Media Monitoring - Operating Smart to Improve Margins Business Landscape
Today's digital world is a tough place for media monitoring and insight services providers. In addition to supporting the global and local mainstream media - which still retains a privileged position within the PR community - there is the ever-growing need to deal with and draw insight from the billions of unstructured conversations across various social media platforms. This is because an unattended comment or blog can have a significant impact on the business performance of their clients.
Competition in this market is intense with many providers operating at low margins, especially for the commoditised, labour intensive, and high volume services like media monitoring where barriers to switching are low. Compounding this is the fragmented landscape of different systems, often legacy processes and teams, that many providers are stuck with. They may have inherited this through series of acquisitions as they sought to become a one-stop for information and software across different geographies.
To address these challenges, media monitoring providers need a new, more integrated approach to technology and operations - one that rationalizes both the organisation and platforms, and frees cash to invest in new higher value services.
The key is to move to a factory model for delivery where geographically dispersed operations are centralised in a shared service centre. Processes for common products are standardised and simplified, although there will be a need to accommodate some localised nuances. These will be supported by teams organised by function, rather than by geography, to allow cross-skilling and efficient management of the peaks and troughs in demand. Therefore, in this model, the focus of the remaining in-country teams becomes one of customer support and sales.
In conjunction with shared service centre, the deployment of a common platform with advanced analytics is essential. A single integration layer will allow the ingestion of content from across all channels, making it easily available to current and future downstream services. Content augmentation through tagging, summarisation and sentiment analysis is automated through a combination of a Boolean rules based engine together with Cognitive and Natural Language Processing (NLP) methods. In the case of quantitative analysis, we see the potential for almost 100% automation, while the more qualitative work will continue to require some level of human intervention, although much reduced and likely to be further impacted as Cognitive and NLP engines develop.
Together with selected use of Robotic Process Automation (RPA) to automate routine reporting and workflow activities, returns from this new integrated approach of 40 percent are realizable, in addition to the increase in operational agility. Furthermore, by treating it as a single programme, the savings from the shared service centre will self-fund the technology investments in the new common platform. Given the challenging business landscape and the need to address falling profitability something surely worth considering!