The Ultimate Guide to Revenue Attribution

Revenue attribution is the process of assigning credit for conversions and revenue to the various marketing channels and touchpoints that contribute to a sale. You can use it to determine which marketing activities are the most effective at driving sales and how different marketing channels work together to generate revenue. In our previous articles, we talked about revenue attribution models, revenue attribution tools, how to do ad revenue attribution the right way, and how I, as a data consultant, can help you get the most out of the revenue attribution process. This time, I’ll gather all that information in one place to make it easier for you to understand the complete process. So, this article will cover: Five questions to ask yourself before acquiring data As I stated, revenue attribution helps you connect purchases with exact activities and touchpoints inside your campaigns. There are five primary questions to ask yourself before acquiring data: Since revenue attribution is not easy, marketers often face challenges while answering these questions. I thoroughly explained these common challenges and the solutions to each of them. Read carefully. Revenue attribution benefits There are plenty of reasons why revenue attribution is of immense importance. I’ll mention only a few essential ones. Leads prioritization It’s crucial to be 100% certain from where a vast majority of your leads come from. And, of course, it varies in different niches. The ecommerce store will probably benefit the most from Facebook or Instagram, while leads for a SaaS tool usually convert the most from LinkedIn.  So, the point is to understand which platform gives you the most leads/conversions and then prioritize your efforts based on that. Better cooperation between marketing and sales teams As a marketer, you can pass valuable information about revenue attribution to your sales team. For example, you can inform them about which keywords (pain points) are the most successful so that they can construct their sales pitches around these keywords.  It could help them understand the target audience better, get a better approach to the conversation, change their pitch accordingly, and, as a result, close more deals.  Accurate monthly reports There’s no way to convince your client or C-level executive that you’re doing a good job other than by showing them your exact results. They have to know if their expenses are worth it. By measuring the profitability of each campaign for a few months, you’ll have a clear picture of what works and what does not. In other words, an accurate attribution process will show which channels and campaigns contribute the most to the revenue. Revenue attribution models Numerous revenue attribution models depend on specific business goals and objectives. In one of our previous articles, I thoroughly analyzed these models. Now, I’ll mention only specific use cases for each of them. There are two groups of revenue attribution models – single-touch and multi-touch. Single-touch models credit one part of the journey, while multi-touch models, as the name suggests, consider multiple channels. Single-touch models 1. The first interaction model  Since this model gives all the credit to the first touchpoint, it’s most useful for early-stage businesses in campaigns created for the initial awareness. 2. The last interaction model The situation is the opposite here – the whole credit goes to the last touchpoint. Therefore, it’s beneficial for short buying cycles without too many touchpoints before converting. 3. The last non-direct click model  Eliminating direct traffic is suitable when you know it comes from conversions you already won on other channels. This model ignores all direct sessions and gives full credit to the last touchpoint the lead clicked through before converting.  4. The last Google ads click model  This model will show you which one closes the most conversions if you’re running multiple ads. Multi-touch models 1. The linear model The linear model is the simplest – evenly applying the credit to every touchpoint. This is the model for you if you want to demonstrate each channel’s value equally. 2. The time decay model This model is slightly different since you give more credit to the touchpoint closer to the purchase moment. So, it’s helpful in long sales cycles. 3. The position-based model It’s a hybrid of the first and last interaction models. You split the credit between these touchpoints based on your priorities.  This model has three variations: U-shaped, W-shaped, and Z-shaped. Learn more about them in this post.  4. The data-driven model Simply get data from a computer and make decisions based on more machine learning. You can consider a data-driven model if you have a lot of data, for example, 30000 sales per year and more than six marketing channels. 5. Your own (custom) model If you want to use different criteria to determine what things are most important to your sales cycles, then feel free to build your own model and test it. It is worth mentioning that going for the most complex option is not always the best idea, and the decision should be based on the business goals and requirements. How can I help you do the revenue attribution the right way? Even though you can do the revenue attribution on your own, it’s still not an easy thing to do. It’s easy to get confused with the information from Analytics, especially if some of the results don’t match your real sales numbers. You’ll likely need expert help to correctly collect data about your most valuable campaigns and traffic sources. On top of that, I do it with security and privacy in mind to ensure that the data is only available to you. Based on data, experts like us can help you:  Besides these duties that are related to revenue attribution, there are plenty of other benefits you can get by hiring a data analytics consultant. You can learn more about my professional duties in this post. Final verdict So, what is the most important thing for you to draw out from this article? Revenue attribution is not easy. If you

