Leveraging data to drive higher ROI
Website browsing data helps you understand what your user was looking for along with the strength of his intent. In the previous post of this series (Data is the difference!), we saw different user data signals that can be extracted from your website or app. This post will focus on using those signals for crafting a personalized message and segmenting valuable customers for ROI optimization.
Communication strategy using Contextual signal
The contextual browsing data signals like category, product visited, etc. can be used to devise a relevant communication message for the user. A customized and relevant message goes a long way in driving a positive response towards the ad. Few examples are:
- Somebody who’s searching for mandarin collar shirts or was found browsing multiple shirts of that type can be shown an ad showcasing other mandarin collar shirts in your collection matching the price range or color that the user was looking for.
- Somebody who put a certain pair of leather shoes in the shopping cart (but abandoned the transaction) can be shown an ad with picture of those exact shoes saying “Your favorite shoes are waiting for you! Use this Coupon code XYZ123 to avail a special 10% discount and make them yours!”
There are two ways you can achieve this:
- Manual method: In your remarketing platform, you can create segments for each category and target these segments to show the top 5-6 static creatives in their respective categories. Needless to say, this method is not scalable if the number of categories or subcategories in your product catalog is huge.
- Automated method: This method is far more effective in delivering a personalized communication and is scalable to the longest of product catalogs. If your remarketing platform supports Dynamic Creatives, the task of Ad generation is taken over by its recommendation engine and is executed for each user in the runtime. Products similar or equal to the ones browsed or cart-ed by the user are filtered out from the product catalog (remember the inferred signals!) and are used to generate an ad specifically for that user.
ROI optimization using Intent signals
The intent signals help you tell an accidental visitor or casual onlooker from somebody who’s seriously considering a purchase. These signals can be leveraged to spend selectively on the users (in terms of advertising cost or individual discount offers) and hence boost your ROI. Since most of the remarketing platforms use RTB to buy inventory from Ad exchanges, this translates into placing high bids on a strong intent user to win every opportunity of serving an impression to him and not bidding or bidding conservatively on someone who is not likely to convert. Few examples of selective bidding are:
- Somebody who was seen on the website regularly (3-4 sessions) for the last 1 week, checking out multiple Items in the Bed category with significant time spent and page views per session can be considered to be deep in the research phase with high category coherence, contemplating a purchase soon and hence, should be bid at aggressively.
- Somebody who put a certain product in shopping cart (but abandoned the purchase), or compared it with other items in the same category or added it to his wishlist is considered to be deep in the purchase funnel and should be bid at aggressively or even offered a personalized discount to give the final push.
Just like in case of contextual signals, there are two ways to achieve this:
- Manual method: In your remarketing platform, you can create segments based on recent views, frequency, funnel stage etc. and set a different bidding strategy for each of them.
- Automated method: If your remarketing platform supports Rule-based bidding, you can assign different bid-boost values to each intent signal like cart, recent views, category coherence etc, so that a user scoring high on multiple intent signals is computed a high bid value as well. Some sophisticated platforms leverage machine learning to compute dynamic bids for each user based on the intent signals. These predictive models decide the weight for each intent signal (in computing the bid value) by looking at the conversions and intent signals registered then.
Limitations of Browsing data based retargeting
This post cannot be complete without mentioning the caveats in browser data based retargeting. Since the entire user browsing pattern is tracked against a cookie here, this method suffers from limitations around cookie persistence and matching.
- Few browsers like Safari don’t allow third party cookie persistence, while some users surf in the incognito mode where cookies can’t be set. Systems with high safety settings flush the cookies after every session, making the retargeting effective only within the session.
- Cookies work in the silos of a browser i.e if a customer uses multiple browsers or devices to access your property, he will be treated as multiple customers and the unified view of his browsing pattern will be lost. This also means a cookie can be retargeted only on the same browser/device. However, the industry is trying to overcome this limitation with cross-device user identification which is essentially tying up multiple cookies/devices using keys like email id.
- If multiple users are sharing the same machine, their browsing pattern gets tracked against the single cookie. The remarketing platform then thinks of them as a single user which compromises the efficacy of remarketing.
Conclusion
Contextual data signals can be leveraged to deliver a personalized and relevant communication for the user based on his past browsing behaviour. While intent signals can be used to optimize the ROI by spending selectively on your retargeted base. Creating segments is the easiest way to use these signals, but if your website has a huge product/category catalog or you want to use multiple intent efficiently without exploding the segment count, you should go for Remarketing platforms with dynamic creatives support and automated bidding controls. Cookie-based tracking used to capture these signals has some limitations which the industry is trying to overcome by using fingerprinting techniques or tying cookies with actual user account.
