Reputation Intelligence in the Digital Age

With advancements in machine learning and natural language processing (NLP) come enticing opportunities for organisations to better analyse their reputations and predict stakeholder behaviour. Some have fully jumped on the AI bandwagon and used these technologies to assess which topics and language use will likely have the greatest effect among consumers and other stakeholders. Others are using tools to alert emerging issues that may pose a reputation risk and become material to the organisation’s future success.

While the advantages of applying technology in reputation intelligence and reputation strategy are worth exploring, savvy operators are learning to tread cautiously.

Reputation is a top business priority

Wherever you turn, business leaders acknowledge the importance of their company’s reputations and we’ve seen in multiple CEO surveys that reputation risk is cited as a top business risk. We have also seen increased legal requirements for boards to show how they are ensuring their companies take the interests of multiple stakeholders (beyond shareholders) into consideration and demonstrate how they listen to stakeholders, consider the impact of the company on the community and environment and govern reputation risks. This all requires a thoughtful approach to reputation intelligence, one that is starting to emerge among savvy companies and often stewarded by corporate communication departments in collaboration with risk, compliance and other specialists.

Advancing Reputation Intelligence

It is with good reason that leading companies are starting to build advanced approaches to reputation intelligence, governance and capability into the fabric of their business. With this comes the recognition that standardised traditional approaches to reputation measurement are no longer sufficient. This type of data gives insufficient depth and breadth of insight and does not stand up against the increasingly stringent corporate governance requirements.

Boards and executive committees are increasingly asking for a comprehensive and relevant understanding of reputation (risk) and its interplay with business success (and failure). Given its centrality in business strategy and operational success, leaving reputation measurement and analysis to a ‘black box’ self-learning machine would be a folly. But there is a plethora of data that can, in part, be processed by the emerging technologies. Given that the power of AI comes in the combination of machine and human analysis, the best approach is to use the right tools to extract value from large data sets and to involve senior analysts and strategists along the way.

Combining natural language processing of the online discussions and survey feedback on a range of topics relevant to the organization (including its own reputation drivers) with predictive analysis of quantitative survey outcomes and datasets of an organisation’s non-financial and financial performance indicators is a first solid application for enhanced reputation intelligence

However, all these insights need to make sense and be actionable. Careful consideration needs be made on the validity and relevance the data from both internal and external sources.

While AI can provide good early warning signals and some direction for further exploration, thoughtful human analysis and implications assessments are still required.

From cross-industry learnings from leading firms, we outline here essential tips for getting underway:

1.       Orchestrate your reputation intelligence around your own reputation framework: Your business and its strategy is unique, so is your reputation. While standardised metrics and relevant rankings and ratings are useful indicators and benchmarks, they are only one part of the full picture you need to strategically build reputation and mitigate reputation risks.

2.       Ensure reputation is a leadership imperative: Involve leaders in designing your reputation framework, setting ambition and acting on reputation intelligence. Ensure adequate reputation (risk) governance at executive and board levels and explore personal incentivisation around reputation cross-functionally.

3.       Make better use of existing reputation data: Explore and integrate the perception and behavioural data you already have, connecting the dots between, for instance, customer surveys, employee surveys, media tracking, materiality studies, analyst recommendations, sales data, and all relevant (non-financial and financial) performance data.

4.       Track but also listen and engage: Although it’s easier to track reputation among publics and online panels, this by far isn’t enough insight. To truly minimise reputation risks and build reputations that lead to organisational success you need sufficient breadth and depth of insights. This should cover quantitatively and qualitatively all the topics and stakeholders that are important to your future success. The most successful firms combine advanced quantitative tracking with programmes that involve stakeholders in shaping future directions.

5.       Combine human and artificial intelligence: The emerging technology can certainly help extract value across large data sets both quantitatively and qualitatively, but the true implications and adequate solutions are only arrived at by involving expert analysts that understand your business and its reputation dynamics.

6.       Integrate reputation across the business: Build reputation understanding and capability into the business, ensuring all big decisions include a reputation implication analysis.

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