We all know that a one-size-fits-all marketing strategy is no longer a best practice. Every customer is unique. When we define a customer, the persona includes user interests, behavior, device variability, location parameters, gender-based decisions, and many more. Any product analytics can derive this information for you, and now it’s high time to use this information to increase your eCommerce conversion rates. eCommerce User segmentation is the starting point for your success.
The power of eCommerce Customer segmentation
Segmentation makes sure that your marketing efforts pay off. Thus all successful campaigns out there have used segmentation, and it’s working for each one of them in precisely targeting their customers. Let’s dive deep into the segmentation process and how to use it in different business scenarios.
How to segment your users?
- Make use of the available data to figure out “what defines your customer?” or “whom do you want to target to achieve a specific goal.”
- Make a list of things that uniquely identify a set of audiences. Define rules for the same.
- Configure the same rules on the user engagement tools like Upshot.ai and set the report generation period (by selecting a date range). You can also use the forever checkbox to filter out every user who joins the platform after a particular start date.
- Make the segment active, and you can use it while configuring your user engagement campaigns.
Tip: Upshot.ai has made the eCommerce customer segmentation process simpler. You can segment your users with a single click readily available at each step of the funnel and each step of the user journey using a save icon or add-to-segment widget.
So if you set up a conversion funnel, you can strategically engage your users who channel through the funnel and interact with the drop-offs at each stage. It can boost your user engagement 10x fold.
Types of Segmentation
- Demographic segmentation
- Psychographic segmentation
- Behavioral segmentation (Based on digital interactions)
1. Demographic segmentation
Demographic eCommerce customer segmentation groups users who have similar user profile characteristics like gender, age, location, marital status, religion, occupation, and special days, to name a few.
– Gender-based segmentation
Every time you log in to a new website these days, a pop-up enables you to update your profile data, specifically gender, to personalize the website. Thus gender adds a filter to everything you hear from the brand from that point. On the back screen, the gender preference gets updated in the user’s user profile data and makes him/her qualify for the gender-based segments and associated campaigns.
It’s just the same with every other type of demographic segmentation we are going to discuss here. Once the segmentation is over and the campaign is live, the campaign varies according to user choice.
– Location-based segmentation
Imagine you have launched a new store in San Francisco. You want users in San Francisco to try the latest range of products at the new store. So your goal is to increase the footprint of your new branded showroom.
KFC, one of the leading retail brands, also stumbled upon the same challenge before. That’s when they started location-based campaigning with relevant banner images of the newly launched stores nearby with relevant banner images of the newly launched nearby stores.
Thus location-based campaigns increase offline store footprints with online marketing. Google Maps has got a similar feature too. Checking on “restaurants near me” will rely on the user’s current location data to fetch a nearby restaurant list.
– Age-based segmentation
Everyone has a smartphone these days. Lots of teenagers access social media on their smartphones, while games are popular with kids. With a lot of marketing taking over personal spaces, how to watch over your kids in online spaces. Parental control is an option, yet not the immediate solution for this.
Identifying this pain point of the parenting partners, brands recreated their marketing strategies for different age groups. A consent form or a captcha is made available to validate the user’s age before displaying anything else.
– Social status-based segmentation
The more information we know about the user, the more we can target them with resonant messages. For example, knowing their profession, birthday, or any personal information can route a calendar for communicating and greeting on their special days.
Lots of teenagers love to get discounts on apparel and fancy stuff when their birthday is around. Budget-oriented families love discounts on frequently bought grocery items.
Brands are strengthening their bonds by wishing their users their big days. It is adding a humane touch and personal bonding to the communications, which is subconsciously increasing the brand image and user loyalty.
– Priorities matter
Priorities and choices vary a lot based on the family status of the user. Being single, users tend to spend more on fashion and gaming, whereas partners with children tend to spend more on children’s education and welfare. Personalizing user experiences based on the user’s family status resonates with their day-to-day activities, and users get more connected with the brand.
2. Psychographic segmentation
Psychographic eCommerce customer segmentation focuses more on the motivations behind consumer behavior. It helps businesses to find what drives the decision-making process of the targeted consumers.
To come up with a strategy for psychographic segmentation, one needs to understand the business and its short-term and long-term goals. Psychographic segmentation involves looking into the ways of living, like lifestyle choices, values, principles, and everything that is invisible yet powerful.
You got two user profiles who are buying fitness-related products of high value (more than the normal user average cart value). User 1 buys a fitness product every month without fail, irrespective of any discounts provided. User 2 buys the same product in multiple quantities when discounted. Both are generating revenue for the business. One is consistent, and the other is discount-driven. We need to separate these two users into two categories, “consistent buyer” and “discount driven.”
Therefore, psychographic segmentation not only varies from industry to industry but also differs at the user level. Every detail of user behavior matters, and a psychographic profile starts to shape up.
3. Behavioral segmentation
As the name suggests, the primary focus of this type of segmentation is user behavior. User app usage patterns, purchasing habits, user journeys for a specific period, and more can speak volumes about their actual conduct and interests.
– Purchasing behavior
Similar to real life, there are window shoppers and serious buyers in digital spaces. It doesn’t matter; however you market a window shopper is hard to convert; in those cases, all we can do is habitual training. In contrast, a serious buyer is highly motivated to purchase with less effort and is good to target frequently with personalized communications.
Loyalty, by concept, is simple but extremely powerful at conversion levels. It’s like incrementing the user goodies for every transaction and making sure that users don’t turn toward your competition. It takes time to create a segment of loyal customers, but it will pay off in the long term.
Psychographic segmentation and Behavioral segmentation look similar, but on a quick note, psychographic segmentation is a subset of behavioral segmentation. It might share similar characteristics because motivations and behavior can cause similar user interactions.
How does Upshot.ai use eCommerce customer segmentation power?
ITC is one of our premium clients powering Mangaldeep, A devotional app with huge devotional content like pujas, vidhi videos, slokas, chants, temples explorer, and many more.
Being an Indian-origin app, Mangaldeep has diversified content supporting all state languages and their related festival information. And most of the users are more comfortable with their native languages than English. Thus, ITC personalized and localized (text conversion to a native language) its in-app content while communicating with its users.
Using the segmentation engine of Upshot.ai, we have segmented users based on their language and state. During festivals specific to particular states, the Upshot.ai team executed specific campaigns only for users in those states, and every communication was localized per the user’s preference.
Segmentation also helped us to find more insights about users from different states, feature adoption, and behavior.
To know more: Check our ITC case study.
Panini is one of our favorites again for making the best use of our segmentation engine. Panini began as an in-store company, selling their NFL, NBA, and FIFA sports cards & memorabilia in physical stores such as Walmart, Target, etc.
As the team found that users who were retained till Day 7 had higher chances of purchasing in the future, a 7-day onboarding campaign is designed for first-time users. As a result of personalized onboarding, users are retained and motivated to make a purchase.
Also, based on the user purchase behavior and content preferences, personalized deal bundles were served, and as a result, sales increased.
To know more: Check our Panini case study.