Commercial Analytics

In a recent piece, I explored the role of efficiency in the success of a quick-commerce organization.

Now, I'm going to delve further into this topic, focusing on efficiency within the commercial function, a vital component of any such organization. This topic carries significant importance as it ties directly to business metrics such as average bill, order frequency, front and back margins, and write-offs, all of which contribute to the company's PnL and are overseen by the commercial department.

For this article, I'm not going to discuss the aspect of efficiency in negotiations. Instead, I'll be sharing insights into how we can uncover untapped potential in pricing, assortment management, and stock logistics with data-based instruments and processes.

The description provided in the article is intended to provide a high-level overview, and I understand that each topic covered could be explored in much greater detail. If you would like more comprehensive information or detailed explanations of specific tools and approaches mentioned, I encourage you to leave a comment. This way, I can address your specific interests and provide more in-depth insights as needed.

1. Pricing

By pricing here I mean the process of setting prices on SKUs, both regular prices and promo prices. Usually, within a q-commerce company with a limited assortment, prices are set manually given the expertise of commerce executives. Still, there are two approaches and tools that might greatly increase the efficiency of pricing.

1.1 Pricing Rules

In today's economic climate, many countries face high inflation, necessitating frequent price revisions. This scenario presents a substantial challenge for commercial teams:

• Consistent price modifications may introduce the risk of inaccuracies, potentially compromising profit margins.
• Additionally, maintaining an accurate and up-to-date pricing level can be demanding, both in terms of time and departmental resources.

The practical remedy to these challenges is pricing automation, utilizing a predefined set of rules for each SKU. Such rules could encompass maintaining a minimum profit margin for an SKU, aligning the pricing index with certain competitors, or establishing minimum/maximum prices. The frequency of price adjustments can be tailored to a company's objectives. For example, Ozon, a prominent player in the Russian marketplace, revises prices multiple times daily to ensure the most competitive rates for certain categories. However, such frequent updates may not be crucial for an average quick-commerce entity.

While the solution may appear simple, it necessitates considerable collaboration from analytics, commercial, product, and development departments. The following tasks are key:

• Analytics: Design a data-flow encompassing competitors’ prices and purchasing costs.

• Commerce: Establish the pricing rules, ideally built upon a transparent pricing strategy.

• Product & Development: Automate the entire process, including data collection, new price computation, and price updates.

  1.2. Pricing Segmentation

In many developed countries, price segmentation and differentiation are often viewed as ethically questionable and, in many cases, legally restricted. However, in this piece, we won't delve into ethical debates for the following reasons:

• Instead of rejecting reality, we must acknowledge that price segmentation is a pervasive practice, varying in flexibility and extent across markets.
• Regardless of a company's stance on price segmentation, the capability to conduct pricing experiments is indispensable for understanding price elasticity and developing effective pricing strategies.

Laws governing pricing modifications within the same brand of stores can range from absolute prohibition (as in Israel) to essentially full approval of pricing adjustments for different users (as seen in Russia). Stringent regulations may limit the elasticity of price segmentation but do not necessarily ban pricing experiments outright. For example, even in Israel, a retailer can form a separate brand with lower prices or conduct pricing tests using personalized discounts instead of standard prices.

So, how does one implement effective price segmentation? The primary objective is to either boost orders with minimal impact on unit economics or, conversely, enhance unit economics with minimal growth disruption. In a more granular sense, the aims of this exercise are as follows:

• Determine the correlation between price levels and cohort retention, frequency for distinct customer segments – essentially, establish the elasticity between pricing and long-term growth.

• Ascertain the relationship between unit economics and price levels.

  
  

There are multiple ways to design a pricing test, but here's a simplified approach:

• For each SKU in the assortment, calculate its importance to customers, also known as the Key Value Items (KVI) index.
• Create several pricing scenarios by modifying prices for top N items based on the KVI index, or proportionally, based on the KVI index: decrease, increase, substantially increase, etc.
• Divide users/stores (subject to legal flexibility in your jurisdiction) into multiple test groups and a control group. Ensure the number of observations is ample for identifying effects within a reasonable timeframe.
• Wait to identify the cohort effect (typically a few months if run by stores), then analyze variations among stores to detect patterns, such as demand elasticity depending on differing real estate prices or number of groceries, etc.

Through analyzing these patterns and identified elasticities, it's possible to derive either a robust pricing strategy or an optimal price segmentation approach.

2. Assortment Management

When it comes to assortment management, it's important to recognize the complexity of this process. Data analytics, while powerful, might not provide a fully automated solution, a reality that became clear to me while trying to develop an algorithm to optimally set assortment quotas: the ideal size of assortment within different categories. Despite our best efforts, this project was unsuccessful. The challenges arose from our inability to confidently estimate cannibalization curves (predicting how category sales would adjust if we added one more SKU) and integrate all pertinent market information such as agreements with suppliers, suppliers' service levels, and the quality of remaining market SKUs.

