How do you merge marketing data for a complete view?

One thing I find cool about merging marketing data is that it allows you to gather information from different places where you interact with customers. This comprehensive view of each customer provides a complete picture of their likes, preferences, and behaviours. With all this information in one place, you can tailor your communication to each customer's unique interests and preferences. This helps your brand resonate with them deeper and increases their trust in your company. Additionally, you can optimize your marketing strategies by analyzing what works best with your audience. This secret weapon is a game-changer that can help you deliver the best possible customer experience and drive more effective marketing campaigns.

A key obstacle to overcome when it comes to measuring and merging data is the recurring inconsistencies among platforms: some proprietary platforms/channels tend to show data that in most cases are more favourable to their own platform when compared to data referencing the same channel performance from a third party application. Manual reconciliation of these inconsistencies is frustrating and a loss of time. Moreover some of the walled garden platforms don't share enough insights, as they keep most of the relevant attribution and performance for them. A good solution would be focused around fixing this disconnect among platforms to get a view that is more realiable when it comes to make data driven decision and budget allocation.

Merging marketing data allows you to piece together a comprehensive view of your audience, their behaviors, and preferences. This holistic understanding enables better targeting, personalized campaigns, and improved customer experiences. Without merging data, you risk incomplete insights and misinformed decisions, hindering your ability to adapt and optimize your marketing strategies effectively.

The first step is to identify all of the data sources that contain relevant marketing data. This could include your CRM system, marketing automation platform, website analytics tool, email marketing platform, and any other system that collects data about your customers and prospects. also Define the common fields, Clean and normalize the data , Merge the data , Validate the merged data . If you are looking to improve your marketing results, then merging marketing data is a great place to start. By taking the time to merge your data, you can gain valuable insights into your customers and prospects that you can use to improve your marketing campaigns.

Well, having multiple source of data is great, but the challenge is in structuring data for concise and clear reporting. So, merging marketing data is not about having all data thrown in a PDF or Google Sheet. You should identify the most important metrics across each marketing channel and categorize them into appropriate categories, labels etc. When someone else is reading your report, they should have clear understanding of different data streams. You should use logical thinking when creating attribution models, otherwise you'll likely end up with duplicate data that will mess with the overall goals.

For marketing data in particular you want to ensure that you unify the attribution windows (look-back windows) closely so that you are not giving one ad platform more time to take credit for a conversion than another.

Effective data preparation is vital for merging marketing data seamlessly. It involves addressing errors, missing values, and anomalies, ensuring data integrity. Standardizing formats, labels, and metrics across various sources promotes consistency. This meticulous process streamlines merging, enhancing the reliability of analyses. By enforcing uniformity in elements like date formats and naming conventions, marketers create a foundation for cohesive data integration. The quality of data preparation significantly influences the success of merging endeavors, emphasizing the importance of diligence in this foundational stage.

Any businesses can effectively prepare their data for merging, enabling the business to adapt a comprehensive and cohesive view of their operations by identifying their data sources , establishing a common identifier, map out fields and centralised their data location. Not forgetting to validate their data.

Let's delve into an illustrative example: merging Social Campaign data with CRM data. The pivotal first step in this merge is pinpointing mutual fields for seamless integration. Take UTM parameters, for instance. When a user is directed to your website via an ad click, these parameters are captured and subsequently stored in your CRM. Such parameters become the vital bridge connecting digital campaigns to specific users. However, before embarking on this merge, it's essential to guarantee the data's integrity. It should be immaculate, consistent, and align with the predefined framework. Properly curated data not only simplifies the merging procedure but also ensures the resultant insights are sharp and actionable.

When connecting online & offline data you must be able to connect the relevant entities: - cookie - for online. - customer ID - for offline. This requires: Gather and manage detailed online data (not aggregated as on free GA). Capability to manage an entity table of cookie-customer ID match. Capability to insert relevant data into the entity table.

1. Data integration platforms to merge data from different sources. 2. Customer relationship management tools (CRM) this allows businesses to keep a centralised view of their customers interactions. 3. Data visualisation and analytics tools for reporting. Tools/platforms such as Salesforce, Marketo, SQL, Microsoft dynamics, PowerBi and Google Analytics have been used to improve business performance.

