Integrating Marketing Attribution & Customer Lifetime Value Through AI

Marketing attribution isn’t a new concept. It’s the inevitable combination of smart marketing and advanced technology. In many ways, it’s surprising this technique didn’t catch on earlier. 

Today, marketers use marketing attribution for maximizing ROI and improving marketing funnels. But that is, by no means, the extent of its potential. In a fast revolutionizing world led by AI, the possibilities and benefits of marketing attribution are endless.

But there is a catch.

Marketing attribution alone isn’t sufficient to give brands the edge over the rest of the field. It has to be integrated with customer lifetime value to be truly useful. Combined with CLV, marketing attribution has the power to enhance conversions manifolds.

That, however, is easier said than done. With all the consumer data pouring out each second, the connection between them isn’t immediately obvious. It takes an experienced mind and a well-calibrated AI-backed algorithm to integrate CLV with marketing attribution. 

Today, we’ll show the connection between these two vital concepts and how they differ. More importantly, you’ll see how AI integrates CLV with attribution modeling and why it’s crucial for business success.

integrating marketing attribution with CLV with AI

A Closer Look at Marketing Attribution

So let’s start by getting to grips with the concept of marketing attribution. In this section, you’ll learn the essence of marketing attribution; it’s importance for modern brands and the fundamental difference between it and customer lifetime value.

What is it?

In technical terms, marketing attribution is allocating the budget according to the purchase pathways for ROI maximization. A bit too complex, isn’t it? Let’s try defining it another way.

It’s the amount of money you attribute to a marketing channel, depending on user preference. Essentially, this means you’ll allocate more money to the marketing of a channel that diverts more users to your business. And that’s why it’s called marketing attribution. 

This concept evolved thanks to the shift to digital. As brands and users began interacting online, businesses needed a way to organize their spending. Today, there are dozens of channels through which users interact with companies. Companies must know how much money to allocate to each channel. Otherwise, they can end up wasting thousands of dollars and missing opportunities to expand their reach to relevant audiences.

Why Do Businesses Need It?

The purpose of marketing attribution, among other things, is to maximize ROI.

But how?

A brand’s presence on each channel requires some amount of investment. The amount of business generated from each channel is the return on that investment.  A company can pinpoint the business generated by carefully monitoring data touchpoints between the user and the brand. Usually, businesses use analytic web platforms such as Google Analytics for this purpose 

Once the analytics map out data touchpoints, brands can accurately estimate the effectiveness of each marketing channel. This analysis also shows the ROI for each channel. Thus, businesses can optimize effective channels and adjust or eliminate investment in ineffective media.

The data collected for attribution modeling also helps in content marketing. Since each interaction between the user and brand is carefully monitored, businesses can pinpoint the exact content or message that helped move the user along the marketing funnel. They can then devise entire content strategies based on the types of content that proved most effective for specific segments.

marketing attribution models

How Is It Different From Customer Lifetime Value?

Marketing attribution tells a business what amount of resources it should allocate to a particular channel based on ROI. 

CLV tells a business the expected money a consumer will spend with a company throughout the consumer lifecycle. As such, marketing attribution and CLV complement each other. One sets the resource allocation priorities for a business, and the other set consumer priorities. Many companies, however, tend to rely on one or the other. This strategy is ill-advised because both measures represent the whole picture. We’ll discuss more regarding this later on.

For now, let’s look at some standard attribution models.

Models of Marketing Attribution

There are several marketing attribution models, but we will only go through the most common ones here.

1. First Touch Attribution

This model is the most basic. It assigns all the weightage to the very first point of contact with the user. For example, suppose a user receives an email as the first touchpoint and then converts into a lead later on. According to this model, the email channel would get the entire credit for that lead. But this model is also misleading because it distorts the perceived effectiveness of the first channel.

