A lack of data is not the problem for most communicators. Nor is access to it. The real challenge is collecting data that moves the business needle – and presenting it to the C-suite in a manner that leaves no question as to the overall value of PR to that brand.
To achieve this takes a certain wisdom as it pertains to data. Converseon calls it “decision intelligence.” This includes clear differentiation between meaningful and “vanity” metrics. This includes employing data to minimize risk. This includes using data to see ahead, not just look back. This includes new, more effective ways of benchmarking than ever before. And much more. It’s all about getting more intelligent about your intelligence. In his responses to the half-dozen questions below, Converseon founder and CEO Rob Key helps you toward that end…..
1. Comms pros firmly believe that a brand’s reputation is important. What are some of the latest, most innovative techniques at comms pros’ disposal to measure this and unequivocally make the correlation to their C-suite?
Key: In our data-driven world, what doesn’t get measured doesn’t get managed. While we all instinctively know “reputation” is an essential lifeblood to business, we have had a dearth of rigorous measures and metrics to prove it. Survey-based approaches tend to be too retrospective and myopic for a real-time, polarized world. Interim metrics – such as “sentiment” or visibility – are often vanity metrics that get lost in a world where there is too much data. Leadership struggles to identify the best data to which they should pay attention.
There’s good news, though. There are now approaches that combine real-time data streams with sophisticated NLP (natural language processing) and predictive analytics that not only tie reputation measures to business outcomes, but also the actionable core drivers.
Comms pros now have access to a rigorous, data-driven, always-on guidance system to demonstrate how reputation investment drives shareholder value and sales that filters noise to get to just the metrics that really matter to the C-suite.
2. Why does reputation matter to an established company whose services and/or products are so deeply ingrained into the larger consumer base as to seem immune to any serious consequences?
Key: To think that no one is immune is a fallacy. The average life of a corporation is 21 years. The average tenure of a CMO is 40 months. Stakeholders are becoming increasingly empowered. The world is increasingly unpredictable. As Warren Buffett famously said, “It takes 20 years to build a reputation and five minutes to ruin it.” Today, five minutes might be generous, as reputations can be severely damaged in an instant. Mindsets that rely on a “steady state” of assumptions are not advisable.
Minimizing risk is one part of the plan to combat this, but there is more. We now have data to show that a good reputation means good business. Our models show, for example, that if a large fast-food chain were to improve perceptions of its environmental efforts, it would likely generate an additional $140 million of revenue per quarter.
Rob Key, founder and CEO of Converseon
3. “Decision intelligence” is a term Converseon often uses. What does it mean? Why is it important to this conversation?
Key: Each day, we create roughly 2.5 quintillion bytes of data. About 80% to 90% of it is unstructured and underleveraged. So, the challenge for organizations is not a lack of access to metrics or data. It’s how to cut through the deluge to get to the metrics that matter, those that contribute specifically to a business outcome (such as sales, shareholder behavior, etc.). Other data might be interesting, but they are all “vanity” metrics at best if they don’t ladder up to the core KPIs that drive a business.
“Decision intelligence” is used to get to the metrics that matter that can predict business outcomes along with simulation capabilities. This enables leaders to assess the risk and opportunity of specific actions before they take them. This is essential to navigate effectively through a fast-changing world where real time is not always fast enough.
4. Social listening will help brands pick up on conversations and perceptions among their audience and key stakeholders, but it only takes you so far. How does “decision intelligence” take a brand further?
Key: Social listening and media monitoring are good starting points for predictive reputation intelligence, but they’re only first steps. Think of them as data aggregators with some limited NLP and dashboards that, by their nature, are reactive and descriptive. Many of these platforms are important partners of ours, so they have an important place in the process. However, companies are drowning in too much data - most of it retrospective and untied to business outcomes. Increasingly, this data is not just bad, but also misleading.
Today, it is NLP and advanced analytics that ties this data to first-party data such as sales. This is transforming the data into predictive and prescriptive decision intelligence. Accessing this capability is also becoming increasingly simple. Many leading social-listening and media-monitoring platforms are offering API (Application Programming Interface) integrations with decision-intelligence solutions such as ours to access the data both through those platforms and through other dashboards.
5. Converseon recently revealed the results of a study focused on the impact of ESG on business equity and outcomes. This is more than collecting data. It is understanding trends over time. How can comms pros go about accomplishing this?
Key: ESG is a good case study. There are hundreds of prebuilt models powering the system, ranging from trust to carbon offsets and diversity & inclusion. The first level of filter is seeing how all the brands align across these in a heat map (see image above) – areas of strength and weakness. This is extremely powerful and the benchmarking is unprecedented, but the story doesn’t end there.
For comms pros accessing this, sometimes this means they are not just looking at five to 10 trends or attributes to make a decision, but at least 30 or 40 (if not more) – and not in isolation, but in combination with each other. This can make it almost impossibly complex. To mitigate that requires an understanding and recognition of the specific attributes that are tied to business outcomes.
For example, if you look at the heat map, you can see that while “labor relations” is a weak area, it's actually “environmental impact” that is most tied to sales. And while “governance” is an important area of focus, it does not impact shareholder value or sales.
This is the highest form of augmented intelligence, the machine and human working together for a better solution that provides much greater focus.
6. More times than most of us would like, data can be viewed skeptically due to biases (even inadvertent) in how that intelligence was collected. How can models best be created to minimize or totally negate such concerns?
Key: There is healthy skepticism towards AI because it has indeed been overpromised in some areas and humans bring bias to data analysis.
The foundation for effective decision and reputation intelligence has to be built on high-quality models that keep humans in the loop, leverage subject matter expertise, and have comprehensive measurement testing (such as F1 precision and recall standards) that are transparent. We believe every machine-learning model deployed should have an associated performance score so users know exactly what they’re getting.
Decision intelligence takes this the next step. With social data, the question of whether it is representative or not often comes up. The response here is simple: If the data is predictive and has quantitative value, it's meaningful.
All data, whether survey or conversational, can have inherent bias. Connecting to outcomes keeps the data honest and allows leadership teams to consume it with confidence. Back testing models against data it has never seen before also shows exactly how well that model has predicted the past so that you have strong assurance of how it will do in the future.
No models are perfect in the world of AI, but many are useful. In the foggy, challenging world of today, this data is an essential part of the business and reputation intelligence toolkit.
Rob Key is founder and CEO of Converseon.