“Research tells us that organizations that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.”
-Research by McKinsey and Company.
By 2023, e-commerce businesses are expected to generate 30$ billion worth of revenue worldwide.
If the marketer’s intent is to stay at the top of users’ minds, then, you need to understand their behavior and serve their needs.
A user browses down the products page and wishlists blue shirt and a couple of other products and exits the page.
Now the marketer has the behavioral insights of the user on page view details, average time spent on the page, interested products, etc.
Based on this behavior, marketers can segment these users and run personalized ad campaigns, like
Behavior targeting audience segments
👉Users wishlist in the last 30days but not added to the cart
👉Users wishlist more than 5 products in the last 7days
👉Users added to their cart more than 5 products but not purchased in the last 7days
By user behavioral targeting the marketers can implement upsell and cross-sell tactics to improve the purchasers.
User behavior data helps improve upselling for your e-commerce business:
When a customer is encouraged to buy anything that would make their primary purchase even better. It’s like an upgrade to an existing order. Marketers often use this tactic in their campaigns to generate more sales.
For example, when a user is looking for a basic wristwatch, it’s a good idea to offer a smartwatch that’s 10-15% more expensive but well equipped with most of the things a smartphone does.
The user pays a little more but winds up with a better product. It’s a mutually beneficial deal.
User behavior data helps improve cross-selling
Amazon is a prime example of cross-selling. The practice of increasing sales by encouraging the user to buy more complements and enhances the products in the cart already.
For example, when a user is on the checkout page for purchasing the shoes, then you could have related products for the shoes like socks, flip-flops, shoelaces, etc.
Another common example is Mcdonald’s, “Would like to have some fries?”.
When you order a classic cheeseburger, in general, you won’t checkout without a drink and french fries. And that’s cross-selling.
First of all, for the average business that offers upsells and recurring products, 70% – 95% of revenue comes from upsells and renewals. Only 5 – 30% come from the initial sale.
And needless to mention, attracting a new customer is 5 times harder than retaining the existing customer.
But is the marketer able to do behavioral retargeting NOW?
With not much buildup, after the iOS’14+ update, the retargeting ad campaigns themselves have taken a huge toll as Facebook and other ad platforms access to user behavior data is severely limited.
Impact of iOS’14+ update on Marketers?
Limited user behavior data:
As Apple has given the choice of opting out from tracking, Facebook and other ad platforms have limited access to user behavior data.
For instance, if you want to retarget the users who have visited your website 30days back or wishlist products, Facebook cannot target the users who have opted out of tracking
Pathetic Facebook custom audience match rate:
Let’s say, you wanted to create a segment of users who “wishlist products for the last 15 days” and run personalized ad campaigns.
Since Facebook has limited access to user behavior data, Facebook manages to match only 20 to 25% of the total audience size.
It has been more than a year since the iOS’14+ update and marketers are getting used to the poor audience match rates.
Does the limited user behavior access stop with iOS users alone?
Similar to Apple’s App Tracking Transparency policy, Google has come up with its own Android privacy update, which too won’t allow third-party apps or website to track their user behavior data when a user opt-out of tracking from personalized ad campaigns.
And Chrome will be phasing out third-party cookie tracking by 2023
The challenge does not end here. Google announced, that, by early 2023 they will be phasing out third-party cookies from chrome.
During the first 10 months of the pandemic, we have seen 10 years of change. And the evolution is subjected to grow towards more user consent and privacy.
However, the only solution that we’re left with is collecting your own customer behavior data i.e.first-party data.
Advanced audience segment limitations on Facebook
For example: In an e-commerce business, users in general, perform various actions and they could be targeted based on their behavior patterns like,
👉Category wise page visitors
👉Added to the cart but not purchased
👉The user added more than 3-products in the cart, etc.
But the audience segment limitations on Facebook will limit you to segment audiences based on their default audience segments like
👉Viewed product pages
👉Added to cart
The future proof solution for the cookieless world – First-party data
Up until the iOS’14+ update, Facebook tracks the website visitor data and stores them. And marketers use it to create segments and run ads.
But after iOS’14+ updates, Facebook is no longer your user behavior data host.
And marketers are recommended to collect their own user behavior data and use Facebook as an advertising platform only.
Own your user behavior data i.e.First-party data.
Along with the CRM data, lead forms data, and subscription data, anybody that visits the website is your first-party data.
