Modeling marketing multi-channel attribution in practice

When you retain a customer, you are retaining their lifetime value – the value of their entire future relationship with your business. Retaining customers means retaining that lifetime value — both in terms of their spend and their power to influence other potential customers.

How do you do this? By understanding the customer journey — the typical lifecycle of a customer — you can identify where you have weak points that cause defection and uncover opportunities to improve.

How many times you have asked yourself: What is the next step I need to take to close his deal? What will this customer ask for next and how can I drive it to him? What is the shortest path to close a deal?

How much do all my marketing and sales activities really cost? How much does one action or marketing channel costs?

All these are common questions marketing and sales are facing with on daily bases.

Luckily, there is an answer.

Knowing how much you spend on each of the marketing and sales channels and activities is from essential importance for your business success.

Channel attribution is an important and useful concept in interactive marketing. It is helpful for many managerial problems. The most obvious question is the budgeting of marketing expenditures for customer acquisition. Also, proper attribution can help to allocate spending across media (mail vs. telephone vs. television), vehicles (list A vs. list B), and programs (gift vs. special price), as well as to inform decisions concerning retaining existing customers. In many cases, companies implementing proper attribution models can achieve a strategic competitive advantage.

Besides channel attribution, modeling customer relationship is fundamentally essential. Once you have successfully modeled channel attribution and your customer relationships, you can gain a unique understanding of your customer, how each of them interacts with your offers and most importantly, you can oversee possible future interactions and outcomes of each.

Marketing multi-channel attribution in practice

I have been experimenting with a couple of different techniques to set up a good LTV model and create a successful model for setting a customer relationship. From all the experiments I concluded that Markov Chain Models (MCM), is most appropriate for modeling customer relationships and calculating LTV.

Here is why:

The most significant advantage of the Markov chain model is its flexibility. The Markov Chain Models can handle both customer migration and customer retention situations. It is important to point out that Markov Chain Models can apply either to a customer or a prospect.

Thanks to its flexibility, the Markov chain model can be used in many situations that were not covered by previous models.

Another advantage of using Markov Chain Models is, the model accounts for the uncertainty surrounding customer relationships. This is possible because Markov Chain Models is a probabilistic model.

As an outcome of using Markov chains, we get a probability and expected value. In that way the model allows one to talk about the company's future relationship with an individual customer.

As direct marketers move toward right one-to-one marketing, their approach will also change. Instead of talking about groups or cohorts of customers, direct marketers will talk about a specific person, John Smith, for example. Marketing and sales will speak about the probability John Smith will be retained, instead of talking about retention rates,

Instead of discussions about average profits from a segment of customers, now we can have conversations about the expected benefit from the company's relationship with John Smith.

Because the Markov chain models give probability and the expected value of a specific action, it can be ideally used for facilitating right one-to-one marketing. Moreover, thanks to that you can introduce the correct personalization to each of your marketing campaigns.

How does Markov Chains model work?

Markov chains model a stochastic model that is describing a sequence of events in which the probability of each event depends only on the state attained in the previous event.

The contents of the sequences are determined by the Markov order, which ranges from 0 to 4:

• Order 0: Doesn’t know where the user came from or what step the user is on, only the probability of going to any page. For example, you have a set of products. Markov chains form order 0 will give you just the likelihood of outcome to happen for a specific Market without knowing any other information ( i.e., buyer history).[caption id="" align="alignnone" width="736"] 0 order Markov Chains[/caption]
• Order 1 - "Memory-less": Looks back zero steps. You are currently at Step A. The probability of going anywhere is based on being at that step.[caption id="" align="alignnone" width="934"] Order 1 Markov Chains[/caption]
• Order 2: Looks back one step. You came from Step A (Sequence A) and are currently at Step B. The probability of going anywhere is based on where you were and where you are.
• Order 3: Looks back two steps. You came from Step A > B (Sequence A) and are currently at Step C. The probability of going anywhere is based on where you were and where you are.[caption id="" align="alignnone" width="1018"] Order 3 Markov Chains[/caption]
• Order 4: Looks back three steps. You came from Step A > B > C (Sequence A) and are currently at Step D. The probability of going anywhere is based on where you were and where you are.

[caption id="" align="alignnone" width="1068"]Order 4 Markov Chains[/caption]

 Example of Markov Chains for multi-channel marketing

Let’s look at an example of the first-order Markov chains. It is called “memory-free” because the probability of reaching one state depends only on the previous state visited.

For instance, customer journeys contain three unique channels C1, C2, and C3. Additionally, we need to add three individual states to each Markov chains graph:

• start - representing a starting point
• conversion - purchase or conversion
• null - unsuccessful conversion.

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Markov chains - Modeling marketing multi-channel attribution in practice