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Find out what big data analytics is and how it's revolutionizing the retail industry.
The retail industry is being reshaped by big data analytics.
From supply chain management optimization so your favorite product is always in stock to deliveries that happen in under 1h that fulfill the immediate customer needs, retail businesses are using predictive analytics to drive growth.
In this article, we will look at what big data analytics is and what are the three main drivers it uses to reshape the retail sector.
Let us start with some clarity surrounding the terms.
Big data is characterized by 3 Vs: an increased Volume, Variety, and Velocity of data that offers a competitive edge to those who are not afraid to wield it.
Namely, big data unlocks a different set of tools.
It offers the artillery of machine learning and artificial intelligence algorithms that can speed up business decision-making, discover patterns in data sets not obvious to humans (more on this later), and optimize and streamline business processes to fatten up those thin margins characterizing the retail sector.
Traditional retail analytics had many data points at their disposal that were used to optimize pricing, stock management, and other business decisions.
For example, the retail sector could forecast customer behavior around certain events based on the purchase history of their clientele.
Black Friday, Cyber Monday, and other critical retail events are red flags for the merchandising teams to get ready.
Loyalty cards were used not just to track customer loyalty, but also to group customers into segments to better understand customer needs. Suddenly, it is not just Jessica buying baby powder, but you get insights into what mothers like Jessica often buy together with baby powder.
With the advent of big data, retail analytics has gone a couple of steps further. It can forecast more accurately, in shorter times, and discover previously unseen patterns.
Traditional marketing strategies focus on segments built on demographics.
But the issue with segments and demographics is that they often get it wrong. Two people from the same demographic have very, VERY different customer needs.
Big data in retail changed the segmentation game. By providing more data, retail companies can narrow down the customer profile to very personalized needs. Effectively speaking to a segment-of-one, aka the customer directly.
Pantene was one of the first ones to do just that.
They partnered with the Weather Channel to get near-real-time weather conditions and predictions. Using weather data metrics and IoT data from mobile phones’ geolocation, they crafted a social media campaign that was highly personalized. In a social media advertising campaign dubbed “Haircast”, Pantene showcased ads for hair products based on the weather condition in micro-locations - from a wet street in Chicago frizzing hair, to the sun-blasted Phoenix boulevards drying curls up, all the ads targeted the real-time user location.
The ads then directed the customers to Walgreens retail stores, effectively bridging the offline-online divide, and creating an omnichannel experience that delighted their customers.
By using hyper-personalization, Pantene witnessed a 10 percent increase in sales at Walgreens.
Multiple factors driving demand can mean that one brick and mortar store is overstocked on skis on a sunny weekend, while another physical store ran out of snowboards just before a 30% rise in customers' demand to buy them.
Be it pricey storage or a missed opportunity at a sale, both situations impact the bottom line.
Supply chain management is the art of stocking exactly as many SKUs as the retail company needs, being mindful of demand by location, and anticipating the surges and dips in customer needs.
Forecast demand is an area where machine learning shines.
Some factors driving purchases and demand are intuitive. For example, forecasting snow ahead of the weather channel and stockpiling winter sports equipment for your outdoors gear e-commerce is a no-brainer.
Sportisimo, for example, used Keboola to create a crystal ball that makes 12 million predictions everyday. This way they are better prepared for seasonal sales, and short term weather effects.
The first step to improving the customer experience in retail is to understand where there is a gap between the customer needs and the retailer’s delivery.
The problem is that once retail companies grow their customer base it becomes increasingly hard to have a personalized and meaningful conversation with each customer about their experience.
Luckily, retail companies can tap into a lot of sources of customer satisfaction data - both internal (surveys, post-purchase satisfaction scoring in online retail) and external (Google Reviews and other social media feedback in the forms of reviews, engagements, comments, etc.)
With the use of sentiment analysis - a branch of machine learning and Natural Language Processing - retail companies can analyze massive amounts of data to understand and extract meaningful insights about their customers.
This is another case, where big data analytics is changing retail. When data points grow beyond the thousands, humans have problems reading and understanding all those customer reviews. But machine learning algorithms can scale and help you understand - and delight - your customers.
Curious how to use big data to revolutionize retail?
Check the following in-depth case studies explaining how exactly data is used to transform and elevate businesses:
More of a hands-on person? Try it for yourself.
