In home furnishings retail, understanding and the ability to forecast seasonal trends is key to staying ahead in terms of your ability to capitalize on demand, respond to the right trend, and keep inventories right-sized. Furniture retail is no different than other retail sections. It is even more ‘seasonal’ than some other industries, where demand shifts from patio sets in the spring to cozy winter décor in the autumn.
With the evolution of data and intelligence tools, you don’t need a crystal ball to predict what’s coming — you just need the right intelligence.
By leveraging 2 types of data sets—your past sales data and eCommerce analytics—furniture retailers can accurately forecast seasonal trends by examining what has transpired before for their business and category-wide trends. Here’s how.
Run Deep Analysis of Historical Sales Patterns
Start by digging into your past sales data. Look at performance over the last 2–3 years, broken down by month or quarter.
For each category of products, identify which products or variants saw the most demand during specific seasons. For example, did white outdoor furniture sets sales surge in Q2 vs black outdoor furniture sets? Did TV Stands with built-in electric fireplaces perform better in the winter season compared to non-fireplace TV Stands?
Segment your data to uncover deeper insights, for example, by a combination of:
- Product type (Like coffee tables)
- Variant/attributes (Color, material, etc.)
- Price range
- Geography
This historical view helps you baseline expectations for each season, for planning, inventory purposes, and trend analysis.
Benchmark Year-over-Year Trends
Use the segmentation you performed before for historical data analysis to compare each segment’s year-over-year (YoY) performance, identify growth trends or anomalies, and successfully forecast seasonal trends.
For instance, if sales of light-colored dining sets, 9-piece maple wood sets, grew 20% last fall compared to the previous year, that may indicate a growing trend you can capitalize on for this type of product.
Once you see a positive or negative trend, investigate why, first by benchmarking to category-wide data. For example, if you identified a segment that dropped 12% in sales Year over Year, but the same segment dropped category-wide (including competitors) by only 3% for that time, it means the problem is likely on your end. Once you know there is an issue on your end, you can go deeper to see if this is related to supply chain issues, pricing, or marketing.
Understanding these patterns helps you make more informed decisions about products, pricing, marketing, and even sales channels.
Track Real-Time Behavior
Historical data is essential to setting a baseline of segments and data sets used to benchmark yourself to past performance and competitors.
Once you have identified your segments, methodically leveraging real-time analytics helps you understand what’s happening now and respond faster.
Data sources are the same: internal (Sales data, digital marketing analytics, etc.) and external (Such as category performance). Tracking these metrics can help you spot early signs of success you’d want to foster and find issues as they only begin.
For example, if you notice a spike in category-wide demand for office furniture with certain characteristics in late summer by leveraging eCommerce analytics, you may want to take advantage of it by better promoting your products that meet the same
criteria.
Identifying and responding to issues or opportunities in real-time could be pivotal to a business. This is a practice you’d want to master.
Benchmark Throughout The Sales Funnel
Once you have mastered analyzing sales results by segment, the next step is to expand the reach of the analysis to earlier stages of the sales funnel, primarily Discovery and Consideration.
The typical eCommerce Analytics suite includes all aspects of the funnel, but as you go wider/deeper, it takes more time and could be more resource-intensive.
The upside is obviously more sales; if you monitor and address issues that hurt your visibility or discovery on relevant eCommerce platforms, more consumers will find your products and consider buying them.
Here, the typical areas to analyze are:
- Visibility on eCommerce/retail platforms (Category rank, Search performance, etc.)
- Consideration phase factors and how others address them (Price, images, descriptions, titles, etc.)
Conclusion
Making a habit of benchmarking past performance against present performance and your performance against category performance is critical to forecasting seasonal trends for success today. This is especially true if you tap into data in real time and respond quickly to findings. By combining sales insights with real-time eCommerce analytics, furniture retailers can anticipate customer needs, optimize actions, and drive growth year-round.
In today’s fast-moving, data-driven retail landscape, this is necessary to win; otherwise, you may be outpaced by others.
