Despite the sluggishness of the so-called recovery, industry experts are predicting a pickup in the housing market early next year. This will have broad implications for retailers of furniture, furnishings, and other home items. How retailers can prepare for this opportunity is a favorite topic for the data collectors and analysts at MasterCard Advisors, the merchant services arm of MasterCard. Here are some ways in which furniture retailers, who’ve gotten pummeled in this recession, can capitalize when the upswing occurs.
While sluggishness in the housing market and changes in the retail environment are leaving furniture merchants feeling besieged, one development is showing great promise as a powerful tool for remaining competitive. The increased use of cards and electronic payment has led to an explosion in the amount of data now available that that can be analyzed to better understand consumer preferences. A recent study by MasterCard Worldwide showed that the data warehouses at the cutting edge of this development are managing over 100 terabytes of information, and are seeing those numbers grow by 20 billion transactions, every year.
A spectrum of ways to analyze big data
New forms of data analysis can be helpful at every level, from a broad macroeconomic view to in-store insights. Most furniture merchants have an intuitive understanding that their business is linked to home sales. Now, though, analysis of transaction data can quantify the connection between the housing market and furniture results (both sales and price level). This has allowed retailers to plan their stock needs more accurately than ever before. With the right experience and analytic tools, this kind of transaction data can be examined to provide insights across a range of levels, from broad sector-wide trends to the regional market to consumer segmentation, at a level of accuracy and speed that was previously unimaginable.
Insights drawn from transaction data can work on a local level. A merchant who wants to explore possible store locations can use it to get a granular picture of both the consumer profile and spending behavior of the population by zip code, as well as current furniture sales in the overall region – information that is invaluable for decision-making. And for an existing store, a broad data set can be used to benchmark performance against the results of a local competitive set.
What kinds of questions can transaction analytics answer?
In the case of one chain, big data analysis helped measure the extent to which two of its stores were cannibalizing each other’s sales. A study of the customer zip codes showed that rather than going to the store near their homes, many people were patronizing the location nearer their places of work, and were visiting the store either during lunch or on their way home. This information led the chain to adjust their marketing and stocking strategies accordingly.
Some of the most exciting work now being done involves understanding and building predictive models for customer behavior. An analysis of the channel and shopping preferences of different customer segments can drive efficient and effective targeted marketing strategies to bring likely customers to the store or website. Once there, knowledge of that customer’s profile will suggest directions for up- or cross-selling.
Merchants already know that major life events are triggers – a move, for example, or a new child, or a home renovation. Current research is identifying the spending patterns that would indicate when such an event has just happened – or even better, is likely to happen. Imagine what kind of marketing strategy you could build around that kind of information – and information is power.
Why make the shift to data-analysis driven marketing?
These new marketing solutions are particularly important for the retail furniture industry. In the best of times, the furniture industry faces challenges that most other retail sectors don’t. Stores have to be large, so merchants are both saddled with substantial real estate costs, and generally find themselves in “destination stores,” away from the malls where they can pick up passers-by. Further, furniture may be one of the most infrequent purchases people make, which means that retailers cannot depend on repeat business – even a satisfied customer may not be back for years.
And these are far from the best of times. Furniture stores face are under pressure from a several directions. First, of course, is the economy. Not only is furniture a major investment for most people, but, unlike cars or appliances or electronics it rarely breaks down, wears out, or becomes obsolete, so in times of economic uncertainty it is a purchase that can be postponed.
Second, the competition has intensified. Increasingly, large multi-category stores are carrying furniture. While they may not have the selection or the range of the furniture-only retailer, they do have the advantages of a regular stream of customers and a level of insulation from economic fluctuations. A second competitive front is opening on the internet, with the growth of online furniture stores. These businesses do not need to carry either the real estate burden or the inventory of brick-and-mortar stores.
Finally, the growth of online shopping is linked to important shifts in consumer behavior as well. Shoppers may not be wandering through the malls, but they are navigating a variety of social networking sites and daily deal messages as well as the Internet. Savvy merchants are making use of all of those tools in their marketing strategies. Additionally, they’re putting up interactive websites that allow customers to see how a piece would look with different finishes and fabrics, and even place it in a photo of their room to see how it would work.
These are some of the reasons why detailed understanding of the economy, the marketplace, and the consumer is so important these days. Furniture has never been an easy market, and it’s not about to get any easier. But while the business may not be getting any easier, using analytics in near real-time drawn from transaction data can help make it a lot more profitable.