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Big Data

I was asked the other day whether Big Data was over hyped and whether it was just another fad that has taken over the online community. The same person indicated that Big Data was about analytics, reporting and statistics, all of which are areas of the online world that seem to have been done to death at the moment. Examining the interaction and touch points between the average consumer and big brands every day online, shows that Big Data is a lot more than just data and statistics. 

Big Data can provide maximum advantage when used to fully understand your consumer. If a consumer loves your brand they will naturally be loyal and want to be part of the club, your job is all about making them feel a part of your brand. Big Data achieves this.

We develop a substantial number of digital solutions for our clients and customers and we get into numerous conversations about conversion and commerce site design. I keep coming back to Amazon, which has fantastic conversion figures and basket value per customer as well as high customer loyalty. Yet the site still looks like it was designed in the late 1990s. Why is Amazon still so successful after all of these years? It’s not about design or presentation, it’s about the understanding of Big Data - how it understands the huge data combination of consumer data and what products they buy, what they are looking for and making links and references between buying habits. All of this is only possible with Big Data, not analytics but rather algorithms that interact with Big Data to change what consumers and products are being linked. Imagine the databases that are held to build up a consumer profile of consumers. I don't even have to buy anything, I can just look for a product without buying anything and in a few days I will get a nicely formatted simple email sent, gently prompting me to make a decision. If Amazon had a list of rules for Big Data, I imagine it might include the following:

1) Collect it. Make sure to collect and store every interaction a consumer makes with you, whether it be buying or browsing.

2) Detail it. Detail all your products and their relationships with categories, hierarchy and genre.

3) Link it. Think about the relationships between product, people, other people, other products.

4) Pattern it. Produce patterns in the data to reveal scenarios to test probability of improving conversion.  

5) Test it. Constantly test your data with consumers.

6) Advance it. Advance your data set so it learns from what works and what gets results.

To improve conversion design only goes 50% of the way, getting on top of your data leads to much stronger results.