Retailers are making use of complex algorithms that help them identify patterns in consumer spending behavior in order to increase sales.
It’s thought consumer retail spending could reach nearly $27 trillion next year, thanks to rising population and higher levels of disposable income in many countries.
Despite economic turmoil in many parts of the world, retail has been growing for the last few years. It has done so with a highly competitive landscape chasing very low margins; the winners in the expanding retail market are those who can gain an edge over their competitors.
Big data and its associated analytics practices are being employed by retailers as tools to help them get ahead.
Retailers are making use of complex algorithms that help them identify patterns in consumer spending and associated behavior. Machine learning is enabling them to analyze huge data sets to identify opportunities, opportunities that their competitors have perhaps yet to spot. For huge retail chains, real-time information processing is giving them visibility into any issues that arise, while helping them respond quickly to minimize losses and maximize gains.
The big data approach is revolutionizing the retail industry, reorganizing decision-making and improving efficiency.
Tight margins often mean that retailers need to operate on a large scale in order to achieve significant profits. Real-time analytics is enabling retailers to implement optimum pricing. This can be highly beneficial to retailers as it helps them adjust pricing in real time to maximize their profits. Big data analytics also helps improve the efficiency of the supply chain to reduce the likelihood of selling out of popular items.
This is helping fashion retailers respond quickly when an item proves popular, making sure the right item in the right color and size is in store. It’s a way to make the most out of their existing capabilities to increase revenue. Scale is no longer the only element that retailers can compete on; retailers can now introduce new efficiencies that can also help them get ahead.
Extreme segmentation
It’s already extremely common for retailers to use segmentation in their market approach. Segmentation groups the retailer’s target audience into clusters based on characteristics such as their age, gender, household income, life stage, and geography.
Big data technology enables retailers to take segmentation even further.
It’s possible to add in a huge number of factors that help understand consumer behavior with even greater precision.
This helps retailers create much more specific segments, often based on the habits and behaviors of very small numbers of consumers. These segments can be constantly tailored and updated using real-time data on how the consumer behaves. Where data is available, it’s possible to use big data technology to combine data from online and offline sources to really get a picture of the consumer across multiple retail encounters.
These complex analytics show not only who the customer is now but also how their needs and expectations might change over time. For brands that wish to retain customers as they mature, it’s a way to understand how the individual’s needs evolve.
It’s an approach that is changing retailers’ understanding of the consumer, as well as deepening the relationship with them. It’s leading to a new level of personalization, enabling retailers to suggest products to a consumer and tailor offers specifically to them.
This has the potential to assist with customer retention and increase conversion rates. Understanding which patterns of behavior are associated with particular buying habits should also help reduce marketing costs. As brands get a better view of their customer, marketing efforts become better targeted and more effective. This should, in theory, help reduce the costs of marketing.
For their part, consumers are increasingly expecting a greater level of personalization based on their existing relationship with a retailer.
There’s also an interactive element to this consumer profiling. Whilst Facebook gathers a huge amount of data on its users based on their behavior, the platform also solicits information directly from them. It’s done this by dropping a module into the person’s newsfeed asking “how interesting is this story to you?” to gauge the type of content it should show them in future. Other retailers are finding different ways to talk directly to the customer about what they want, in addition to gathering and analyzing data about them.
Data analytics is one way for retailers to begin to understand new markets they venture into. For businesses trying to reach new audiences in an unfamiliar culture and language, data analytics can be a helpful way to understand the consumer as well as their interactions with the business. This method is one way to create a more accurate picture of an audience that may have very different habits than the home market.
Competitive insights
Big data analytics does not just focused on the customer. Retailers are also using this approach to yield insights about their competitors and other forces affecting the market. Product pricing can be tracked in real time across all key competitors, helping the retailer adjust their own pricing accordingly. Retailers track other elements including offers and promotions that are being run in their market by other retailers.
Other elements can also be included in the analysis. For ice cream or seasonal clothing vendors, this might include aspects such as the weather temperature. For certain purchases, the weather is likely to affect retail behavior. This can then be included into analysis that will inform the supply chain. For some products it is also appropriate to track the cost of other goods. Car manufacturers may find it important to consider the cost of petrol and interest rates as these are both likely to affect car sales and how customers choose to buy vehicles.
Retail remains a tough business to thrive in long-term. Big data analytics is a significant cost and organizational hurdle for retailers to implement, but once embedded into the organization, it can yield results that offer a significant competitive advantage.
It’s important that businesses implementing big data practices make effective use of the insights offered.
Big data can offer valuable insights that can be used to inform decision making. Implementing an analytics program needs to be accompanied by organizational change so businesses can act effectively on the data insights they are accessing.