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Data Consultant – Client Strategy

Marketlytics is a user data consultancy and advanced analytics provider, helping clients understand their user behavior. We collaborate with a network of clients and partners across 26 countries including F500s, fast-growing startups, and global e-commerce brands. We are looking to add Data Consultant – Client Strategy to the team. The role will require working with a team of Data Engineers, Analysts, and Scientists to streamline the development, testing, and deployment of data pipelines and products. What you need to have to join the clan:  At least a Bachelor’s Degree in Computer Science or related discipline. Self-taught individuals with a similar level of knowledge are also welcome A clear understanding of Data, Data importing, Data Cleaning, and Manipulation  Hands-on experience with Data Analytics Strong analytical skills Ability to perform root cause analysis on data and processes to answer specific questions and identify opportunities for improvement  Experience with Project Management and Leading Experience with Client Communication  Experience with Requirement Gathering and Documentation Quick adaptability to new technology and the ability to research troubleshooting techniques and best practices. Interest in building end-to-end solutions that leverage strong system designs principles to build scalable solutions At least one of: 2 years of experience working with SQL or R, Python 2 years of experience building and optimizing data architectures, data pipelines, and datasets. Familiarity with Cloud Native Stacks (AWS, Azure, or Google Cloud) Experience with Data Mining & Analysis Experience with working with BigQuery, Snowflake. What you’ll be required to do: Depending on the project, you will be paired with a Technical Lead and you will be responsible for deliverables ensuring timely delivery and project success from the technical point of view. You will be deployed on a number of engagements simultaneously. Where you will be expected to double as a Technical Architect of at least one engagement while being Consultant Data Strategy on others.  You will build and handle relationships with clients, and teammates. You will be the point of contact for the client.  You will strategize, scope, and translate project requirements from the client into clear functional specifications and work with the team to transfer them into solutions.  You will be the owner of project documentation such as scope, sprint doc, WBS, and tickets. Be able to do the first level of r&d and technical research to support the client and technical team in making the right choices  You will track and update project status to management and be the knowledge point for the project. Key Behavior:  You are a Self-starter and Driver: you are resourceful, proactive, and solutions-oriented.  You are a Multi-tasker: you thrive in a fast-paced environment while maintaining a positive “can do” attitude. You are a doer: you can handle last-minute changes and can figure out how to get things done You value teamwork: you enjoy building strong, trustful relationships with your colleagues in the team. You have a great sense of humor You have a passion for service and helping those you work with shine.   The benefits you’ll get from being part of the family:  Compensation is strongly tied to performance (Base + Bonus) Profit Sharing Flexitime  Paid Vacations Health Coverage Mentoring  Learning & Growth Opportunities  How to make us notice you? Send us a brief message at careers@marketlytics.com along with your CV and don’t forget to mention what makes you a good fit! This is a full-time position based in our Karachi office. We’re an equal opportunity employer and encourage all applications. The salary is negotiable based on existing expertise and the market.

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Data & Analytics Trainee

Job brief We are looking for Data & Analytics Trainee who is passionate about launching their career in user analytics, reporting, and data analysis. The successful candidates will help build systems to collect customer information, work with clients to understand their needs, analyze data and create insightful reports to help clients run their businesses better. We will provide initial training for the first four months. After successful completion of training and based on your performance, you will be promoted to Data Associate. Qualifications and Experience Bachelors in any field: Business, Computer Science, Engineering, Statistics, or similar. Analytical and problem-solving skills Communication skills, verbal and writing. Skills that will be cool to have (but not an issue if you don’t): Having any of the following skills will be a plus Knowledge and experience with online marketing tools. Familiar with tools like Google Analytics  Familiar with Javascript. Understanding of User Behaviour  Key Responsibilities Help design and configure data collection systems for mobile and web (for example, google analytics, mixpanel) Measure and report on digital marketing and client information datasets. Create data visualizations for performance reporting dashboards that allow users to quickly understand trends and make better decisions or drill down for further analysis. Communicate analysis with a focus on helping make better, faster decisions. What we offer Compensation tied to performance (Base + Bonus) Profit Sharing Flexitime Paid Vacations Health Coverage Mentoring Learning & Growth Opportunities Mentoring & Exposure to a wide variety of business models and customers Process To apply, please send your CV to careers@marketlytics.com. Only shortlisted candidates will be contacted. P.S. Once you’ve sent the CV, don’t forget to read through our step-by-step Hiring Procedure right here to know what and when to expect.

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