Moving from Media Monitoring to Industry Intelligence

Media monitoring reports often contain a wealth of information that can help companies navigate fast-changing landscape and spot both opportunities and issues on the horizon. Unfortunately, these insights tends to stay buried in the inbox of the PR and communications department and business decision makers lose out on a valuable source of information. So how can communications professionals – both in-house and agency – elevate their insights to the C-Suite to the point that they affect business decisions?

The biggest problem lies in relevancy as executives are often bombarded under an avalanche of requests and reports, from a multitude of sources, and a multitude of agendas. “FYI” is now code for “Safe to Ignore”, and “Urgent” is the new “Regular work”. With this constant amount of background noise, creating a report that stands out in terms of clarity and relevance is essential.

Here are some ways to evolve the way functional groups view and work with communications teams.

Understanding what’s important to internal teams 

As communication professionals, we understand the impact of the media, and media intelligence in achieving business outcomes. However, this understanding might not be clear for all business functions, and there are plenty of anecdotes about inter-departmental friction because of this.

Instead of trying to educate and convince your colleagues, communications professionals should make the effort to understand not just company business models, but also work processes.

For example, with the expiration of the Production and Innovation Credit (PIC) scheme, the Productivity Solutions Grant (PSG) introduced during Singapore’s Budget 2018 will take its place to provide small and medium-sized enterprises with funding support for off-the-shelf solutions to improve productivity. If your sales team has products that can be funded with governmental grants, changes to grant structures could impact their sales strategy.

Gathering this feedback also helps better define your parameters on your media monitoring platforms, allowing these solutions to pick up content that you know will be more relevant for your internal target audience – which is why a well-constructed media intelligence report will also be critical in making sure the report is read by key stakeholders. It should be constructed in such a way that the most important information is highlighted in a concise manner.

The challenge lies in balancing providing enough information while making the report concise enough for a quick read.

A few questions every communications professional should ask themselves when sharing information to other internal teams:

  • Is this relevant information outside of a communications/PR function?
  • Is there a “bigger picture” or strategy that can be derived from coverage?
  • How does this impact other functions? What kind of actionable insights can be extracted from this coverage?
  • Is there any non-media information that should be shared to provide more context? (e.g. Links to relevant blogs, in-depth research papers, new online resources)
  • How can intelligence reports also highlight the importance of a communications function?

Reverse Engineering Competitive Strategy 

A competition’s major strategic campaign usually creates some ripples in the media, even if they are not explicit about it. A savvy enough communications professional can piece together not just what is being said to the media, but also better understand “behind-the-scene” strategy.

For example, if a competitor has announced a MoU with a major educational institute, and opened an incubator lab, they may be pivoting for a strong push among start-ups. Or if a brand has signed on a celebrity outside of their typical demographic, and released products targeting a niche market, it may signal a larger strategic push in another direction.

Once a potential competitive strategy has been extrapolated, the information could be used by other functions to pivot and gain the competitive advantage.

Developing Next Steps 

To bring intelligence to the next level, it needs to have actionable insights. While most business functions should have an idea of what next steps are, PR and communications professionals can also value-add by including potential next steps – including suggesting possible new lines of inquiry.

The old business adage of “Don’t bring me problems, bring me solutions!” resonates here – and while PR and communications teams are unlikely to have a fully-fledged solution to most problems, the next best thing they can provide is a way forward to helping teams collaborate to come up with a solution.

For example, if a government agency announces a new budget allocation for smart cities, PR and communications teams can provide links to various RFPs that are part of this new allocation. If a competitor announces a new product, PR and communications teams can provide an updated portfolio of the competitor’s products.

Organisations face little issues with data collection. Where they struggle most is in analyzing it, picking out the most important data points and then developing a strategy around it. This is where presenting information in a standard format is key to avoid alternative interpretations of data, encouraging cross-departmental collaboration and streamlining processes to mitigate the risks of duplicating work. Ultimately, aligning each department’s strategy into achieving the organization goal.

While elevating your media monitoring requires extra time and thought, turning data into value-added insights in collaboration with different departments will potentially help your organization make that next critical decision.

Interview With Mimrah Mahmood, Regional Director Of Media Solutions, Meltwater

Tell us about your role at Meltwater and the team/technology you handle.

I’m the APAC Director for Media Solutions at Meltwater. My main focus is to help companies in the region with customized technology solutions that accurately measure media performance and bring insights that can be used to improve their business. For example, our acquisition of Klarity last year allowed all our clients to benefit much more from insights on WeChat, Weibo, Youku and Line than was previously available in the region.

DataSift brings similar types of value with its privacy-by-design to increase our scope of work when it comes to consumer research based on social media data. We are constantly increasing our unique proposition to our clients by bringing new advancements in smart crawling, predictive analytics, etc.

Why is it important to have open data science platforms? What role does Meltwater play in providing a better adoption rate of Data Science platforms?

Open data science platforms are more agile in nature and increase the use cases by inviting as many students and startups to contribute. It is a relatively new space and needs the input of as many individuals to help increase the maturity.

Meltwater is uniquely positioned because we are among a handful of companies that mine its own data via our proprietary crawling technology — including unstructured external data beyond just mainstream media and social media data that is tokenized for data enrichment. Often, data science teams lack the access to relevant data needed for running, training, and testing purposes. Meltwater’s Shack15 is an example of an open office concept that allows startups and entrepreneurs to work out of existing Meltwater offices using this open data science platform.

