The importance of various data signals for Remarketing
User Remarketing is one of the effective techniques used by marketers today to get their site-visitors to purchase on their online store. While SEM, social and branding campaigns drive new traffic to your website, remarketing encourages the site visitor to go further down the purchase funnel by showing a highly relevant message, while being selective in going after the users so as to optimize your ROI. Remarketing depends on user data (website, CRM, social, emails etc) to be able to do so and the success of any remarketing campaign largely depends on how good the data collection is. The browsing pattern of the user on your property (website or app) can be a great asset to you when it comes to remarketing. The footprints users leave behind on your property can help you understand what he was looking for and how strong was his intent. For an existing customer the browsing data can be combined with purchase history or profile data to deliver even more effective messaging.This blog touches upon the key data signals marketers should focus on extracting and the mechanics of extraction.
Types of User data signals
The schematic representation below represents different signals you can capture under browsing data on an ecommerce site.
While this list covers most of the important data signals one can use, it by no means represents an exhaustive set. Also, other verticals like travel sites, finance, classifieds etc, may differ in terms of contextual signals but the signals depicting the intent strength remain pretty much the same across verticals.
Mechanics of Signal extraction
Since most of the online businesses (like ecommerce, travel, classifieds etc) allow user to access the website/app content without logging in, majority of the browsing data is tracked against the cookie. Remarketing platforms use pixels or tags to track the browsing pattern just like any web analytics solution does. The remarketing pixel should be placed on every page of the website, either directly or through a tag manager solution like Google Tag Manager.
Extracting Direct Signals
With every page load, the pixel fires, drops a cookie (if the user doesn't have one) and tracks details like timestamp, page url, referrer url (source of traffic), device type etc against that cookie on the server-side. Other signals like category, product, and funnel can be extracted using one of the following ways:
· Parsing the URL, if it is structured.
· Pixel crawling through the page and reading the values from certain DOM elements.
· Website owner passing the values explicitly to the pixel as parameters (params).
However, not all the signals can be captured at the time of pageload like user going through review section, photos within the product page or clicking a button (like add to wishlist) which doesn’t reload the page. In such cases, pixel is configured to listen to such user interaction signals and trigger a server call when it happens. Alternatively the website owner can fire the pixel explicitly with appropriate parameters when such an event happens.
Extracting Inferred Signals
Signals like recency, frequency, time spent on page, category coherence are computed on the server-side from individual events recorded by pixel. Product attributes like price, brand, offer etc can be inferred by looking up product_id (captured as a direct signal) in the catalog (usually provided programmatically as an xml feed). In absence of an xml feed, these signals can be captured as direct signals from the page itself by passing key values as params in the tag.
The above mechanics are applicable when the pixel is placed on your desktop or mobile-optimized site. Incase of a mobile app where cookies don't exist, the user is identified by device-id which needs to be passed to the pixel. The pixel is deployed using a tag manager SDK already integrated with your app.
Marketers who have user history or profile data can also bring these high value signals for leveraging in their remarketing campaigns. Such information can be passed via params and subsequent audience buckets can be created for remarketing. For eg: a fashion ecommerce site can pass whether the user is a male or female based on the profile data of its logged in user. The richer the data extraction about the user, the better will be the audience segmentation which will enable a high ROI for your campaigns.
Website browsing data is instrumental for an effective remarketing strategy. Different signals (like product, funnel, frequency, recency etc.) can be extracted by placing a remarketing pixel on your website and are used for crafting a personalized message and segmenting valuable customers for ROI optimization.
RevX platform provides the best-in-class data collection and segmentation functionality in the industry today. We have built advanced remarketing predictive model that leverage direct and inferred signals to predict the bid price and customize the marketing message.