Boost Marketing Intelligence with In-depth Analysis after Data Integration
Customers have shifted their expectations and behaviors in response to our increasingly digital world. Businesses must accelerate data integration to keep up.
Data integration is a challenge for all organizations. There is so much marketing data constantly being generated, and integration requires ongoing updates and thoughtful innovation. With the right marketing intelligence platform like Salesforce, modern marketing professional can continually integrate data across new channels and platforms to get a clear understanding of their campaigns. From there, marketing intelligence yields optimization and analytics with rich insights that maximize ROI and deliver better customer experiences.
Unlock Marketing Intelligence with Salesforce
Marketing Cloud Intelligence was purpose-built to solve data integration challenges for marketers and technologists of all levels. Intelligence automates the entire integration process, from discovery to retrieval to harmonization, giving marketers total control over their data while ensuring the flexibility to evolve as the team’s needs change and grow. Learn more about how your data integration can optimize the impact your marketing budget while increasing your operational efficiency.
Everyone knows that the marketing function as critical for driving growth. To drive growth and improve the customer experience, marketers need to embrace a combination of people, process, and technology to create a single system of record. This works to build a marketing intelligence strategy.
This marketing intelligence strategy must be built on a solid data foundation. Because consumers are more connected than ever, marketers have more information than ever. While 98% off marketers around the world emphasize the importance of having a complete, centralized view of all cross-channel marketing, 75% still evaluate cross-channel marketing performance in silos. The result is marketers are swimming in a sea of disorganized data; and 59% are still using manual are partially manual processes to connect it all together. But this challenge is also a major opportunity. With a proper data integration strategy, marketers can turn this information into a foundation to drive decisions and optimizations.
What Is Data Integration?
By integrating disparate and unconnected data sources, marketers can get a complete view of marketing performance. They can integrate everything from campaign and budget records to analytics and customer relationship management (CRM) data to paint a full picture of their marketing performance. These combined datasets unlock insights that aren’t available in siloed datasets. With this information, marketers can optimize their spending to make future campaigns more efficient and effective.
Data integration, however, isn’t easy. Manual data integration was the #2 most selected challenge by marketers for evaluating marketing performance, behind only employee resources and skill sets. Marketers must wrangle facts and figures with a future-proof mentality. This is a complicated task that requires the right people, process, and products to tackle three main phases—discovery, retrieval, and harmonization. In this guide, we’ll explore each phase and the requirements to be successful.
Data Discovery with Salesforce
To integrate your data, you first need to understand what your data looks like and where it’s stored. This is the purpose of the discovery phase. Each source requires its own discovery process, in which all data fields (such as geography, cadence, and campaign attributes) must be identified and labeled. The discovery phase requires coordinating with stakeholders from across the organization. You must engage each channel owner to properly analyze the data in detail and create a data roadmap, ensuring that all perspectives—from top-line leadership to on-the-ground analyst validation—are covered
In order to successfully optimize your ROI, you need to know your data. This requires:
People:
• Solution engineers/data analysts to data architect
• Technical project/program managers to oversee the process
• Channel owners to relay information about the sources
Process:
This discovery phase typically takes 1–3 months per project (depending on the amount of data). You must also get validation from all stakeholders (leadership down to analysts) which is a variable time commitment.
Product:
• Data providers to collect various data extracts
• Data architecture to design the overall plan for integration
How can a marketing intelligence platform help?
People:
A centralized system facilitates collaboration across all stakeholders—IT/business intelligence, marketing, executives—to ensure scoping and discovery experts all contribute
Process:
Built-in AI can facilitate project management and streamline data-mapping processes
Product:
Automates access to data provider extracts and provides prebuilt architecture schemas built from years of aggregated marketing expertise
Retrieval of Data for Application
Once you’ve discovered and located all your data, it’s time to access the information programmatically and link it all together. This is the retrieval phase.
The most effective way to retrieve your data is through an automated system. When manually integrating data, 29% of marketers spend at least one week per month collecting, cleansing, and modeling data for reporting and analysis — time that could be freed up for deeper analysis with automation.
Manually retrieving and transferring data can be extremely laborious and time-consuming. Without automation, teams of analysts have to manually search for specifics on each platform and aggregate it all into a master reporting system. Fundamentally, this manual approach isn’t scalable.
However, automation is not easy. There are two main ways marketers retrieve data through automation: API-connected retrieval and non-API programmed retrieval.
