Technique 6 minutes, 35 second read Nathaniel Spohn, VP, EMEA, Fivetran
The marketing department is a gold mine of data, but too often analytics practices are ineffective, resources are underutilised, and results are unimpressive – that’s according to 58 percent of marketing leaders in Gartner’s research. This is due to a ‘problem’ of both quantity – with a constant influx of marketing data from hundreds of platforms – and of quality – with marketers spending a long time manually consolidating data that, by the time they reach an actionable insight, can be several weeks old.
Effective data analysis can reduce human error, speed up time-to-insight, and create a 360-degree view of the customer, which is key to improving decision-making far beyond the marketing department. CMOs need to know five key things about the state of data analytics today that will help them reach a high level of efficacy and bring their marketing operations up to speed.
Effective data analytics first requires data centralisation
All modern marketing teams rely on a vast array of digital tools in their day-to-day work, 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 ActiveCampaign, 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 social media campaigns and track paid advertising effectiveness.
Alone, these platforms and apps enable marketers to undertake campaigns, organise data and carry out native reporting – but together, they are capable of much more. When data across disparate systems is combined and centralised, it gives a 360-degree view of the customer, so issues can be spotted as soon as they appear and remedied before they can have a wider impact for the business.
A holistic view of data lets marketers move beyond the ‘what’ and ask ‘why’
Having a holistic view of all marketing data is crucial to unveil previously hidden patterns and trends, and quickly spot new opportunities to connect a brand to its customer base.
Think about the tools listed above – it’s likely your organisation uses a few or all of these, and they were all originally onboarded to fulfil a purpose. But once they become part of the technology mix, its users often start overlooking the ‘why’ and focus on ‘what’ exactly they can achieve within the specific suites. Centralising data sources empowers marketers to ask big-picture questions again.
Centralised data enables a deeper insight into marketing KPIs
It’s common practice for marketing teams to set key performance indicators (KPIs) and report on these metrics on a recurring basis. But when teams have access to all marketing data in one place, they can dig deeper into KPIs and do more elaborate reporting.
Return on investment (ROI) analysis is one area where a deeper understanding translates into better performance. By combining the data from every advertising channel, marketers can understand which are generating the most leads at the lowest cost of acquisition and allocate additional funds to that channel. Attribution analysis is another great example.
Whether it’s paid advertising, free demos or chatbot interactions, it’s important to know which customer acquisition method is the most effective and which should be credited after each acquisition. By applying different attribution models to their activities, including multi-touch attribution, marketers can understand what customers interact with before and after they buy.
Combined, these two metrics can also help map the customer journey, which can also be further analysed. For instance, do you know how a customer went from never having heard about your brand to being a customer, or even better, an advocate? With more data at hand, marketers can build out a bigger picture and map journeys, find new ways to accelerate the sales cycle, and even recognise the signs of churn.
The modern data stack is crucial for making data-driven decisions
When data is at the centre of marketing analysis, it can create a data culture where employees and stakeholders alike can view crucial information and have strategic conversations about the direction of marketing spend, as well as overall company initiatives.
For example, the content marketing team might consider a recent ebook campaign a success based on the number of leads the gated content generated. But if the ebook was promoted across paid, organic and employee advocacy channels, marketers need more data to determine which channels delivered the most leads.
This is where the modern data stack comes in. With this approach, data is directly loaded into cloud data warehouses where transformation can quickly take place, followed by analysis and reporting through Business Intelligence tools.
In this flexible, multi-layer model, the data integration step can be automated to help marketing teams quickly and reliably access all the relevant data under one roof, and manipulate the data easily within the cloud to understand correlations and causations in customer journeys.
By centralising all the data and making it analysis-ready, the marketing team can make more informed decisions for future campaigns.
All common data analysis errors have roots in human error and manual processes
Human creativity and emotional connections are key to building relationships with customers, but when it comes to data analysis, human error is often the biggest barrier to reaching actionable insights. There are many ways marketers can reduce this risk and at the same time eliminate manual processes which are a drain on productivity and valuable analysis time.
There are three common issues that stand in the way of truly data-driven marketing strategies:
- Data silos bridged by manual processes. When there is no automated process for the data to travel from its source to a place of analysis, marketers are often forced to move the data manually, copying and pasting between spreadsheets. This is rife with the potential for human error and the mundane work can quickly cause burnout.
- Ensuring data accuracy. When data is housed in different apps, platforms and CSV files, centralising the data and putting it into an analysis-ready format is cumbersome and often leads to long delays in reporting. In a Dimensional Research study, over 4 in 10 analysts admitted to using data which was on average at least two months old. Outdated data is inaccurate data, and misleading insights can manifest in missed opportunities and lost revenue.
- Building and maintaining data pipelines. The data pipeline is 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 analysed). Its uninterrupted functioning is crucial for proper data synchronisation, but often data teams’ resources are unnecessarily exhausted with fixing pipelines when breakages occur and making changes every time new data sources are added.
All of these human-led processes risk compromising mission-critical projects – it’s all lose, no win. Here is where automation can be a welcome addition to the data analysis and reporting capabilities of a marketing team.
Start today by asking the right questions
The pandemic has accelerated the speed of digital transformation for enterprises across the world. To make marketing a driving force of business growth, CMOs must evaluate their data strategies and ask some important questions:
- Which tools does the marketing team use and for what purpose?
- What are the top KPIs for the department and what data supports each metric?
- How many manual hours does it take to assemble the data in the correct format?
- What is the impact of human error?
- What is missing from the company’s modern data stack?
By answering these questions, marketers can have an effective and honest conversation about the ways the marketing team – and the company as a whole – can drive more value from data analytics and Business Intelligence investments.