A home retailer with a 3D planning solution has access to a rich mine of highly detailed data on how consumers are envisioning their dream homes.
Big data’s impact on the home retail industry has been transformative. Faced with the dramatic changes of new channels replacing old, increasing costs, flat customer confidence, and discounting and intense competition, retailers have embraced analytics in a big way. With the market growing at 7.7% per annum towards a global value of USD 316 billion by 2024, the stakes are high: consequently, big data helps forecast demand, improve customer service and boost sales.
However, a fast-changing consumer who spends most of the purchase cycle online, comparing prices extensively and using social media for validation even before purchase, means retailers must understand trends before they materialize if they are to stay ahead.
Trend cycles becoming shorter
One of the most significant challenges home retailers face today is the accelerating momentum at which trends change. Even just a few years ago, a small number of staple collections with an annual portfolio update was the norm across the industry. This is completely different to today’s reality. New ‘looks’ and portfolios must be released every season, or even more frequently, to capture consumers’ interest and tempt them in-store, where they can then browse, compare prices, and envision their dream designs.
As a result, home retailers need to work like the fashion industry: spotting emerging consumer preferences, predicting how these will play out into the major home retail trends of the coming season, and creating collections accordingly.
The good news is that big data analytics can do a lot of the heavy lifting in this area.
Understanding big data
Big data is a buzzword that has been around for almost a decade. What exactly is it?
It is the combined collection of traditional and digital data from inside and outside the company, which is generated through the interaction of millions of consumers with the business. When properly analyzed in conjunction with today’s machine learning techniques, this data is a rich source of insight and continued discovery about trends within the company and out in the marketplace.
The billion-dollar question facing every home retailer is: “What are the must-have items in the next forecasting period?” They traditionally invest millions in employing designers, conducting market research, and combing the world to answer this question.
No doubt, when businesses were smaller, human analysts or market researchers could periodically produce reports on trends and changing consumer preferences, but web-scale businesses are simply too large and fast moving to be broken down in this way. On the other hand, it is well within the capabilities of an intelligent algorithm: and this is exactly where big data comes into its own.
Harnessing the power of big data to predict home retail trends
Here are some ways by which home retailers can make use of big data to predict trends.
Forecasting algorithms can be deployed to unpick trends from web search and browsing habits and even the activities of consumers on the company’s website or store to identify which types of colors, products and design styles are causing a buzz. Are consumers going for single colors in upholstery, or are they looking for vintage looks, or is it the period for geometric patterns?
Another popular technique is sentiment analysis, which allows home retailers to understand the context in which certain products are being discussed online, especially on social media. For example, let’s take a minimalist look: is it being called ‘clean’ by most consumers, or ‘bare’? Is a particular room layout being described as ‘cozy’, or ‘cluttered’? By working out whether the prevalent sentiment for a particular range or look is positive or negative, home retailers can make strategic inventory, pricing, or promotional decisions.
While it is expensive for retailers to understand browsing behaviors of shoppers in-store, these trends become transparent when consumers shop or browse for products online. It is possible for retailers to not only understand the types of products for which consumers are searching, but also the types of terms they are using to find them. Is there a spike in searches for ‘tower shelf’ or ‘feature wall’, for example?
A home retailer with a 3D planning solution has, at their fingertips, access to a rich mine of highly detailed data on how consumers are envisioning their dream homes. From furniture placement, to color choice, preferences in lighting, and much more: home retailers can apply simple analytics techniques to the types of designs that consumers create to understand what current products/product sets are getting the most attention from consumers, and then make appropriate recommendations. This type of predictive analytics can be lucrative. Amazon’s product recommendation engine drives 35% of cumulative company revenue.
Pricing strategies can be informed by how consumers select and deselect products into their designs at the budgeting stage. An omnichannel solution, where consumers are able to go into the store with their designs and consult with in-store experts, can make this data even richer, as sales staff will be able to assist with ‘get the look for less’ strategies.
By analyzing how consumers group products in their designs, in-store merchandising strategies can be refined, and specific promotions can be developed to make it easier for consumers to achieve the look they have in mind. The results can be surprising: Walmart famously discovered through analytics that shoppers tended to buy beer at the same time as nappies, for example. In a home retail scenario, insights might not be as surprising, but they can certainly reveal new product groupings.
It has been estimated that, at present, businesses use just 5% of all the big data in their possession. It is undeniable that a lot of the data generated by a business might be overwhelmingly difficult to analyze, but focusing on highly rich insights, offered by technology such as 3D planning solutions, can help home retailers predict trends and make important business decisions in time to address trend-conscious consumers.