oculus360 - Dec 20, 2018

How To Optimize Your Merchandising with AI & Machine Learning

The marketplace isn’t what it used to be, but that isn’t a bad thing when a brand knows how to appeal to its target audience. What was once a relatively straight shot down the commerce highway is now filled with potholes, detours, and sudden twists and turns. If a brand fails to adequately heed those pitfalls, its journey towards conversions can quickly become messy, complicated, and imprecise.

Download Case Study: AI-driven Merchandising Optimization for Top 100 Retailer

However, no matter how complex or fickle the modern marketplace becomes, companies are not helpless in their efforts to identify and engage their target customer segments. Merchandise optimization in a vast digital landscape has become a critical component of realizing lasting success, particularly when fueled by the forward-looking and in-depth insights provided by AI and machine learning.

As vanguards in AI-driven insights to guide a brand’s message and product offerings based on the voice of the customer in online conversation, Oculus360 (O360) is lending some key best practices for merchandise optimization in the digital age. Specifically, we offer brand insights and innovation by leveraging AI and machine learning in their merchandise optimization strategies, including six critical factors to maximize engagement and conversions. By placing particular attention and emphasis on these factors, brands can develop the competitive advantage needed in an unforgiving environment.



Given the sheer scope of the digital landscape, it can be easy for an individual consumer to get lost in the mix. Personalization is a broad yet powerful concept that can counteract that corrosive dynamic by creating a unique shopping experience that caters to an individual’s specific tastes, expectations, needs, and preferences. Like the attentive salesperson that assists a shopper in a brick and mortar setting, a brand that tailors the experience to the individual, rather than trying to appeal to a larger, more general demographic and set of affinities, is much more likely to find sales success and enduring brand loyalty.


When a brand adopts a personalized approach in its copy, creative, and overall marketing message, it allows the audience to both notice and act upon its unique voice. Using O360’s work in personalization to demonstrate the importance of the concept, Lincoln Motor Company recently used insights culled from O360’s AI-driven platform to highlight the specific attributes favored by their target demographic sets. Such a strategy yielded significant and immediate results, with 4% increased revenue and 5% higher click-through rates stemming directly from a personalized, curated voice in their campaigns’ digital ad content.


AI Drives Personalization

Similarly, Amazon estimates its recommendations engine drives up to 55% of total sales based on its ability to tailor those recommendations to the individual. Before algorithmic modeling and AI, such an effective technique was simply impossible due to the overwhelming amount of data needed to refine the results into something insightful. Brands can now use the intelligence and computing horsepower of advanced technologies to identify a shopper’s preferences and mold the customer journey with messaging that speaks directly to the individual.




As alluded to in our Amazon example, recommendations are a subset of a broader personalization theme that, given their impact, deserve a closer look at their specific benefits and abilities. When a retailer provides the individual shopper recommendations based on their personal tastes and shopping behavior, they are exposing the consumer to products and ideas that the shopper would otherwise rarely, if ever, think of or discover on their own.

Traditionally, retailers were forced to make recommendations based on general audience preferences since they lacked both the consumer data and the ability to generate recommendations according to individual tastes. Brands pushed bestsellers out of simple popularity rather than intimate knowledge of personal likes, dislikes, and expectations. However, those days are now long gone and brands can, in essence, create customized individual campaigns and make recommendations based on those personal preferences, a strategy that was previously impossible.


Machine Learning Personalizes Recommendations

As demonstrated by Amazon’s recommendations engine, machine learning can now generate recommendations that are far more compelling for each customer, drastically improving the chances of them making a purchase. A machine learning platform can analyze consumer data from a variety of different channels and process it using algorithms that categorize the data in real time by occasions of use, demographic segments, customer affinities, or virtually any other audience variable. Just as importantly, these tech-driven recommendations can evolve over time as the customer progresses down the sales funnel, making them continually pertinent to the environment and circumstances. In other words, one size doesn’t have to fit all anymore.



Visual Merchandising

Retailers have long understood the importance of the look and feel of a sales floor to appeal to the customers. Just like the meticulous attention brands devote to nearly every facet of the ambiance and visual flow of a physical sales space, visual merchandising is quickly taking root in the digital environment as well. Now working hand-in-hand with its brick-and-mortar counterparts, visual merchandising allows a brand to develop an identity and narrative, using such concepts to maximize engagement and, when coupled with other facets of merchandise optimization, conversions as well.


Visual merchandising plays on our natural preference for visual stimulation and information. As recent research shows, 67% of online consumers state that product images play a pivotal role in their purchasing decisions. How a brand displays inventory to the consumer and incorporates design elements into their digital storefront develop the emotional bonds between brand and consumer that are so essential in driving conversions. Those bonds begin to form a mere 100 milliseconds after a consumer first glimpses a website, further emphasizing the absolute importance of effective visual merchandising in digital commerce.


