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Moving From Big Data To Intelligent Data

Many have called 2014 the year of Big Data, meaning that marketers are now numbers-rich, but they are still insight-poor. Despite the opportunities and efficiencies that Big Data promises to open up, marketers are still being held back from truly unlocking these and moving from big to intelligent data.

Marketers have long leveraged data from a huge variety of sources, including transaction data, CRM databases, and market research, to inform strategic and tactical marketing decisions. Add to that the ever-growing volume of consumer behaviours that can be digitally tracked through in-store sensors, TVs, computers, gaming consoles and mobile devices, and it’s hardly surprising that many marketers are struggling with how to best extract value from the vast quantities and variety of data available to them. 

Until now, big data efforts have centred around knowledge gathering and exploration, but all this knowledge is meaningless without the right context and the ability to narrow down which individual measures are the most important to a business. Cutting through the noise of big data to the measures that matter is what will make a difference. For example, one German social media platform found they could distill all of the complex data accumulated from their site down to one key metric: daily active users.  An increase in this metric is a leading indicator that site users are happy, recruitment is going well, and advertising revenue is on the rise.

Simplifying KPIs

In 2015, we’ll also see more marketers focus their data strategies by simplifying key marketing objectives and KPIs by finding the smallest number of KPIS required to help steer the business.  Which metrics are nice to know, but only really helpful when optimising within a specific touchpoint? And which are actually central to the brand’s overall success? For example, bounce rate may be importance for the website manager to know, but could have no correlation with long-term sales growth. By identifying data points that correlate most strongly with brand and sales data, marketers can understand which fast-moving metrics are the best indicators of growth and which are the key touch points for increased marketing investment and focus – and make that move from big to intelligent data. 

To further accelerate this in 2015, we’ll see marketers invest in not just the right technology, but also in the talent that can best support them analytically - because powerful data processing platforms can be costly and, at best, only marginally useful when marketers lack a firm understanding of the quality of the data and appropriateness of its applications. Companies are already recognising the importance of investing in the brainpower needed to make big data smarter - “Statistical & Data Analysis” was what that topped LinkedIn’s recently released global list of the 25 Hottest Professional Skills of 2014. 

Maintaining Investment

But investments, whether in talent, technology, or partners, need to be carefully considered and maintained. Brands need to continue investing in their new hires’ growth and data skills to maximise their contribution to marketing objectives. While data mining and statistical analysis are hardly new in the world of marketing, the content, platforms and applications (mixing online and offline data, or integrating different devices) for these are, and they’re changing all the time. Today’s statisticians and data professionals need to wrap their heads around a whole raft of new metrics relating to online performance, social media resonance, video viewing and location-tracking data, all across multiple devices, to uncover the impact on each to driving digital and in-store sales. 

Due to the newness of availability and access to all these variables, analytic talent with deep expertise in making connections across these types of data points is scarce. The ability to accurately interpret data analysis and translate insights into the right action requires input from individuals with a unique blend of industry expertise and analytic prowess – and marketers need to continuously test and learn.

First Steps In Predictive Analytics

With the right analytic expertise and a test-and-learn culture in place, marketers can even begin to venture into the arena of predictive analytics, leveraging those “must-have” metrics to anticipate which audience segments or consumer actions will result in a specific outcome. Marketers can work with their teams and their knowledge of predictive analytics to drive brand equity and sales across retail and communication channels; and use data-based evidence to support further investment in the right areas. 

Working with a European confectionary brand recently, Millward Brown combed through multiple data sources to quantify the value of paid, owned and earned activity on social media in driving both short-term sales impact and long-term brand health. By evaluating consumer interaction across key touch points and quantifying the causal relationship each interaction (or sequence and combination of interactions) had on sales and brand equity measures, the brand was better able to prioritise its future activities, and maximise sales.  Similarly, we worked with a US telecoms company to examine the on-site and competitive site activity of current customers to identify online predictors of churn. By continually tracking churn indicators, the telecoms company is able to detect early warning signals of customer defection and employ counter-measures to mitigate risk.

Marketers are currently facing the front end of a steep learning curve as they themselves test and learn how to harness the power of big data efficiently.  Through further experimentation, the focus next year will shift from “big” to “intelligent” data, and ultimately, human knowledge and expertise applied to big data assets will be recognised as the key to translating the hype over big data into reality – and results.


Margaret Hung
Senior Vice President, Solutions
Millward Brown Digital

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