Mimrah Mahmood is the APAC Director for Media Solutions at Meltwater. He is a commercially driven business and sales leader with 10+ years of experience in Software-as-a-Service (SaaS). Across Asia-Pacific, he helps organisations to create best-in-class frameworks for PR and Marketing reporting, as well as advising leading companies on data tracking, competitive intelligence and actionable insights. He has a proven track record of recruiting, training and developing top performers across the region.
Meltwater helps companies make better, more informed decisions based on insights from the outside. We believe that business strategy will be increasingly shaped by insights from online data. Organizations will look outside, beyond their internal reporting systems to a world of data that is constantly growing and changing. Our customers use these insights to make timely decisions based on real-time analysis.
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.
Thank you, Mimrah! That was fun and hope to see you back on AiThority soon.