How Advanced Analytics & AI Can Help Category Managers in Retail

Want to select the right assortment based on your customer's preferences data? And set the right price and discounts compared to your competitors on top of that?

Continue and discover how Advanced Analytics and AI can help you make data-driven decisions in your category management.

The growth of research in the domains of data science, machine learning and AI enables data-driven organisations to take things even further. These new technologies and techniques enable a new way of extracting patterns from data, of deriving insights from data.

Insights that help data-driven organisations to better understand what is happening and why it happened. Insights that permit a retailer to predict what will happen, anticipate and prescribe actions, applying ever more advanced analytics.

This article focuses on 3 jobs-to-be-done in Category Management where Advanced Analytics & AI can create the most impact with your data:

1. Hook customers to your categories of choice, by applying advanced analytics and AI

Growing categories in a changing retail landscape

Ever since the dawn of category management, retailers have learned the importance of growing a category, not merely brands. The importance of teaming up with manufacturers. With the global decline of the power of brands, the latter has become even more crucial.

With a growing amount of sources to buy from, customers have shifted tactics. Shoppers more and more pick different sales channels for different categories, in which they hope to find a product that helps them get a specific job done. Where one household used to buy the bulk of its supplies in one or two physical stores, said is now scattered amongst various online & offline channels, providing different levels of convenience, pricing strategies, assortment sizes or delivery options.

As such, known touchpoints are vanishing and retailers need to take necessary actions to recreate vibrant and dynamic categories able to lure people in. There is a trend towards adapting physical stores to the requirements of a store’s individual shopper base. Creating such individualised store assortments makes the effort for a retail organisation even more challenging, as the complexity in procurement, inventory management and adjacent functions intensifies.

Increased data-driven category management is essential to empower category managers to excel at their task. Current business practices push category managers towards managing short-term decision-making, as lack of time, scattered data and legacy systems impede developing a new practice focussed on long-term growth.

As internal and third-party data is brought together to enable advanced analytics, machine learning algorithms can plough through large sets of data and help discover insights unheard-of.

Improve your understanding of the brand - shopper relationship

The use of advanced analytics can help unravel the dynamics between shopper and brand on the level of a single item. By joining loyalty card data, online & offline purchase data, competitor insights, financial data and various other sets of data, retailers can perform, for instance, improved substitution analysis, determining the crucial and non-crucial items for a category.

Enhanced loyalty analysis can increase the retailers view on buying behaviour, influencing the procurement strategy for different products and pack sizes. They can help further determine the impact of pricing and discounting on brand vs white label.

Advanced analytics can furthermore be used to rebuild trust between manufacturers/vendors and retail organisations. The introduction of the category management methodology led manufacturers and retailers to increased cooperation, working on goals that benefitted each party. As retail started dominating the relationship, confidence was weakened

Today, both can thrive from the exchange of data to create appropriate items that enable a retailer to get the most out of managing a category.

2. Drive growth with a Pricing Strategy that is fuelled by Advanced Analytics and AI

In search of the optimal price point

Finding the optimal price for a given product has always been essential in order to walk the path of profitable growth in retail. Yet, a reluctance to invest in the appropriate tools remains present. Applying gut-feeling and mere cost-plus calculations are in a significant number of situations still predominant when setting prices.

As traditional retailers shift from the single-channel model to an omnichannel model, digital processes such as the act of buying in an online store enables a retailer to get a better understanding of customer behaviour. Through clicks and views, retailers have been improving their ability to quantify and analyse the psychological impact of their offer pricing.

Using such data to determine the optimal price point for a given product for a given group of people or at a given time is gaining importance. Customers are looking for more personalised offers. They expect to receive differentiated product or service offers. Such enables a retailer to subsequently set a different price as a result of a different willingness to pay.

The advent of advanced analytics and AI, driven by data science techniques such as machine learning algorithms, enable retailers to move away from intuitive or less state-of-the-art price setting methods. The technology can influence several aspects of the retailers pricing strategy.

Setting the initial price of an item

Category managers consider multiple factors when they set a price for a new item. Data that is brought together includes the initial markup, information on the performance of a similar item, competitor pricing of similar items, etc. The challenge they face is weighing each factor correctly as well interpreting the data correctly to find out what drives the perception of value.

There certainly is an increased importance of price perception and greater price transparency, which results in competitors scraping each other's online prices or comparison engines tracking and predicting prices. With such a given, it is essential to get the initial price right. Especially for retailers with thousands of different items, such seems a daunting task. Advanced analytics can significantly improve the accuracy of such pricing decisions and subsequent decisions related to the procurement of items and managing inventory.

The move towards more dynamic pricing.

With business processes going digital, retailers are provided with the opportunity to change prices in (near) real-time based on supply and demand indicators.

They are not pioneers in this matter. Airlines or event organizers have been utilizing the technique for decades, factoring in parameters such as the week of the year, weather, time until the event, etc. Large tech companies such as Uber have made it an essential part of their business strategy.

For retail, dynamic pricing can be used as a tool to respond towards a competitor move, towards inventory turnover or sudden spikes in demand. Especially in retail sectors where online retailers have a large market share, there is a shift towards algorithm-driven pricing changing the price of an item up to a dozen times per day. What is holding traditional retail back? Apparently, it is the fear for a ‘black box solution’. Category managers need to be able to understand the logic behind a tool that uses advanced analytics and AI, in order to accept the recommendations and feel confident when making decisions. Such leads to a returning request for customized solutions that extensively factor in certain specificities of an organisation, contrary to off-the-shelve tooling.

3. Use discounting wisely in order to drive growth, using the insights provided by advanced analytics & AI

Finding out what promotions are creating an impact

Finding the optimal mix between pricing and promotion in a given category can be a tough row to hoe. Certainly, as retail seems to have found itself in a permanent state of ‘discounting’ due to a changing - more digital - retail environment.

Nonetheless, retailers continue to lose profit as they remain hesitant to scale back the volume of such promotions. Often, the reason is both simple and troublesome. It is not clear which ones are working.

With the number of sales channels growing considerably (own stores, own webshop, third-party stores, third-party marketplaces, etc) it is becoming increasingly difficult to test and assess the impact of a promotion. Thus, the promotional weapon isn’t being used as a lever to drive growth.

Applying advanced analytics & AI enables a retailer to sift through the data and find patterns that indicate which promotions are truly effective. It can also help determine whether the same strategy can be run for a different item with similar traits, tweak parameters and test scenarios to determine their impact.

This further necessitates the need to create the right experimental design setting to determine what is and isn’t working. Each promotion should therefore be linked to a clear target. A measurable metric such as profit, margin, traffic, basket size, etc to enable the analysis of historical performance.

As such, it can demonstrate whether it is meeting the objectives the retailer has put forward and can, therefore, be bookmarked as an ‘excellent’ promotion. It can form the basis for developing guidelines for future promotion calendars or promotion strategies that do drive growth.

Hyper-personalisation of discounts

But there is more. Applying advanced analytics and AI enables hyper-personalisation of discounts that focus on persuading the right individual and the correct item with a specific discount. Data stemming from the aforementioned variety in sales channels enables a retailer to shed light on general client conduct and the conduct of a loyal customer. It permits a retailer to understand at what moment a promotion has the most impact on a given customer.

Levering data to test and better prepare promotion calendars, as well as using insights for customized discounts at the level of an individual shopper or shopping cart, will prove to be the most rewarding when retailers use promotions to drive growth