You might have heard the fairytale of the media business that went swimming in the data ocean and found treasure. But chances are you already knew this is a costly lie. Even a company that claims to have done this when pressed may admit it was a bit more complicated than that.
We have seen an incredible amount of money wasted on white elephants sold on this belief. From our experience, here is where the data strategy for Digitising Events has gone wrong.
Data Misconceptions when Digitising Events
There is a misconception that the journey starts with data. If we keep mining more data, it will provide us with the needed insights to generate value, goes the mantra.
So, we go to the IT department, which builds vast data spaces and designs sophisticated algorithms to capture as much data as possible.
We must be more strategic and selective about gathering data at our events to generate better insights.
But more data does not necessarily mean more helpful information. And this is a crucial point: quality over quantity. We must be more strategic and selective about gathering data at our events to generate better insights.
The temptation promised by ever-more accessible technologies for data gathering, automation, and analytics is to do this first. However, the variability of big data needs a responsive analytics framework that allows you to save time and effort. Goals and outcomes should dictate the tech, not vice versa.
Define Objectives: The Core of Data Strategy for Digitising Events
Your starting point, therefore, is not data. Instead, you should begin by asking the right and usually complicated questions: What are you seeking to influence or understand from the data? And what actionable insight can it enable?
The absence of clearly defined and agreed-upon outcomes is like sailing without a destination. The setting of this destination, which is a large part of the CVI + CVO Frameworkâ„¢, is a business and operational decision which does not require a data analyst. As an example, the core outcome of a webinar for a media business is selling engagement. Registrants who don't watch your sponsor's content are significantly less valuable than those that do.
Once defined, your data analyst can more clearly identify the indicators and interactions that can influence the outcome.
Create Relevance and Context in Data Analysis
The next critical point is creating relevance. In an age where we can collect many data points, there is a temptation to focus on a manageable number or, worse still, many numbers, preferably big ones, and not factor down to the relevant numbers linked to the intended CVO.
Collecting and identifying relevant numbers can be more complex. For example, if using PPC to advertise an in-person event, the manageable number is looking at the page's traffic from this channel. The more challenging number is the one that predicts how many registrants from this channel will attend, thereby creating a positive feedback loop to optimise PPC activity.
Moreover, in identifying relevant numbers that are Commercially Valuable Interactions (CVI), context matters and will help guide the data points you need. Are you going to limit yourself to structured data? Will you be collecting both structured and unstructured data? These questions are crucial for the analytical strategies that will follow.
Some critical questions from the CVI + CVO Frameworkâ„¢ to identify relevant measures are: Are they influenceable? Are they directly linked to a CVO? What skills would be needed to work these numbers, and in what timescale?
It is no coincidence that the new version of Google Analytics (GA4) changes much of the narrative towards engagement as a critical metric and gives us much more flexibility to collect custom events and build our attribution models.
In addition, awareness of context prevents you from treating data as means to confirm your inclinations, which can lead to confirmation bias and subjective decision-making.
Consider what happened to Nokia: complacency and misguided obstinacy left them on the opposite end of the smartphone revolution. They completely missed the big picture and drowned in useless details.
Turning Data into Actionable Insights
An effective data strategy for Digitising Events should be able to transform data points into timely and actionable insights. But, again, more data does not mean better insights.
Data insights can take the form of processes that eventually improve your company's performance or operations that reduce your risks while optimising rewards. They could also lead to discovering new markets, products, and/or services. Equally important, insights provide you with a better understanding of your existing clients so that you can deepen loyalty and increase lifetime value.
An effective data strategy should be able to transform data points into timely and actionable insights; but more data does not mean better insights.
In the context of Digitising Events, where the goal is to deliver engaged attendees, collecting all questions asked by attendees and combining these with structured data only you have, like the seniority and company size, may allow you to field large language models like ChatGPT to get near-real-time insight into market dynamics.
Another easy mistake is to measure SEO effort with too much bias towards visits and page views, which can create a false feedback loop to go for big numbers. This may be at odds with engaging a specialist audience and thereby creating lower commercial value.
When viewed this way, data analysis is not just about the data; it needs enabling tools and specialist knowledge guided by an appropriate framework to make sense of the data. An incisive analytical framework like the CVI + CVO Frameworkâ„¢ allows you to connect the dots.
Furthermore, this framework is flexible enough to process new and emerging data. After all, data analysis is an iterative process. In this sense, analytics is just part of the voyage; you define the destination.
Leverage the CVI + CVO Frameworkâ„¢
The CVI + CVO Frameworkâ„¢ gives clear and quantifiable measures that generate business value. Without these insights, you relegate your dashboards and data to interesting curiosities with no perceived business value. It aligns data generation with clearly-defined outcomes and, most crucially, empowers data experts who may not have deep domain knowledge of Digitising Events to create value.
When Digitising Events, we must be able to show value in terms of interactions and knowledge exchange. To enable this, a data warehouse approach is the only way -- but we now have a reason for it to exist beyond a cool engineering exercise.
Finally, as you dive deeper, expect to encounter novel and unusual patterns that might or might not be helpful to your organisation. For example, using a large language model showed a disproportionate amount of 'low level' questions indicating a less expert audience than desired.
If you match data science with the right specialist for the job, these patterns and trends can become insights. It is where the CVI + CVO Frameworkâ„¢ is most beneficial. It is your compass to navigate this vast and treacherous data ocean, never losing sight of your destination.