The quintessentially Indian Kirana shop is slowly metamorphosing into the mall next door. Retail in India has completely transformed, but that need not mean losing out the advantage of a small set-up connect. We must look for ways where the power of data can drive our traditional family-connect, by leveraging analytics to create the Segment of ONE!

Today, retailers harness the power of the data that is generated from every single transaction that we place at stores, e-commerce sites or mobile applications. And tend to create extremely personalised offers based on buying patterns. Have we then come to a full circle?

Retail In India — A History Of Personalised Service

Till the early 1990s, the largely unorganised nature of retail made it dependent on strong personal customer connect. Local areas had shops that had loyal customers for generations, and the bond was very strong. They used a very clear customer strategy, i.e. personal connect with internalised data!

With the launch of the organised retail sector through departmental and multiband stores in Bangalore and Mumbai around 1991, a new world of possibilities opened for the retail segment. It was the decade of economic reforms, and opening up of the economy, driving increasing incomes and rapidly changing lifestyles. By the end of the 90s-decade global brands had entered the fray, and soon the neighbourhood shop was overpowered by a deluge of tempting brand launches. The family connect restricted to smaller towns, but they too soon joined the bandwagon. One big thing that these evolving “organised retailing” drove in India was the ability to scale brands and operations across the country, and this has reshaped the market ever since.

By 2006, organised retail had taken a stronghold in large parts of the Indian markets, and the retail industry was using PoS systems — entering the era of data awareness. Customer data soon became priceless, and the basis of all marketing strategies. As per India Brand Equity Foundation (IBEF), in 2012, the Indian retail sector stood at USD 518 billion and reached US$ 950 billion in 2018. By 2020, it is expected to touch USD 1,150 billion (USD 1.1 trillion), almost doubling in a decade! 

Globally the biggest retail stores by now had turnovers that equalled the GDP of a small country — it was a game of volumes after that. The size of data being gathered was humungous, and it was just a matter of time before this data became the soul of retail decisions. But the biggest credit for this growth goes to the fast adoption of data analytics — leading to much better and business-focused insights to strategise growth on.  

With the advent of the Cloud, smarter PoS and mobile technologies, data grew exponentially. A recent PWC survey maintains that almost 90% of their retail respondents have been dependent on the analytics of this data for growth strategies.

But there is more. 

Best Of Both World: Data-Driven Personalisation And Hyper-Personalisation

Clearly, as data took centre stage, the bigger brands found it increasingly difficult to keep the personal touch alive. As product strategies were directed by analytics, it became an economy on the conveyer belt — mass production, mass marketing, and mass sales were happening. In this deluge of data and analysis that could point towards a gold mine of revenue — personalisation was lost.

Since the inception of this decade, a renaissance has been in the creation. While “Data Science” often gets the limelight, it is always not recognised that many of these data science algorithms existed on paper for decades. It becomes quite a challenge to believe that Neural Networks, the class of algorithm that fuels AI today, was theorised in 1951. The missing link was the access to stacks of data required for these data-hungry algorithms, and more essentially the computing power to implement them practically. This has been possible due to the rapid evolution of “Data Engineering” space, where distributed and commoditised data have been consumed to engineer storage, accessibility, and faster as well as cheaper data computability.

Upon convincing properly, retailers can leverage the power of data engineering and data sciences to mimic the brain of the Kirana shop owner for analysing individual customer, their behaviours, and their decision making process minutely. This development has given retailers the best possible way to power up the personalised, hyper-personalised driven data, and new offerings for a Segment of One. 

It no longer must be a choice between “Delivering the Personal touch” and “Operating at Scale”. It is a decision to harmonise the two with the help of analytics. In a rapidly converging world, it is perhaps time to amalgamate this top of the line technological ability with the good old human touch – the strength of the traditional Indian retail outlet — catering to each family in a unique way. This is further driven by the availability of massive data, which sets through varied sources and ubiquitous power of computing that hustles up neural technologies. Thus, retailers have the ability to do nifty things like implementing ML and AI to drive much better insights on customer personalisation. 

The use of analytics does not limit retailers to reports and sporadic predictive models. For enabling the required competition and leading spree, they require to cultivate the brains of Kirana shop owners to their level. Personalised experience depends on analysing individual customer characteristics. Specific brand loyalty, eliciting unprecedented demands, seasonal buying patterns, lifestyle shifts, preferred communication are few of the data one needs to analyse for a more detailed outlook and here data analytics is the best possible way to get them. 

Perhaps it is time to laud the fact that analytics, powered by data engineering and data science, has brought Retail customer-connect to a full circle. The boom in data collation points and the ease of availing extremely evolved analytics, has provided the ultimate weapon to retailers — the ability to cater exclusively to each customer — the Segment of ONE.