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Real-time customer profiler and its application

When it comes to building an effective marketing strategy, acquiring data is one thing and actioning on it is another. Mostly because the data is scattered and hard to perceive, and unifying it is a whole other task. 

It is only smart to invest in a tool that helps you not just see the bigger picture but derive insights to attain it. There are quite a handful of options to choose from, but when it comes to finding the right fit for your business, it can be a hard find. Apart from pricing, features, integrations, and support, you need a tool that helps you understand, and engage your target audience. 

Verfacto offers built-in features that can leverage data to fuel smarter marketing decisions. How? Thorough offering you the source of truth about the customer journey, their experience, and dedication to your brand.

You can cross-share this data with other tools you use. Based on the first-party data only, you can empower the whole marketing team by integrating the real-time data with any marketing tool running on your shop: Klaviyo, Google Optimize, Hubspot, Meta Pixel, Google Pixel, etc.

A specific example of how Verfacto powers your marketing is through one of its features “Real-time customer profile” which enables, per se, building segments of customers who were noticed on a website with an “unfulfilled buying intent” after returning to you for the 3rd time in a row from a “Direct” channel last week and pass this segment to Klaviyo.

Going granular and understanding a customer’s journey can help e-shop owners understand how to bring them to a conversion point. Let’s understand in detail how the Real-time customer profile works and what you can do with it (if you’re interested in this topic, take a look at this article about real-time data analysis)!

How does it work?

Verfacto keeps track of each customer session on your website through a tracking script. Once you set it up, Verfacto is able to retrieve raw data as well as aggregated data – which combines to make a customer profile. 

All of this is made possible through configuring the user object  to a global javascript variable “vfUserData.” Thos helps track and update entire users journey – from its first visit to becoming a loyal customer.

Here is a sample JSON object of a “window.vfUserData” variable that is returned for each visitor coming to your site.

				
					{
  "attention_rate":-34,
  "atc_rate":-34,
  "recency_score": 4,
  "frequency_score": 3,
  "c_aov": 48.2,
  "c_days_since_last_purchase": 19,
  "c_ltv": 189,
  "session_count": 31,
  "days_since_last_visit": 2,
  "is_returning": true,
  "last_channel": "Email",
  "cap_scores": {
    "affiliates": 0,
    "direct": 0.87,
    "display": 0,
    "email": 0,
    "organic_search": 0.09,
    "other": 0.03,
    "other_advertising": 0,
    "paid_search": 0,
    "referral": 0.01,
    "social": 0
  },
  "most_frequent_device": "mobile",
  "s_discount": true,
  "s_fullprice": true,
  "s_loyal": true,
  "s_refunders": true,
  "s_sales": true
}
				
			

All attributes are divided into several logical groups:

Behavior in the current session

A group of attributes that are mainly based on the data from the current session:

  • attention_rate—as the name implies, this attribute reflects how much user’s attention you currently have [-infinity … +infinity]
  • atc_rate—a real-time prediction of an “add to cart” event based on all users’ past activity.

Use case: These metrics are mostly used for on-site marketing. Instead of using generalized time or event based triggers, use predictive behavioral metrics to determine best timing.

Customer historical data

A group of attributes that identify the historical information about purchases and the monetary value of a customer:

  • c_aov—average order value of a customer (AOV)
  • c_days_since_last_purchase—days since last purchase
  • c_ltv—lifetime value of a customer (LTV, or CLV).

Use case: Segment your customers based on their buying behavior. Advertise expensive products to high AOV customers. Or determine users buying cycles and communicate with them using days_since_last_purchase.

Behavioral historical data

A group of attributes that identify the historical information about a visitor:

  • session_count—the amount of sessions a visitor made across all devices
  • days_since_last_visit—days since last visit
  • last_channel—the channel identifier of the previous session
  • cap_scores—acquisition profile scores that identify preferred or most frequent channels that a visitor returns to your website from
  • most_frequent_device—a visitor’s device preference.

Use case: Knowing a customer’s days since last visit lets you give a personalized message: a warm welcome after a break, or showing where he left off last time. If you are looking for advanced strategies, identify the customer’s preferred channel and exclude him from other platforms. Spend money only on effective ads.

Segments

A group of attributes that identify predefined segments that a customer/visitor belongs to:

  • recency_score—a score from 1 to 5 based on your customer interaction history
  • frequency_score—a score from 1 to 5 based on all customer profiles that are present
  • s_discount—true/false value showing whether a customer prefers discount goods
  • s_fullprice—true/false value showing whether a customer usually buys for full price
  • s_loyal—true/false value showing whether a customer profile falls into the definition of high value customers
  • s_refunders—true/false value identifying customer profiles that tend to make returns of ordered goods
  • s_sales—true/false value identifying customer profiles that tend to wait for bargains.

Use cases: Most common use case is RFM segmentation. Real-time profiler, can feed customer segments back to Meta business or Google ads seamlessly, so you can use laser focus targeting or have improved lookalike audiences.

How to use Real-time Profiler for your marketing strategy? 

Understanding your customers motivations is key to serving them better, which ultimately brings tangible value to your business. 

Studying raw customer data brings little to no value. Aggregated data, on the other hand, enables you to see trends and patterns hidden from the human eye that can be converted into business opportunities. Here’s how?

Building lookalike audiences on Facebook

If you just started advertising on Facebook and you don’t have a lot of customer data yet, interest-based targeting is a good solution to start searching for your audience.

You can begin by testing different interests to see what works and gain some traction. Once you start building your customer base and gather enough Facebook pixel data from your website, you can upscale your targeting methods and use lookalike audiences.

For example, you could set up a custom pixel that fires an event when a user with a higher LTV comes back to visit your website.

				
					<script type="text/javascript">
  if (vfUserData.c_ltv >= 150) {
    fbq('track', "ViewContent", {
      value: vfUserData.c_ltv,
    });
  }
</script>
				
			

This enables you to create a Custom Audience on Facebook and set up a lookalike audience. The beauty of this approach is that your audience will always be up to date.

Email marketing and Klaviyo metric-triggered flows

Klaviyo is a great tool that not only solves a number of email marketing and SMS marketing problems out of the box but also has great integration capabilities. One of them is the metric-triggered flow—an option that allows you to queue people for a flow when they take a certain action.

For example, you could track a custom event “Recency” and later on configure the flow in a way that a special welcoming email would be sent to your old customers who abandoned browsing your shop.

				
					<script type="text/javascript">
    var _learnq = _learnq || [];
    _learnq.push(['track', 'Recency', vfUserData.recency_score]);
</script>
				
			

Next Step? Access audience insights with Verfacto

With Verfacto’s Real-time customer profiler, you can collect and leverage advanced customer data with most of the marketing tools available on the market.

While we are just getting started with Real-time customer profiler feature, watch out for upcoming articles to uncover its capabilities to boost your marketing and business.

To try it out, simply sign up for Verfacto. In case of any difficulties or product-related questions, contact our support team or book a demo with one of our experts.

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