Importantly, it shattered the communication model first mooted in 1949 and which underpinned the marketing industry's behaviour for decades.
In terms of this model, the flow of communication was linear, from marketer to channel to consumer. That is, a marketer, the originator, created and transmitted a message through a channel which decoded it for the consumer, the target.
Today, computer-mediated communication dominates, and we - marketers and consumers - are deeply enmeshed in it, not on either end of it. And consumers are no longer targets. The networked world means that all connected individuals are potentially exposed to the marketer's message. This is passed on, spread quickly and modified; consumers have become participants and generators of communication.
But as marketers grapple to come to terms with how this has changed the way they interact with consumers, few have realised that the ever increasing channels and information sources will change, have changed, the way marketers gather data and conduct research.
Data is proliferating at a rate that would shock even Alvin Toffler - visionary author of Future Shock. This is more than just oodles and googlepexes of data, but massively complex, interconnected, continuously modified and viral streams, growing exponentially.
In my opinion, tomorrow's marketers must rely on this data to inform their decision-making, and not just the feedback they receive from customers through the connected social media space.
That's a seemingly controversial opinion. Effectively, I'm saying marketers should downgrade the current attention given to Facebook posts, LinkedIn discussion groups, Twitter feeds and so on. How can I justify that? The answer is just one word - 'representativeness'.
Granted, while feedback may provide some accurate data, it cannot be extrapolated or generalised. This is because feedback suffers from a self-selection bias and is controlled by the consumer. In other words, the customer that chooses to contribute and offer feedback may not be representative of all your customers. Typically customers that feel strongly, positive or negative, are the ones that choose to participate. This has a role to play, and we must engage on these platforms, but not take major decisions based on this content.
The only viable alternative is research, because research gives the market researcher control of the sample. And, in the ideal world, the application of intelligence leads us to insight and strategy.
We can define intelligence as the routine of collection, collation, interpretation and dissemination of information.
When it comes to collection, there's a vast array of tools that continue to spring up to amass data, both quantitative and qualitative. Think of updated eye-tracking, neuroscience brain monitors, rich behavioural data for mining, social network site mining, pop-up and interactive surveys, proprietary on-line panels, physiometric measures of response, ethnographic research, crowdsourcing, new projective techniques, GPS tagging, video surveillance and network analysis, to name a few. In fact, Dashboard has developed its own proprietary platform called ATI (Automatic Telephonic Interviewing).
The potential trap is to harvest data that has little value. Focus (on the problem, issue or data needs) and selection of the most appropriate way to collect the data, taking channel independence and privacy into account, are key. Often clients are sold data collection solutions that are sexy, but are not designed to do the job at hand. The insight value lies in collecting accurate, cost effective and timeous data, regardless of which collection tools are used.
Wherever and however we get our data, we need to collate and aggregate it, or pull it together before we can make any sense of it, and unlocking trend insight depends on how well this data is organised. For example, we wish to combine our customer satisfaction measures from web-based interactions, call centres and transactions, which each get collected separately. Fortunately many great tools (software and web-sites) exist including digital curation utilising metadata.
It is also important to critically evaluate the data before interpreting it. Consider these questions:
• Purpose - what was the original data intended for? Is there a hidden agenda? Are there biases?
• Completeness - can it address your issue entirely, or adequately?
• Time assessment - when was it collected and is it still current?
• Definitions - have these changed over time or do they fit your need?
Interpretation of data is and will remain the key human aspect of the intelligence chain. A close relationship with the suppliers of the data enhances this function, from design through to output.
Computer-mediated communication has, fortunately, provided us with additional means to short-cut our way to analysis using a technique called collaborative filtering. Also known as semantical ergonomics, this is based on the premise that those who agreed in the past will tend to agree in the future and taps into a network of agents (or people with similar interests or on-line groups).
Once data has been collected, collated and analysed, disseminating or sharing the findings and insights is key. It must be timeous, succinct and relevant ... and has also been helped tremendously by the wave of new tools that continue to pop-up and be developed.
In conclusion, combining intelligence and systems provides marketers with the insights they need to be successful in today's computer-mediated communication world.
However, this can only be achieved by harnessing the power of technology. Being channel independent is the holy grail, but there should be a clear strategy for each channel for collection and dissemination. Where possible, move to using real-time information, with data visualisation and live dashboards for better dissemination and advanced database management that can integrate unstructured data as well.
