The top barriers to implementing Attribution Modelling
The following are some of the biggest barriers in your organization which you must overcome before you can implement attribution modelling:
- A work culture that is not data-driven
- Lack of business agility
- A work culture that does not encourage innovation
- Presence of organizational silos and other operational inefficiencies
- Non-alignment of goals and KPIs across an organisation
- Lack of authority
- Lack of expertise
- Lack of tools, technology, and processes
- Data collection issues
- Data integration issues
#1 A work culture that is not data-driven
It is common for companies (especially larger and older companies) to not be data-driven. And this can create huge problems in your work.
The following are typical characteristics of a work culture that is not data-driven:
- Decision-makers in your company do not use, trust, and value the data.
- All important business decisions are made on faith, intuition, personal experience or HIPPO (highest paid person’s opinion), instead of data.
- Decision-makers do not really understand ecommerce or cannot differentiate between offline and online marketing.
- Little to no investment has been made in collecting, integrating, interpreting, and reporting data. This investment can be in the form of:
- Hiring a full-time analyst, external consultant or setting up an analytics department.
- Using free and paid web analytics tools such as Google Analytics, Adobe Analytics, Optimizely, Supermetrics, etc.
- Allocating time and resources for carrying out testing and conversion optimisation.
- Using well-defined processes and frameworks.
If your company is not data-driven then you have got bigger problems than attribution issues.
Your time would be better spent on first creating a data-driven work culture, rather than worrying about attribution.
#2 Lack of business agility
According to Wikipedia, Business agility is “the ability of a business to adapt rapidly and cost-efficiently in response to changes in the business environment.”
Technology is changing at an unprecedented pace.
Almost every week something new is being developed and introduced. Almost every week something old is being discarded or labelled ineffective. We also know that customers adopt new technologies faster than most businesses do.
We live in a world of constantly changing marketing conditions and customer needs. In order to respond quickly to these changes, we need to be agile. In order to capitalise on new marketing opportunities, we need to be agile.
In order to make super timely decisions, we need to be agile. Being agile means being able to move quickly and easily.
It is the ability to adapt rapidly and cost-efficiently in response to changes in the marketing environment.
In an agile environment, you deliver solutions weekly if not daily.
How quickly and how often you can create value for your customers determines how agile you are as a business.
In attribution modelling, success does really not come from the level of insight you get from data analysis. It comes from your ability to make timely changes.
In order to be in a strong position, your company must be adapted to a dynamic work environment.
In a dynamic work environment not just incremental changes but radical changes to strategies, systems, and processes can be introduced within a short span of time (within a couple of days) with little to no top-down approvals and bureaucracy.
This can be achieved if your company’s work culture encourages innovation.
Related Article: How to Use Agile Analytics to Quickly Solve Your Conversion Problems
#3 A work culture that does not encourage innovation
The work culture which does not encourage innovation can not foster a dynamic work environment. And you need a dynamic work environment in order to be agile.
The following are typical characteristics of a work culture that does not encourage innovation:
- Employees are not allowed to be self-directed. They are not allowed to make, test and execute their own decisions.
- Ideas, goals, KPIs, and vision are not openly shared among employees.
- There is little to no culture of testing and optimisation.
- Creativity and ‘out of the box’ thinking is not encouraged, promoted, recognised or rewarded.
#4 Presence of organizational silos and other operational inefficiencies
Organisational silos are dysfunctional departments within an organisation. They are commonly found in publicly traded companies, government organisations and other large organizations.
These silos have the following characteristics:
#4.1 Each department works to protect its own interests first and places its own goals ahead of the organisation’s goals. As a result, organisational goals are often not shared across departments and there is generally conflict of interest and resistance to any change.
#4.2 Each department tends to withhold information and data.
#4.3 Each department communicates more within than outside. As a result, mistrust, miscommunication, and a lack of cooperation haunt the working environment.
#4.4 If you have got organisational silos in your company then it is almost impossible for your company to think about optimisation holistically.
Organisational hierarchy, bureaucracy, and internal politics are other operational inefficiencies that can act as a barrier to fixing the attribution issues.
#5 Non-alignment of goals and KPIs across an organisation
The non-alignment of goals and KPIs occur when the core business objectives and the corresponding business KPIs are not shared across your organization. This results in teams and individuals measuring success differently.
And when the success is measured differently across your organization then attribution modelling is unlikely to work.