What is the importance of Machine Learning (ML) in marketing technologies? How does it affect Meltwater’s client base?

Automation is critical to scale business operations, and through machine learning, organizations are able to reduce the manpower needed to qualify or route leads. The new MarTech stack for most companies will allow them to use it as a key differentiator against peers in the industry. This differentiation comes from how much smarter your stack is against competitors, which mainly depends on their ability to train and improve the machine learning capability.
Meltwater’s customers already benefit from machine learning through smart alerts. Smart alerts here refer to the client being able to input a set of wish list triggers that they want to be updated on as soon as it happens. If you are the CEO of a Fintech company, you might be interested in any acquisition talks that are accelerating within that space for example. In future, we look forward to bringing more AI-enabled analytics that will further equip our clients with the latest advancements in predictive analytics modeling.

What is the ‘State of Media Monitoring’ technology in 2018? How does AI/ML influence this dynamic state?

Media monitoring as an industry has been very slow in adopting new technologies. Even now, a majority of media monitoring is carried out the same way it was two or three decades ago, with manual clippings of newspaper articles sent over to the clients via email. In Japan, the world’s third largest economy, a majority of the clients still receive media monitoring clips by fax or physical post. Over the past two years, especially in the US, we are observing consolidation among industry players as a measure to future-proof themselves by adopting new technologies.
AI/ML has vast potential in the media monitoring space. At Meltwater, we’ve been advocating the use of media monitoring beyond tracking owned brands. This includes Competitor Intelligence (CI) and Business Intelligence (BI) to generate actionable insights. AI/ML allows us to alert our clients on triggers that could have potential business implications based on what was mentioned in mainstream or social media. Advancements in AI/ML will enable brands to better pick up on reliable and contextually relevant alerts.

How could media intelligence platforms influence social media buying behaviors?

Buying behaviors can be influenced by producing content that resonates well with the customers. If customers can better relate to the content, it increases the likelihood of them taking the next step in evaluating before ultimately making their purchase.
Media intelligence enables companies to narrow in on the type of content that resonates well with different customer demographics. Customer-driven content, agile content, content marketing – all try to improve this ideology. We’ve been working on educating the industry that having access to these datasets, which can help bridge the gap between customer preference and the brand’s content.

What is AI’s role in ensuring user privacy while concurrently delivering scalable data and social insights?

Ensuring user privacy needs to occur earlier at the data collection stage, and this can be done via better policies and guidelines instead of simply relying on AI. Meltwater’s latest acquisition of DataSift is a great example of how data can be harnessed while the backend technology is built on as a privacy-by-design approach. DataSift has been able to bring in customer insights to its clients from Facebook, Twitter and LinkedIn while keeping the user anonymous. By aggregating the insight, it allowed customers to still get their main findings, without being allowed to go back upstream and invade the privacy of individuals.

Tell us about the emerging trends and innovations in AI that support the push for greater privacy in the industry?

I think AI brings the topic of privacy to the forefront. Harnessing AI allows companies to mine data at a large scale. Cambridge Analytica was a timely reminder of how scale thrust the topic of user privacy into the forefront, such that brands are compelled to take a more rigorous approach in safeguarding their databases. These steps are vital to today’s evolving social media landscape as it continues to mature.

What does DataSift’s acquisition mean for Meltwater’s customers in Asia?

DataSift has been able to showcase the power of privacy-by-design in the global social networks like Facebook, Twitter and LinkedIn. Our acquisition allows us to bring this technology to Asian-centric networks such as Weibo, WeChat and LINE.
In the next 12 months, our customers in Asia can build consumer insight dashboards that unlocks new uses with AI/ML. DataSift’s greatest strength is in its ability to deliver an intuitive and easy-to-use interface for data science enthusiasts. This enables them to train data, use the pre-stocked enrichment engines, and build custom enrichments that can improve the way we tag content, alert triggers and generate predictive analytics.

How do you differentiate between REAL-Human behaviors from bots in your audience data analytics? Do bot analytics impact influencer marketing results?

Bots are repetitive in nature in its current form. This allows our data science platform to detect them quickly and exclude them from our results. However, as bots evolve to become increasingly sophisticated, altering the way they behave, technology must be equipped with the ability to detect them based on context. Anytime data is harnessed to derive actionable business insights, it is critical that the data itself is representative of the customer.

What are your predictions for AI in marketing technologies in 2018-2022?

We expect to see a lot more utilization of real-time predictive analytics in media intelligence as machine learning enable brands to quickly pick up on shifts in consumer patterns while generating actionable insights. Data collected for Competitor Intelligence (CI) and Business Intelligence (BI) will be increasingly used to support brands in making informed decisions, minimizing uncertainty and wastage in marketing spend. Machine Learning could potentially empower brands to do more, for instance with sentiment analysis, helping brands eliminate uncertainty on how their audiences will react by tailoring the right message, and ultimately, stay ahead of competition. But as AI in marketing technology gets fine-tuned in the coming years, we anticipate more demand for talent in ensuring data hygiene, who will be key to ensuring the validity and consistency in data used to support marketing decisions.

A version of this post appeared earlier in AIthority.com.