Data as all-encompassing
The importance of analytics and use of data has only increased as the years have gone on. Historically, both B2B and B2C companies who use analytics have been companies that have found success and become market leaders, from supermarkets, price comparison sites and hotel chains. Whether it’s accurately targeting paid spend, optimising price points or understanding what drives customers to convert through a contact form.
Yet now with the introduction of LLMs, how companies use and collect their data is becoming ever more important, particularly as customers become more aware of how companies are using their data. In our day-to-day life, whether it’s changes in sports terminology (such as “xG”) or Spotify showing us our listening patterns, customers are now much more aware of the data around them, and have an expectation that it should be used to enhance experience.
For those in B2B marketing still trying to find their way through the world of analytics and data, there are several base fundamentals when it comes to using analytics as a means to drive growth and further understand both the business and your customers.
Data integrity & accuracy
The most important consideration is data accuracy and integrity, without which analysis would be of no use to anyone within a B2B company.
Ensuring that the output of the sources that the analytics software relies on is accurate is key to success. Whether that’s the data within your CRM such as Salesforce or the data layer which GA4 uses to collect data within its events.
There’s then the linking of the data. Is the data going to the correct platform and are the parameters such as traffic source coming through as expected? For example, when a GCLID is within the URL, does the lead come through as paid?
There are also several smaller pitfalls that data tracking can fall into, such as poor cookie banner and consent security policy implementation, and bad practice when it comes to the adding of platform’s code (such as Hubspot and GA4) onto the website. While often overlooked, these things can be resolved with a sturdy testing plan that ensures best practice and data alignment.
Input metrics
Once the data and tracking is confirmed to be trustworthy and accurate, the more open process of how to use that data to benefit the business begins.
This can be done by defining the inputs and outputs of both the website and the datasets. Inputs are the metrics that the teams across a business can control; the existing capacity of a fleet, the price being shown to a customer, stock availability for products that the customer is searching for or the conversion rate of a contact form.
Tracking these will give insight into how the levers a business can pull impacts both the customer journey and the customers’ interactions. They can help answer questions like ‘what happens if the price of a product increases?’ and ‘how many B2C visitors are completing the contact form?’ or ‘is there a certain product that is leading to a high exit rate for customers?’
Website & CRM tracking
Once you’ve determined your input metrics, this will inform what tracking to build on the website and within the CRM system. Scoping out what is possible to track with the technical team or data agency will maintain expectations and give an understanding of where future development might be required.
With the defined inputs, the tracking can be built, and you can begin collecting data to enrich the pool of information of the business. The levels of testing that were done on the initial datasets will once again need repeating, and while it can extend the process, it will remove the doubt around data accuracy that can so often undermine any analytical insights.
Finally, when new tracking is built, the data will need time to reach statistical significance, meaning there will be a window of time before decisions can be made regarding this new data (typically 90 days is a reasonable window).
Output metrics
Once your tracking is tried and tested then comes the outputs. These are the KPIs and measurements that marketers use to define if something has worked or not, whether that’s a campaign, UX changes or pivot in organic strategy as examples.
Don’t forget that these are not metrics that can be controlled, and their outcomes are influenced by what is measured in the previously discussed inputs. It is easy to fall into the trap of bean-counting metrics such as transactions and revenue, when we’re marketers and not accountants.
As marketers, it is not possible to control a customer’s intent, but we can amend the website and product to meet that intent. How these outputs are displayed, is equally important as to how they’re defined. What is considered success will lead to how these outcomes are judged. Campaigns that exist to raise awareness or to drive MQLs must have those metrics clearly defined. Using the wrong metric can lead to incorrect business choices when determining next steps post-campaign.
Of course there are metrics that key stakeholders across the business will be most invested in, so ensure that they are clearly displayed in a dashboard or a report, allowing stakeholders of all technical and marketing abilities to understand the progress that is being made.
Analysis as top of mind
While it can be easy to take shortcuts when it comes to proper data-led marketing decisions in the early stages, once done thoroughly and to best practice, having it at the core of any team and business can lead to stronger growth and understanding of customers.
It’s easy to have systems in place for business analytics and to make do for extended periods of time, but the above is a process that anyone who uses analytics within their business should review. Beyond this, there are further steps such as server-side tracking, enhanced conversions, A/B testing and data modelling that can further drive growth and scale up analytical insights, and push forward data-led marketing within every team.
Looking to improve your B2B business’s data and analysis? Get in touch.
