According to industry analysts at Gartner, 58% of marketing leaders are unimpressed with the marketing data and analytics their companies produce. Why? Because many leaders aren’t using a modern data stack to power those analytics. Marketers are spending too much time rounding up marketing data and not analyzing and building insight off of it.

Let’s explore four points you can use to prove to your CMO that its time to bring your company’s marketing analytics into the modern age.

Effectively manage a multitude of data sources

Any modern marketing organization uses dozens (if not hundreds) of platforms, apps and suites in its day-to-day work, including:

• Content management systems: such as WordPress, HubSpot, Contentful and more
• Ad tech: Facebook ads manager, LinkedIn Ads, Twitter Ads
• Customer relationship management (CRM): such as Salesforce, Hubspot
• Social Media: data around paid and organic social media efforts across LinkedIn, Facebook and Twitter
• Marketing automation platform: such as Marketo, UberFlip and ActiveCampaign
• Lead enrichment services: such as Clearbit, Fullcontact and ZoomInfo

While each of the above have native reporting capabilities, there are limitations. To have a full view of your customer and holistic insights, you must combine the data from these disparate systems.

Conversation starter: Ask your CMO and marketing directors which tools your marketing team uses and why.

Fully discover marketing KPIs via the modern data stack

Each subgroup in the marketing organization has key performance indicators that they track and report on. Most of these metrics revolve around ROI (return on investment) analysis, attribution analysis and customer journey analysis. Let’s break each of these down and see how a modern data stack enables you to dig deeper into these key metrics.

• ROI analysis. By combining the data from all of your advertising channels, you can understand which are generating the most leads at the lowest cost of acquisition and allocate additional funds to that channel.
• Attribution analysis. Chances are your company has deployed many avenues to acquire customers –paid ads, demos, chatbot and so forth. Knowing which deserves credit for a customer acquisition is the job of attribution modeling. You can apply different attribution models to your efforts, including multi-touch attribution, to understand what your customers interact with before and after they buy.
• Customer journey analysis. ROI and attribution are certainly part of the work to figure out your customer journey. How do customers go from not knowing about you to being engaged, loyal fans? All of that mapping makes up your customer journey. With more knowledge here you can understand the signs of churn or how to speed up the sales cycle.

Conversation starter: Ask your CMO how the department determines ROI, attribution and customer journey metrics and what data supports each metric. How many manual hours does it take to normalize and assemble the data in the correct format? Has your CMO considered the impact of human error?

Examine barriers to proper data analysis

There are a few team and technology-related issues that get in the way of a truly data-driven marketing strategy. When data is at the center of marketing analysis, the employees and stakeholders can have holistic conversations about a marketing campaign’s effectiveness.

Say, for example, content marketing considered a new ebook to have been largely successful based on the number of leads the new gated asset generated. However, if the ebook was promoted across paid, organic and even employee advocacy channels, you need to go a step further to determine which channels resulted in the most leads. By connecting all the data and analyzing the trends, such as which channels are more successful than others, the marketing team can make more informed decisions for future campaigns.

What stands in the way of data analysis on marketing teams?

1. Data silos bridged by manual processes. Many times, marketing managers are merging data by copying and pasting between spreadsheets. This is rife with the potential for human error and also causes burnout.
2. Ensuring data accuracy. If your data is housed in apps, platforms and CSV files, centralizing and normalizing the data is cumbersome and often leads to a delay in reporting.
3. Building and maintaining proper data synchronization. If your data team’s duties include building pipelines, consider the opportunity cost. Beyond the initial build, this team will have to maintain the pipeline as breakages and source changes offer, risking mission-critical projects.

Conversation starter: Ask your CMO if automation would be a welcome addition to the data analysis/reporting capabilities of your marketing team. This is a great opportunity to share the benefits of a modern data stack.

By leading with the above points, you’ll have an effective and honest conversation about the ways a modern data stack can help your marketing team with data and analytics.

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