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Recession or no recession, marketing budgets are getting smaller. Marketers are being asked to do more with less.
The upside of this is that it provides clarity to the priorities marketers need to make with the budget they have. Time and money work the same way in that regard. Given an unlimited number of hours in a day, you’d accomplish everything. But that’s not how life (or budgets) work.
Over several informal conversations with marketing leaders at over 20 companies across a range of industries, we asked what struggles, pain points and wish lists dominated their day and influenced their spending decisions.
Specifically, we asked what their priorities were when making budget allocation decisions.
One clear desire rose above the rest — Reporting and Analytics. If they were given free money to spend on anything they liked, increasing their reporting and analytics capabilities regularly came out on top among the wish lists.
Related: How to Grow Your Business With Marketing Analytics: The Ultimate Guide
The marketer’s wish list
Here are the top 10 results, in order of importance, of how these marketing leaders told us they would stack rank their priorities against their budget. This was not a list of pre-set options, but rather what they volunteered themselves that simply laddered up into the categories below.
Take a look and see if your priorities match:
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Reporting / Analytics
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Machine Learning / Artificial Intelligence
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Stability
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Audience Growth
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Customer Journey
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UI and operational efficiency
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Privacy and Trust
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Loyalty
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Content
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Simplify Stack
Why Reporting / Analytics?
The first question, of course, is why? What makes Reporting and Analytics so important that it so far outpaces the other items on this potential wishlist?
For starters — ROI. Marketing departments have to constantly justify every action and every dollar through the results they achieve. Marketers (and those they report to) need to see that their efforts are performing as expected in terms of direct attribution (read: revenue) across all channels — email, mobile, and so on.
That leads us to autonomy. Marketing teams would prefer to analyze the results of their campaigns themselves directly from the platform they use, rather than rely on a separate IT or tech department to pull data for them.
Not only is this more efficient from a time/resources perspective (eliminating the back-and-forth request/response/request/loop), but it also makes the insights gained more actionable within the marketing team and the campaigns they manage.
Automation is another one. Marketing teams are trending smaller as budget is pulled away into building IT and tech-focused groups like marketing automation. So, marketers say they’re spending too much time on data creation and the manual tasks behind that effort, and would prefer platforms with built-in automation wherever possible to help them.
This includes connecting data and analysis functions directly with the CRM platform they use, as well as proactive predictive customization to automatically implement campaign changes based on pre-set parameters.
And finally, monitoring is a big part of the data/analysis equation. The ability to monitor incoming data and make rapid changes as needed is a logical place to invest data and analytics dollars. This includes robust A/B testing capabilities with the ability to rapidly and dynamically modify tests on the fly, as well as the ability to monitor the entire customer journey.
Preferably, this monitoring can take place through a single dashboard that compiles all datasets from across the platform (or integrates data from multiple platforms) to reduce the number of multiple screens or handoffs necessary with most systems today.
Related: 5 Analytics Tools to Supercharge Your Marketing Strategy
What to report/analyze?
The ability to report and analyze data is one thing. Knowing what data to focus on is another.
Revenue was a common data point the marketers we spoke with wanted to measure. Partnering with a technology company that can track web behavior and tie it back to channel performance is a key data point. What marketing emails, ads and other tactics are driving the most revenue, and why? If something outperformed historical trends, what was the differentiating factor? Could a change in one channel drive a shift in channel share?
Engagement stats like clickthrough is another important metric to monitor because engagement often leads to revenue. Conversions are important.
And finally, ensuring that interested customers are being serviced properly through digital channels to avoid involving a human intervention, which can tie up resources and ultimately slow down a conversion. Imagine receiving a push notification with a coupon code but then not being able to redeem that code upon checkout.
The “human” cost associated with any digital channel snafu can be expensive in the form of customer service representatives ultimately needing to complete the transaction. Keeping the activity online and completing sales in a single session is the mark of a well-functioning marketing campaign that drives both engagement and revenue.
Ultimately, the number one goal is to avoid sparking a phone call to customer support. A phone operator can only assist one person at a time, while a website can serve thousands.
On paper, good data reporting and analysis seem obvious. Time and time again, good data and analysis result in improved ROI. But in the reality of the fast-paced marketing world, carving out the time needed to both collect and analyze data can be difficult when doing so remains a manual process.
That’s why companies should seek out and demand automated reporting and analytics features from their marketing platform providers. Revenue modeling and channel attribution are too important to be left to chance. Working with a platform that can easily automate this kind of performance reporting, and then using AI to detect the small shifts in these results, gives marketers the insights they need to optimize their efforts in real time.
In other words, the same tools that marketers use to automate marketing outreach should make collecting and extrapolating data just as easy and automatic. This allows marketers to spend more time making the data more actionable for more personalized communications — and ultimately, more meaningful relationships.
Related: 10 Tools Helping Companies Manage Big Marketing Data