1) API-Connected Retrieval
An API is an application programming interface — like a menu. There are many different APIs that allow marketers to retrieve information from databases. While APIs streamline the retrieval process, the time commitment for marketers can be cumbersome, especially when juggling multiple data sources.
A single API might take 2–4 weeks to plan and develop, but the real time effort is in the dedicated updates and maintenance. Given the fast changing nature of marketing, this might be a time-intensive effort. Depending on how many marketing channels and data sources you have, this process may merit a full-time staff and years of ongoing work. This is where technology with out-of-the-box API connectors can help, as the creation and maintenance — the most egregious part of the process — is taken off your plate.
2) Non-API Programmed Retrieval
Some marketing channels, however, have not yet developed their own APIs. This presents challenges for developers who are trying to automate their data retrieval. To retrieve information from these channels, you can program retrieval through cloud storage, private storage, or internal databases.
This programmed retrieval process requires the same kind of development and upkeep as API-connected retrieval, but the process itself ranges in complexity depending on the source. To handle non-API programmed retrieval, you need to build a system agile enough to process any type of file, smart enough to categorize all your data, and fast enough to retrieve data in real time.
So, whether you’re automating via API or not, this retrieval process requires:
People:
• Developers, engineers, and IT to program the retrieval and maintain upkeep
• Channel owners to answer questions about platforms as they arise
• QA and technical support analysts to support issues in setup and ongoing maintenance
Process: you must not only invest heavily in the upfront engineering of these retrieval approaches, but in the ongoing maintenance and updates. The data providers are not static and require constant attention. This is especially true if your marketing objectives evolve.
Product:
• Data retrieval to access and extract data in an automated and ongoing basis
• Data warehouse to store the extractions
How can a marketing intelligence platform help?
People: all stakeholders in the marketing department can retrieve data with all clicks, no code, across various integration methods (both API and non-API alike)
Process: the entire retrieval process from creation, maintenance, and update is automated for you, so you can focus on data action and insight rather than data discovery and maintenance
Product: out-of-the-box, automated API and universal connectors partnered with storage methods that are accessible for all members of your organization.
Harmonization of Data & Market Intelligence
Once you’ve discovered and retrieved your data, it’s time to connect and prepare it for analysis. Disparate data sources, even when integrated into one common platform, still can’t provide insights until they’re unified and connected. That’s the purpose of the harmonization phase.
During harmonization, you connect data sources, create a common semantic layer, and turn data points into a story that you can use to optimize your campaigns. After harmonization, you’ll have a unified dataset you can use as a single source of truth for all your marketing.
With this single source of truth, it’s easy to manage your data and get the answers you need from it whenever you have questions. Any level of marketing analysis is possible, and all future marketing inquiries, across all stakeholders, can be easily audited.
Marketers recognize the importance of this step as well: data quality was named as absolutely essential or very important for driving growth and improving the customer experience by 79% and 77% of marketers, respectively — more than any other option.
The harmonization phase takes place in two steps: merging data sets and classifying data.
1) Merging Datasets
Related sets of data need to be connected based on relevant hierarchies (such as channel, product, campaign) and rules that account for relationships between data. This relationship management requires data architecture, semantic calculations, and metric formulas to relate datasets. You must also automate this logic so that all future data is merged per your business needs.
2) Classifying Data
Each data source comes with its own structure, outputs, and naming conventions. To harmonize your data, your teams need to develop a unified system for renaming and classifying data from different sources, so you can perform the same analysis across all of it. This requires automating a system that takes raw data and enriches it with the custom logic for your business. Ultimately you then get a process that turns information into insights.
This harmonization process requires connecting all of your disparate teams, gathering their requirements, automating data enrichment, and creating validation processes all in one platform.
Once complete, you have data-driven insights across all of your data prepared for optimization. But, this requires:
People:
• Data analysts to write business logic and calculations
• Channel owners to answer questions about platforms
• QA and technical support analysts to help with issues
Process: you have to align multiple stakeholders around common project plans first. Then, the IT team has to engineer and automate with help of the QA/support analysts.
Product:
• Data warehouse to store the data
• Data architecture and logic platform to write the business logic and semantic enrichment
• Automation platform to monitor discrepancies and update net new data
How can a marketing intelligence platform help?
People: marketers can utilize the marketing data models and semantic layer enhancements to enrich their raw data, freeing time for analysts to do more advanced analytics
Process: automate your information architecture merging and simplify your classification with expert-built processes
Product: marketing intelligence includes data storage, data lakes, data models, and semantic layer logic to automate the entire process end-to-end