Shoppers and retailers face the heat alike
While retailers compete harder to attract customers with differentiated propositions, creating right offers and winning loyalty is still a challenge.
The retail landscape is still uncertain— supply chain disruptions, inflation, and political instability across a few key geographies have taken a heavy toll on retailers and consumers alike. Shoppers are unhappy with rising prices, and, unfortunately, retailers haven't been able to do much to tame that trend.
For their part, retailers are trying to do their bit to ease the cost of living by lowering prices wherever they can but are under pressure to deliver commitments made to investors and shareholders.
Getting better at basics
A data-led approach can help retailers pull strategic levers that matter and win loyalty.
Customers want to believe that their favorite retailers see, hear, and recognize them. In the current macroeconomic environment, the simplest way to improve end-to-end profits without targeting consumers is to use a data-led approach across all business functions and processes to optimize KPIs and achieve better business outcomes.
Here’s how retailers can pull the levers that matter and win loyalty across all seasons by fine-tuning their merchandising strategies across assortments, pricing, and promotions.
Improve margins with customer-centric assortments
Retailers should curate customer-centric assortments while delivering the highest margin.
Winning at the shelf edge has never been more important than now. This is the time when retailers start to make changes to discretionary space in stores, to accommodate seasonal ranges such as back to school, Halloween, and so on. These lead us to the busiest time of the year where bespoke Christmas ranges become the focus of both discretionary and in-fixture space.
By failing to take a data-driven approach to range, retailers often focus just on profit and inadvertently delist products that are high priority for customers but have lower margins. This ultimately risks sales and customer loyalty.
Building a store-specific product mix goes beyond identifying cash and margin drivers. By leveraging AI-powered retail merchandising platforms with sophisticated models that consider all available data, retailers can achieve the perfect combination of customer-centric and profitable ranges. Key considerations for assortment optimization:
If I remove a product, what is the halo effect on customer loyalty? The product may be unique and of high customer importance.
Am I offering a wide enough variety before adding similar products?
Are the number of choices available engaging or confusing shoppers?
What is the customer mission for each store? Does the store range reflect that?
With AI, assortment managers can:
Simulate the impact of removing products on customer behavior and overall profit.
Determine how to group stores based on numerous variables, to ensure that similar types of stores carry similar ranges.
Build assortments aligned with store missions and customer needs.
Avoid product sales cannibalization by ensuring that new product launches fulfill a specific customer need rather than duplicating existing ones.
Analyze huge data sets to look for key patterns in consumer behavior and associate them with specific range or space requirements.
Make pricing transparent
Retailers should show transparency in pricing and rationalize promotions to make it easier to shop.
Despite inflationary stress on household budgets, the long-term outlook is starting to look more positive. While customers are on the lookout for great deals and every opportunity to stretch their finances, they are also cynical of retailers’ fair price promises. Retailers must be cautious about being perceived as manipulative by customers who are suspicious that they increase prices using the veil of war and/or fuel prices when a lot of products are unaffected by them.
The relevance of loyalty scheme prices is also being challenged, especially when some products follow a promotion on-promotion off cycle; customers know that membership prices defy logic when the same products are available at a discounted price a few weeks later. With more price comparison options available to them, there is greater awareness amongst consumers about what the standard price of products really is, and retailers would do well to consider an every day low price (EDLP) approach rather than follow an increase-and-decrease price plan, which puts customer trust and brand perception in jeopardy. Price and promotions optimization platforms will create win-win situations—make promotions impactful and memorable for customers and deliver value for retailers.
Key considerations for price and promotions optimization:
When should I run a promotion and for how long?
Which of my promotions are doing well?
Should I risk increasing prices for seasonal essentials to capitalize on the demand?
With AI, pricing managers can:
Simulate the impact of price moves on the customer basket.
Include cost-to-serve into pricing decisions.
Get recommendations on when to sell at full price and when to markdown.
Maximize pricing outcomes without compromising on the value proposition of the product or the brand.
Optimize space for enhanced CX
Retailers should build space plans that reflect each store’s footprint and drive a positive customer experience.
While planograms have always been key drivers of customer experience and sales during the peak season and beyond, it is foolish to adopt a one-size-fits-all approach. Stores have huge variations and maximizing planogram efficiency requires an accurate understanding of the store profile, customer needs and expectations, key category inputs, space, and sales forecasts. Wherever possible and justified, planograms should be store-specific.
The store layout should reflect how customers shop, and using their likely journey, adjacencies and sizes of departments and categories need to make sense. Space planning decisions must include key customer trends and shopping missions, and these should be regularly reviewed to ensure that external factors have not driven a change which needs to be considered.
Retailers must ensure that customers can buy what they want, when they want it, from their stores. Products must have enough space to ensure that they are available and stay on sale at peak trading. Rising fuel costs are forcing customers to reduce the number of trips they make, if availability in their chosen store is poor, they are likely to shop elsewhere.
Key considerations for space optimization:
Do the sales of a specific category justify its space on shelf?
Do key customer segments and growth areas have the right share of space?
What percentage of sales are shopped from the promotion location vs the standard on-shelf location?
How can I optimize the amount of space given to each department and category along with the right flows and adjacencies?
How do we plan stores to create the optimum shopping experience for customers?
With AI, space planners can:
Simulate the impact of space adjustments on categories and sales.
Get recommendations of optimal space for thousands of store-category combinations in minutes.
Simply rationalize store space decisions based on product adjacencies.
Make the most of available space without costly remodeling.
Challenges and success factors
Merchandising strategies need to be flexible enough to allow retailers to change course at short notice.
During the busiest time of the year, agility is key. Pricing analysts, category managers, and space planners must have clear executable strategies to ensure that key sales seasons throughout the year deliver the expected growth and profit. But retailers also must respond very quickly to changes in competitor prices or the need to change planograms and floorplans to accommodate new services or trending product groups.
Many retailers, however, do not have the framework or insights for taking key decisions quickly: What dictates a price change? When should you change it? Should you raise prices or markdown? And finally, how do you execute it across the business in an agile and seamless way? To compound things further, there are cross-functional impacts, contractual obligations, conflicting priorities and accountabilities, or often no agreed plan or processes to deliver change quickly.
While agility is key, having robust processes, systems, and governance with clear definition of roles and responsibilities is extremely important. A RACI (responsible, accountable, consulted, and informed) model that defines responsibility, accountability, and a process administrator who applies the process is essential to deliver change of any type.
AI for all seasons
A data-led approach is key to thrive during the holiday season and beyond.
This year’s autumn and winter seasons will be crucial for most retailers; they must grow sales and profit in a very challenging environment. They must win customer loyalty to guarantee strong performance. They must have the right products available at the right price throughout the peak holiday season. By leveraging the right technologies and simple and efficient processes and systems, retailers can optimize their ranges and prices quickly and give customers what they want, when they want it. An AI-powered, retail merchandising platform can be a key driver for making strategic decisions accurately and quickly.