In a previous post, we explored the future of digital transformation and grocery shopping. In this post, we further this exploration with the use of Big Data.
The term Big Data first emerged in the 1990’s but has gained more prominence in recent years due to the advancement of technology and in particular, greater data storage, interlinked systems and the Internet of Things. Big data in retail generally describes combinations of datasets that are too large to be managed using off the shelf software or systems. Big Data is often described using ‘4 V’s’ of
Volume – Quantity of generated and stored data.
Variety – Nature and type
Velocity – The speed at which the data is generated and processed
Veracity – The quality and value of the data
In recent times, a 5th V has also emerged, that of
Value ”the ability to transform a tsunami of data into business.”
In an earlier article, we discussed digital transformation and grocery shopping and in this article we delve further into the concept of the 5V’s in this important area of retail.
Over the past decade, the grocery industry has become increasingly complex due to online shopping opportunities and ever advancing, supply chains and data capturing systems. These systems therefore generate massive amounts of data Volume/Variety. Supermarkets and shops operate thousands of stores and hundreds of thousands of products which are sourced, shipped, stored and sold generating a great variety of datasets.
Grocery companies also hold a wide variety of demographic changes, promotions and campaigns relating to customers. For example, supermarket chain Tesco has currently over 16 million active members of the loyalty programme Clubcard, whereas thanks to dunnhumby customer data science platform, the company analyses unstructured data from customer emails, social media, video, and elsewhere that does not fit into their normal database. They have over 2.5 million followers on their Facebook page alone.
Supermarket Sainsbury’s run Teradata, which is a smart data warehouse to deal with variations driven by customer purchase behaviour. However, if customer data is treated individually, different data types associated with customers segmentation lead to an excessive amount of data. A large amount of logistics data of no particular structure is also generated by RFID (digital tag) technology, which provides the opportunity to help the planning process to improve the supply chain Volume. Asda supermarkets, unlike the loyalty schemes followed by its competitors, use lots of different data types from the quick checkout route, `Scan and Go’, to capture the customer behaviour in the long run.
Grocers also use rule-based decision support systems to keep the appropriate stock available during their distribution process Velocity. This is also helpful to reduce wastage of products with short expiration dates in order to avoid stock-piling or shortages (Veracity). They also gather assorted customer data from their loyalty programmes to make better decisions related to promotions, customer market segmentation and marketing.
Grocery marketing and big data in retail
Marketing for supermarkets is big business and uses volumes of varied data from grocery stores that are automatically shared with suppliers in real-time. Data is also generated by blockchain or other technology, grocery companies in order to provide real time offers to customers in store or online which demonstrates Velocity. This data can also improve estimations for decision making and Veracity.
For example, data such as delivery performance, weather forecasts, transactions, customer demand forecast, footfall and other historical datasets are now used to provide further insights (Value). This means for example, products could be marketed depending on the weather forecast. If data shows that a heatwave is approaching, automated digital advertisements such as Pay Per Click marketing campaigns can automatically adjust to highlight personalised adverts for swimwear, paddling pools or beer depending on the customer. Furthermore, the supply chain could automatically be adjusted to supply more of these products into stores so that they do not sell out during a heatwave.
The Future of Big Data and grocery shopping
The illustration from Brew Dog demonstrates their vision of grocery shopping in the near future.
As part of our commitment to sustainability, we are going to open 4 @BrewDog Drive Thrus.
They will be beer collection points, hubs for electric vehicle deliveries & hubs for closed loop, zero waste packaging such as growlers, mini-kegs & returnable bottles.#BrewDogTomorrow pic.twitter.com/UYLcBkFxQN
— BrewDog (@BrewDog) July 9, 2020
Whilst drive thru shopping is nothing new, what is new here is the underlying uses of big data. For example, each of the things you can see in the picture collects data. So for example, the digital screens with pricing can be adjusted dynamically based on big data. The windmill, solar panels and electric car charging and waste facilities all add to the environmentally friendly proposition, but equally, the hardware can link via the Internet of Things. Sales data and weather data can also dynamically adjust stock control and marketing campaigns including pay per click and social media posts. Aggregation of customer feedback data through surveys and social media posts can also be processed together and dynamically affect new product designs, open source beer recipes and crowd funding campaigns.
These just provide some brief examples of how grocery stores can use Big Data in retail, but there are many others. If you’d like to share your thoughts or comments, please drop us a line. You can also read more about the use of data to create value in our new book Strategic Digital Transformation.