Consequently, I believe that assortment can be defined properly only within a colaboration between analytics and the commercial department. Analytics in this case can assist commercial managers in 3 ways:

1. Data accessibility: Enabling commercial with necessary tools and BI-reports to promote data-awareness and data-driven decision making

2. Assortment Strategy: assisting in defining a transparrent assortment strategy
3. Category Reviews: regular procedures targetted on category potential identification

Let’s deep dive in each field separately.

Based on this experience, I'm convinced that successful assortment management requires a collaborative approach between analytics and the commercial department. Here, analytics can play a critical role in supporting commercial managers in three areas:

1. Data Accessibility: Provision of necessary tools and business intelligence reports to foster data-awareness and facilitate data-driven decision-making.
2. Assortment Strategy: Guidance in formulating a transparent assortment strategy.
3. Category Reviews: Routine procedures aimed at identifying category potentials.

Let's delve into each of these areas in detail.

2.1. Data Accessibility

Specific recommendations for data accessibility are challenging to prescribe, as the required tools and reports can vary significantly. At the very least, commercial managers should be able to extract various commercial metrics (Sales Quantity, GMV, Revenue, Write-Offs, OSA etc.) at a daily level, with various levels of detail (by SKUs, stores, categories etc.) readily available. In the E-Grocery companies where I've worked, this kind of dashboard was referred to as the Commercial PnL. As for other dashboards, open and clear communication between the commercial and analytics departments is critical, ensuring that analysts are responsive to the challenges and requirements faced by commercial managers.

2.2. Assortment Strategy

An assortment strategy is essentially a set of guiding principles that aid category managers in managing assortment. These principles are bespoke to each category and align with the broader brand image and development trajectory of the service. The structure for such a strategy may be laid out as follows:

• Each category should have a defined role. This role is a strategic classification within a logical framework.
• Each category role should be governed by specific management principles, which are applied uniformly to all categories within that role.

Different perspectives exist on how to define category roles. For instance, Walmart's framework classifies categories along two dimensions: the development status of a category and whether there's an intention to develop it further. However, I find the following approach more useful:

Short description of the roles:

• Traffic Generators: These are the SKUs that drive customers to shop. Common examples include staple items like dairy products or fresh bread. Although these categories typically see high volumes of sales, they seldom offer a high front margin.
• Money Generators: These are the SKUs that customers often buy impulsively or without premeditation, much unlike eggs or milk. Examples might be chips, chocolates, or beer. While these categories offer significant variety, the penetration of a single SKU in an order is usually low. Despite this, Money Generators significantly contribute to the overall profit margin.
• Supplementary SKUs: These are the SKUs that are only occasionally used, like dishes or birthday candles, within a grocery store. Their function is to draw customers into the store over competitors or to boost the average bill.
• Focus SKUs: These are the SKUs that hold importance for both customers and retailers due to their profitability. A subset of Focus SKUs is the KVI (Key Value Items), which are items that a specific retailer is renowned for ("Retailer ABC is the go-to place for fresh fish").

Please note, correctly assigning category roles may vary depending on customer profiles. For instance, some customers may primarily use your service for ordering snacks, while others opt for ready-to-eat meals. Therefore, more customer-centric approaches could be explored in alignment with the overarching brand strategy.

Delving into the guiding principles for each category role would indeed be quite comprehensive, meriting a discussion of its own. If you're interested in this topic, I encourage you to comment below. Still, here is how I see the overall strategy and goals for each category role:

• Traffic Generators: be on par with direct competitors but do not differentiate; target metric - penetration into orders or sales in items
• Money Generators: differentiate from competitors and address growing demand; target metric - commercial profit, abs
• Supplementary SKUs: cover the basic offer not trying to differentiate; target metric - penetration into orders
• Focus SKUs: offer the broadest and highest quality assortment, differentiate from competitors; target metric - SKU / Category retention or LTV

Feel free to comment on the topic, as it seems there might be a lot to discuss.

2.3 Category Reviews

A category review is a process where analytics is directly deployed to ascertain potential deficiencies and investigate growth opportunities within distinct categories. However, it's vital to note that, if mishandled, this can become a contentious issue for commercial managers. Firstly, it could potentially be perceived as an unwarranted audit of their work. Secondly, if executed improperly, it can erode the trust between the commercial and analytics teams.

I won't delve into the full methodology of a category review in this discussion, but it largely concentrates on identifying uncovered customer attributes by constructing what is referred to as a Customer Decision Tree (CDT). The CDT is a systematically organized map of customer needs, each of which can be met by a range of interchangeable SKUs, referred to as customer attributes. The creation of a CDT is generally an analytical process using clustering techniques based on internal sales data. However, it can be further enhanced with insights from commercial managers or customer feedback gathered from surveys.

