We recently discussed how shopping behavior impacts frictionless needs and the technology options retailers should consider to eliminate pain points in the shopper journey. Consumers expect a seamless journey at all touchpoints, and if a brand fails to deliver on this, they will lose to their competitors and fail to keep up. 

But once the right technology is implemented, does it guarantee smooth sailing from there?

Not necessarily. We’ve seen the challenges that retailers face when the solution doesn’t deliver on its full promise, leading to frustrated customers and low return on investment. Retailers obviously want to deliver real value to customers and their bottom line with these frictionless initiatives, but to do so requires addressing the biggest challenges in terms of consumer adoption, technology and cost. 

Here are the common roadblocks that retailers must address to successfully bring the ‘frictionless retail’ experience they are striving for.

Low consumer adoption

Implementing frictionless technology is a waste if consumers don’t use it. The key to high consumer adoption is to ensure that there are great benefits to the technology that will make their life easier, not harder. Some of the top reasons that deter shoppers from using the technology include:  

• Process inefficiency – Like we mentioned in our last blog, customers want to avoid long checkout lines. Offering new options like curbside pickup or BOPIS is a good start, but it doesn’t address the problem on its own if executed inefficiently. Customers who opt to use these services are looking for new convenience and don’t want to spend a lot of time waiting for their order. Retailers need to ensure the items are ready to go and the pick-up process is quick and easy. 

• Lack of inventory visibility – We’ve also discussed how frustrating it can be when an item a shopper wants is out-of-stock or there’s limited assortment. If online channels don’t accurately display item inventory visibility in real-time, shoppers will become disappointed when the item is not available in-store when they want it.

• Lack of personalization – Customers want a personalized shopper experience where the retailer truly understands his or her wants and needs. All frictionless technology options should make it easy for them to find and buy the items they want as conveniently as possible. Retailers can cater to shopper preferences with features like integrated loyalty programs and individualized recommendations on their mobile app. 

• Underdeveloped concepts and implementation – In May 2018, Walmart pulled the plug on its Mobile Express Scan & Go service after receiving negative feedback from customers who were experiencing issues bagging items and scanning weight-based items. If every part of the process isn’t designed with the worst-case-scenario in mind, frustrations for too many will pile up and the experiment will fail.

Unresponsive technology

There’s nothing more frustrating than when technology doesn’t work the way it’s anticipated to. Technical errors will create IT problems, which is why the right processes must be put in place to ensure there’s little interruption and the system can support high traffic now and in the future. Here are two main issues to consider: 

• Network issues – Often times, retailers rely on cellular networks through consumer devices to power their in-store technology. As a result, they’re not able to ensure stable and quality service, especially as people consume more and more bandwidth. That’s why in-store customer Wi-Fi is always essential to provide the best available Internet connection and ensure smooth service.

• Computing issues - As retailers look to achieve frictionless retail, they can utilize both cloud and edge computing to maximize their potential. While edge may have the benefit of speed, cloud has power and capacity, as well as security for both sensitive company and customer information. Retailers must strike the right balance and have the flexible and scalable technology to provide the combined capabilities that is best suited for each solution.

High cost and resources

Finally, costs – up-front, long-term and opportunity costs - are fundamental considerations for any business decision. Where these resources are allocated are often influenced by executive perception and organizational culture. Large companies often have leaders who run the business with old-school thinking, putting the business at risk of underestimating the risks of maintaining the status quo while also underestimating the problems of investing in promising technologies without the right deployment plan.  

However, innovative retailers will find that if they are addressing their customers’ unique demands and implementing the solutions correctly, they will see an increase in ROI and become even more equipped to serve a convenient and enjoyable experience for all current and future customers. Frictionless retail may seem like it requires a daunting effort, but with a smart, long-term plan, the transition will be incredibly worthwhile.

To learn how GK Software can help you remove roadblocks and address these key challenges, contact us here and start creating a truly frictionless experience today.

At first glance, Dynamic Pricing Systems and Recommendation Engines may not have too much in common. Dynamic Pricing is meant for targeted price optimization so a retailer can sustainably grow revenue and profit. Recommendation Engines, on the other hand, ensure that customers receive excellent online service, even though there is no sales associate at their side while they shop.

While Dynamic Pricing Systems and Recommendation Engines operate in different worlds, they have one very important thing in common: they generate added value from data and focus on the retailer’s most important asset: customers. After all, customers are the ones who ultimately decide on the success or failure of a retailer. In this context Dynamic Pricing Systems and Recommendation Engines have even one more thing in common: The most successful retailers (especially Amazon) have been using both technologies for years!