Core business objectives are the results you want to achieve, improve, or maintain as an organization.
Core business goals/objectives are set at the organisational level and focus on measuring the overall performance of a business.
Your core business goals could be:
- Support and maintain the company’s core values.
- Increase/maintain market share
- Increase profitability
- Improve brand retention
- Provide excellent customer service etc.
A business KPI is a metric that is used to measure the performance of a business in achieving its core business goals.
For example, if one of your core business goals is to acquire more customers then your business KPI can be the ‘customer growth rate’.
When core business objectives and business KPIs are shared across your organization, everyone becomes aware of what their company is trying to achieve. This helps teams and individuals in setting up their goals and KPIs which align with your business KPIs.
In many companies, employees have a hard time understanding how they are adding value to the business bottom line and whether what they are currently doing, is really worth the time and investment.
And this happens because core business goals and business KPIs are not shared across the organization.
Everyone in your company must be pushing towards the same organizational goals. This is the only way to ensure maximum productivity and profitability.
And this can happen only when there is an alignment of goals and KPIs across your organization.
#6 Lack of authority
One of the top requirements for fixing the attribution issues is to make your company think about optimisation holistically.
For this to actually happen, you must have the ability to do the following:
- Be able to shape the work culture of your company so that it becomes data-driven.
- Have the ability to encourage different teams/departments to work together, share information and data, and put the company’s interests before their own department’s interests.
- Be able to secure buy-in and support from top management (preferably C-level executives i.e. CEO, CTO, CMO).
As you may have realised by now, this is all easier said than done.
Changes, especially the radical ones, come from the top. People at the top of your company are in a better position to introduce new practices and policies and can encourage (or force) others to take them on and develop them.
They are more immune to internal resistance and company politics than the people in middle management (managers, supervisors, etc.)
So for the changes to really happen, you need to either be one of the C-level executives or you need to directly report to a C-level executive.
Without such authority or position, it is highly unlikely someone can shape their company’s work culture or break organisational silos.
#7 Lack of expertise
Attribution modelling is an advanced stage of business analysis where we determine the most effective marketing channels for investment. There is almost always a lot of money at stake.
An incorrect attribution can result in a huge monetary loss for you or your client.
Therefore you need a dedicated resource, preferably a web analyst, who can execute attribution modelling in your organisation.
This person must have the following skills:
- A good understanding of the maths and stats behind web analytics.
- A great understanding of the company’s business model, business objectives, products and services, target market, and competition.
- A great understanding of both offline and online marketing operations and day-to-day business activities.
- The ability to communicate well across all departments/teams.
- The ability to conduct both qualitative and quantitative tests.
- An eye for detail.
- Be really good at data interpretation
- Be courageous. Yes, you heard it right. The web analyst needs to be courageous enough to call spade a spade.
If the analyst is too scared to point out faults in existing systems and processes, too scared to confront top management executives and cannot stand up for what they believe is right then executing attribution modelling may not be for them.
#8 Lack of tools, technology, and processes
You will need certain tools and technologies to carry out attribution modelling. These tools should assist in:
- Data collection
- Data integration
- Data visualisation
- Data interpretation
- Data forecasting
- Creating and executing attribution models.
You will also need processes and frameworks to carry out your optimization tasks on a day-to-day basis. The process you follow must be well-defined. It must have a clear start and a clear end.
Without a well-defined process in place, you will end up doing what I call random optimisation.
Random optimisation occurs when you optimise a website without any clear objective. You identify problems (based on some industry best practices) and then you rush to fix them.
You fix the problems in a hope that it will somehow improve the business bottom line.
Random optimisation also occurs when every second or third day you ask yourself this question “what should I do next?”
If your company already has a data-driven work culture then it will value analytics and testing. It will help you get all the tools and tech you need to implement attribution modelling.
On the other hand, if your company does not value data, and treats analytics as some sort of a side project, then you may have a hard time getting all the required resources for your work.
#9 Data collection issues
You need accurate data in order to do an accurate analysis. Any conclusions based on flawed data cannot produce optimum results.
There is no point in carrying out attribution modelling if you have got major data collection issues. So you need to identify all the critical data collection issues in your organisation.
These issues can relate to the way you collect data through:
- Analytics software (like Google Analytics or Adobe Analytics)
- CRM
- Phone calls
- Point of sale systems
- Internal order systems
- Shopping cart
- Accounting software
- Data Warehouse etc
Often, businesses start data analysis under the assumption that their data is highly accurate. But this is never really the case. I have audited hundreds of web analytics accounts in my career.
