The use of marketing dashboards has been new for a long time. Its purpose is to help teams analyse their performance. Campaign managers take a look at the number of impressions, clicks, conversions, the amount spent for each lead and the engagement metrics on multiple platforms. However, due to the rising competency of artificial intelligence in terms of data processing, the well-known dashboard could cease to be the only way to obtain marketing insights.
There is a new approach on the way – conversational analytics.
Marketers get the chance to turn to an AI assistant with a precise question instead of opening various dashboards and going through numerous reports such as “Which campaigns returned the highest quality leads this month?” Technology is capable of comprehending the query, processing related information, revealing patterns and formulating the answer in an accessible format.
The changes are more than just exchanging charts for chat as they alter the way marketers handle data.
Dashboards require their users to figure out the location of the information and the metrics they need. In contrast with dashboards, conversations open with business questions. For instance, the marketing manager may be interested in finding out the cause of the decrease in leads or even which sections of the audience have been performing well.
Artificial Intelligence allows collecting data together from different reports. For instance, data about the campaign performance, customer relationship management system, website activities, email performance, and sales results can be viewed together as long as the systems are connected properly.
This helps make marketing analysis available for teams that do not spend entire days with spreadsheets and analytics apps. Decision-makers are able to follow up on their queries and immediately switch from observing to investigating rather than waiting for a report.
However, conversational analytics doesn’t completely eliminate the need for dashboards. Visual reporting is essential for tracking the trends, analysing performance over time, and providing a common view of meaningful metrics. The only change is that the role of dashboards will become more targeted.
The success of this AI model is fundamentally tied to the accuracy of its training data. If name formats are inconsistent, the CRM is partial or there are no clear rules for assigning credit, the AI will be unable to take the unreliable data and generate reliable insights.
At the same time, there’s a change in the set of skills marketers ought to be focusing on. Being able to interpret a dashboard and knowing, for example, what data it displays are hardly sufficient these days anymore; one of the most valuable aspects will be the capacity to formulate the right questions, clarify the issues, and be able to scrutinize the AI-powered analytics.
Which might eventually facilitate faster marketing changes, to some extent. Rather than spending hours collecting and organising necessary data, marketing teams will spend more time analysing data and deciding how to act afterward.
Consequently, the field of marketing analytics may not be dashboard-less in the future. Rather it may consist of the use of light dashboards and conversation-based analytics, where dashboards provide the structure while artificial intelligence allows the marketers to explore the questions behind the analytics.
The main difference lies in the fact that the marketers will not have to search for their answers in a particular place.
