Customer World

Last week, Unilever announced it had acquired Dollar Shave Club.  Tech Crunch carried an interesting article on the $1 billion acquisition and the challenges reputed & established FMCG brands face with the onslaught of innovative and emerging brands. 

FMCG companies have been living in an era of mass marketing for over 100 years now. They have working on the premise that if they  create a great brand and have an efficient supply chain, then the sale is done. Hence, over the last few decades, they have been working on building an efficient supply chain and ensuring the stocks are placed ahead of competition and replenished efficiently. As far as the customers were concerned, if they had a top-of-mind recall about the brands and if there was availability when they landed in a retailer's store, the sale was completed. However, digital disruption and democratization of technology is transforming the  FMCG industry. Leaders like Unilever, P&G, Nestle, L'Oreal and the like have been reeling under this disruption.

The thinking of FMCG companies has to move from managing the supply chain to building a robust demand chain. The FMCG companies' supply chain approach  is about 3P strategy- " Place, Push & Purchase". However, the demand chain approach requires a drastically different mindset which is a 3F strategy " Find, Fill & Fullfill". The customer behavior here is about "Discover & Buy" rather than "Reach & Buy". Also, it is no more about replenishing retailers'' stock but it is about replenishing customers' stock. This for FMCG companies turns their marketing thinking on its head. However, the customers are lapping it up, as they are able to get value & convenience like never before. This also throws-up new big data opportunities about customer purchase behaviour for FMCG companies as new direct-to-customer digital channels are emerging to engage with customers and they now need to understand the insights that arise out of these huge data sources.

The era of direct mass marketing is around the corner for FMCG companies and they need to serve these customers in new ways in the future.

These three - Deep Learning, Personalization and Privacy are indeed oxymorons.  The fact that when they come together for contextualization, relevance and differentiated customer experience - privacy takes a back seat!  When Privacy takes centre stage, then Deep Learning & personalization take a backseat! The real question, is from a customer point of view, can they work in harmony? How do companies use them effectively?

What deep learning can do is well documented. When you listen to this talk by Jeff Dean on Large scale deep learning at Google, you will realize the power of its capability. The most important difference you will see is how Google has embedded that in its products and services.This then brings the issue of privacy to the center of the debate, as this is the data of the customer that Google analyzes & monetizes. However, when it comes to the synergy between Deep learning and personalization, all the data that is used to understand this customer, effectively means personalization can be done like never before. So, the next time you walk-in to your favorite store, or your bank, or your online store, or your health care provider - the personalization possibilities thro' virtual assistants can create a phenomenal experience to the customer. Hence, if deep learning helps customers make their life simpler, then what's the problem with privacy? I think if this is done with the permission of the customer, then puts to rest the debate.

However, this has a lot of learning for the other industries - as the customer pays and owns their products, unlike Google where products are sometimes used for free. Take for example an automotive brand - the applications of deep learning, personalization is phenomenal. With most cars fitted now with navigation systems and telematics devices, the data this can throw about the customer, their driving habits into their automotive platform is phenomenal. The driving habits, the engine data, travel data, innovative use of public data all represent great deep learning application opportunities for the automotive brands to drive personalization & differentiation. Here, privacy is something of little consequence or debate as any assisted customer service will only enhance the customer experience. The same goes with banks - where the usage of the mobile app, online banking, payment apps, shopping data  thro' credit cards, public financial data etc. will only create opportunities for deep learning applications and personalization for enabling personalized financial solutions.

The question really is not will they but they will have to do it soon.