Each account had at least one or two issues that seriously stood in my way of getting optimum results from data analysis. You need to make sure that your data is at least reasonably accurate before you start attribution modelling.
#10 Data integration issues
Data integration is the key to fixing attribution issues. Without proper data integration, your attribution modelling is going to be flawed.
In order to minimise the number of missing touchpoints in your conversion path, you need to integrate as much data as possible from different data sources. These data sources are (but are not limited to):
- Google Analytics.
- Google Ads.
- Facebook Ads.
- Google Search Console.
- Google Merchant Center.
- Google Sheets.
- MS Excel.
- Phone calls data.
- CRM data.
- Point of sale data.
- Data from customer support service.
- Data from internal order systems.
- Financial data.
Following are the main advantages of data integration:
- You can quickly track various aspects of your marketing campaigns.
- You can analyze the overall performance.
- Take timely decisions.
- Correlate all of your data with business bottom line impacting metrics like revenue, cost, gross profit, etc.
Without proper data integration, you will always get a ‘silo’ view of your marketing campaigns.
You need to create a robust data integration system in order to carry out any meaningful analysis.
In fact, if you are a big organisation then it is completely pointless to collect and analyse data without proper data integration.
If you are really serious about carrying out attribution modelling then you have to invest in data integration tools and technologies like Google BigQuery.
Note: These are not the only barriers you should be concerned about. The actual number of barriers to fixing attribution issues can vary from business to business.
Other Articles on Google Analytics Attribution Modelling
- How to analyse and report the true value of your SEO Campaign
- How to valuate Display Advertising through Attribution Modelling
- Shopping Cart Analytics Tutorial
- 6 Keys to Digital Success in Attribution Modelling
- How to Measure and Improve the Quality of SEO Traffic through Google Analytics
- How to explain attribution modelling to your clients
- Default and Custom Attribution Models in Google Analytics
- Understanding Missing Touchpoints in Attribution Modelling
- What You Should Know about Historical Data in Web Analytics
- Model Comparison Report Explained in Google Analytics Attribution
- Data-Driven Attribution Model in Google Analytics – Tutorial
- Conversion Lag Report Explained in Google Analytics Attribution
- Selecting the Best Attribution Model for Inbound Marketing
- How to do ROI Analysis in Google Analytics
- Conversion Credit Models Guide – Google Analytics Attribution
- Introduction to Nonline Analytics – True Multi Channel Analytics
- Conversion Types Explained in Google Analytics Attribution
- Attribution Channels Explained in Google Analytics Attribution
- Differences Between Google Attribution & Multi-Channel Funnel Reports
- Introduction to TV Attribution in Google Analytics Attribution 360
- Conversion Credit Distribution for Attribution Models in Google Analytics
- Conversion Paths Report Explained in Google Analytics Attribution
- Attribution Model Comparison Tool in Google Analytics
- Touchpoint Analysis in Google Analytics Attribution Modelling
- Attributed Conversions & Attributed Revenue Explained in Google Attribution
- Which Attribution Model to use in Google Analytics?
- Google Attribution Access and User Permissions – Tutorial
- Conversion Path Length Report Explained in Google Analytics Attribution
- How to set up a data-driven attribution model in Google Analytics
- View-Through Conversion Tracking in Google Analytics
- Offline Conversion Tracking in Google Analytics – Tutorial
- How to Create Custom Attribution Model in Google Analytics
- 8 Google Analytics Conversions Segments You Must Use
- You are doing Google Analytics all wrong. Here is why
- How to Use ZMOT to Increase Conversions and Sales Exponentially
- Connected Properties Explained in Google Analytics Attribution
- Marketing Mix Modelling or Attribution Modelling. Which one is for you?
- How is attribution modelling helpful for ecommerce and non-ecommerce websites?
