How do you keep your marketing analytics funnel updated and relevant in a dynamic market?

To keep your marketing analytics funnel updated and relevant in a dynamic market, it’s essential to continuously monitor data and adjust key metrics based on real-time trends. By frequently reviewing and refining your KPIs, you can stay aligned with evolving business goals. Incorporating advanced tools like GA4, automation, and machine learning ensures you're keeping pace with changes. Regular feedback from customers provide further insights, helping to adapt strategies. Agility is key, so you can quickly pivot based on market changes or competitor actions, maintaining an edge in a shifting landscape.

As you begin the process of defining your goals and metrics for each stage in the funnel, its equally important to define the data sources that will help to define and serve as the basis of those metrics. Going through this exercise allows you to identify what data you have on-hand to properly define and represent these core metrics.

Keeping your marketing analytics funnel updated and relevant in a dynamic market requires a proactive and data-driven approach. One thing to focus on is Automation. Automation plays a crucial role in maintaining a relevant and up-to-date marketing analytics funnel. And by leveraging automation in these aspects, marketing teams can not only streamline their processes but also respond swiftly to market changes, ensuring that the analytics funnel remains relevant and effective in a dynamic environment.

Defining funnel goals and metrics was the first step for us to not only measure our performance and identify bottlenecks in the sales cycle, but also to figure out tactics to improve conversion rate in each stage and across them. All funnels need to be viewed from the lens of the customer segments you are targeting. Each segment may have different metrics, as well as different bottlenecks

To craft a marketing analytics funnel that truly resonates with your business objectives, infuse each stage with clear, actionable goals and precise metrics. For instance, in the awareness phase, aim not just for broad visibility but for meaningful engagement, tracking not only impressions but also the quality of interactions. As you move to conversion, focus on the depth of customer commitment, measuring not merely the quantity of leads but the conversion rate and customer lifetime value. This approach ensures that your funnel is not just a pathway, but a rich, data-driven narrative of your customer's journey.

This is such a crucial step in the overall measurement process. One needs to think of their tooling not just from a short-term perspective, but also from a mid-long term perspective and ensure that the tradeoffs of using certain tools at the start of your journey do not harm you in the long run. As they say for all data-driven insights, garbage in, garbage out. Collecting data from different systems can be a grueling process and one needs to keep in mind the right extraction and ingestion tools to use along with connectors that can collect data from your source systems.

Having an overarching data strategy in place in terms of future state tooling for this effort should always be a first step. Because marketing data tends to come from a variety of 3rd party sources, you should identify and research data extraction and ingestion tools that provide connectors to your specific source systems.

Given the diverse origins of marketing data often stemming from myriad 3rd party sources, by meticulously identifying and researching cutting-edge data extraction and ingestion tools. you need to pinpoint tools equipped with connectors tailored to seamlessly integrate with your specific source systems.

As part of an overall strategy, the teams should ensure they are leveraging the appropriate information from these sources to accomplish the business objectives. Many of these sources have multiple views of the information (different attribution windows, etc) that must be reconciled prior to incorporation into a broader data strategy.

Integration of data sources involves identifying and combining various sources to create a unified and consistent view that provides a complete and accurate picture of your marketing channels, users/ customers and their engagement at all touchpoints, platforms and stages of their lifecycle. The process of data source integration includes data replication, data ingestion and data consolidation. This may encompass customer data (demographics, location, attributes, etc), contact details (if collected by consent), account records, etc that is collected from diverse origins through APIs or direct interface access and fed into a data lake or data warehouse passing through an ETL tool.

The impending deprecation of cookies is poised to impact all stages of the marketing analytics funnel. Currently affecting 1% of Chrome Enterprise users, this change is expected to be fully implemented for 100% of users by the end of August 2024. The limited ability to track users across website visits poses challenges for attribution and understanding their journey through the nurturing process. However, the cookie deprecation also provides an opportunity to reassess our approach to marketing funnel measurement. Shifting focus towards first-party data targeting and server-side tracking allows us to adapt to the future without reliance on third-party cookies while still maintaining the ability to measure campaign effectiveness.

For marketing analytics in particular, especially use cases that leverage several disparate data sources, its recommended to extract, load, and transform that data within a central data platform (i.e. - DWH, Data Lakehouse) for the purposes of reporting out. Again, the ingestion / extraction, transformation, DWH/ DLH and visualization tooling should be determined prior to project inception in the form of a data strategy assessment. When building the reporting layer, its important to prioritize high value use cases as defined by business stakeholders.

Downloading the relevant metrics from LinkedIn analytics, Google Analytics (or manually when taking note of meetings, interviews, etc.) enables me to create insightful visuals and KPI's on Power BI to track for example, the metrics relevant to the various stages of the funnel, the conversions between the stages, etc. and how they evolve in time and correlate with relevant activities I undertake (e.g. linkedin posts).

Visualising and analysing funnel performance through charts, illustrations, funnel visuals showing drop-offs, stage wise ingestion or stage wise transition provides marketers clarity to identify issues pin-pointing the areas of improvement, emerging trends and patterns, and help devise insights that will propel their marketing efforts towards success. There are various tools that help with this, like google sheets or Excel, Looker Studio, Tableau, MS PowerBI, Google Analytics, Mixpanel, etc. The key is to have a robust data collection, integration and management process.

We know that every day the user journey changes. And before, users who traditionally used Google as their only search source now find recommendations in videos on Tiktok or YouTube. This type of scenario can shorten the journey, leading the company to explore other communication channels. The point here is to evaluate this trend by consulting your data and build new journeys whenever necessary.

This is where input / output dashboarding can play a central role. Tracking leading indicators (inbound web inquiries for example) and their downstream affect on lagging indicators (number of conversions into paying customers) is a great mechanism to test the overall effectiveness of your marketing strategy leveraging your analytics solution.

Embrace Continuous Feedback and Iterative Data Insights- Implement a system for ongoing feedback collection and data analysis. This involves regularly reviewing funnel performance data, customer feedback, and market research to identify new trends and patterns. Use these insights to iteratively refine and adjust your funnel strategies. This could mean altering customer segmentation, tweaking messaging, or revising channel strategies based on what the data reveals about customer behavior and preferences. By adopting a continuous, iterative approach to data insights, you can ensure that your marketing funnel remains dynamic, data-driven, and closely aligned with both market realities and customer needs.

Once the funnel is created, visualised and analysed, the funnel tactics charted out need to be tested and optimized through various experimentations (A/B test or Multivariate test). Segmentation and targeted campaigns are the best way to test and optimize funnel tactics. Messaging through Push notifications, emailers, SMS, or a combination of various channels depending upon insights that drive nature of the message (content and creative) and desired level of user engagement.