Social Media & Big Data – Declared preferences vs. Discovered preferences


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The other day, I logged into a local eCommerce site – to buy a book. In India, it’s a leader in eCommerce customer experience with very high Net Promoter Scores, a well-established metric with direct correlation to customer experience. I have not met anyone in India who has used this site and not recommended it to others. Like any forward-looking website, it allowed me to login using my Facebook/Gmail credentials, rather than asking me to create another username and password, which I would obviously forget. So my experience started on a positive note. Out of curiosity, I started to browse the “Recommendations For You” section to try and decode their algorithms. Next I tried the same process with Amazon, which did not allow me a Facebook login (or did I miss it?). And it dawned upon me that there are things that these e-tailers know about me because I told them that (the declared preferences) and there are things that they will infer about me based on my transactions with them (the discovered preferences). There would be two primary sources of declared preferences – my social media profile and any additions I make on their website to my profile like a phone number, an explicit addition to my “wish list” etc. And there would be another two primary sources on discovered preferences – my past transactions with them and my interactions with my social media website (including associated clickstreams). This is the perfect marriage of Social Media and Big Data.

Social Media

There are things that I do on my social media profile – let’s take Facebook as an example – where I declare my preferences of music, my date-of-birth, my relationship status, my photos, etc. Some of this data is available to businesses, if I give a merchant access to my profile. These are explicitly declared by me and companies can use this data to substantially improve their interactions with me since they know that much more about me. So having separate logins – in my personal opinion – is useless . Whether you are a local e-Commerce site or even as complex as a bank, if you are not exploring ways to use social media logins (Facebook, Twitter, Gmail, LinkedIn etc.) to your site, you are really not sincere about knowing your customers and serving them optimally, no matter how much you harp upon “We live to serve” in your print or TV ads. Social media sites are called that because they help customers be social. And so should businesses – all of them, not just for lip service but for transaction-enablement.

Big Data

Next comes all the status updates, ‘Likes’, comments, check-ins that I do on Facebook, activities which reveal a little bit about myself every time I interact with these sites. Facebook graph search and Facebook Home, of course, have now opened an even bigger Pandora’s Box in my opinion. Add these to the customer transactions with your company, the clickstreams of the customer on your website, install the processing power to do statistical analysis around the combination of all this structured and unstructured data and you are well on your way to your big data analytics strategy. But how much Big Data is useful and how is it useful? What can companies do better with Big Data Analytics that they could not before?


Part of the answer is in the problem itself – how intrinsically predictable something might be?

(And as far as human predictability is concerned, just think about your spouses, kids and parents before answering. That should give you an idea of how predictable your customer is going to be.)

So what’s the point?

How can you create value for me – your customer? Big Data, Social Media, Predictive Analytics, any technology on which a company invests money, it must create value both for the company and its customers. And ideally it must achieve this in a way whereby it improves the quality of the overall customer experience and reduces the cost of operations for the business. So how can you create value for your customer and yourselves?

Firstly the businesses must have end objectives in mind – not something as broad as “revenue increase by 2%” but something more well-defined “revenue increase by 2% from existing customers through existing products”. The key word(s) here is “existing”. If you are talking customer acquisition or new product launches, you might need different approaches than what I am about to talk next. The intrinsic predictability of your problem is vital to finding a solution for that problem.

When you have defined the problem as clearly possible with potential for predictability – you start looking for points of commonality between your product line and your existing customers. Now all of a sudden it begins to make sense to learn more about your existing customers and how they consume your existing products. To study that you start mining the Big Data of your company’s enterprise datawarehouses, transactional systems and of course the social media profiles of your customers. And you can start the journey to discover preferences of your customers in levels of granularity that makes it meaningful to establish relationship between customer segments and product segments with higher degree of correlation. An improved matching of product profile with customer profile since you now have more data points – both about the customer and the product. So both the declared preferences and discovered preferences are valuable

Discovering these preferences and patterns of your customers and products is meaningful only if you are confident that you are leaving that 2% money on the table. A product like a book in unlikely to be bought by the same customer again but a perishable or fast moving consumer good (FMCG) would have a typical consumption period after which it can be recommended yet again. So if you know when the customer last bought it, you can recommend again after a certain period.

Finally, as usual I want to state that business is always about ROI and if you have an opportunity to invest your money in something that can give substantially higher returns with the same investment, then forget big data, forget social media… But till then happy analysing social media and happy discovering customer preferences… and to start with let your customers become your friends on Facebook and login to your website with their Facebook credentials…

Abhishek Singh
Currently, Abhishek holds the responsibility for conceptualizing, implementing and managing the IT product strategies for Infosys subsidiary, EdgeVerve, in the Digital space. Prior to this, several years at Singapore Airlines as well as his years of entrepreneurship ingrained in him the importance of customer experience.


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