2. Last Touch Attribution

In contrast to the previous model, the last touch model assigns full credit to the last touchpoint before the final sale. This model is easier than the former since it is easiest to track the last contact point for purchase. However, it doesn’t account for any prior interaction. So you won’t get a complete picture of how the user got to the last touchpoint. This may lead to faulty assumptions and suboptimal budget allocation later on.

3. Linear Attribution

Perhaps the most straightforward attribution model of all is the linear attribution model. It assigns equal weightage to all the channels leading up to the sale. So if a user receives an email, searches for the website on a search engine, finds a blog, clicks a link, and then lands on the website for purchase, all the funnel channels will get equal credit.

4. Time Decay Attribution

The time decay attribution distributes credit among channels according to recency. That means the most recent touchpoint gets the most credit, and then each prior channel gets progressively less credit. So the time frame is an essential metric in this attribution model.

5. The Full Path Attribution

The full path attribution model sketches a W-shaped credit distribution. Essentially, this means that significant points in the customer journey are assigned the most credit. The touchpoints in between get lesser credit weightage and, consequently, lesser budget allocation. This model’s benefit is that it monitors post-sale interactions and gives them the same credit as pre-sale activities.

Marketing Attribution & CLV – The Big Picture

We already know what separates CLV and marketing attribution. Now let’s look at what unites them. 

Marketing attribution may tell businesses how they need to allocate their media budget, but it doesn’t show the whole picture. Let’s explain this with an example.

Suppose you use one of the data-driven attribution models to track user interactions with your brand. According to that model, organic channels such as search engines drive the most customers to your website. That’s great; now you know that you have to increase media spending on Google Ads. 

But wait! How many of the Google directed customers come back for another purchase? Which of them only purchase once? How many of them have a higher one-time purchase revenue than the average?

If you aren’t asking these questions, you aren’t looking at the big picture.

This stage is where customer lifetime value comes into play. CLV helps answer these questions and complements marketing attribution models. The latter alone isn’t enough to predict a user’s long-term profitability regardless of the channel of acquisition. That is the job of customer lifetime value.

Similarly, only tracking profitable customers isn’t going to help you if you spend a fortune acquiring them. Without marketing attribution, you’ll never know the right mix of media spending to optimize customer acquisition. 

So marketing attribution and CLV are like yin and yang. They work together to reveal the best strategy to target users and optimize marketing channels. 

But what’s AI got to do with all this?

Unifying Marketing Attribution & CLV with Artificial Intelligence

Artificial intelligence is the bridge that connects customer lifetime value with marketing attribution. AI is rediscovering CLV in a whole new way with novel techniques. Companies are now able to calculate customer lifetime value with the use of advanced analytics. This method allows them to accurately predict the CLV for an individual consumer, which is a huge advantage. 

But the real benefit of using AI for marketing attribution and CLV is that it provides customer valuation. A customer’s life cycle consists of three stages; acquisition, development, and retention. This valuation helps allocate the budget (also an objective of marketing attribution) to the most lucrative channels and consumers. 

It’s usually hard finding a connection between the channels that bring the most profitable customers since there’s so much data. With AI, businesses no longer need to spend countless hours analyzing the data. Instead, the algorithm uses predictive analytics to pinpoint the optimal budget allocation and predicts future CLV. 

The beauty of AI is that it can pinpoint the customer life cycle stage through data analytics. So you know which customer is ready to make a big purchase, which channel they are coming from, and how much budget you need to allocate to retain that customer, all thanks to AI.

What’s in it for You?

Data-driven attribution models do not require meticulous attention to setting rules for credit allocation. This step is both time-consuming and demands technical experience, which puts new business models at a disadvantage. With AI, you don’t need to do any of that. The artificial intelligence algorithm monitors each touchpoint between the user and the brand. It then builds a model on its own that accounts for the most critical touchpoints. 

Not to mention, AI-backed CLV analysis automatically integrates into the attribution modeling. You’ll get better insights into how your users are interacting with the brand and which channel is performing most effectively.

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