Example of a CDT built analytically:

3. Stock Logistics

Efficient stock logistics is essential to ensuring high levels of availability while minimizing write-offs and store overload. The key elements of efficient stock logistics are the following:

• Proper availability measurement
• Accurate and automized stock replenishments

3.1. Proper availability measurement

Availability is a critical metric within every retail operation, influencing both immediate sales and long-term customer loyalty. Therefore, in order to accurately understand the situation and prioritize efforts aimed at enhancing availability, it's imperative for retailers to develop a robust measure of availability. This measure should align with the customer's perspective of availability, that is, it should have a strong correlation with key performance indicators such as order conversion rates or average bill.

Availability is usually measured with a metric called out-of-stock availability (OSA). Still, this metric is often an inaccurate indicator of availability due to the following reasons:

• (1) Traditional retailers often lack real-time data on stocks, thus limiting OSA estimation to once or twice a day. This snapshot approach does not accurately represent the dynamic nature of stock availability. For instance, if you measure the OSA at 7 a.m. right after restocking, sell out all the stock by 1 p.m., and cease operations for the rest of the day, the OSA wouldn't reflect the true availability scenario.
• (2) OSA does not account for the fact that a retailer might have multiple similar SKUs that satisfy the same customer need (as per CDT). Take eggs as an example; If a retailer has two SKUs of eggs and one of them is sold out while the other remains, it wouldn't impact sales but would decrease the OSA.
• (3) OSA does not consider the varying importance of different SKUs to customers. For instance, while calculating OSA, eggs and birthday candles would be given equal weight, despite the fact that eggs are generally of greater importance to customers.

Here is the logic solution for the problems listed above:

• (1) To overcome the challenge of time-specific OSA measurement, retailers need to have real-time access to stock data. Then, OSA should be estimated as the total number of hours each SKU was present, divided by the total number of hours each SKU should have been present (the number of active SKUs multiplied by the number of operational hours of a store).
• (2) SKUs should be classified using the concept of the Customer Decision Tree (CDT). For instance, if you have two bottles of milk from different suppliers that are indistinguishable for customers, these two SKUs are within the same CDT property. Measure availability by CDT properties using the same approach as in point (1). This metric is called Group OSA (GOSA).
• (3) Finally, to account for the varying importance of items, weight properties by # orders. Although more sophisticated methods for measuring item importance could potentially be devised, # orders seems to accurately illustrate the relative importance of a property for customers. This resulting metric is called Adjusted Group OSA (AGOSA).

Once AGOSA has been determined, it should serve as a pivotal business KPI. A recommended practice is to establish weekly meetings involving both commercial and supply chain managers. During these sessions, the team can review customer properties with low AGOSA scores (these could be properties with a high weight or low GOSA). The team can then collaborate on strategies and activities aimed at improving AGOSA for these identified properties.

3.2. Accurate and automized stock replenishments

The topic of automated stock replenishment is extensive enough to warrant a dedicated textbook. For the purposes of our discussion, however, I'll briefly outline the key elements that underpin an effective stock replenishment system:

• Accurate and transparent demand prediction by SKUs and stores

Creating a strong stock logistics system requires accurate sales predictions for each day, SKU, and store, and these predictions should cover at least the next seven days. These predictions should be clear and open, and we should be able to easily change them if something unexpected happens or if there are mistakes. A good way to do this could be to make these predictions step-by-step. We start by estimating the total sales, then estimate the share for each category, and finally look at the share for each store and SKU.

We need to remember that predicting sales is usually quite hard for SKUs that aren't sold often. In these cases, the prediction is more about the chance of an item being bought within a day, rather than a normal sales forecast. So, for retailers with a lot of different products, getting a completely accurate forecast might not be possible.

• Autoorder algorithm that estimates the economically optimal order

Beyond sales forecasting, it's essential to develop an integrated auto-ordering algorithm. This should incorporate various factors, such as sales projections, minimum supply quantities, suppliers’ delivery schedules, etc., to calculate the optimal quantity to order from suppliers. For retailers operating with a Distribution Center (DC), the system's complexity escalates. This scenario necessitates a two-step procedure: firstly, estimating the optimal order for the DC, and secondly, determining the ideal order from the DC to individual stores.

It is vital that the auto-ordering algorithm does more than just crunch numbers based on sales forecasts. A sophisticated algorithm should evaluate the economically ideal level of OSA that maximizes commercial profit and calculate the necessary order volume needed to achieve this target OSA level. To achieve this, one needs to statistically identify the multiple relationships listed below:

• Operational analytics responsible for demand prediction corrections

Regardless of the sophistication of sales forecasting, it is crucial to have a specialized team tasked with monitoring and adjusting sales predictions and automated order suggestions. This is primarily due to instances where the system may not account for variables like public holidays, internal trials, or promotional campaigns.

This dedicated team should also be equipped to manage logistical intricacies, such as preventing store overload and managing stock acceptance queues. Additionally, the handling of new SKUs, which lack historical sales data, falls under their purview, necessitating an individualized approach to their management.