Data Analytics: Five Truths To Know By CMOs
Data Analytics : Marketing departments hold a treasure trove of data, but too often the analytics implemented are inefficient, resources are underutilized, and the results aren’t up to par at least that’s what 58% of CMOs surveyed say in a recent Gartner survey .
This result is due to a problem of both quantity with a constant stream of marketing data from hundreds of platforms and quality with marketers spending considerable time manually consolidating data that, when it ends up generating useful information, often reflects a dated reality.
Effective data analytics can reduce human error, accelerate the generation of useful information, and create a 360-degree view of customers, all of which are essential to improving decision-making, far beyond simple marketing departments.
To achieve this, marketing departments need to know five truths about implementing data analytics, which will help them achieve a high level of efficiency and take their operations to a new level.
Effective Data Analytics First Require Data Centralization
All marketing teams today rely on a wide range of digital tools for their daily tasks, including:
- Content management systems (CMS) such as WordPress and Contentful
- Customer relationship management (CRM) tools such as Salesforce and Hubspot
- Marketing automation platforms such as Active Campaign, Marketo and UberFlip
- Lead enrichment services such as Clearbit, Fullcontact and ZoomInfo
- Social media platforms such as Twitter, LinkedIn and Facebook – both to track organic campaigns and advertising campaigns on these networks.
Individually, these platforms and apps enable marketers to run campaigns, organize data, and report but together they are capable of much more. When data from disparate systems is combined and centralized, it gives a 360-degree view of the customer, and problems can be identified as soon as they appear, and corrected before they have a greater impact on the business.
A Holistic View Of Data Allows Marketers To Go Beyond
Having a holistic view of all marketing data is essential to unveil hitherto well-hidden patterns and trends, and quickly identify new opportunities to connect a brand to its customer base.
Each of the tools mentioned above were originally installed to fulfill different objectives. But once they are part of a mix of technologies, their users often start by neglecting the ‘why’ and focusing on what they can achieve with the tool in question. By centralizing data sources, marketers can once again ask the right questions.
Centralized Data Provides A More Accurate View Of Marketing KPIS
It is common for marketing teams to establish performance indicators (KPIs) and report on these metrics on a recurring basis. But when teams have access to all the marketing data in one place, they can better analyze these metrics and produce more elaborate reports.
Return on investment (ROI) analysis is an area where better understanding translates into better performance. By combining data from all ad channels, marketers can understand which ones generate the most “leads” at the lowest cost and allocate more investments to those channels. Attribution analysis is another excellent example.
Whether it’s paid advertising, free demos, or chatbot interactions, it’s important to know which customer acquisition method is most effective and to which to attribute each acquisition. By applying different attribution models to their business, including “multi-touch” attribution, marketers can understand what content customers interact with before and after their purchase.
Combined, these two metrics can also help trace the customer journey, which can be better analyzed. For example, can we know how a customer went from being ignorant of a brand to being a customer, or even better, a promoter of that brand? With more data at their disposal, marketers can have a more complete view and trace journeys, find new ways to accelerate the sales cycle, and even recognize signs of abandonment.
A Modern Data Stack Is Essential For Making Data-Driven Decisions
When data is at the center of marketing analytics, it can create a true data culture, through which employees and stakeholders alike can visualize strategic insights and drive strategic thinking about driving marketing investments, and projects involving the entire company.
For example, the content marketing team may consider a recent ebook campaign to be successful based on the number of leads the content generated. But if the ebook has been promoted through different channels (paid, organic, etc.), marketers will need more data to determine which channels have generated the most leads.
This is where the “modern data stack” comes in. With this approach, data is directly loaded into data warehouses in the cloud where its transformation can be quickly carried out, followed by the analysis and reporting step via Business Intelligence tools.
In this flexible, multi-layered model, the data integration step can be automated to help marketing teams quickly and reliably access all relevant data in one place, and easily manipulate it in the cloud to understand correlations and casualties in customer journeys. By centralizing all the data and transforming it to be ready for analysis, the marketing team can make more informed decisions in future campaigns.
Most Common Inconsistencies In Data Analysis Are Due To Human Errors
Creativity and human interaction are key to building a relationship with customers, but when it comes to data analytics, human error is often the biggest obstacle to generating effective information. There are many ways for marketers to reduce this risk and at the same time eliminate manual processes that reduce productivity and waste valuable time.
Three common issues affect data-driven marketing strategies:
- Data silos linked together by manual processes. When there is no automated process to get data from its source to where it’s analyzed, marketers are often forced to move it manually, via copy-paste between spreadsheets. This results in a high potential for human error and a large number of repetitive tasks that are quickly tedious.
- Ensure up-to-date data. When data is hosted in different applications, platforms, and CSV files, centralizing and transforming it into a ready-to-analyze format is complex and often results in long delays in producing reporting. In a survey conducted by Dimensional Research, more than 4 in 10 analysts admitted to using data with an average of two months of seniority. Outdated data is by definition inaccurate, and the misinformation it generates can result from missed opportunities and lost revenue.
- Build and maintain data pipelines. A data pipeline is made up of a series of processing steps that data goes through between its source (raw data) and its destination in the cloud data warehouse (where it can be analyzed). Its uninterrupted operation is crucial for proper data synchronization. But often the resources of the teams in charge are unnecessarily allocated to repairing pipelines when breaks occur and changes must be made every time a new data source is added.
All of these manual processes risk compromising strategic projects individually or together. This is where automation advantageously complements a marketing team’s data analysis and reporting capabilities.
Asking Yourself The Right Questions
The pandemic has further accelerated the digital transformation of businesses around the world. To make marketing a central part of future growth, marketing departments need to evaluate their data strategies and ask themselves several important questions:
- What tools are used by their marketing team and for what purpose?
- What are the main performance indicators of their department and what data each of them depends on?
- How many hours does it take to manually assemble the data in question in the right format?
- What is the impact of human error in this process?
- What is missing from their company’s modern data stack?
By answering these questions, marketers will be able to gain an effective and reliable view of how their marketing teams – and their businesses as a whole – will be able to maximize the value of their investments in data analytics and business intelligence.