Retail isn't an easy place to be these days. The environment is omnichannel and ever-changing. Competition is rising and retailers are struggling to understand how to best meet customers’ merchandise preferences.

Fortunately, analytics are driving profitability and market share for smart retailers. Let’s take a look at the four hottest trends in retail analytics:

Trend 1: Volume and climate clusters are so #oldschool

Remember when the only option for shopping was getting into your car and driving to the store? Those days are long gone. I typically window shop from my couch in comfy yoga pants.

To understand your customers' merchandise preferences, it's critical to break down the silos of channels. Look across channels to understand what products individuals are purchasing online, what products they're purchasing in store and understand what products have opportunity within a local market.

Retailers need to use analytically-driven clustering techniques to cluster local markets or trade areas by similar merchandise preferences and selling patterns.

Trend 2: Attribute analytics are all the rage

Merchandise attributes are ways to describe products such as color, sleeve length, silhouette, fabric and so forth. The merchandise attributes that are analyzed by retailers can vary by area and also range in number.

More attributes are not always better. Analyzing too many attributes can become overwhelming, and, quite frankly, the juice may not be worth the squeeze. Correlation analysis can be used to understand which merchandise attributes are correlated. This can help narrow the number of attributes down to a manageable size.

But what draws customers into the department and what drives their purchasing decisions may also be understood through attribute analytics.

I know I always head toward the Jessica Simpson heel display because I know the price point is right, the styles are cute, and the shoes are actually comfortable. Unfortunately, I can't wear heels that aren't comfortable, no matter how cute they are. I end up looking like a baby deer trying to walk in stilettos. It’s not a good look. Now, I know that brand is driving my purchasing decisions for heels, but what about every other customer?

You can use analytics to statistically determine what merchandise attributes are the most important to customers -- and then, within those attributes, the specific values of those attributes that have opportunity.

We can understand, for example, whether it's brand or fit drives customers’ decisions and then, within that, which brands or fits have the most opportunity by cluster.

These insights can drive assortment planning decisions and help you to move from product-centric to customer-centric assortments.

Trend 3: Manual sales planning is out, statistical forecasting is in

Throw out your post-it notes reminding you of previous events. Clear your calendar reminders for past promotions. Let your merchants focus on the art of analytics. Using a statistical forecast allows retailers to predict future demand. For new items and previous items, analytics can determine the significance of events and promotions and account for outliers and anomalies in the data.

The statistical forecasting process automates the ability to account for promotions and holidays, giving your merchants back more time in their day!

The statistical forecast has also been proven to increase accuracy by over 50 percent. The increase can equate to a significant optimization in inventory, which leads to an increase in gross margin and profitability.

Trend 4: Steer clear of the loser loop

You can have the most amazing assortment, completely tailored to your customer preferences. But if you don’t have their size in stock, it's an epic fail. Traditionally, historical point-of-sale data has been used to determine what the size curve should be of a given product for locations. The problem is that if you didn't stock the size, then you didn't sell the size, ultimately creating a loser loop.

The other aspect here is the use of imputation techniques. Imputation sounds like a word out of your high school biology or genetics textbook, but don’t be scared. In statistics, it’s the process of replacing missing data with values.

In this example, imputation is replacing missing sales due to a lack of inventory. Each time a store sells out of a size, it creates what is referred to as an out of stock. When stores have items out of stock, they miss out on sales. Sometimes this concept is referred to as lost sales.

However, more advanced statistical imputation techniques can create a much more accurate result. For example, being able to take into account when in the product life cycle a stock-out occurred gives a much more intelligent perspective.

If the location didn't have inventory on hand, then determining where in the product life cycle the item was at is the next step. If the item was at a meaningful point in the life cycle, then the lack of inventory could have affected the demand.

This means that if the item was about to be marked down to clearance the next day, the fact that the item was out of stock is actually a good thing. However, an item being out of stock during the beginning stages of the life cycle weighs more on the effect of the demand.

Enabling analytics to determine what the true size demand is for a given product down to a location level, will ensure that your customer finds the right product, in the right size. This process not only creates a better customer experience but also avoids markdowns, increasing your overall profitability.

To learn more about creating a data-driven assortment, as well as additional insights in to retail analytics, come see me at the SAS booth at NRF 2017! And check out my new book Style & Statistics: The Art of Retail Analytics, launching November 30, 2017!