AI Helps Choose the Ideal Digital Assets

AI is already helping online retailers maximize the impact of their visual merchandising efforts by pairing content and digital assets like graphics and embedded videos to individual preferences. In the near future, the continued advancement of different optical technologies will allow brands to leverage 3-D images and even elements of VR and AR into their digital storefronts, once again propelled by AI-powered insights and visuals tailored to personal affinities.


Product Assortment

Knowing the precise assortment of products to carry that best appeals to customers and maximizes sales has long been the holy grail for retailers. Traditional analytics can analyze scan movements to help a retailer have a general idea of what to stock but lack the ability to look down the road with any degree of clarity or accuracy to foresee trends and shifts in demand. Put another way, those traditional methods relied on past sales data to inform future product inventory decisions based on rudimentary techniques.


In the digital marketplace, the ideal product assortment requires a far more prescient, insightful perspective to engage the audience and anticipate consumer demand and interests. Likewise, it must also take into account the competition’s product offerings, either mirroring or using a contrarian approach depending on the goals and circumstances. Such abilities require both consumer data and the tools necessary to draw insights from them to guide a brand’s product assortment.


Using Technology to Guide Inventory

AI and its machine learning subset bring a range of benefits to brands in their constant search for product assortments and inventories that best lend themselves to their audience’s needs. First and foremost, predictive analytics aren’t relegated to historical scan movements but utilize actual consumer behavior and purchasing decisions to predict future trends and shifting audience preferences.


The combination of AI, consumer data, algorithms, and processing power act as a crystal ball of sorts, giving retailers an accurate tool to look into the future to anticipate the most effective product assortments rather than merely reacting to changing consumer needs after the fact. AI also offers retailers a far more accurate ability to categorize products by different attributes, occasions of use, styles or themes -- nuanced associations to make the ever-important search process within a digital store more reliable and capable of matching the audience’s needs.



Product pricing might not be the sole determining factor in conversions, but it’s certainly an important one. In the digital marketplace, the typical consumer will search for a product across a range of different outlets to find the best for their price range in conjunction with consumer commentary and reviews.


Like product assortment and inventory factors, retailers have traditionally used historical sales data and static forecasting models to establish price points. However, in this hypercompetitive, extremely dynamic digital sales environment that shifts by the second, brands need a far more accurate way to set pricing, one that accounts for both consumer expectations as well as competitor price points.

Dynamic Price Modeling Through Machine Learning

Of course, pricing is another area of retail operations that can greatly benefit from technology. Machine learning platforms are already on the market that create price optimization models and find an optimal balance between product pricing, consumer demand, and profit margins. These models, once again, use algorithms to blend real-time pricing data with forecasted trends to let companies adapt on the fly while also constructing future pricing strategies. Since they collect data from the company and the marketplace itself, these intelligent models flex with the market and become more accurate with additional iterations.



Promotions take many different forms in physical stores as well as digital markets. Whether a price promotion, exclusive product offering, or any other type of promoted benefit, they are specifically designed to attract consumers by providing them with a distinct advantage that isn’t necessarily available elsewhere.

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The sheer competitiveness of retail in the modern age, particularly in the digital space where information and audience preferences change so rapidly, places additional burdens on brands to create promotions that positively impact metrics like click-through rates, ROI, conversion ratios, and many others. However, since the underlying goal of any promotion is ultimately to improve the bottom line, brands must always weigh the benefits provided to the consumer by the promotion against the effect it has on revenue and profitability. In such a fluid sales environment, this notion is rarely simple or straightforward to achieve.

AI Balances Profit and Appeal

An approach that leverages AI’s ability to collect data and machine learning to analyze that data is already proving to be a powerful approach to creating effective promotions that benefit all involved. These advanced methods find equilibrium points that maximize both profitability and audience appeal, mitigating risk for the brand without sacrificing impact on its targeted consumer segments.


O360 Lends Powerful Insight

AI and machine learning are already instrumental components in finding retail success in the digital marketplace, a trend that will only grow in importance with time as merchandise optimization ultimately separates successful brands from also-rans. However, even these advanced and powerful technologies will fail to provide a significant improvement in conversions if pertinent, timely consumer data does not drive them.

READ NEXT: Using AI to Analyze Your Brand's Category

This dynamic is precisely where O360 lends so much value and strength to the equation, mining the vast reservoirs of consumer commentary spread through the online environment to fuel the platforms that improve each of the discussed factors within merchandise optimization. Referencing our original metaphor, if applications leveraging AI and machine learning are the vehicles that help brands reach their goals along the conversion highway, O360 provides the fuel that propels those vehicles in the first place.

Written by oculus360