"The Irony With Big Data Often Is That The More Data You Have, The Harder It Is To Detect Trends"

Interview with Stephen Dale, General Manager, APAC, Digimind


Digimind is a social intelligence solution designed for brands and agencies to enhance marketing and communications strategies through a data-driven approach. They enable clients to effectively plan, execute, and analyze their strategies by turning consumer and market data from online and broadcast channels into comprehensive and actionable insights.

Stephen Dale is General Manager, APAC, at Digimind. With over 10 years’ experience in sales and marketing, Stephen has cultivated a deep knowledge of deploying social listening and competitive intelligence in the organization. Since 2013, he has been instrumental in spearheading Digimind’s presence in the APAC region, while continuing to advise the world’s largest B2B and B2C companies on mastering digital transformation.

Stephen was also a guest speaker on Channel News Asia, where he lent his insights on trending topics such as the MH370 disappearance and the ALS Ice Bucket Challenge.

We sat down with him ahead of the Digital Marketing & Strategy Innovation Summit in Hong Kong, which takes place this April 18 & 19.

Could you tell us a bit about the problems Digimind is trying to solve?

We work with companies to identify their business goals, gather relevant data, and provide them with the means to tell stories and make decisions with data through data visualizations, dashboards, and reports. Our main goal is to empower clients with deep consumer insights and accurate KPI metrics so they are able to excel in their roles, and leverage social intelligence for more than just brand reputation, but also for competitive intelligence, product development, and more.

What kind of metrics should organizations be looking at when trying to measure their success on social media?

Reach and engagement are still the most popular metrics for measuring relevance and interactivity of a brand’s activities on social media. With that said, it’s equally important to look beyond vanity metrics and also consider hard metrics in order to get a more complete picture of your ROI.

Click through rate, cost per click, volume of sales, and new customers acquired will give you the most direct, quantifiable measure of how much returns your social media activities have delivered. Additionally, it’s important to always tie these back to your overall business objectives (be it driving revenue, growing market share, or increasing brand awareness) by looking at social data alongside other data sources such as website and sales data.

How can brands maintain loyalty and meaningful engagement in the over-crowded digital space with the use of social intelligence?

Social intelligence can help detect trends in consumer discussions around your brand, competitors, and the market. With this information, brands can better understand consumers and grow communities by developing experiences and content that aligns with these trends. Netflix does this very well, with one example being their recent Facebook video which linked Yishun’s odd reputation for negative news to their show, Stranger Things.

Additionally knowing which channels your audience and on and how they use it can help brands develop their community growth strategies. Understanding which channels to focus on and having an engagement plan for user-generated content allows community managers to focus their efforts on developing loyalty and advocacy by acknowledging individuals and creating a sense of community with followers.

How can companies use data to personalize content and marketing campaigns? How important is location data today?

A common aim of marketing personalization is to drive engagement and eventually conversions among customers by demonstrating that your brand understands their needs or aspirations. Companies can use data to personalize content and marketing campaigns by understanding what’s trending among target audiences and creating content around trends.

Content personalization is also important for regional and international companies. Organizing data by geolocation helps brands localize campaigns to increase relevance and engagement by understanding local consumer preferences. One of our clients, a household soap brand, used insights from social media to understand the nuances of APAC markets to tailor ad content such as products featured and choice of words used. This allowed them to better connect with consumers by using terminology and products that they knew resonated with them.

How important is it to track customers' journey and have you seen a change in browsing behaviors? (e.g. move to mobile, moving between social platforms to purchase, etc) What challenges does this bring for data analysis and for digital marketers looking to understand their ROI on social?

Understanding which channels customers are on during the awareness, purchase, and retention phases allows marketers to optimize content strategy and influence purchase decisions by sharing the right content at the right time and the right place.

However, gone are the days of a linear customer journey. Customers today have the luxury of deciding when and where to interact with your brand, be it on mobile, app, social media or in-store. All the more, they often interact on more than one platform, which means marketers need to optimize multi-channel marketing to ensure experiences on all potential touchpoints are consistent.

The challenge with multi-channel marketing is that tracking and attributing ROI to specific marketing activities can be tricky. To do this accurately, digital marketers need to ensure their data does not exist in silos but instead combine and analyze data sets from multiple sources such as Google Analytics, social analytics, and revenue to determine the extent of which marketing activities impacted their company’s bottom line.

How important is unstructured data to the analysis of understanding social media output and do you believe machine learning has helped improve our ability to exploit it?

Unlike structured data, unstructured data is often harder to make sense of. But once you are able to make sense of it, you can answer the ‘why’ of many instances. As an example, companies can understand brand sentiment and customer trends and behaviors by analyzing unstructured social data.

Due to the sheer amount of data involved, machine learning has added significant value in social listening by allowing companies to analyze multiple data variables simultaneously. It helps arrange data in a unified, structured format that then provides details on how they interconnect to form patterns and trends that could potentially impact businesses, all without any human involvement.

In addition to the who, what, when and where of consumers, machine learning makes unstructured data far more valuable to marketers by letting them understand not just whether a brand was mentioned, but also the context in which it was mentioned.

What other challenges are there around social media analytics that new technologies are helping to solve?

Data privacy is a key challenge for social media analytics. The recent situation involving Cambridge Analytica and Facebook highlights the immense responsibility companies have when dealing with personal data from social media. At the same time, data privacy laws such as the EU’s GDPR mean that social analytics tools need to provide innovative ways to account for personal data, while still being able to provide companies with in-depth consumer insights. There are a number of ways providers can help clients achieve this, such as by anonymizing personal data or through privacy by design, which are both features Digimind offer.

With that said, the accuracy and integrity of data collected is another challenge. The irony with big data often is that the more data you have, the harder it is to detect trends. Thus a seemingly simple yet important technological feature in social listening tools, that allows organizations to be more data-driven, is the ability to collect relevant data, visualize it in a manner that is simple to understand, and share insights across the organization easily.

What do you foresee to be the biggest disruption/innovation in digital marketing in 2018?

I think artificial intelligence will continue to bring about some of the biggest disruption and innovation in digital marketing this year. With more data available, expectations of marketing teams from both internal and external stakeholders are higher. Marketers are responsible for how they make use of their resources, and need to feel confident that their decisions will positively impact their marketing objectives. At the same time consumers today expect marketing to be personalized. A study done by Adobe Digital Insights found 65% of Millennials in APAC prefer ads based on their interests, with a third of the same demographic believing advertisers are not doing it well enough.

AI has the ability to affect the entire marketing process, from research, planning, execution and evaluation, allowing informed decisions to be made for stronger marketing strategies, and a higher amount of personalization for users.

You can hear more from Digimind, as well as other leading industry leaders, at the Digital Marketing Innovation Summit. View the full agenda here.


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