Implement Target Recommendations

Start with a strategy.

Illustration showing recommendations strategy

  • What items do you want to recommend? First, think about what items you want to recommend. This could be products, videos, or content.
  • Where do you want to show recommendations? Next, think about where you want to make recommendations. Broadly what channels (web, mobile, in-store, a kiosk, and so forth). What parts of the customer journey will contain recommendations? What pages on your site will contain recommendations?
  • How will you determine if recommendations are successful? Suppose that you have an experience without recommendations and an experience with recommendations, or you have two different types of recommendations. How would you determine which experience was a better experience for your customers? Some metrics might be more difficult than others to measure. For example, the impact of recommendations on Customer Lifetime Value is often difficult to directly get to. So it is often easier to get to a less abstract metric and one that is more concrete, for example, revenue per visit, average order value, or number of clicks. In some cases you might be looking to minimize a metric, for example, the number of support calls.

After you come up with your strategy, you are ready to start the implementation of Target Recommendations.

There are three broad steps involved in creating your recommendations implementation:

Illustration showing the steps to create your recommendations implementation

  1. Teach Target about your context or products.
  2. Capture user behavior.
  3. Get recommendations with the right context.

Teach Target about your context or products

When you start with Recommendations, you pass information about every item you want to recommend. Target offers several integration options to create your catalog.

Illustration showing how to teach Target about your context or products

The simplest, and most frequently used method is to send a CSV file on a daily or weekly basis from your product information management system or from your content management system. But you can also pass information on the data layer from your page using the Adobe Target Javascript library, leverage our APIs to pass information directly from your source system, or use our Adobe Analytics integration if you are already passing catalog data to Analytics.

Sometimes, you might want to use multiple options together, for example, passing most data daily via a CSV file and passing inventory updates more frequently via an API.

Your IT department will usually be involved in helping set this step up.

Whichever method you choose, you should include metadata about each item in three categories:

Illustration showing metadata information for your catalog

  • Data that you want to display to the user receiving the recommendation. For example, the name of the movie and a thumbnail image URL.
  • Data that is useful for applying marketing and merchandising controls. For example, the rating of the movie so that you do not recommend NC-17 movies.
  • Data that is useful for determining the similarity of items to other items. For example, the genre of the movie or the actors that are in the movie.

Capture user behavior

Next, you should add tags or leverage you existing Analytics implementation to track the conversion events (such as views and purchases) that drive Target algorithms.

Illustration showing how to capture user behavior

You need to ensure that Target is aware of the items that your users are viewing and purchasing. If purchasing isn’t relevant to your context, you might want to track a different type of conversion event, for example, downloading a PDF, completing a survey, subscribing to a newsletter, watching a video, and so forth.

If you are already using Target to run A/B Tests activities on your site, you might have already completed this step. Or if you are already using Adobe Analytics to report on site visits and conversion behavior, you can use Analytics as your behavioral datasource. If not, it’s easiest to set this up using a tag manager such as tags in Adobe Experience Platform. It’s also possible to send offline or in-app interactions to Target via real-time API.

Get recommendations with the right context

Pass information about the user and context at the point of interaction to Target to return relevant and personalized recommendations.

Illustration showing how to get recommendations with the right context

Besides user behavior in aggregate, you need to pass Target the specific context where recommendations are being shown. This includes information about the page and information from the user profile. Target uses this information to make personalized recommendations. For example, on a retail website, you want to know the product and product category that the visitor is currently viewing. You also want to know information about that user (favorite brand, favorite product category, loyalty tier, and so forth). This information is important so that Target can filter items and improve the personalization of recommendations.

Build your first Recommendations activity

What is a Recommendations activity?

Illustration showing the parts that make a good recommendations activity

A Recommendations activity is made up of the following components:

  • Audience: Who should see these recommendations?
  • Criteria: What items should be recommended?
  • Design: How should the recommended items be displayed?

Illustration showing what makes up a recommendations activity: Audiences, Criteria, and Designs

Out of the box, Target includes 14 built-in audiences, 42 built-in criteria, and 10 built-in design templates. You can customize each of these items or add your own. We’ve had previous webinars about building audiences in Target. This section focuses on defining the criteria, which define which items will be recommended.

Target uses the concept of the criteria card. A criteria card is like a recipe for personalization.

Criteria card illustration

It is important to choose or create the right criteria to achieve the personalization results you desire. A criteria is like a funnel that takes you from your entire catalog to your final set of recommendations.

Funnel illustration

The following sections describe the various parts of this funnel and how they work in Target:

Static filters (collections and exclusions)

Static filters are broadly applicable rules related to catalog attributes that you don’t expect to change frequently.

Collections and exclusions illustration

For example, in a content context, you might want to include all movies in recommendations, but exclude movies rated NC-17. In a retail context, you might have multiple brands in different parts of the world, but you want to recommend only products available in the United States. You might also want to exclude products from a regional private label.

These are all catalog attributes that are broadly applicable that you might want to use in multiple recommendations and you don’t expect them to change frequently.

Algorithm (recommendation key and logic)

The next step is to choose a recommendation key and logic. This is where you decide what is the basis for your recommendation.

Algorithm illustration

The first thing you need to choose is the recommendation key. The recommendation key is what you are “looking up” to choose the recommendation. This is what you are basing your recommendation on.

You might base your recommendation on:

  • The item the visitor is currently viewing
  • The category the visitor is currently viewing
  • The item the visitor last purchased or added to the shopping cart
  • A custom attribute related to a visitor or an item

Based on these keys, you then choose the desired recommendation logic:

  • Items with similar attributes
  • The most-viewed items in a particular category
  • Customers who bought this item also bought these items
  • A custom attribute

Out of the box, Target includes a portfolio of algorithms.

Portfolio of algorithms illustration

  • Popularity-based algorithms include Most Viewed and Top Sellers.
  • Content-based algorithms include Content Similarity.
  • Item-based collaborative filtering algorithms include Viewed/Viewed, Viewed/Bought, and Bought/Bought. Note that “bought” can be any conversion.
  • Personalized algorithms include Recently Viewed, Site Affinity, and profile-enhanced collaborative filtering.
  • Bring-your-own algorithms let you use your own custom algorithms.

Online business rules

The last step is applying online business rules. This is where you empower your algorithms with domain knowledge and current context based on what the visitor is doing on your digital property.

Online-business rules illustration

For example, in the content context, you might want to exclude movies that the visitor has previously watched, recommend movies by the same director, or boost movies in the same genre. In the retail context, you might want to exclude out-of-stock products, show items in a price range of $5 to $500, or boost items from the same brand.

Demo

After you complete the tasks illustrated in the recommendation funnel describe above, you are left with your final recommendation. To watch an in-product demonstration inside Target, the demo begins at 21:00 in the Adobe Target Basics Webinar, linked to below.

Adobe Target Basics Webinar: Introduction to Recommendations

Introduction to Recommendations

Target


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