Marketing data has never been easier to access, analyse & visualise. As analytics platforms have grown in popularity, they’ve become cheaper, more user-friendly, and easier to integrate with the broader MarTech stack. Advances in the technology is a double-edged sword for marketers – on the one hand it allows you to deliver better targeted and more efficient campaigns, on the other, so can your competitors.
Gone are the days of the set and forget campaign, in which channels and creatives are selected in advance and adjustments are made to future campaigns based on the findings of the ‘post-campaign report’. Remaining competitive, particularly in the oversaturated digital media landscape, requires adjustments to campaigns mid-flight – capitalising on opportunities as they arise and cutting your losses where and when your activity isn’t delivering. Making these adjustments requires access to complete, accessible, up to date information about your campaigns. A decade ago, centralising marketing data in this way wasn’t realistic for small marketing teams with limited technical skillsets. These days, with the changes we’ve seen in the MarTech platforms, there are no excuses.
Why centralise your marketing data?
Tracking multiple touchpoints
Any effective campaign should reach people via multiple touchpoints, both on- and offline. These multi-channel campaigns present challenges when it comes to getting a consolidated view of the user journey and making direct comparisons of the efficacy of all channels in your marketing mix. The campaign manager might be reviewing conversion data via Google Analytics and impression data on Facebook, while relying on periodic reports from an external agency on above-the-line activity, often with a host of unfamiliar metrics. On the strength of this information, that same campaign manager is expected to make assessments of what’s delivering the strongest ROI.
If this assessment is to be accurate, data from each of these sources needs to be consolidated into a single view that ties delivery against campaign objectives (say e-commerce sales) directly to activity on each respective channel. This view should ideally be as complete a representation of the user journey as possible, but in reality it won’t be perfect. You may never know whether your most recent customer signed up because of an email you just sent them or a radio ad they heard weeks ago. The key is to consolidate what you can and compare consistent metrics across channels wherever possible.
All marketers will be well acquainted with the inconsistencies the data presents us with when making comparisons of metrics between channels. A ‘conversion’ that’s configured to measure the same thing across Google Analytics, Facebook and DoubleClick will often yield very different numbers depending on the source you’re looking at. Consolidating your data into a single view will help you identify these inconsistencies, rectify tracking issues when they arise, and make better-informed decisions on which to consider your ‘source of truth’.
Consolidated data from multiple channels may help you pick up on trends and insights that you aren’t necessarily looking for. For example, you might observe that your top performing Instagram ad is delivering next to no website engagement, or that campaign landing page A is far more compelling to LinkedIn audience X than landing page B.
How to centralise your marketing data
Before you can centralise your data, you’ll need to ensure that tracking for each channel is configured in a way that allows you to easily integrate and reconcile data between sources. For example, you’ll need to use consistent naming conventions for your campaign names, creatives, and tracking codes across all your ad platforms. Conversion pixels will need to be implemented to track the same website actions across channels, ideally using a third-party tracking tool such as Google Marketing Platform.
Data from your campaign channels should feed into some form of data warehouse. The data warehouse is essentially a place to store your data, define relationships between tables, and transform those tables into a consistent, easily queried format. Your data warehouse can range in complexity from a simple Google Sheets-based structure to more complex SQL databases using tools such as Redshift or BigQuery.
Many of your marketing platforms will easily integrate with your data warehouse using free plug-ins or APIs. You may also consider an aggregation tool like Supermetrics or TapClicks. These tools collect data from a huge range of digital channels like Google Ads, Salesforce and Facebook, which you can then feed into your data warehouse.
Ultimately, what marketers need to make timely decisions on campaign optimisation, is a live, interactive dashboard that surfaces critical information and allows for the exploration of trends and relationships. Where your data warehouse functions as the back-end, a data visualisation tool like Looker Studio or Power BI operates as the front-end. These tools are very intuitive and easy to learn, but powerful enough to process huge volumes of data and create compelling visuals.