Intent-based customer segmentation is a technique of targeting in marketing to predict the outcome of customer segments. For instance, if a customer browses through an eCommerce store, with intent-based customer segmentation, retailers can predict whether they would make a purchase or if they are merely browsing.
Imagine that you have a loyalty marketing mobile app for your eCommerce store. Let’s say that your app has 50M+ downloads and has over 100M subscribers to date globally.
Now, let’s say you want to launch a viral campaign using targeting in marketing. And maybe your goal is to maximize the signups in the next 30 days. So, let’s imagine that you need to get another 50K signups.
Tell me, what will be your first step in achieving this goal?
If you thought like me, you’d probably bank on intent-based segmentation for eCommerce to identify the audience most likely to meet the goal.
As you leverage a data science engine like Ingage, you can create an intent model-based targeting in marketing for the goal by considering different data points as inputs.
Here are a few for example:
- Frequency of user visits
- Clicks on a specific campaign
- A user’s most recent visit
- No. of user transactions in past
So, as you explore various data points and analyze them using targeting in marketing, you can come up with three potential outcomes for this intent-based segmentation:
- Most likely segment
- Moderately likely segment
- Least likely segment
Investing in the most likely segment would help you to achieve your goal.
Let’s now get a deeper understanding of the various types of intent-based segments for eCommerce.
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You would have products over a diverse range of prices. Some could be affordable and some might be meant for the posh folks.
Now, if you want to introduce a new pricing scheme for your products, then you might want to consider the spending intentions of your customers. So, with customer segmentation, you would know which customer segment of your customer base would purchase product X for price Y.
To create the spending intent-based segment, go to your customer data platform (CDP) and set the filter to the following conditions.
- Purchase is greater than [enter value here]
- The last transaction date is [enter a value here]
With these filters for targeting in marketing, you could list the customers who can spend beyond the set threshold. Also, by specifying the transaction period, you could discover customers who frequently purchase from you than those who don’t.
This intent-based segmentation is useful to tell customers who are just browsing from those who are actually interested in buying from you.
Those who are just browsing through might display random behaviors. So instead of pushing your way through the uncertain, you might want to look at the following characteristics that characterize purchase intent by targeting in marketing.
- Add to cart
- Price filters
- Product brand filters
Depending on your niche industry, you can add as many possible filters as you need to create the segment that is game for purchase.
So, once you find out the segment that is willing to pay, you can leverage the insight to tweak your marketing campaigns.
Furthermore, this exercise will help you understand how your customers approach the purchase decision. It will shed light on the complexity of the purchasing process if there is any. You can also find out barriers along the path to purchase and remove all hurdles.
Best of all, you can pinpoint those behaviors that are most and least predictive of purchase-making with this technique of targeting in marketing.
Customers can be at different stages of their buying journey with your brand. Some might be just getting to know your brand. Some might be your ambassadors.
While all your customers are important to you, you must realize that your most loyal customers are the most valuable. So, you can’t afford to treat all your customers the same way. You do have to deliver red carpet attention to your loyal customers.
But how do you go about this, discovering loyal and potentially loyal customers?
Thanks to AI-based customer relationship management systems such as Ingage that you can predict and mark customer loyalty. You can log in to your Ingage account and head over to the Segmentation module.
Go to the Intelligent Customer Segmentation tab and tune the RFV to loyal customers. In a matter of minutes, through its machine learning intelligent algorithms, Ingage will create a loyal segment from your entire customer base.
Based on the produced report, you could then find out the key behaviors along the customer journey that lead to loyalty. You can also handpick customers for loyalty programs and delight them.
This targeting in marketing will help you to easily maximize the value and revenue from your most loyal customers.
Recommended Reading: 17 eCommerce Customer Segmentation Examples to Try Out!
As far as your eCommerce website is concerned, you need to keep an eye on your home page, product page, and check-out page.
To segment your customers based on their behavior on the homepage, you can use the following ideas.
- Clicks on pages with sale
- Categories that users frequently browse in
- Products looked at
- Brands that have greater inclination and attraction
You can spice things up by integrating other filters such as gender, demographics, geography, and the likes, to get precise results.
Then, for your product page, you might want to segment customers based on the filters they use. You can see if users click on product recommendations. Discover if there is any conversion such as adding products to wishlist, cart, chat option, and so on.
And finally, you need to segment your customers based on the time they take to convert. In case, the user is new, do they convert in their own time or is there a resonance between old conversion rates? That is one possibility to explore.
So, here are some questions you might want to consider while creating conversion segments.
- How much time do my regular users take to purchase a product?
- Is cart abandonment influenced by price?
- Do I see a peak in cart abandons at the end of the month?
- Are my sales escalating on payday?
- Are my products added to the cart first or are they directly purchased?
- How do my customers respond to offers, discounts, campaigns, and likes?
1. Choose a Platform
If your existing CDP or CRM supports intent-based segmenting, then great! If not, you might want to consider an AI-based segmentation software like Ingage to get started.
Recommended Reading: List of The Best CRM for eCommerce That Your Competitor Uses to Drive Profits!
2. Fix a Goal
Once you have chosen your segmentation platform, you would then have to define your eCommerce segmentation goals.
Using filters and events, you can define the behavior you want o to encourage or discourage. So, this could vary from augmenting purchases to reducing cart abandonment.
3. Let Your Segmentation Platform Predict the Outcome
After defining your goals, wait for your segmentation platform to run its in-built data science models to predict the likelihood of you meeting your goals in the stipulated timeline.
4. Discover Your Intent-Based Segments for eCommerce
After running the data science engine, you will be given a report of the intent-based customer segmentation that will assist you in achieving your defined goals.
It will show you which users from your customer base are most likely, least likely, and moderately likely to meet your goal. With targeting in marketing, you can interact with them and nudge them towards your sales goals.
5. Engage and Scale!
This is your playfield!
You have all the data you need. Now, it’s time to engage each of these eCommerce segments with targeting in marketing to meet your goal. Using this approach, you are eliminating the guessing game around the right content or right audience for your marketing campaigns.
Now that you know the power of eCommerce intent-based segmentation, don’t hesitate to leverage its power for customer acquisition and customer retention. You can get started right away by creating your user account in Ingage.
Click here, and we’ll give you a personalized one-on-one walk-through of eCommerce segmentation.
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