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In a , I explored the role of efficiency in the success of a quick-commerce organization. recent piece 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: : Design a data-flow encompassing competitors’ prices and purchasing costs. Analytics : Establish the pricing rules, ideally built upon a transparent pricing strategy. Commerce : Automate the entire process, including data collection, new price computation, and price updates. Product & Development 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. Enabling commercial with necessary tools and BI-reports to promote data-awareness and data-driven decision making Data accessibility: assisting in defining a transparrent assortment strategy Assortment Strategy: regular procedures targetted on category potential
identification Category Reviews: 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: : Provision of necessary tools and business intelligence reports to foster data-awareness and facilitate data-driven decision-making. Data Accessibility Guidance in formulating a transparent assortment strategy. Assortment Strategy: : Routine procedures aimed at identifying category potentials. Category Reviews 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: 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. Traffic 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. Money Generators: 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. Supplementary 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 ( ). Focus SKUs: "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: be on par with direct competitors but do not differentiate; Traffic Generators: target metric - penetration into orders or sales in items differentiate from competitors and address growing demand; Money Generators: target metric - commercial profit, abs cover the basic offer not trying to differentiate; Supplementary SKUs: target metric - penetration into orders offer the broadest and highest quality assortment, differentiate from competitors; Focus SKUs: 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.

