6 Ways to Optimize Segmentation in Retargeting

Just in time for the highest traffic time of the year, Adlucent revealed that consumers respond primarily to personalized advertising experience, with 71% of respondents preferring ads tailored to their interests and shopping habits.

Experienced marketers know that today’s possibilities for segmenting data are endless, and agree that a precisely-targeted advertising campaign is fundamental to success. Today, new technologies allow us to personalize customers journeys and attract potential buyers with offers fit to their individual needs at points never previously seen before. Together with my team at RTB House, I chose 6 of the most promising ways you can segment your audience, products, or user activities to drive a successful advertising campaign.

#1: Product Category

You will probably agree with me, that product category segmentation is one of the most robust segmentation types. It builds upon products viewed by a potential buyer so that you can run unique campaigns creatives in smaller segments. For example, an online retail store trying to reach “high-fashion customers”, can easily separate out activities by promoting selected models or brands of handbags or footwear to people who browsed items within the same category.

#2: Product Value

If your goal is to increase sales of high-value or high price point products, you should know that modern personalized retargeting can be optimized also towards specific inventories. Thanks to AI, Self-learning algorithms will choose items to display in the same cost-range as products already viewed by the user.

#3: User Activity

Advertising activities can be also focused on specific types of users. For example, if you are advanced in retargeting you know that it is possible to run an additional campaign focused on users, for instance, who have not “visited the store’s website for a long period of time (e.g. 14 or 30 days)” or those who have “bought a sweater within the last three months.” These kinds of tactics help to build a brand awareness, maintain a long-term relationship and keep an eye on potential buyers. By showing dedicated notes on ads, exclusive discounts or tips about upcoming sales, you can plan long-term strategies to keep your brand top-of-mind.

#4: User Engagement

You can also focus your advertising activities looking at users engagement, like those who haven’t signed up for your company’s email yet. With information gathered through a custom tag or shared by a customer, thanks to personalized retargeting you can easily identify and inform this group of “unsubscribed users” about special promotions for registered users. This is useful for growing new subscribers while avoiding double-engagement for those who are already subscribed.

#5: Purchase Intensity

Campaigns can also be segmented by the intensity of purchase or the number of offers viewed by visitors, shoppers and buyers, added to cart, or bought. You can run separate campaigns with different messages and creatives dedicated between power-users who buy often (e.g. send a special discount for returning clients) and those who rarely or never purchase from your website (e.g. send a promotional code for a first-time purchase).

#6: Browsing Device

You also should be aware of consumers segmentation according to their browsing device: desktop, mobile, TV or any other device used to search for a product online. Advertising campaigns on different platforms enable you to run cross-device campaigns, target people on-the-go and take the advantage of not only timings in their offers, but the screens most likely to be used.

Summary

Today’s performance-based marketing campaigns can be targeted with so many different variables that you should discover which ones work best for your business and your client’s needs. When combined with deep learning algorithms – currently the most promising subfield of AI-oriented research – it is possible to get more reliable, richer, machine-interpretable user descriptions of customer’s buying potential.

Therefore, I have to boast that RTB House implemented a new upgrade to its recommendation mechanism using a combination of deep learning and computer vision. The new method enables ultra-precise predictions of possible user’s buying needs, leading to product recommendations up to 41% more efficient than previously. The final display is based on a full spectrum of information, which takes into account not only the referencing patterns made by other users with a similar buying profile but also what was previously presented on creatives. At the end of the day, improved performance brings a higher return on ad spend and helps to multiply ROI.