With a Recommendation Engine, you as a retailer can control the customer experience of every single shopper. You are able to specify at which customer touchpoints what products or product groups are to be offered. The AI then selects the products that best match the customer’s interests. From there, you can decide whether the AI should only show products that are directly geared to the customer’s interests – offering the best possible service – or whether it should also recommend additional products that offer your business high margin, turnover or sales potential.

In most cases, the combination of both approaches is worthwhile as it achieves customer satisfaction and serves your business targets profitably. Additionally, I would like to emphasize a great benefit that you generate here quite incidentally: You gain a deeper understanding of your customers’ interactions with products, such as:

• Which items are very high in demand.
• Which items they buy frequently or notably rarely.
• And – maybe most important – you will see, which items often end up in shopping baskets or on watch lists but are ultimately not bought.

With all this information, you’re able to optimize inventory levels and also make price decisions dependent on the most important indicator of all: the customers. With dynamic price optimization, you can supplement your pricinge processes with a system that makes statistically valid and profitable decisions – for your entire product range. More importantly, the AI uses your existing data (e.g. those from the Recommendation Engine) and converts them into additional income because it runs processes that a pricing or category manager cannot perform:

• The AI calculates the daily demand for each individual item in your assortment.
• The AI automatically considers a range of influencing factors, such as stock level, target sales date or crucial competitive information.
• The AI aggregates this information and derives a valid sales forecast for each individual item.

Based on this, a daily updated price is calculated for each product within the framework of the pricing management specifications. Retailers still have full control over the strategic direction of their pricing; The AI only controls the processes that cannot be handled manually – neither qualitatively (mathematical accuracy and inclusion of ALL relevant pricing factors) nor quantitatively (for all items in the assortment).

To close the circle, Dynamic Pricing Systems and Recommendation Engines can support each other and maximize the common added value. Recommendations can ensure that customers are made aware of very specific products – for example, those that you as a retailer would like to sell as soon as possible. If these products are also given an attractive price by the AI, you can boost your sales in a very targeted manner and avoid disadvantageous discounts.

Would you like to learn more about the effects of both systems in place? Just a few keywords about that: Bundle pricing and couponing! You will find out more in my next blog post – or, in the meantime, sign up to be the first to get this information. :-)

Mrs Pricing

In our last blog, we explored how shopping behaviors impact frictionless retail needs. We looked at eight different types of purchases  that uniquely affect the shopping experience a customer expects. We also explained the common friction points retailers need to address for each of these customers.  

Today, we’ll identify the different frictionless technology options retailers can leverage to eliminate these pain points. These technologies are paving the way for retailers to achieve the ‘frictionless retail’ they envision – one based on the unique needs of each experience. Only with the right solution can retailers deliver the speed, ease and convenience that today’s shoppers demand. 

Here are just a few of the many different frictionless shopping services retailers can provide based on each stage of the shopping journey, ranging from the prominent to the possible:

1. Browsing

• Mobile ordering and order-ahead
  
  The increased familiarity and adoption of mobile shopping has made it easier than ever for customers to order ahead. Buy online, pickup in-store (BOPIS) saves customers valuable time from browsing the aisles for items, eliminating friction points for normal purchases like grocery trips where shoppers want to quickly get in and out of the store. Customers making symbolic purchases will also appreciate order-ahead so they can be confident that the items they want are in stock.
  
  This technology is also a great fit for recurring purchases since loyalty programs can be easily integrated into a consumer’s mobile app. A digital loyalty program allows retailers to send personalized offers directly to customers’ personal devices, providing a new level of excitement and value for shoppers on the go and keep customers coming back. 
  
  
• Connected cars
  
  Connected cars will soon become more common as automakers integrate in-car shopping into their infotainment systems. Like mobile shopping, connected cars will allow drivers to place an order through their car and pick up their purchase when ready. This gives on-the-go consumers who are making normal or recurring purchases another option to quickly browse and order the items they need in their own vehicle.

2. Transaction 

• Scan-and-pay/Tap-to-pay
  
  Scan-and-pay has become increasingly popular in retail environments where people don’t want to wait in line. From grocery stores to convenience stores, this technology has been key to blending the online and in-person experience. The biggest advantages for retailers implementing this technology are increasing customer satisfaction and converting customers into loyal fans and brand ambassadors. We discuss the opportunities and challenges of scan-and-pay technology in our blog here.
  
