5 Ways Big Data Can Help Marketers Increase Retail Sales
Retailers have always had the advantage when it comes to collecting consumer data - from what customers frequently buy to shipping addresses and data collected through customer reward programs. As retailers added on e-commerce, they gained even more data, such as which kind of advertising caused visitors to click on an item or what kind of marketing labels encouraged a sell. With big data platforms such as on-premise Hadoop or Hive as a Serivce, marketers can help retailers take their analysis of the consumer even further to not just increase sales but also cut down on unneeded expenses. Here are five ways how.
1. Eliminate Ineffective Draw-Ins
Promotions and a diversified product offering are supposed to draw in new clientele, but many of these enticements fall flat. Unpopular inventory ends up on the clearance rack frequently, and some discounts don’t seem to bring in the crowds looking for a good deal that retailers are hoping for. Often this is the result of failing to understand who the consumer really is and what their interests are. For some consumers, quality is more important than discounts. Big data analysis of both online and in-store sales can help to find these broader insights that can be hidden if you aren’t looking at the data as a whole.
2. Improve Social Media Marketing Tactics
Many resources are wasted on marketing tactics that are much like a shot in the dark. Yes, marketers have transactional information and can adjust messaging and channels based off of what they know about the customer, but knowing the individual interests of each customer and reaching them at the right time and place was well out of the reach of retailers. Big data, however, makes that personalized type of marketing possible by using social media and click stream data to find out what the individual is thinking about at the moment.
3. Discover the Most Lucrative Store Layout
Most retailers experiment with layout, placing certain products together and playing with the design and the atmosphere of the store. Of course, experimenting takes time and resources, and knowing for sure why a customer bought a product is difficult to say, even if you ask them. Some retailers have started using big data to collect information on where customers go in their stores and which products they look at. Some do this through the signal of a customer’s smartphone, following the signal from the phone as the customer moves around the store. By combining this with other factors, such as product placement, retailers can determine which layout seems to encourage customers to stay longer and purchase a product.
4. Find the Perfect Staff Number
Much like store layout, staff number is something retailers should also consider. Of course, hiring extra staff costs money, and if the customer isn’t wanting more staff interaction, it could be an investment that is causing more harm than good. Changing up staff also creates instability among employees, which also has its own negative effect on revenue in terms of staff’s work ethic and employee turnover. Rather than basing this decision on a manager’s observation or mere sales data, which can confuse causation and correlation, retailers can run controlled experiments to test customer to staff ratios before making any operational changes.
5. Optimize Prices
Price is one of those factors that doesn’t have an easy formula because it is not just about the wholesale cost of creating the item but also about its perceived value. Some brands have higher prices just because their brand is associated with the item, not because the item is particularly expensive to make. Getting the wrong price can take away from that brand value by cheapening a product, or it can deter a consumer from buying because it is overpriced. Big data can help to clarify the process by collecting data on a customer’s behavior online or tracking a customer’s movements in-store to see how certain prices influence whether the customer buys a product or not.
Overall, big data is a natural addition to retailers’ already abundant reservoir of consumer data. By stepping back and taking in the big picture, retailers can cuts costs and optimize their stores to be as profitable as possible.
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