In God we trust; all others must bring data” – W. Edwards Deming
Machine Learning an elixir for new world technology
The world of analytics is now talking about Support Vector Machine (SVM), Nave bayes, expectation maximization using nave bayes, random forest, bagged regressions et al. everything is about adapting learning and self-evolving algorithms that augment the understanding of the customer with every successive digital footprint.
For e-commerce businesses of this era data mining and machine learning algorithm plays an important role in the following areas
- Product search
- Product recommendation and promotions
- Fraud detection
- Business intelligence
- Anticipatory purchases
- Pricing management
- Supply chain management
Providing an example is the best way to understand how data science works and why is it so useful. For instance, a customer service centre has basic systems that allow employees to check the customers name, email, phone, address whenever they are calling the centre. In this way, the employee can see what this customer has bought in the past and they can skip the explanation in the beginning. However, with the help of tools that rely on data science, employees will be able to get more information about the caller like their return history, the ratings they gave to the company in different surveys, the amount of money theyve spent on products/services etc. In other words, thanks to data science, the employee will not only figure out what the customers problem is, but they will also understand the frame of mind of each customer.
We are living in a digital era where customer is the king. Many businesses have capitulated to this new realm and have started interacting with customers dynamically. Today the customers are free to navigate a merchant (e-commerce) website any way they fancy. Also the merchant can display content and place offers dynamically based on how a given customer interacts with his website. To add to the complexity purchase decisions are not necessarily made on the first visit itself. Internet savvy customers now have all the information at their fingertips to land themselves the best deal.
A merchants dilemma
When contemplating a purchase customers go through something which marketers call the AIDA journey:-
- A: Attention/Awareness attract the attention of the customer
- I: Interest of the customer
- D: Desire – convince customers that they want and desire the product or service and that it will satisfy their needs
- A: Action lead customers towards purchase
In most scenarios, a customers site navigation on the day of the purchase is mere execution of a decision that has been made even before the customer lands on the site the customer has been on the site before; the customer is aware of what is on offer; the customer knows exactly how to get to the page on the site where they can choose the product they desire. In fact, the pages visited on the day of the purchase are often not causal to the purchase, just simply correlated.
In the digital world, the focus is dramatically shifting from prediction to classification. The selling and buying is now all happening in a real-time environment where the two players are interacting with each other, and repeatedly. The merchant has the leverage to influence the customers behaviour through customized offers based on behavioural segmentation and contextual targeting. All the merchant wants to understand is who the customer is and that will determine what offer to place.
Warming up to Big Data
With all the technological breakthrough happening around us, the big data movement however is still in its infancy, while Hadoop heavyweights like Cloudera, Hortonworks, and MapR Technologies are giving us plenty of sandboxes to get started with analytics and even lightweight application frameworks to jumpstart specific use cases like fraud detection and making personalized recommendations, getting a big data analytics application built and deployed requires a lot of handholding today.
What’s your take? Leave a comment.