  Grocers like Ahold Delhaize have also implemented tap-to-pay technology, allowing customers to tap shelf labels with their loyalty card to buy an item, and their purchases will process through their mobile app. It’s another way to provide shoppers a convenient and easy shopping experience on their most personal device. 
  
  
• Cashierless Cameras
  
  Amazon Go paved the way for the cashierless store trend, and a snowball effect is taking place as other grocers adopt this technology in order to deliver an excellent and frictionless customer experience.
  
  By using cameras and/or sensors, retailers can track those who come into the store, which items they grab and allow them to pay digitally without ever having to physically check out. Similar to scan-and-pay, this removes the major pain point of long checkout lines and allows for contactless payment – a major factor in a post-pandemic world.  
  
  
• Shelf Sensors/RFIDs
  
  A more affordable alternative to cameras, electronic shelf labels (ESLs) and smart RFIDs give retailers flexibility to adjust pricing in real-time. Retailers can manage millions of labels by displaying the optimal price at the right time with strategic data, increasing efficiency and basket size.
  
  Shoppers making “saving” and “interim” purchases are looking for sales and discounted items that have a good price point, such as at apparel or home goods stores. To remain competitive and still make a profit, retailers can use these shelf sensors to quickly display markdowns and promotions in real time. Dynamically changing the price of these items protects margins while appealing to the target customer.

3. Last Mile

Like we mentioned in our last blog, the last mile is essential for impulse purchases. With more shoppers moving online, this will look differently than candy at an in-store checkout line. Instead, it will be based on the recommendations at the checkout page that will have customers “add to cart.”

• Pick-up in-store/Curbside pickup/Delivery
  
  With mobile ordering and order-ahead as a browsing option, retailers will also need to offer reliable last mile options for a seamless end-to-end shopping experience. Whether a customer chooses pick-up in-store, curbside pickup or home delivery, it’s important that the order is completed seamlessly. 

• Autonomous drone delivery
  
  Autonomous flying drones are creating new opportunities for quick and reliable last-mile delivery.  According to Gartner, more than a million drones could be carrying out retail deliveries by 2026.  More and more retailers are investing in this technology to enable faster transportation, reduce operational costs and provide customers easy and contactless delivery. Recently, CVS, with UPS, announced plans to deliver prescription drugs with drones to retirement communities in an effort to provide safe and efficient deliveries during this time of crisis.

Deliver the right experience today with frictionless tech

With so many technology options available, retailers will first need to evaluate the main friction points that their customers care about, and from there, determine which of these frictionless options will lead to greater shopper satisfaction.  

To get started on addressing your customers' unique needs with frictionless shopping technology, contact us today.

I can report from the experience of many projects: Markdown Optimization is one of the topics that retailers are particularly concerned about – across all branches. Because markdowns – and especially write-downs – limit gross profit. Therefore, it is important that retailers proactively manage their sales and keep write-downs as low as possible. This is not least essential for the overall success of a company. The particular challenge with markdown pricing is, that those products whose prices need to be intelligently reduced, are already at the end of their product life cycle. As a rule, the following applies to such products:

• the demand for these items is constantly decreasing and customers expect reductions
• Pricing managers lack the time to price these items in a targeted manner and keep losses low
• these items cost storage capacity and sometimes cause additional disposal costs

For pricing managers, these special circumstances create a real bottleneck: in order not to sacrifice margin needlessly, they would have to regularly determine which items of their entire assortment meet these criteria, and for those that do, what the appropriate price reduction for each one should be. This would take far too much time. In the end, a “spray and pray” approach to reductions are generally accepted as a necessary evil, even though they are strongly detrimental to margins. It’s akin to a watering can – only some water willreach plants, and the rest will be wasted on the ground. You can hardly earn margin with such a watering can approach; a more direct nozzle is needed.

From an economic point of view, it is essential to rethink the pricing process for the markdown optimization. The following objectives must be combined:

• Daily price adjustments based on demand to achieve margin-saving sales (= abolition of the watering can)
• Adherence to all sales targets and optimal use of storage capacity
• Streamlining of processes and reduction of manual effort

Fueled by AI, these solutions can forecast sales per product for the coming days based on the latest transaction data (current clicks, created shopping baskets, purchases, etc.). Depending on the specified target sales date, the daily price is then determined for each item. If the target sales date is close and the sales forecast is poor, the price is reduced. If the sales forecast is good, the price is either minimally reduced or is even increased.