- Conversion Time & Interaction Time Explained in Google Analytics Attribution
- How to Allocate Budgets in Multi Channel Marketing
- Data-Driven Attribution Model Explorer in Google Analytics
- Google Analytics Attribution Beta and Project Tutorial
- Google Ads Performance Report Attribution Beta
- Google Attribution vs Google Analytics Multi-Channel Funnel Reports
The following are some of the biggest barriers in your organization which you must overcome before you can implement attribution modelling:
- A work culture that is not data-driven
- Lack of business agility
- A work culture that does not encourage innovation
- Presence of organizational silos and other operational inefficiencies
- Non-alignment of goals and KPIs across an organisation
- Lack of authority
- Lack of expertise
- Lack of tools, technology, and processes
- Data collection issues
- Data integration issues
#1 A work culture that is not data-driven
It is common for companies (especially larger and older companies) to not be data-driven. And this can create huge problems in your work.
The following are typical characteristics of a work culture that is not data-driven:
- Decision-makers in your company do not use, trust, and value the data.
- All important business decisions are made on faith, intuition, personal experience or HIPPO (highest paid person’s opinion), instead of data.
- Decision-makers do not really understand ecommerce or cannot differentiate between offline and online marketing.
- Little to no investment has been made in collecting, integrating, interpreting, and reporting data. This investment can be in the form of:
- Hiring a full-time analyst, external consultant or setting up an analytics department.
- Using free and paid web analytics tools such as Google Analytics, Adobe Analytics, Optimizely, Supermetrics, etc.
- Allocating time and resources for carrying out testing and conversion optimisation.
- Using well-defined processes and frameworks.
If your company is not data-driven then you have got bigger problems than attribution issues.
Your time would be better spent on first creating a data-driven work culture, rather than worrying about attribution.
#2 Lack of business agility
According to Wikipedia, Business agility is “the ability of a business to adapt rapidly and cost-efficiently in response to changes in the business environment.”
Technology is changing at an unprecedented pace.
Almost every week something new is being developed and introduced. Almost every week something old is being discarded or labelled ineffective. We also know that customers adopt new technologies faster than most businesses do.
We live in a world of constantly changing marketing conditions and customer needs. In order to respond quickly to these changes, we need to be agile. In order to capitalise on new marketing opportunities, we need to be agile.
In order to make super timely decisions, we need to be agile. Being agile means being able to move quickly and easily.
It is the ability to adapt rapidly and cost-efficiently in response to changes in the marketing environment.
In an agile environment, you deliver solutions weekly if not daily.
How quickly and how often you can create value for your customers determines how agile you are as a business.
In attribution modelling, success does really not come from the level of insight you get from data analysis. It comes from your ability to make timely changes.
In order to be in a strong position, your company must be adapted to a dynamic work environment.
In a dynamic work environment not just incremental changes but radical changes to strategies, systems, and processes can be introduced within a short span of time (within a couple of days) with little to no top-down approvals and bureaucracy.
This can be achieved if your company’s work culture encourages innovation.
Related Article: How to Use Agile Analytics to Quickly Solve Your Conversion Problems
#3 A work culture that does not encourage innovation
The work culture which does not encourage innovation can not foster a dynamic work environment. And you need a dynamic work environment in order to be agile.
The following are typical characteristics of a work culture that does not encourage innovation:
- Employees are not allowed to be self-directed. They are not allowed to make, test and execute their own decisions.
- Ideas, goals, KPIs, and vision are not openly shared among employees.
- There is little to no culture of testing and optimisation.
- Creativity and ‘out of the box’ thinking is not encouraged, promoted, recognised or rewarded.
#4 Presence of organizational silos and other operational inefficiencies
Organisational silos are dysfunctional departments within an organisation. They are commonly found in publicly traded companies, government organisations and other large organizations.
These silos have the following characteristics:
#4.1 Each department works to protect its own interests first and places its own goals ahead of the organisation’s goals. As a result, organisational goals are often not shared across departments and there is generally conflict of interest and resistance to any change.
#4.2 Each department tends to withhold information and data.
#4.3 Each department communicates more within than outside. As a result, mistrust, miscommunication, and a lack of cooperation haunt the working environment.
#4.4 If you have got organisational silos in your company then it is almost impossible for your company to think about optimisation holistically.
Organisational hierarchy, bureaucracy, and internal politics are other operational inefficiencies that can act as a barrier to fixing the attribution issues.
#5 Non-alignment of goals and KPIs across an organisation
The non-alignment of goals and KPIs occur when the core business objectives and the corresponding business KPIs are not shared across your organization. This results in teams and individuals measuring success differently.
And when the success is measured differently across your organization then attribution modelling is unlikely to work.
Core business objectives are the results you want to achieve, improve, or maintain as an organization.
Core business goals/objectives are set at the organisational level and focus on measuring the overall performance of a business.
