Marketing departments quickly adopted big data analytics and obtained good results. Many companies—such as Amazon and its noteworthy and effective personalized marketing powered by big data analytics—use big data–based marketing analytics to outthink their competitors. However, according to a recent survey by Kantar TNS, one of the largest research firms in Europe, delivering meaningful personalized marketing is still a big challenge for organizations.
Nevertheless, utilizing big data definitely is the first step for smart marketing. Sometimes, even a little insight such as the best timing for sending emails exposed with big data analytics can lead to some significant improvements. And, having all marketing being data driven is always a good thing, but it can also be dangerous if only incomplete data and some simple analyses are used. Incomplete data and simple analyses can create biased estimations or even lead to disasters. Many bad examples were identified in 2016 as the year of bad analytics by some people, including a FICO partner.
To go beyond incomplete data and questionable analyses, at least, three steps of good data science work need to be performed. First, we need to create more complete data. Big data does not equal complete data. Data completeness helps us, at least, to avoid misrepresentation and model misspecification biases. The marketing data set from the past for any company is always a good place to start, but we need to merge it with other data sources. We need to merge the readily available internal data with some external data sets such as census data and open data, which are publicly available and easily accessible. With time and location data added in, we then need to go further by merging weather data and social media data, and applying text mining to generate new features.
Second, with more complete data in hand, we can then apply modern data analytics to derive causal structures from our data.