E-Commerce in India is valued at USD $10 Billion in 2014 and expected to cross USD $15 Billion in 2015 (Report from IAMAI). India has barely scratched the surface of web ecommerce potential but is poised to go ballistic (in a good way) in mobile commerce. An interesting tidbit here is that over 5.5 million 4G enabled mobile devices have been sold but less than 100K 4G subscribers are there, mainly because of the limited availability of 4G network in India. But with reports suggesting that 4G Internet will be launched countrywide in 2015, it is not far fetched to think mobiles may soon monopolize shopping in India.
But rules of old world no longer apply. (Old = 5 years ago.... and I may be too generous here.) While there is general trend of preferring native apps over mobile web or hybrid apps, retailers really care about nailing just one thing : "Engaging User Experience".
What exactly makes a shopping experience "engaging"? The way I think, it is the complete shopping experience that presses all the right psychological, emotional, ethical and rational buttons to make shopping a gratifying experience at conscious and subconscious levels. The experience starts with how I hear about a retailer, my first impression on visiting the retailer, rich information centered around not just products, but also how products gel with my lifestyle (or my aspirations), ability to navigate, locate and visualize what I want, non intrusive but always on hand sales associate attuned to my level of knowledge of what I want to purchase, not having to shop-hop.... list goes on.
Yes, we need cool UI to give the initial wow factor, but we could do with functional UI that does not get in the way of the shopper. Functional does not mean run-of-the-mill, factory cut UI. It should still stamp the retail store's individuality. Beauty is important - world does not take the retailer seriously otherwise, but as mobile shoppers mature, skin deep beauty will ring in hollow. The days of having cookie cutter approach to providing shopping experience are numbered and coming up with custom cookie cutter pattern alone will no longer set a retailer apart. When I visit an e-commerce retailer in my mobile, I expect the product recommendations to match my previous browsing and purchase history. I only notice it if they don't. Rules of engagement are evolving and rules based engagement, any which way we package it, is no longer dynamic enough. The rules engine now needs to think. Not only think, but reason! And this intelligence must be available for both shoppers (as a guidance/advisor/sales associate) and for retailers (to enable marketers and merchandisers digest large quantities of data and discover patterns in a meaningful manner).
IBM has forayed in to the world of cognitive computing and has thrown open Watson analytics with natural language supported exploratory visualization for business associates that takes analytics to new heights without the steep learning curve. The free trial is available for everyone to give this a try. Free version has some limitations : For example data storage is capped at 500MB, record size cannot be more than 100K rows and 50 columns. But it will give a great insight into the promise that this new age solution holds. Imagine being able to ingest scores of spreadsheets from your ERP systems and other analytics tools and gain insights using natural language queries.
How do you do it? Visit IBM Watson Analytics, use your IBM id (register one if you don't have one - it is free). You will receive an email to validate your account. Once validated, you are taken to a landing page where you can upload your data - for example recent sales data. Once uploaded it suggests starting points for exploring your data. You can also view the useful sample tutorials for tips on analyzing your sales or marketing data. These insights can now lead you to make decisions that go over and beyond regular formulaic decisions. Once these insights are available, WebSphere Commerce solution can now factor these relations into their recommendation or guidance engine.
I played around with just a dump from a sample ORDERS table of WebSphere Commerce. My intention was to find my order spread by currency (aka region) in a given time frame. It was as simple as dumping a report about all sales order into a CSV, dragging and dropping that CSV onto IBM Watson Analytics and going through various recommended and custom visualization schemes.
I could type in natural language questions and it responded with graphs. As an example, I could make out that total sales value indicates that my store's sales in Japan (presumably due to value of Yen) far outstrips my sales anywhere, but number of orders say my USA sales was easily more than sales of all other countries put together. I could then drill more into the data to understand average value of sales from each region and type of products selling. This helps me tailor my recommendations, promotions and campaigns more effectively. Many analytics tools may do this in one manner or the other, but IBM Watson does this while supporting natural language queries putting it within reach of any retail business user and not requiring a PhD in data science.
I have barely scratched the surface of this tool's capabilities. Next time you wonder why you are able to navigate to a page showing your preferred brand, color and size of t-shirt within a couple of taps without ever disclosing this information to the retailer, you may have to thank Watson for it.
Has anyone played around with Watson analytics? What are your thoughts? I welcome you all to share your experience.