Your core business goals could be:
- Support and maintain the company’s core values.
- Increase/maintain market share
- Increase profitability
- Improve brand retention
- Provide excellent customer service etc.
A business KPI is a metric that is used to measure the performance of a business in achieving its core business goals.
For example, if one of your core business goals is to acquire more customers then your business KPI can be the ‘customer growth rate’.
When core business objectives and business KPIs are shared across your organization, everyone becomes aware of what their company is trying to achieve. This helps teams and individuals in setting up their goals and KPIs which align with your business KPIs.
In many companies, employees have a hard time understanding how they are adding value to the business bottom line and whether what they are currently doing, is really worth the time and investment.
And this happens because core business goals and business KPIs are not shared across the organization.
Everyone in your company must be pushing towards the same organizational goals. This is the only way to ensure maximum productivity and profitability.
And this can happen only when there is an alignment of goals and KPIs across your organization.
#6 Lack of authority
One of the top requirements for fixing the attribution issues is to make your company think about optimisation holistically.
For this to actually happen, you must have the ability to do the following:
- Be able to shape the work culture of your company so that it becomes data-driven.
- Have the ability to encourage different teams/departments to work together, share information and data, and put the company’s interests before their own department’s interests.
- Be able to secure buy-in and support from top management (preferably C-level executives i.e. CEO, CTO, CMO).
As you may have realised by now, this is all easier said than done.
Changes, especially the radical ones, come from the top. People at the top of your company are in a better position to introduce new practices and policies and can encourage (or force) others to take them on and develop them.
They are more immune to internal resistance and company politics than the people in middle management (managers, supervisors, etc.)
So for the changes to really happen, you need to either be one of the C-level executives or you need to directly report to a C-level executive.
Without such authority or position, it is highly unlikely someone can shape their company’s work culture or break organisational silos.
#7 Lack of expertise
Attribution modelling is an advanced stage of business analysis where we determine the most effective marketing channels for investment. There is almost always a lot of money at stake.
An incorrect attribution can result in a huge monetary loss for you or your client.
Therefore you need a dedicated resource, preferably a web analyst, who can execute attribution modelling in your organisation.
This person must have the following skills:
- A good understanding of the maths and stats behind web analytics.
- A great understanding of the company’s business model, business objectives, products and services, target market, and competition.
- A great understanding of both offline and online marketing operations and day-to-day business activities.
- The ability to communicate well across all departments/teams.
- The ability to conduct both qualitative and quantitative tests.
- An eye for detail.
- Be really good at data interpretation
- Be courageous. Yes, you heard it right. The web analyst needs to be courageous enough to call spade a spade.
If the analyst is too scared to point out faults in existing systems and processes, too scared to confront top management executives and cannot stand up for what they believe is right then executing attribution modelling may not be for them.
#8 Lack of tools, technology, and processes
You will need certain tools and technologies to carry out attribution modelling. These tools should assist in:
- Data collection
- Data integration
- Data visualisation
- Data interpretation
- Data forecasting
- Creating and executing attribution models.
You will also need processes and frameworks to carry out your optimization tasks on a day-to-day basis. The process you follow must be well-defined. It must have a clear start and a clear end.
Without a well-defined process in place, you will end up doing what I call random optimisation.
Random optimisation occurs when you optimise a website without any clear objective. You identify problems (based on some industry best practices) and then you rush to fix them.
You fix the problems in a hope that it will somehow improve the business bottom line.
Random optimisation also occurs when every second or third day you ask yourself this question “what should I do next?”
If your company already has a data-driven work culture then it will value analytics and testing. It will help you get all the tools and tech you need to implement attribution modelling.
On the other hand, if your company does not value data, and treats analytics as some sort of a side project, then you may have a hard time getting all the required resources for your work.
#9 Data collection issues
You need accurate data in order to do an accurate analysis. Any conclusions based on flawed data cannot produce optimum results.
There is no point in carrying out attribution modelling if you have got major data collection issues. So you need to identify all the critical data collection issues in your organisation.
These issues can relate to the way you collect data through:
- Analytics software (like Google Analytics or Adobe Analytics)
- CRM
- Phone calls
- Point of sale systems
- Internal order systems
- Shopping cart
- Accounting software
- Data Warehouse etc
Often, businesses start data analysis under the assumption that their data is highly accurate. But this is never really the case. I have audited hundreds of web analytics accounts in my career.