You can utilize nearly any tool to merge marketing data, but think of your marketing data as pre-click sources (ad platforms) and post-click sources (Google Analytics/Salesforce). When you merge the sources together are you merging down to the creative name of your ad campaigns? Do you understand what it means when people say that reporting your ROAS from your ad platform is the practice of operating in a data silo and why that's bad (double counting of sales as one example). Additionally, most people are not trafficking the creative level insights into their sources of truth (Google Analytics/Salesforce/etc) properly. Just do a single day pull from both sources pre + post click by creative & you can likely answer that question.

Merging marketing data is facilitated by a range of tools, offering marketers flexibility based on specific needs. Google Data Studio, known for its user-friendly interface, seamlessly integrates with Google services for easy data connection and visualization. Power BI, a cloud-based platform, provides a comprehensive solution for transforming and analyzing data from various sources. Tableau excels in data visualization, enabling exploration of data from multiple channels. SQL, a versatile programming language, serves as a foundational tool for querying and joining data from diverse sources. Selecting tools aligned with data complexity and analytical depth empowers marketers to maximize the potential of merged marketing data.

For merging online and offline data you can use marketing automation platform which has this capability e.g Hubspot, Marketo, Eloqua… A CDP is quit a new solution supporting this capability.

Marketing data can be merged in the following ways: 1. Create a Marketing Data Lake using Snowflake or other systems to store raw data and process it to ingest to the Customer Data Platforms (CDPs). 2. A Customer Data Platform (CDP) is an emerging marketing technology/system that ingests different forms of marketing data from multiple sources/databases. This data gets segmented as macro and micro segments to target both anonymous and deterministic audiences through 1:1 personalized digital campaigns across various digital touchpoints. The performance of these campaigns can be monitored (campaign dashboards) through the integration of BI tools. #cdps The CDPs will be the future of marketing enablement in the world of cookieless future.

This is a good start, but keep in mind your reporting from Google Analytics will be last touch and thus inherently bias the data story to favor your lower funnel marketing tactics. Consider using a Multi-Touch Attribution solution that using click-stream data + impression/view through data so that you allow a model to learn how the upper funnel tactics influence the overall customer path to purchase.

Merging marketing data with SQL offers a potent and adaptable method for creating a unified view of customer behavior and campaign performance. This involves identifying relevant tables, determining common join keys, and constructing SQL queries for seamless data integration. The advantages of this approach include flexibility in data manipulation, efficient processing of large datasets, customization for specific requirements, integratability with analysis tools, and automation for streamlined and up-to-date insights. Ultimately, SQL emerges as a robust and scalable solution, empowering marketers to glean comprehensive insights into customer behavior and overall marketing effectiveness across diverse platforms.

The example in the article body is oversimplified. Need to take the structure of the data into account. The structure of data will heavily depend on what tool you use to extract it (completely custom API call, 3rd party tool with pre-defined schema, BigQUery Data Transfer).

Merging marketing data with Google Data Studio offers a user-friendly approach for obtaining a comprehensive view of marketing performance. By creating reports and blending data from sources like Google Analytics and Google Ads, marketers can seamlessly connect and display information using common identifiers. While challenges may arise in maintaining data consistency, the benefits are substantial. Google Data Studio enables the creation of visually appealing, customizable dashboards, providing a unified perspective on marketing data. This streamlined approach empowers marketers to derive actionable insights, make informed decisions, and navigate the complexities of multi-platform data analysis.

Google Data Studio (now Looker Studio) only allows merging on a superficial (already aggregated) level, but it can still be useful. Data blends can be tricky to get right, but they do work. Need to find the common dimensions e.g. date and campaign ID & name, and then bring together the required metrics from the various data sources. Need to be very thorough with data validation with the source systems, because the joins are harder to control in Looker Studio itself.

Once you implemented successfully a solution to merge online and offline data, you need to deploy reporting and analytics solutions in order to gain the business value. Few examples: - Using online behavior as an intent data, to generate communications in other direct channel. - Understanding customer behavior & improving customer experience. - Building a 360 customer view. - Improving targeting….