At the end of the day, retail data analytics is all about delighting your customers. As you already know, retail customers expect an engaging experience that's also personal – we all like to feel recognised and valued. Whether someone is shopping online or in a physical store on the high street, you can do a superb job of delivering an experience they'll value and inspire them to come back for more because you've taken the time and made the effort to use data analytics to learn their needs and habits. When you use this crucial retail technology-driven insight to boost customer satisfaction and also streamline your operations, you are onto a winner.
Retail data analytics helps businesses like yours retain existing customers, attract new ones through word-of-mouth, great reviews and recommendations, and can also enhance customer lifetime value or LTV, something that every good marketer knows is incredibly important.
So, at the end of the day, retail data analytics concerns the process of analysing large amounts of data to inform smarter decisions made on a reliable statistical basis. Manipulating that data helps you to improve your business operations and make more sales, take advantage of the power of loyalty and stock more of the items your audience loves best of all. It even helps perfect back-end processes like supply chain and inventory management.
We've seen a major shift in consumer purchasing behaviour recently. Google’s 'Zero Moment of Truth' research reveals how 70% of consumers research services online before buying in a bricks and mortar shop. This means retailers need to connect the dots to produce genuine insight into how consumers behave. Combine big data with business intelligence and the insights you get have real-world value.
How is analytics used in retail?
How is analytics used in retail? Retail data analytics uses big data technology to optimise the overall customer experience, make the supply chain more efficient, and manage inventory with a lot more confidence. So how does retail data help to drive sales, improve operations, and make the retail experience better for every single customer?
Analytics lets retail businesses identify valuable insights by measuring differences in demand across customer segments, knowing which items are key-value items, clustering their stores into zones, and looking into shopping behaviour across channels.
Big data means reliable data
Human intuition is deeply flawed, something you should never rely on in business. The conclusions you get from retail data analysis and the patterns you spot are statistically sound, because the data pool is so large and unbiased. Even a small retail business can gather surprisingly large amounts of data-driven insight quickly, then use the findings intelligently to inform future plans.
Retail analytics lets you derive valuable insights
Measuring the differences in demand across different customer segments can result in unexpected gems of knowledge. You might discover that your online customers buy entirely different items from your in-store customers, which means you can adjust your online advertising to encourage more online sales. You might realise that improving the in-store experience in a relatively simple way drives more purchases than you could have imagined. Maybe placing one product next to a product that complements it results in a higher profit. Unless you analyse the data, you'll never know.
Pinning down your key value items
A key-value item is something you sell at or below the original cost, which inspires customers to buy other, more expensive items. Obviously, this is a great thing to know, and take advantage of, because it can have a dramatic positive impact on your overall bottom line. There's more. Analysing your data can also allow you to manage your pricing so it maximises sales. You might discover that you sell 50% more of a product when you reduce the price by 10%, for example.
Retail data analysis lets you cluster stores into zones
This is one for retail businesses with multiple outlets. Store clustering means bundling together stores that are similar to each other into a segment and assigning stores with different characteristics to different segments. When you cluster shops by their common attributes and performance patterns, you can achieve better customer satisfaction as well as better supply chain efficiencies.
You can understand shopping behaviour across multiple channels
Where your business is concerned, every tiny piece of customer insight matters. These days it's relatively rare to find a customer who only buys via a single channel, but did you know that the older the consumer is, the more likely they are to shop via just the one channel? Younger shoppers are far more likely to use three or more channels to buy. When you analyse the purchasing dynamics of multi-channel shoppers – those who use two or more channels all or most of the time when shopping – you can benefit from a great deal of useful insight. You find out how your multi-channel customers behave, whether or not their behaviour is different in a meaningful way from mono-channel shoppers, and what their relationship is with the technology they use, and all of this helps hone your marketing efforts to a fine point.
How is data gathered?
Modern retailers focus on the total customer experience, end to end, which means data can be gathered in a multitude of ways. Every touchpoint matters, from bricks and mortar shops to apps, websites, telesales and customer contact centres, email marketing, EPOS data, Customer Relationship Management systems and actual footfall. When you harvest this data, analyse it for patterns and transform the knowledge into actions, it can sky-rocket your bottom line.
It’s no surprise retail is at the forefront of today's data-rich business models. Your challenge is to collect the right data, process it in the right way, then take appropriate action based on your findings. If you'd like to understand how KFP can help you tap into the incredible power of big data, contact us for a fascinating discussion.