Attribution Modeling – How much credit to be given to which touchpoint?

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attribution modeling Marketing Football

What is attribution modeling?

soccer
Soccer field example

When does a goal happen in soccer, which player should get how much credit of it?

  • Should the striker get the most credit for the goal?
  • Should the last person who passes the ball to the striker get the most credit for the goal?
  • Should the first person who initiated that rhythm of passes get the most credit for the goal?
  • Should every person who passes the ball get equal credit?
  • Should the latter ones get more credit of the goal?

In marketing, we are facing the similar kind of problem, that when the conversion happens. Then how much credit should be given to which channel in marketing?

attribution modeling Marketing Football
A customer’s journey for landing on the website

Why we need an attribution model?

In today’s world, every company is spending big amount of their budget on marketing

  • To gauge the effectiveness of channels
  • To measure the impact of communication with customers
  • To determine ROI
  • To decide which actions to take

Types of attribution models:

  • Rule-based attribution model
    • First Click Attribution Model

      This is the closest proxy we have for “how did they hear about us in the first place?”
      This is useful if you need to know which keywords genuinely helped make you known to the user.
first click
  • Pros:
    • Easy to implement
    • Helps to know New Customer Acquisition Channels
    • Insight into drive awareness campaigns
  • Cons:
    • No influence of subsequent touches
    • Too much credit to lead gen programs
  • Last Click Attribution Model

    Most useful when the final touchpoint really was the deciding one, e.g. for impulse purchases or very price-sensitive decisions.
last click
  • Pros:
    • Easy to implement
    • Insights into drive conversion campaigns
  • Cons:
    • No influence of prior touches
    • Too much credit to converting campaigns
  • Equal weight Attribution Model

    Gives equal weight to every touchpoint. Doesn’t matter when that click is happening.
equal weight
  • Pros:
    • No fighting over who gets credit
    • Helpful for longer revenue cycles with many clicks
  • Cons:
    • Low-impact touchpoints get a high credit
    • No importance to high impact touchpoints
  • Time Decay Attribution Model

    “Latter you are in click-chain, more credit you will get”. If users can’t remember about who showed up on the first few clicks than those were probably worthless in the final decision.
time decay
  • Pros:
    • Focused on all touchpoints
    • Helpful for longer revenue cycles with many clicks
  • Cons:
    • Artificially inflate importance to latter channels
    • Low-credit to acquisition channels
  • Positional-Rule based Attribution Model

    “What makes you aware and What makes you purchase from us?”
    If you needed to get on a shortlist but you don’t care if the user interacted with you again until they made their final decision, this model replicates that level of credit.
Positional
  • Pros:
    • High credit for acquiring and converting channels
    • Helpful for longer revenue cycles with many clicks
  • Cons:
    • Less influence for middle channels
  • Data-driven attribution model: Second way of looking at attribution

    “Proper distribution to each channel”
data driven
  • Pros:
    • Helps to know proper attribution to each channel
    • No set of rules to skew data
    • Less guesswork
    • Better decisions
  • Cons:
    • Not Easy to implement
    • Require statistical knowledge
    • Require a lot of data to know statistical validity
  • Shapely Attribution Model
shapely formula

How much value does each channel have?
Or in other words
How much value a channel brings to already existing list of channels?

let’s consider a customer journeys

shapely path 1

In this customer journey, the customer has a 1% chance of making a purchase.

shapely path 2

Now when we add a display to the customer journey customer has 2% chances of making a purchase. This means there are 100% more chances of making a purchase when we add display as a channel to the current customer journey.

Let’s consider we have 430 total conversions that happened thought the paid channels. Out of these 50 conversions had only social as the channel in the journey, 30 conversions had only Display as the channel in the journey, …, 60 conversions had Social as well as Display as the channel in the journey, …, 100 conversions had Social, Display as well as Retargeting as the channel in the journey.

shapely data 1

The contribution of each channel is the total time the channel appears in the customers journey. e.g. for Social, it is
[50(where social appears as the only channel in customers purchase journey] +
[60(where social, display occur together) – 30(where display occur only)]
+ …. +
[100(where the social, display, retargeting occur together) – 80(where display and retargeting occur only)]

shapely data 2

Weightage of each channel is contribution of that channel divided by total contribution of all the channels.

shapely data 3

The final contribution of channels is different from all the rule-based models.

  • Markov-chain Attribution Model

    Probabilistic Model
    Relies on current state to predict the next state

    let’s consider 3 customer journeys

Journey 1: Customer comes from Social channel, then after a few days customer comes from Display channel, then from Retargeting and finally makes a purchase.

markov path 1

Journey 2: Customer comes from Display channel, then after a few days customer comes from Retargeting channel but never makes a purchase.

markov path 2

Journey 3: Customer comes from the Display channel only and never makes a purchase.

So, we have 3 customer journey’s as follows:

markov data 1

Since Markov Model is the state change model. It is based on the current as well as previous stage only. So, we need to transpose the data and make the probability of the customer going from the initial stage to the next stage.

markov data 2

Once we have done the transformation of data, we get this kind of chart for all the channel in the customer journey:

markov conversion 1

Total chances of making a purchase is (33% * 100% * 66% * 50%) + (66% * 66% * 50%) = 33%

markov conversion 2

what will be the impact if we remove social channel from the customers journey is (33% * 100% * 66% * 50%) + (66% * 66% * 50%) = 22%

markov conversion 3

Effect of removing social media is 22%/33% = 0.66
Similarly, finding the impact of removing other channels.

Effect of removing:

  • Social Media: 22.2% / 33.3% = 0.66
  • Display: 1
  • Retargeting: 1

Weight % of the effect of removing on total conversion, is the effect of removing the channel divided by the effect of removing all channels multiplied by the total conversions that we have in the journey, which is one in this case. This can be calculated as:

  • Social Media: 0.66 / (0.66 + 1 + 1) = 0.25 * conversion (1) = 25%
  • Display: 1 / (0.66 + 1 + 1) = 0.375 * conversion (1) = 37.5%
  • Retargeting : 1 / (0.66 + 1 + 1) = 0.375 * conversion (1) = 37.5%
markov conversion 4

The final contribution of channels is different from all the rule based models.

We will be properly able to attribute the revenue to different channels for revenue for new customer acquisition.

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