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Top 3 differences: Event analytics vs statistics. Is it possible to measure unmeasurable

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Top 3 differences: Event analytics vs statistics. Is it possible to measure unmeasurable

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Event analytics vs statistics. Some use these terms interchangeably. Are they identical? Well, not completely. Read the article and learn the 3 main differences between event statistics and analytics.

Both event statistics and event analytics are used for the numerical description of the event. Both are based on measurable information about events, invited and present participants and their behavior. Both use data gathered on a specific group. However, there are some differences between data analysis and statistics.

The first and fundamental difference lies in the essence of each concept. Let’s look first at the definitions of event statistics and analytics.

event-analytics-vs-statistics-top3-differences

Event Statistics: Meaning

According to The Oxford Dictionary of Statistical Terms, ‘Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to a scientific, industrial, or societal problem, it is necessary to begin with a population or process to be studied. Populations can be diverse topics such as “all persons living in a country” or “every atom composing a crystal”. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.

In case of events, statistics will refer to data collected on a very specific population segment:

  • event guests
  • event organizers
  • event partners
  • event sponsors
  • event participants

Event statistics, to put it simply, is all the data that you collect during the organization of an event in a tool such as Eventory,as well as during the entire planning process that leads to data collection and data processing that comes later.

Event data analytics: Meaning

In Predictive Modeling and Analytics J. Stickland’s says that ‘Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.

Of course, in the case of events, the data analytics will refer to the data collected in relation to the event. It will use data from various sources like Eventory, Facebook, Google Analytics or even a marketing automation platform. It will result in translating raw numbers into actionable insights that will allow you to not only describe the event but also to evaluate it. Data analysis leads also to defining target audience behavior. Also, as an analysis result, you should be able to spot some major tendencies and trends among the target audience. These insights will be important from the point of view of organization/co-organization or participation in a similar event in the future.

Ok, so what are the main differences between event statistics and analytics?

Event statistics vs analytics: Difference # 1 Methodology

Event statistics are most of all data. As I have already written, the term refers to the entire process of collecting it – from planning how you do it, through the selection of methods and the execution of the process, to ordering the data set. It is also a statistical description of a part of the population. The description contains raw facts. It doesn’t require far conclusions and hypotheses about the group.

Event data analytics is a much narrower concept. The analysis you conduct on the collected event statistics should lead to showing some results. Interpreting the data you have collected, showing the most important trends, verbal description and visualization – this is the essence of event data analytics.

Example

Let me illustrate it for you:

Data on the audience, which the speaker collects via live votings, is event statistics.

Cohort analysis of this data and its interpretation placed in the post-event report is data analytics. The outcome is very specific information, like for example how big is the part of the group that agrees with a given statement, and how many attendees doesn’t support it.

Event statistics vs analytics: Difference # 2 Mutual dependence

statistics-for-analytics

Data analytics and statistics are blending to some point. Statistics play a vital role in data analysis. Data engineers use statistics for analytics as a subject of study. Simply put – no statistics, no data analysis, because there is no base material. Therefore, raw statistics are the starting point for the analysis of event data.

A partial analysis is also an element of statistics. The process of organizing data, which leads to a general description of the population, also uses elements of data analysis. However, the analysis conducted as part of the statistics leads to the statement of facts – a description of the population. In turn, data analysis per se is already a conscious process of using data to forge hypotheses about this group and find their confirmation.

Example

It’s easier to understand this with an example.

The statistics will be information about how many people have registered for the event, as well as the number of people who passed through the check-in.

The analysis will be an indication of which part of the population did not reach the event and providing an explanation of why did this happen. This information is priceless in many situations, for example when you work on your event marketing persona.

Event statistics vs analytics: Difference # 3 The result

In statistics, the outcome will be a set of ordered data about the event, its participants and processes that accompanied it. Event statistics will be for example:

  • information about the result of marketing activities
  • the number of leads acquired during the event
  • information about how the participants of the event move around the venue and so on.

In turn, data analysis will translate this information into a language understandable for the beneficiaries of the data, for example, organizers, partners or event sponsors. The result of the analysis should be specific insights like :

  • with whom it is worth to cooperate in the next edition,
  • in which marketing channels you should invest next time,
  • do you need to increase the budget,
  • how the increased budget will affect the achievement of event goals.

Example

The statistics will tell you what venue traffic looked like.

The analysis will give you specific information that next year you should invite company XYZ to the event because they attracted you a crowd of visitors and prepared the best exhibition booth design. It will also tell you to increase the price of the exhibitor’s package for XYZ because they came home with a bunch of fresh marketing leads. It will be justified by the fact that the venue traffic was biggest near XYZ company’s stand. What’s more, the attendees stayed there for a long time and were actively scanned the company’s QR business card.

Wrap up

If you are serious about organizing events, you need both event statistics and data analysis. Event statistics is the whole ‘engine’. It allows you to plan how you will collect data and what data it will contain. What’s more, this concept also covers all the information collected itself and their initial organization.

The data analysis is slightly narrower. By analyzing the data, you go beyond the first layer of data and consciously look for data patterns that lead to actionable insights. Only the data analytics will allow you to draw strategic conclusions about the event, guests and the target group. It will also allow you to present them in an attractive form in pre- and post-event reports, which are one of the most practical applications of gathered statistics combined with their thorough analytics.

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