Commercial Analytics

Photo by on Chris Liverani Unsplash Safety rules were written with blood. That statement sounds familiar to every soldier around. Although we are not dealing with a risk to human lives, losing your expensive Bitcoins by making mistakes trading is definitely not a fun situation. So, how we can avoid those mistakes in our trading? How to be mostly on the green side? First, it is important to note that to trade right requires attention and your one hundred percent focus. Secondly, trading is not for everyone. The following tips are easy to internalize because these tips were “written in blood” (my own blood). However, it’s still difficult to apply them in real-time. After all, we are not rational human beings. Have a reason before entering each trade: Start a trade only when you know why you’re starting and have a clear strategy for afterwards. Not all traders make gains from trading, since this is a zero-sum game (for everyone who benefits someone else loses on the other side).The Altcoins market is driven by large whales (yes, the same ones responsible for placing huge blocks of hundreds of Bitcoins on the order book). The whales are just waiting patiently for innocent little fish like us to make mistakes. Even if you aspire to trade on a daily basis, sometimes it is better not to earn and do nothing, instead of jumping into the rushing water and exposing your coins to losses. From my experience, there are days where you only keep your profits by not trading at all. Target and stop when starting a trade: For each trade we must set a clear target level for taking profit and more importantly, a stop-loss level for cutting losses. A Stop-loss is setting the level of loss where the trade will get closed. Here again, it is important considering a number of factors when choosing a stop loss level correctly. Most traders fail when they fall in love with a trade or the coin itself. They may say, “Here it will turn around, and I will get out of this trade with a minimum loss, I’m sure”. They’re letting their ego take control of them and unlike the traditional stock exchange where extreme daily movements are considered 2–3% in value, Crypto trades are a lot more riskier: in my life as a trader I’ve seen a coin dumping by 80% just in a few hours! And nobody wants to be the one who is left holding it. Meet FOMO (fear of missing out): Indeed, it really isn’t fun to see such situations from the outside — when a certain coin is being pumped up like crazy with huge two-digit gains in minutes. That bold green candle yells at you “you are the only one not holding me”. At exactly this point you will notice lame people flooding the Crypto forums and the exchanges’ Troll boxes to talk about this pump. But what do we do now? Very simple, Keep moving forward. True, it’s possible that many may have caught the rise ahead of us and it can continue raising, but bare in mind that the whales (as mentioned above) are just waiting for small buyers on the way up to sell them the coins they bought in cheaper prices. Prices are now high and it’s clear that the current coin holders only consist of those little fish. Needless to say, the next step is usually the bright red candle which sells through the whole order book. Risk Management: little pig eats a lot, big pig gets eaten. This statement tells the story of the market profits from our perspective. To be a profitable trader, you never look for the peak of the movement. You look for the small profits that will accumulate into a big one. Manage risk wisely across your portfolio. For example, you should never invest more than small percentage of your portfolio in a non-liquid market (very high risk). To those trades we will assign greater tolerance — the stop and target levels will be chosen far from the buying level. The underlying asset creates volatile market conditions: Most Altcoins are traded according to the Bitcoin value. Bitcoin is a volatile asset (relative to FIAT) and this fact should be taken into consideration, especially in the days when the Bitcoin value is moving sharply. Bitcoin and Altcoins have an inverse relationship in their value, i.e. when the value of Bitcoin rises then Altcoins are losing their Bitcoin value, and vice versa. When Bitcoin is volatile, our conditions for trading are kind of foggy. During fog we can’t see much ahead, so it is better to have close targets for our trades or not to trade at all. Tips for trading : Most Altcoins lose their value over time. They simply bleed their value away slowly (sometimes rapidly). Altcoins Take this into account when holding Alts for the medium and long term, and of course choose them carefully. What kind of Alts are recommended for the long term? Remember, this is only when there is a reason for making a trade. The projects/coins that have a higher daily trading volume and which have a widespread community behind them, with continuous development, are here to stay with us: Ethereum ETH, Monero XMR, Factom FCT, DASH, are all leading coins and traded the most volume daily. You should follow the coin’s chart and identify low and stable periods. Such periods are likely to be a consolidation period by the whales, and when the right time comes, accompanied by a good press release of the project, the pump will start and they will sell in profit. A word about public ICOs (crowd-sales): Many new projects choose to make a crowd-sale where they offer investors an early opportunity to buy a share of the project (tokens or coins) in what is meant to be a good price for the tokens. The motivation for the investors is that the token will be traded from day one on the exchanges and would yield a nice profit to the ICO participants. In recent years, there have been many successful ICOs, both the project itself and especially in measuring the yield for investors. Coins doubled, or tripled, their value and much more in relation to their value on the crowd sale. Augur’s preliminary crowd-sale (we reported on it ) yielded investors a phenomenal 1,000% for their investment. Okay, but what’s the catch here? Not all the projects benefit their investors. Many ICOs proved to be complete scams, not only were they not being traded at all but some projects disappeared with the money and we have not heard from them right up to this day. previously here So how do you know if you should invest in an ICO? It’s not about science, it is important to pay attention to the level of seriousness of the project and its team. Look for the project’s website (does it look like a child has built it during computer school?), Who is the team behind the project — Are they hiding behind nicknames or proudly present themselves on their website? Pay attention to the thread (does it exist at all?) and how the team members respond to technical questions. Is there a large community behind the project? Expect to see a Slack gathering its community. Watch out the amount raised: A project which had raised too little will probably will not be able to develop over time, a project which had raised huge amount — there won’t be enough investors left out there to buy coins on exchanges. And most importantly is risk management. Never put all eggs in one basket and invest too much of your portfolio in one ICO. Bitcointalk A final tip — practical steps to implement right away: Fees, fees, fees: Multiple trade actions = More fees. It’s always advisable to post the command (maker) and not to buy from the order book (taker). In Poloniex exchange, the difference is 0.1% in favor of the maker. That’s quite a bit. Traders with no pressure: Don’t start trading unless you have the optimal conditions to make the decision to start a trade and know when and how to get out of it. Pressure almost always creates losing trades. Wait for the next opportunity, you will get there. Setting goals and placing sell orders: always set your goals by putting sell orders. You don’t know when a whale will pump your coin up to catch your command (and pay a reduced fee on the “maker” side, remember?). A successful strategy regarding this is placing very low buy orders. About a week ago a crazy dump occurred, selling off Augor coin down to 25% of its value! After a short while the market recovered slightly and anyone who had low buy these low orders could easily double or triple their investment. Placing buy orders requires special care, don’t wake up when you’re far away from the market to find your buy order is suddenly higher than the current market price! Buy the rumor, sell the news. When major news sites publish articles it is usually exactly the right time to actually get out of the trade. You have made a good trade, but as always, the moment you sold your coin runs up again! First, meet this guy — Murphy’s Law. Secondly, read over what was written previously here and never enter position again under pressure. As long as there is profit — you are ok. Go on to your next trade and don’t find yourself losing it. Leave your ego aside. The goal here is not to be right on your trades, but to make a profit. Do not waste resources (time and money) to try to prove that you should’ve been entering that trade. Remember, there is no trader who never loses, at least sometimes. The equation is simple — get the total profits to be higher than the total losses. What is short? long? how to leverage your trades? Follow here to our for beginners crypto margin trading our special article about five must have Crypto websites. Follow Update 2018 We’ve published another crypto trading tips and mistakes guide, you can . read it here Do you have other tips to contribute? We would love to hear them in the comments and by contacting us. Comments Originally published at cryptopotato.com on December 20, 2016.

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