Generally, 98% of the website visitors are anonymous visitors. They could have landed on your website by a Google ad, Facebook ad, a social media meme or newspaper ad or television ad, or an organic search.
Usually, brands ignore or do not notice these anonymous website visitors and invest their resources, energy, and money in the remaining 2% of known visitors.
To improve audience match rates and event match quality – You need First-Party Data
First-party data is more than just email id and contact number.
From both known and anonymous website visitors, we collect Browser ID, Click ID, IP address, User-agent, Facebook ID, and Google ID along with the first name, email id, and demographics.
With these data points of the known and anonymous website visitors, Facebook could match beyond 70-80% of the audience easily.
Read here to know how first-party data improved the Facebook custom audience match rate beyond 80%
Similarly, as per Facebook’s recommendation, 5/10 is considered to be a decent match quality. But we have pulled a minimum of 6 or 7/10 in the campaigns we run.
Read on how to improve the Facebook event match quality using first-party data.
Basic e-commerce audience segments every brand must have
It’s 2022, and as marketers, we need to move away from the old-school audience segments of “added to cart” and “all website traffic” to smart audience segmentation.
With the first-party user behavior data collected, marketers can retarget the audience based on the website behavior like,
👉Users who added to the cart but not purchased in the last 15days
👉Users who have a wishlist of more than 10 products in the last 30days
👉Users who have visited the add_to_cart page more than 5times in the last 7 days
👉Users who abandoned their cart for more than 60days
Advanced e-commerce audience segments every brand must have
The majority of the users who have interacted with an e-commerce website will probably be stuck in the mid-funnel segments (visitors who check out the website more than 2-3 times, visitors who added products to the cart, and visitors who wishlist products) and are mostly untouched.
70% of the high-intent audience are stuck in the mid-funnel.
You can segment these audiences in the mid-funnel based on user behavior. Like,
Mid-funnel audience activation segments
👉Wishlist products for more than 7 days
👉Wishlist products less than 3 days
👉Product _viewed_more than 3times
👉Cart_abandoned for more than 30days
👉Segment the users who have a wishlist of more than 10 products
👉Segment the users who are first-time visitors and provide them “newbie discount codes”
Unlike the bottom and top of the funnel, mid-funnel is the most overlooked and ignore funnel. We recommend splitting the budget across the funnel in the ratio of 50:30:20 (tofu:mofu:bofu).
Read the following blog to learn more about mid-funnel strategies using first-party data.
Here’s a sample sheet on how our clients segment their data and plan their campaigns based on the funnel, user behavior, and messaging.
Upselling audience segments for e-commerce brands
Increase upselling with personalized audience segments
E-commerce marketers can predict and model the users based on their purchase history. Some of our e-commerce clients have been implementing upselling tactics and retargeting their users based on their purchase history like,
Here’s how one of our mattress clients utilized our segment features for an upsell
👉Customers who added a single-cot mattress were segmented and offered a discount over an upgraded version of the mattress which was good for their back pain and extra spongy.
Note: Based on the Purchase history and age group, users can be retargeted with the relevant products. And products like mattresses can be sold specifically for the age.
Fashion brand’s upsell techniques:
👉Female customers who had higher cart values were retargeted for cosmetic products on offer.
👉Customers who purchased top-wear clothes were retargeted for combo offers along with bottom wear.
Cross-selling audience segments for e-commerce brands
Some of our client’s favorite audience segments are,
👉Retargeting the user with pillowcases, bed-spreads for the user who added pillows to the cart.
👉Retargeting the user with storage containers and utensils for the user who added utensil-basket.
👉Retargeting the user with phone cases, and mobile accessories for the user who added mobile to the cart.
How India’s leading fashion brand WFW implemented advanced audience segmentation:
WFW is a women’s fashion brand that implemented advanced audience segmentation for its users based on user behavior, purchasing habits, average order value, and many other factors.
Some of their audience segments are:
👉Female customers who had higher cart values were retargeted for cosmetic products
👉Customers who purchased top-wear clothes were retargeted for bottom-wears
👉Predict the possible next purchase of customers based on their purchase history
👉Predict the first purchase of a website visitor based on their website behavior
👉Segmenting the plus-size customers based on the PDP(Product Detail Page) visits.
To read more about the smart audience segmentation.