Marketers have long struggled with effective, repeatable ways to measure the true drivers of demand for their products or services. Even in the era of "big data" – the latest catchphrase for the vast amounts of information organizations are collecting about customers' and prospects' behaviors and activities – marketing teams remain challenged to draw a straight line between increased demand and specific elements of the marketing mix. The "truth" remains an elusive metric.
In some ways, availability of data is making the problem worse. The traditional databases upon which traditional marketing analytics were built were not designed to handle the scale (terabytes upon terabytes) of information now being collected. Because these systems have trouble handling large data sets, marketing analysts often resort to an old standby from the offline world: sampling data.
Unfortunately, looking at one-tenth of your customer data is like searching every 10 inches of a haystack for your needle. Worse, sampling data often leads to skewed or misleading conclusions that may cause you to spend money in areas that only appear to be driving demand (search, for example) but are, in reality, less effective than other activities.
Today's marketers need a different technological approach to harnessing big data to understand the true drivers of demand, for example in order to optimize multi-channel marketing spend. As marketing metrics shift from eyeballs to outcomes, companies can't afford to make resource allocation decisions based on fuzzy math, intuition or the status quo.
Big data, in other words, requires big analytics – the ability to mine large and more complex data sets for more predictive insights. These analytics must integrate the silos of information that currently live in isolation across most organizations (and even within the marketing function) in order to provide a consistent view across online and offline activities.
If this sounds like a daunting task, well, it is. But for CMOs who lead the effort to develop competencies around big analytics, the effort will be well worth it. Because increasing the breadth and depth of customer insights will lead to more efficient and more effective marketing spend.
Big data is already there; can you develop the big insights that must follow?