Companies have long used data analytics to improve their business processes and functions, making it easier to capture and retain customers. But as more consumers crave personalised shopping experiences, more businesses are realising the value of leveraging big data through the eyes of the customer.
How to Capture Customer Data for Analysis
How to Put Customers First with Data Analytics
Leverage Data to Thrive in a Customer-Centric World
Big data is aptly one of the biggest game changers for modern enterprises. For the longest time, only massive corporations could afford the cost of data-crunching business intelligence tools. That’s all changed today.
If you’re a small business owner, you now have access to a wide range of data analytics platforms that provide insights to make accurate projections, streamline operations, and increase revenue.
The advent of data analytics also represents a turning point for customer relationship management. As customers gain more information about the products they want, their expectations on the quality of these goods and services, as well as the level of personalised service they receive, has also grown.
In an increasingly customer-centric world, it has never been more important for you to leverage customer data to improve your products, address customer feedback, and enhance your customers’ overall buying experience. Customers are now more discerning than ever, making it necessary for you to take the time to understand what they need from you.
In fact, research by McKinsey shows that organisations that use customer behavioural insights outperform their competition by 85 per cent in sales growth and at least 25 per cent in gross margins.
How to capture customer data for analysis
Today’s businesses can collect all kinds of data from virtually every touchpoint of the customer journey. For the average business with digitised touchpoints, sources of customer data analytics can include:
- Mobile app usage
- Social media habits
- Media consumption
- Website browsing history
- Purchase history
Not to mention that data can also be obtained through offline channels, such as point of sale systems, in-store environments, and loyalty card usage too.
All of this information comes together to create data fingerprints of your customers, which you can then use to serve them better.
How to put customers first with data analytics
To explain how and why you should leverage customer data, we take a closer look at how three areas of business can benefit from data analytics.
Retail is an especially data-rich environment where analytics can solve many of the core challenges faced by such businesses. These include:
- Finding ways to improve customer conversion rates
- Creating personalised and effective marketing messages that resonate with your target audience segments
- Predicting and avoiding customer churn
- Reducing the cost of acquiring new customers (i.e. customer acquisition cost) and improving customer retention rates
A simple but effective technique to drive conversions through analytics is to use customer data to predict future purchases. For example, if you’re an omnichannel retailer with an e-commerce store, you can analyse your customers’ browsing habits, wish lists, and purchase histories to identify their current and future retail needs and interests.
Using a product recommendation system, you can then upsell and cross-sell items that a customer would most likely be interested in based on their past shopping behaviours. For customers, the key benefit is spending less time window shopping, with the system providing each consumer with a showcase of products they’re most likely to buy.
This same dataset can also be used to craft targeted retail marketing messages and ads on a per-user basis. You can remind users about an abandoned item in their cart and notify members of your loyalty program that items in their wish lists are now discounted.
In offline retail, the emergence of people-tracking technology has given retail firms the ability to track in-store shopper movement, including where they stop, where they don’t, and how much time they spend looking at a display. This helps retailers adjust the layout of their displays based on foot traffic and popular merchandise, increasing the likelihood of a purchase. This also allows you to provide a customer-centric shopping experience designed to help shoppers find what they’re most likely going to buy in as little time as possible. Throw in a promotion or two, and your customers get to walk out of the store happy and better engaged with your brand.
Big data analytics can help foodservice companies understand customer needs and preferences by acquiring real-time information about consumer purchases, product inventory, and even competing establishments.
For example, a restaurant can glean valuable insights from their customers’ most preferred, most requested, and least favourite items on the menu. These insights can then be used for product development and menu improvements, giving customers better food choices and better-tasting meals.
Beyond customer satisfaction, customer analytics can prove invaluable in helping restaurants to make adjustments to their inventory and know when to stock up certain perishable ingredients over others. This results in customers getting fresher and better food (a godsend when only certain fruits and produce are in season) and lower food wastage.
Still on the subject of wastage, food and beverage manufacturers and suppliers can also use data analytics to identify and track their stores or locations that produce high waste levels.
These companies can then use this data to improve their supply chain, understand peak and off-peak periods in demand for certain food products, and plan aggressive promotions to sell products that are closest to their sell-by and expiration dates. Customers are much more conscious about their purchasing decisions and appreciate brands that are actively trying to be more sustainable and less wasteful.
Supply chain management is an area where big data science and analytics have obvious applications. According to Deloitte, 79 per cent of organisations with superior supply chain capabilities, also known as “supply chain leaders,” experience revenue growth that’s significantly above average. In contrast, only 8 percent of organisations with unoptimised supply chains, or “supply chain followers,” experience the same level of revenue growth.
This isn’t surprising; for decades, statistics and quantifiable KPIs have been the guiding hand of supply chains. However, the kind of analytics that’s changing the game is real-time data analytics. It’s also helping customers enjoy better prices and better product quality.
For example, a pharmaceutical company that integrates big data into its supply chain can closely monitor inventory levels, shipment movements, and gross sales in real time. All of this data can then be used to conduct real-time price optimisations based on current supply, demand, and anticipated trends, ensuring that customers get the best prices without hurting the company’s margins.
That same company can also use barcodes, sensors, and RFID tags to track and improve supply chains with highly-perishable and temperature-sensitive goods, reducing wastage and ensuring these products make their way to the consumer undamaged, in its original form and is safe to use.
Leverage data to thrive in a customer-centric world
Companies that ignore customer data analytics are leaving money and significant improvements to their customer service on the table. Obtaining the right data and taking the time to analyse what it says about your customers gives you a deeper understanding of your customers’ purchase habits, lifestyle preferences, and pain points.
For years, companies have advocated the use of analytics from a business-process standpoint, using data to acquire more customers, retain those customers, and win them back should they leave.
The customer-centric approach, however, looks at how data analytics can give consumers what they want, whether it’s better food selections, deals and discounts, or better overall customer experiences. It means putting yourself in your customers’ shoes and making decisions based on how they think, and not in the context of your business processes and goals.
At DataVLT, our easy-to-use analytics platform will help you discover actionable insights about your customers, giving them a personalised buying experience that so many of today’s consumers want. Contact our team today to learn more.
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