The omnichannel customer experience now encompasses a multitude of channels, objects and behaviours. This involves collecting, processing and analysing behavioural data of varying complexity.
Today, web analytics tools collect user data from all channels, thereby serving as an information exchange gateway.
Analytics, once seen as being at the end of the digital marketing chain, is or is tending to be back at the centre of the digital ecosystem, positioned between the marketing strategy and end tools such as personalisation tools, data science and machine learning.
Data monitoring is a crucial process if a high level of quality and, more specifically, reliability is the aim. This process involves verifying the validity and continuity of tracking at a set frequency.
That's why monitoring tracking is essential, as it sounds the alarm and escalates the information to the dedicated teams so that the problem can be resolved.Tracking can be monitored in several ways:
Every site or application now needs to meet one or several strategic objectives that apply at a company-wide scale. This means it's important to define how these objectives are measured to observe, steer and improve results and performances.
To assess objectives accurately, it's essential to measure the entirety of user journeys from end to end and across all channels.
Alongside monitoring, specific tracking needs to be able to be put in place to detect anomalies on the site, notably 404 pages, or the version number of releases. This information can be used to quickly identify anomalies and correct them as soon as possible.
Specific analytics tools are available for detecting these anomalies using specific detection algorithms.
Although analytics tools can be used to make predictions as such, they're nevertheless an important data source for machine learning solutions.
For example, these can be used to increase content relevance by recommending the right visual element for each user. To achieve this, the machine learning solution needs to be continuously fed with each user's data reflecting his or her interests and preferences.
The ability to collect relevant and actionable data, process it and export it to serve a machine learning solution will increase its effectiveness.
The data collected by analytics tools can be used by other tools such as A/B testing, personalisation, ad delivery and emailing tools.
Sharing an audience with another tool allows you to either deliver your content more effectively or deliver the right message to the right people.
This introduction is an extract from the chapter dedicated to Web Analytics in the Yearbook 2019 (Valtech_) ; click below to download the long version:
Resources on Web Analytics
Video of the @Berlin2019 conference
Web Analytics is a central concern for marketing decision-makers since the implementation of the GDPR. While some solutions are consolidating into galaxies of tools with facilitating synergies, others are specializing in specific use cases and more flexible business models. How are the players in this market positioned in terms of challenges that, although numerous, are increasingly critical for brands seeking to offer a seamless experience?