Let me tell you this, it is not growing at a considerable pace, it is freaking booming! Just to give you a sense of how it is booming – Sales in 2017 was clocked at USD 3.53 trillion, it is poised to grow at 4%. It is also on the cusp of a technology revolution.
The power of mobile and digital technology has enabled retailers to craft cutting-edge experiences for their customers.
The present customer of the retail industry is an extremely fickle one. The newer generation of shoppers have little to no brand loyalty.
It actually is true. A report from the CMO council stated that 54% of consumers would consider switching in favor of better content and offers.
Aren’t we willing to switch to brands that offer a better buying experience and product range? Of course we are, the phrase “Customer is King” has been drilled into our heads repeatedly.
Retailers are also seen to be investing in a range of unknown brands that can offer similar quality as compared to the bigger brands, at more competitive prices.
There is also the entire world of e-Commerce that has taken off in a big way which has given many retailers the decisions to make between brick-and-mortar and digital presence.
The retail industry is driving all these innovations home due to insights into the customer’s data that includes buying and product browsing behavior.
The industry analysts are also able to get feedback in a faster way and craft a new journey for their customers.
The technology enabling these amazing insights or fueling the new age customer journeys is big data.
To put it simply, your last batter in the 9th inning smacks a homerun, Big Data being the batter!
The Retail Industry : DISRUPTED.
Disrupting Agent : BIG DATA!
Big data is the technology that powers the crunching and analysis of extremely large data sets.
The criteria for the data sets to be ‘big data’ is their volume (their size), their velocity (the speed of incoming data) and the variety (the different types of data).
Big data, when applied to retail is able to transform it in unimaginable ways. Here are a few that I thought were most relevant and impactful:
1. Understanding Customers & Learning Shopping Habits
Now that the customer is in power, retail companies should focus on understanding each customer individually to offer her a more personalized experience. Big data can help retailers to understand their customers by tracking their transactions, browsing behavior, preference for specific products, shopping trends, and social media habits. This way, retailers can cater to the customers in a more personalized way via targeted advertising, product recommendations, and pricing.
2. Analyzing Brands & Providing Better Product Recommendations
In order to provide customers with better product recommendations, it is essential that retailers understand each brand separately. Big data can analyze a brand’s social approval using social media analysis and brand website traffic analysis. The conversions from a product’s landing page to checkout also provide an insight into the level of consumer appreciation of the brand. This data can further help retailers to provide accurate product recommendations to a customer and increase conversions.
3. Building Promotional Strategies
Retailers invest a lot of their resources into advertising and other promotional campaigns in the hope of boosting sales. However, sales could be driven better if these advertisements reach the right customers. Big data can help evaluate the needs of a customer segment by bringing into consideration the buying practices of a consumer and other relevant but often missed information like, let’s say, the upcoming weather conditions. This data can be used to supply relevant advertisements to prospective buyers and consequently drive sales. US hotel chain, Red Roof Inn, employed big data techniques for light cancellation analysis and weather conditions. The company then used this data to send out offers on accommodation accordingly. This strategy eventually led to a 10% growth in its business.
In conclusion, big data has immense potential to help retailers stay ahead of their competition and offer better services to their customers. Apart from the above big data use cases, there are many other specific applications of big data in retail that can drive sales revenue and also take customer retention upward.
Retail Analytics To Retail Intelligence
Data Science technologies can now help retailers to think beyond retail analytics to retail intelligence. These technologies can help retailers take a big leap by not only collecting and measuring data but also creating models that can learn on its own. AI and Machine learning can help retailers infer from unstructured data, images, and videos to provide crucial insights and intelligence. It can help them not only understand trends but also predict it with higher accuracy.
Let’s look at how Retail Intelligence is disrupting retail and creating new opportunities-
Micro Customer Segmentation
In traditional retail, customer segmentation is done by macro-segmentation. A retailer segments its customers based on age, gender, demography, etc. Retail data intelligence will empower retailers to adopt micro-segmentation by uncovering many layers of customer data to build a 360-degree buyer persona.
Smart Product Recommendation
Product recommendations will move beyond suggesting items based on purchase history and browsing behavior to context-aware recommendations. Machine Learning driven recommendation engines will self-learn from the data to extract deeper insights into what, why and how of a buyer’s needs.
Retail intelligence will enable retailers to adopt Predictive pricing wherein pricing will be set at store levels based on the prevailing market conditions. Various internal and external sales drivers like weather, time, demand fluctuations and inventory, etc., can be factored to adjust product prices in real-time.
Predictive Inventory Management
Data Intelligence will help retailers monitor the stock situation in real-time and predict inventory requirements by analyzing customer behavior, market trends, weather patterns, etc.
AI and ML-driven Retail Intelligence offer enormous potential for retailers to build smart, context-aware processes, deliver compelling experiences, drive cost advantages, and empower the workforce.
Data has already disrupted the way retailers do business. Data Intelligence will further drive it. Retailers who can optimally leverage their data will get a competitive edge.