Loyalty programs used to be the primary way retailers could collect data about their customers. But opportunities for data capture have increased as more businesses embrace an omnichannel model. Coupled with the data collected through digital marketing tactics, retailers can now pursue new avenues to increase their sales.
Data can be collected from in-store interactions via a customer loyalty or credit card, through the retailer’s app, their website, and through marketing emails and digital ads on social media or Google.
In 2020, the global big data analytics in retail market size was valued at $4,854 million. By 2028, it’s projected to reach a massive $25,560 million. Retailers can collect data from every interaction the customer has with their brand, online or in-store. But to provide true value, you need to analyze it.
Big data analytics gives retailers a much richer understanding of their customers and their shopping habits. In this article, we’ll discuss four ways retailers can use information collected by big data analytics to boost their sales.
The customer needs to be at the heart of every decision a retailer makes.
Dividing your customers into segments allows you to identify key characteristics about them. Here are some examples:
Segmenting your customers allows you to target them more effectively. For example, instead of offering every customer a money off coupon for a specific brand of shampoo, you could offer it only to those who regularly buy that shampoo. Alternatively, you could offer it to customers who do not buy frequently into the category.
Everyone wants to be treated as an individual. And personalization is a key way to connect with your customers in an omnichannel world - 80% of consumers are more likely to purchase when brands offer personalized experiences.
Through your retail POS data, you can collect data such as what products a customer is buying, how much they’re spending, and how often they shop with you. Through receipt marketing, you can target promotions which are customer-specific, based on what you know about them. This increases the likelihood that they’ll use the promotion.
Online analytics can also help retailers offer personalized product recommendations to their customers. Analyzing product browsing history or items in abandoned shopping carts can give you a good indication of what a shopper is interested in.
Amazon is famous for its use of personalization. Through its recommendation algorithm, it personalizes its online store front for every customer, based on customer interests. It’s so effective, 35% of their sales are generated through recommendations.
Knowing what your customers buy means you can cross-sell similar products to them at the time of purchase. For example, if they’re purchasing shampoo, they may also need conditioner. Or if they’re buying a pre-packaged sandwich between 12pm and 2pm, that may be the perfect opportunity to offer them a drink and packet of crisps as part of a meal deal.
You can also use market basket analysis to uncover more opportunities for cross-selling. Market basket analysis is a data mining technique used to uncover customer purchase patterns. For example, people who buy red wine are also likely to also buy cheese. This type of analysis can also uncover further opportunities for cross-sell.
The types of data you can collect in-store is huge. From your best-selling products, the busiest times of day, and how many customers return to your store in a week, the information you can uncover is almost unlimited.
Make the most of this data to tailor the in-store shopping experience to meet customer needs. For example, if you often experience a lunchtime rush, make sure there’s adequate stock of your most purchased lunchtime items such as sandwiches and drinks.
But remember, retail is an increasingly omnichannel industry. Customers may compare the prices online and in-store or view the product in-store before purchasing it online.
View the whole customer journey, including every touchpoint they have on and offline, as one holistic experience. This will improve customer satisfaction and loyalty, making them more likely to continue shopping with you.
Big data analytics isn’t necessarily easy. The mammoth amount of data you can collect means it can quickly become overwhelming. That’s why it’s important to have an analytics team who will help you make sense of the information.
But get the analytics right, and you’ll uncover lots of opportunities to create a better customer experience, improve your marketing campaigns, and ultimately, drive more sales.
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