Each account had at least one or two issues that seriously stood in my way of getting optimum results from data analysis. You need to make sure that your data is at least reasonably accurate before you start attribution modelling.
#10 Data integration issues
Data integration is the key to fixing attribution issues. Without proper data integration, your attribution modelling is going to be flawed.
In order to minimise the number of missing touchpoints in your conversion path, you need to integrate as much data as possible from different data sources. These data sources are (but are not limited to):
- Google Analytics.
- Google Ads.
- Facebook Ads.
- Google Search Console.
- Google Merchant Center.
- Google Sheets.
- MS Excel.
- Phone calls data.
- CRM data.
- Point of sale data.
- Data from customer support service.
- Data from internal order systems.
- Financial data.
Following are the main advantages of data integration:
- You can quickly track various aspects of your marketing campaigns.
- You can analyze the overall performance.
- Take timely decisions.
- Correlate all of your data with business bottom line impacting metrics like revenue, cost, gross profit, etc.
Without proper data integration, you will always get a ‘silo’ view of your marketing campaigns.
You need to create a robust data integration system in order to carry out any meaningful analysis.
In fact, if you are a big organisation then it is completely pointless to collect and analyse data without proper data integration.
If you are really serious about carrying out attribution modelling then you have to invest in data integration tools and technologies like Google BigQuery.
Note: These are not the only barriers you should be concerned about. The actual number of barriers to fixing attribution issues can vary from business to business.
Other Articles on Google Analytics Attribution Modelling
- How to analyse and report the true value of your SEO Campaign
- How to valuate Display Advertising through Attribution Modelling
- Shopping Cart Analytics Tutorial
- 6 Keys to Digital Success in Attribution Modelling
- How to Measure and Improve the Quality of SEO Traffic through Google Analytics
- How to explain attribution modelling to your clients
- Default and Custom Attribution Models in Google Analytics
- Understanding Missing Touchpoints in Attribution Modelling
- What You Should Know about Historical Data in Web Analytics
- Model Comparison Report Explained in Google Analytics Attribution
- Data-Driven Attribution Model in Google Analytics – Tutorial
- Conversion Lag Report Explained in Google Analytics Attribution
- Selecting the Best Attribution Model for Inbound Marketing
- How to do ROI Analysis in Google Analytics
- Conversion Credit Models Guide – Google Analytics Attribution
- Introduction to Nonline Analytics – True Multi Channel Analytics
- Conversion Types Explained in Google Analytics Attribution
- Attribution Channels Explained in Google Analytics Attribution
- Differences Between Google Attribution & Multi-Channel Funnel Reports
- Introduction to TV Attribution in Google Analytics Attribution 360
- Conversion Credit Distribution for Attribution Models in Google Analytics
- Conversion Paths Report Explained in Google Analytics Attribution
- Attribution Model Comparison Tool in Google Analytics
- Touchpoint Analysis in Google Analytics Attribution Modelling
- Attributed Conversions & Attributed Revenue Explained in Google Attribution
- Which Attribution Model to use in Google Analytics?
- Google Attribution Access and User Permissions – Tutorial
- Conversion Path Length Report Explained in Google Analytics Attribution
- How to set up a data-driven attribution model in Google Analytics
- View-Through Conversion Tracking in Google Analytics
- Offline Conversion Tracking in Google Analytics – Tutorial
- How to Create Custom Attribution Model in Google Analytics
- 8 Google Analytics Conversions Segments You Must Use
- You are doing Google Analytics all wrong. Here is why
- How to Use ZMOT to Increase Conversions and Sales Exponentially
- Connected Properties Explained in Google Analytics Attribution
- Marketing Mix Modelling or Attribution Modelling. Which one is for you?
- How is attribution modelling helpful for ecommerce and non-ecommerce websites?
- Conversion Time & Interaction Time Explained in Google Analytics Attribution
- How to Allocate Budgets in Multi Channel Marketing
- Data-Driven Attribution Model Explorer in Google Analytics
- Google Analytics Attribution Beta and Project Tutorial
- Google Ads Performance Report Attribution Beta
- Google Attribution vs Google Analytics Multi-Channel Funnel Reports
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
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Master the Essentials of Email Marketing Analytics
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Attribution Modelling in Google Analytics and BeyondSECOND EDITION OUT NOW!
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
About the Author
Himanshu Sharma
