How Big Data is changing the marketing landscape
Big data refers to the growing complexity, volume, velocity and variety of information.
What is Big Data Marketing?
According to Eric Schmit, Google’s CEO, today we generate more information within two days, than in all our history before the year 2003. Such information can be used to completely transform the marketing landscape. Long gone are the days of mass marketing (in which a whole segment would receive the same advertising message, and which only relevant to a small minority of its recipients).
Contemporary consumers surf the internet on a daily basis, leaving a wide array of information about who they are, their interests, whom they relate to, where they shop, etc. All this information is collected and can be used to create very detailed profiles about consumers.
Why is Big Data Marketing so important?
Advertising messages completely surround our daily lives, and many times these messages are not really relevant to our needs, or just simply arrive at the wrong place and at the wrong time.
Why should I care about the 50% discount on motocicle repairs that Groupon sent this morning, when I don’t even have such a vehicle? On the other hand, I was very happy to receive Amazon’s mail offering an extra lens kit for the DLSR camera I bough some weeks ago.
As it was discussed in another article in this blog, the use of ad blockers is on the rise. This tendency is a direct result of excessive industry practices that are still being used.
Segmentation with the goal of creating targeted and relevant communication from companies toward their consumers is extremely important since it creates a genuine relationship between both parties. Bombarding them with annoying advertisements is doing just the opposite.
In the following chart we can observe the main differences between the classical approach to marketing, and Big Data Marketing:
(Image source: Phil Hendrix)
Big data marketing avoids looking at market through a few key segments, and begins understanding it as individuals. Mass communication stops, and real time and personalised conversations begin taking place. Finally the consumer goes from being a recipient of information, to being co-producer.
What are the main challenges big data marketing faces?
As you may now be aware, big data marketing is very important in this day and age, but it is still surrounded by confusion, fear and even uncertainty. There are three main challenges to the successful implementation of big data marketing within organisations:
- Knowing which information to collect: There is so much information, that the problem is no longer about scarcity but about over-abundance. Knowing how to focus marketing efforts with overflowing sources of information becomes a key.
- Knowing how to analyse this information: There are two main issues when analysing big data. First the market of tools for Big Data is larger and larger each day, with such a diverse array of tools it can be difficult to select the tools that fit within your company’s requirements. Second, many times this information is not structured and requires complex processing in order to become useful.
- Obtaining actionable insights from this information: All this information is practically worthless unless it creates valuable insights that allow the business to improve its services, gain a better understanding of its clients, or obtain some sort of competitive advantage.
What are some practical examples of Big Data Marketing?
- Profiling and micro-segmentation: Through a wide array of tools companies are now capable of obtaining large sets of non-relational data and attribute those to specific individuals. This information can include historic navigation information, geolocation data, non-structural data from social media, historic shipping information, etc. Such information allows companies to create micro-segments which are much more specific than what was possible many years back in the analogue world.
- Dynamic content: Using the previously mentioned information, which can be acquired with the use of tracking cookies, websites can now display dynamic content that changes according to each visitor. This content is much more relevant and persuasive than static content. For example, amazon provides personalised recommendations based on historic navigation information:
- Sentiment analysis: Thanks to advances in artificial intelligence and natural language processing, many companies are now able to extract and categorise subjective opinions based on non-structural data such as that which can be acquired from social media. For example the tool twitrratr allows users to analyse sentiment on a given twitter topic.
Hope you enjoyed this post!
Dirk Breeuwer – 329445
Big Data Marketing: Engage Your Customers More Effectively and Drive Value – Lisa Arthur
Data Science for Business: What you need to know about data mining and data-analytic thinking – Foster Provost