Helping users of your website or mobile properties find items of interest is useful in almost any situation. This is why the concept of personalized marketing is relevant. However, when I reflect on what one-to-one marketing is all about, it breaks down into three tiers:
In this article, I will:
A massive differentiator of SAS within the martech space is a focus on the analytics lifecycle. Only by recognizing and fully supporting the phases around data, discovery and deployment will brands get a complete process to take advantage of impactful insights. Everything SAS does is built around the recognition that organizations need to get from data to value, with the analytics lifecycle as the underlying principle. We have it down to a science, and algorithmic recommendations is one of many customer personalization challenges currently supported.
Behind the scenes, SAS Customer Intelligence 360 uses collaborative filtering based on the idea that people that share preferences for certain products are likely to share preferences for other products. The method groups users into “neighborhoods” where users in the same neighborhood have similar preferences. Product recommendations for a user are generated by analyzing navigational and behavior user patterns and estimates preferences for products. These preferences are converted to implicit ratings as input to the collaborative filtering method.
There are a few challenges every recommendation system must overcome:
SAS Customer Intelligence 360 uses two flavors of algorithmic techniques in a hybrid approach that combines collaborative filtering and content-based (item) filtering to deliver recommendations, while overcoming the scalability, sparsity and cold-start problems.
User-centric collaborative filtering is the main technique used by Proc RECOMMEND, because it takes the implicit ratings of users for items as inputs, and outputs the recommendations for individual users. The following methods are supported in SAS for custom use cases:
SAS Customer Intelligence 360 exploits matrix factorization using singular value decomposition (SVD) for automated recommendations. If you’re unfamiliar with the benefits of SVD, please check out this link. Specifically, it proves effective in combating the scalability and sparsity challenges common in recommendation systems. However, the cold-start problem presents itself with subsets of visitors, and the resulting impact is the lack of sufficient information to derive meaningful recommendations using matrix factorization. In these cases, item-centric filtering methods prove beneficial when a visitor’s implicit inputs are insufficient.
Content-based (item) filtering is the main technique for link analysis, because it uses inputs of the item attributes and a profile of the user’s preferences. The algorithm will recommend items that are similar to those that a user liked in the past (or is examining in the present). Various candidate items are compared with items previously rated by the user, and the best-matching items are recommended. Two areas of application supported in SAS for custom use cases are:
SAS Customer Intelligence 360 approaches link analysis by analyzing the implicit data inputs to define the association rules to be used for recommendations. The rules are transformed into a graph that consists of nodes and links. The centrality of a node in a network is a measure of its structural importance. There are many centrality measures in graph theory worth considering.
SAS Customer Intelligence 360 ultimately uses all these measures, scores the data, and produces a next-best recommendation for a user when insufficient information is available to apply the Proc RECOMMEND method.
It is important to note that users are not limited to automated recommendation targeting. Here is a great paper on building custom recommendation engines in SAS Viya that you can use with SAS Customer Intelligence 360.
Here is how you can build and deliver recommendation targeting in SAS Customer Intelligence 360. Recommendations are available for web and mobile channels (highlighted below in blue borders) from within a SAS Customer Intelligence 360 task.
At a high level, the process workflow for a marketer consists of the following:
Step 1: Create the visitor behavioral event as the basis for recommendations – for example, a product view event.
After selecting the event type, users can select a web page view or mobile app action as inputs for data collection.
The user then selects the page(s) where product views will take place. One option is to navigate to the page (I will use the sas.com website for this example).
Lastly, users define the key data points from the product pages that will ultimately feed the recommendation engine. For user-centric or item-centric approaches, the user simply needs to define the ingredients, such as Product IDs, Product SKU, etc. to be captured. This step is important because it provides the inputs for SAS to run automated analytic procedures. These attributes are scraped from the page and become part of the visitor’s state vector. The values are fed into the algorithm which subsequently informs SAS Customer Intelligence 360 to obtain the correct content for a recommended product(s), and deliver into the visitor’s website or app session.
Step 2: Select the creatives to be used to display recommendations
The user now needs to specify the family of creatives available for selection and delivery for every visitor session that triggers the recommendation system.
Next, the software will request the user to pick the URL template that will deliver allocated imagery.
The URL template is a one-time configuration based on your selection of Recommendation targeting at the user or item level. Users leverage the template to create a reusable URL containing parameters, such as %%Product_ID%% or %%Product_SKU%%, which SAS uses to locate creatives. For example, a URL template can contain a Product_ID of “SCI-01”, and when deemed algorithmically relevant during a visitor journey, the content can be uniquely selected for delivery, such as the example highlighted in blue below.
Step 3: Define the spot(s) where recommendations are to appear
Spots are the specific areas of the digital experience where users want the recommended product creatives to display. Visual selection tools accommodate the designations.
Step 4: Make a task to display recommendations in the spot(s)
Now that a user has the data collection, creatives and spots ready, it’s time to turn on the recommender task.
A menu will walk the user to select their preferred options. Users can select “product-centric” to make recommendations based solely on associations of product views across all visitors. Users may also select “user-centric” to make recommendations that take additional visitor behavior into account. Mapping back to how the user specified the collection of relevant data, the product identifier is also available for designation.
The ability to map the task to other targeting criteria can be used to further qualify which visitor sessions should receive targeted personalization.
Step 5: Publish the task
The moment of truth arrives to publish and unleash the personalization workhorse on a web or mobile experience.
Step 6: Monitor results against a control group
An important part of evaluating recommendations is whether the products being proposed make business sense. The inner workings of a recommendations system can be opaque, so it can be difficult to construct test cases to be certain whether the generated products are optimal. It’s more of a numbers game and something of an art to figure out how appropriate the recommendations are. And appropriate in this sense also reflects the business strategy for using recommendations in the first place.
Once recommendations are being served, it is natural to ask how well they are performing. Most brands will have an idea of the baseline they want to compare against. In many cases, organizations simply promote popular products. SAS Customer Intelligence 360 includes the ability to form a control group consisting of a user-designated number of popular products. A random subset of visitors is served using a “most popular products” strategy, and the performance of the algorithmic recommendation system is compared to this group. The results are presented in an easy-to-understand tabular form showing comparative conversion and lift rates.
Whether it is A/B, multi-arm bandit, multivariate testing, recommendation systems or propensity model targeting for varying flavors of personalization, SAS Customer Intelligence 360 showcases its analytic strengths and reinforces our vision to be the leader in bringing advanced techniques to digital marketers.
Consumer behavior is challenging to predict, but by using multiple algorithms, SAS Customer Intelligence 360 can shapeshift to meet the demands of the modern consumer.
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