You get what you measure, but marketing measurement may be simply too outdated and incomplete to deliver best possible results. Marketing teams struggle with complex technology and many data sources. Time consumed by inefficient processes and tools may become as a surprise to many marketing leaders. According to Gartner's Marketing Data and Analytics Survey 2020, analytics only influences 54% of marketing decisions. Here's 5 things which may cause marketing measurement to fail the promise of analytics and how to fix them to get better results from your data driven marketing. Its a combination of data, technology and people.
Measurement gaps⚠️
Marketing measurement is based on fairly static dashboards, with a ton of metrics, but they lack the connection from business goals to core indicators to activities.
Various roles in a team cannot drill down from business goals into metrics that are relevant to them, failing to get insight on how they are contributing to those goals.
Data points from various sources are not connected and analysis of data requires using many tools and custom reports. These measurement gaps lead to Inability to make a clear connection between marketing investments, activity and enterprise value.
Actions to take:
✅Agree on business goals, core indicators and volume and effectiveness of activities
✅Centralized data and holistic marketing data model
️Blended funnels⚠️
Blended funnels hide what is actually working and where you should allocate your budget because every lead and revenue opportunity looks to be the same in value.
Most companies don't have good enough data to segment funnels, due to tool focused taxonomy setup, which causes the data produced to be low quality and too low in granularity. The same applies to using AI to your data, if there is not enough variability it's impossible for algorithms to find relevant insights.
Actions to take:
✅Segment funnel to see what created the demand and what captured the demand, where did the prospects entered the funnel, which segments are being closed fastest and the highest rate.
✅Setup taxonomy and context on as detail level as possible and focusing on the customer buyer journey instead of getting data look good in another tool. Do this now! You'll increase data quality instantly and you lay the foundation for using AI.
️B2B marketing teams focusing on leads instead of revenue opportunities and customer success⚠️
Leads may have been the leading indicator of sales in 2013, but that's not often the case anymore. If your cost per lead is 20€, the cost of 500 unclosed or unfollowed leads is 10 000 € and that's excluding non working cost. Together with segmented funnels you can see which activities produce the best results. Optimizing for revenue opportunities helps you create a higher quality pipeline.
Actions to take:
✅ Focus on revenue opportunities and go further into customer success to get insights on what channels and campaign types drives ROI.
✅ Analyse data in CRM and feed the learnings into customer acquisition activities to turn your mindset into flywheel motion.
️Developing measurement like an IT project⚠️
Measurement and capabilities has to be continuously improved, but in most companies marketing measurement is based on dashboards that are developed in an IT project like week's long release cycles by high-priced data scientists or data engineers. A lot of time is wasted in marketers communicating requirements to technical experts who then go and implement changes or develop new features to the dashboards and then release and fix bugs. There might even be only one person who knows how the metrics on the dashboard are calculated and how the data is filtered which reduces agility and is a business risk.
Actions to take:
✅Go for continuous improvement process, where everybody can make improvements in minutes instead of days. Imagine the whole marketing team collaboratively extracting insights and improving measurement every day.
✅ Eliminate bottlenecks like dependency on technical skills and only one or a few experts being capable of deploying improvements.This gives you agility to react to change in the market.
️Too much time spent on data analysis⚠️
Many teams lack modern tools to analyze data fast. Data is spread around in many applications and analyzing the data on a deeper level is done by looking at multiple reports, many tools, dashboards and joining data manually using spreadsheet and csv files. Manual data preparation causes a lot of errors and does not scale to larger data volumes and detailed data. Team members have to wait for custom reports and they lack access to all the data. When going through reports and tools, people only get answers to questions they are asking and miss deeper insights.
Actions to take:
✅Automate data processing tasks to create an data asset that grows in value.
✅Start using modern AI for automating analysis tasks and uncovering deeper insights and getting answers to the questions you didn't think about asking.
Conclusion
Marketing measurement destroying revenue comes down to 2 things:
Insufficient data quality
Inefficient analysis
What if you could make better marketing decisions, how would that effect your business ?
What if you could get 99% of time spent on analyzing data back, how would you use that ?
Summary on how to improve marketing measurement for increased ROI
Fill in measurement gaps
Use segmented funnels
Focus on revenue opportunities
Develop measurement with continuous improvement process
Use modern AI and automation for analyzing data
How Madtrix can help
Madtrix is a powerful data platform for marketing, which automates data integration, data processing and data modeling and powered by search and AI enables marketing teams to uncover their own insights from all marketing data without any technical skills.
Start improving your data quality and make better decisions today!
Learn more about Madtrix data platform for marketing and book a strategy call with an expert!
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