore bold"><td>Total Score</td><td>44</td><td>24</td><td>4</td></tr></table></div><p>Now the affinity profile reflects two strong scores (engagement and recency), enabling us to showcase products the user is more likely to engage with at any given moment in time. And through the normalization of site data, can even uncover &#8220;long tail&#8221; attributes correlated with a purchase, ensuring results aren&rsquo;t skewed by demoting frequently-seen or always-popular attributes.</p><h2>Releasing affinity into the wild</h2><p>The straightforward method of affinity calculation described above is simple and effective &ndash; it leverages available product metadata to better uncover relationships between attributes from different product fields based on the two light formulas above. This makes it easy to communicate to stakeholders and thus emphasize the importance of good-quality attributes in the product feed &ndash; it also incorporates newly released products naturally, without the need for any additional setup or configuration. But the future of affinities is a lot more flexible, thanks to the help of machine learning, which introduces opportunities for increased ranking power and more complex correlations to be uncovered.&nbsp;</p><p>These smart affinities can adjust the variables used in calculating scores, as opposed to leveraging the same formula with a static weighting system for all visitors. For example, by adjusting the weight of one attribute to focus on long-term behavior while another is geared toward the current session. Trained using past customer sessions which culminated in conversion, Deep Learning models can assign weights to different interactions and time scopes for each attribute as well as surface correlations between attributes. Then, based on a real-time user session, it can rank an attribute value, say color, by the probability of being added to the cart or purchased, even for colors or brands the individual hasn&rsquo;t been exposed to before. And through a greater degree of data normalization, critical contextual nuances such as the type of site and product offering, date, and time, will all be taken into consideration during calculations.&nbsp;</p><p>You could say, affinity modeling is becoming significantly more personalized. And with such much variability, teams need a lot more visibility into how affinities are generated, analyzed, and actioned. Today, most affinity data exists in a black box, meaning those leveraging it to optimize their <a href="https://www.dynamicyield.com/article/personalization-guide/">personalization</a> efforts have very little insight into how the algorithm at work calculated an affinity score. But &#8220;this is your affinity score, take it or leave it&#8221; isn&rsquo;t good enough anymore. All of the data around affinities and attributes should be exposed, customizable, and personalized depending on the unique needs of the business</p><h2>The beauty of user affinity</h2><p>The only way to unleash the full power of user affinities for predicting what an individual wants is through exposing and providing as much control over that information, as well as how it&#8217;s used, as possible. Therefore, it’s important to partner with a technology provider that will allow you to not only play with all of user affinity&#8217;s variables and leverage them in relative verbose ways, but also understand the output of those efforts per campaign.</p><style>.single .affinity-table.mobile{
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