Top 4 data challenges among agency clients
Marketing agencies recognize the need to be at the top of their data game to provide the best return for clients.
That’s why it takes two to tango.
If you don’t have clients’ buy-in for embracing a data-driven approach toward their marketing efforts, untapped potential and money gets left on the table.
These are the most frequent obstacles our clients face, and how to overcome them.
1. Tracking is an afterthought
UTM parameters are a marketer’s best friend when measuring down-funnel performance measurement. They give us incredible visibility into exactly what drives performance across all digital channels.
However, the reality is that many organizations simply don’t have a UTM structure to properly attribute data in their marketing campaigns.
Some of the common critical pitfalls are:
- Inconsistent templates from channel to channel.
- Missing or duplicated parameters.
- Mismatched templates at different levels, such as having one for account level and another for campaign level.
A consistent, cross-channel UTM template can be as simple as an Excel spreadsheet.
Ensuring that it is adopted across the entire marketing operations team can immediately improve attribution and measurement insights.
Furthermore, as we move more toward machine learning and automation, clients must capture click-level IDs to measure performance, provide feedback to the platforms that offer offline conversion tracking and further optimize campaigns and bidding strategies to down-funnel goals.
While Google and Facebook are currently the only platforms that offer offline conversion tracking, we have to anticipate that this will become more widely adopted sooner than later and eventually become the best practice.
To take full advantage of offline conversion tracking, the client has to do the legwork in setting up their martech stack to capture and pass these IDs through. They also need to create internal reporting and dataset schemas to export this information back into platform APIs.
2. No centralized data management strategy
Often, greener companies lack cohesive data infrastructure, and their data is siloed and disorganized. It lives all over the place in different formats (Google sheets, CSV files stored in a folder that has no access controls and lead data that lives only within their CRM).
When we see these kinds of issues, we also tend to see that the organization’s various departments handle their data differently.
Marketing operations should be considered integral and aligned with organizational goals, and data management practices should reflect that.
Having a centralized data warehouse solution and a data operations team that transcends individual departments forces the entire organization to align with its data storage practices and definitions.
Getting everyone onboard with a more modern approach to data can seem daunting, but it pays dividends in the long run.
3. Analysis paralysis
The volume and granularity of data available to us as marketers are almost limitless and will only continue to grow.
It is easy for an organization to fall into the pitfall of spending too much time analyzing every piece of data instead of zeroing in on what’s important and actionable.
When an ad manager or client comes to our BI department with a new dataset or visualization request, marketers should always ask:
- “What is the result you hope to achieve with this request?”
- “Will the data drive actionable insights and facilitate decision-making?”
- “Is the request a nice-to-have?”
Actionable is the key word here.
Because of vast data availability, it can seem daunting if an organization doesn’t have someone asking these kinds of questions to steer the ship toward a thoughtful and focused approach.
Data analysis typically falls into three categories:
- exploratory
- descriptive
- prescriptive
As marketers, we want to focus our efforts on the last two. In other words, what is currently happening, what do we want to happen and what do we need to change to get us there?
While there is a time and place for more exploratory analysis, it’s essential to not take our eyes off the prize and the insights that truly matter for a client’s bottom-line goals.
4. Lack of data culture in the organization
We hear the term “data culture” thrown around quite a bit, but the phrase can come across as a bit nebulous and sound like a substanceless buzzword.
Ultimately, all of the plights above can be encapsulated in one overarching challenge: a lack of decisive, holistic data management direction.
Data culture has to be embraced at the executive level and implemented top-down. If marketing operations speak a different data language and define important organizational goals and KPIs differently than financial operations, that’s a problem.
When we see a lack of data culture and a disorganized approach to handling and storing data, most likely, a company hasn’t put the right people and tools in the right places.
A company must be willing to invest the time and resources into finding data leaders who can guide:
- Philosophy at an organizational level.
- Implementation at a departmental level.
We can do our part as marketing data experts to guide our clients toward fixing some of the low-hanging fruit in the short term, like improving tracking and measurement. Still, it ultimately falls on the shoulders of their organizational leaders to foster a data culture that is forward-thinking and open to change to set them up for long-term success.
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