In contrast to the watering can approach, an algorithm re-evaluates each individual product every single day – making the top priority the retailer-specifiedsales date. Measured against this, the algorithm only reduces the price of the products that would not be sold by the target date. All other products are either only very gently discounted or – if there is demand – the price is even increased again.

Across all products, you can then create a profitable balance between aggressive selling and consistent utilization of your margin potential. The sales process is thus margin-friendly, your goods go out of stock on a predictable timetable and you reduce your markdowns considerably.
The best thing is: the manual effort is remarkably low. How low? You should see it live!

Mrs Pricing

One of the reasons why retailers increasingly rely on AI-based Dynamic Pricing is the huge amount of data and influencing factors that need to be considered for price determination: Article master data, purchase prices, sales quotas, inventory levels, competitor data, promotions, real-time transactions, historical data, seasonal trends, regional factors, weather influences – to name just a few. The crucial point is: the amount of data available is huge. Huge, but full of potential – especially in the age of AI! Because an artificial system is easily able to process large amounts of data in seconds and derive meaningful analyses. Every day I see in projects how great success can be achieved with comparatively little effort when work processes are digitally supported and thus optimized. In the area of dynamic price optimization, these successes are particularly visible because they can be measured numerically and the ROI can thus be clearly determined.

What do you need now to use an AI for your price optimization? The list is quite manageable, as you will see:

• Sales data & transaction data
• Product master data
• 4 to 6 weeks time for AI prices in the online shop (for stores about 4 weeks more)

The AI needs your sales and transaction data to calculate the demand for each product in your range. Keyword: price elasticity. (If you need a short recap, you can find it here). This forms the basis for every price decision of the AI. At least all sales information is needed: Which items were sold at what price. All transaction information, which can be used additionally, improves the AI’s forecasting quality and enhances its results again: viewed products, created shopping baskets, cancelled shopping baskets, saved watch lists or entered search terms, to name just a few examples. It makes sense to make this data continuously available – either via real-time tracking (a powerful Dynamic Pricing software provides this feature) or e.g. via data feed from your SAP CAR system.

Product master data is the digital representation of your assortment and thus an important tool for dynamic price optimization. They usually provide a lot of different article information: product ID, master-variant assignment, current price, RRP, lower and upper price limit, seasonal identification, brand, color, size, stock level, expiration date or target sales date and much more. An AI can use these attributes for various tasks:

• The existing supply is combined (via the inventory) with the existing demand – this is how the AI calculates optimal prices in line with the market.
• Using the best-before or target sales date, the AI recognizes when items are going out of stock and prices them down slowly according to their demand, in order to reach zero stock on the given target date and at the same time to work in a margin-saving manner.
• Using attributes such as colour, brand, size and alike, an AI is able to identify similarities between products and can thereby also calculate optimal prices for products with low data availability, longtailers or even new products.
• The price limits determine the AI’s scope for price optimization – i.e. which price it may not exceed or fall below.
• By means of the master-variant assignment, the AI implements family pricing and/or maps product relations for you (e.g. appropriate price range between low-budget and branded products).

Product master data is usually transferred automatically to the AI software once or twice a day with a CSV import or can also be transferred automatically via SAP S4/HANA.

Finally, I would like to share with you some experience about the time you should plan to integrate an AI for Dynamic Pricing into your processes. The implementation of a price optimization software is carried out in three steps:

1. The software is delivered as a cloud service.
2. The automated delivery of input data is set up.
3. The AI is configured and set to your objectives via a Web GUI.

Afterwards the system goes live and optimizes your prices continuously. For projects in e-commerce, this process often takes no longer than 4 to 6 weeks, since many processes – by their very nature – already run digitally. The shortest implementation phase that I have ever seen so far even lasted only 3 weeks (until today one of the most remarkable projects ever)! In stationary retail it usually takes a little more time, but here too, 9 to 12 weeks is very good for planning.

Now only one question remains: When will you go live?

Mrs Pricing

If you haven’t heard already, we recently launched GetMyGooods, an app that plays a critical role enabling simple grocery ordering and pickup while ensuring the protection of everyone involved. The app is designed to provide retailers a way to instantly deploy buy online, pick-up in store service by eliminating the need to integrate with existing systems. That way, retailers are able to focus on ensuring their customers can safely get the products they need. 

GetMyGooods is built on the idea that consumer behavior is changing because of the COVID-19 pandemic, and the adoption of contactless retail will accelerate as retailers and shoppers limit exposure to potential carriers. 

Let’s explore how the app overcomes system integration hurdles and where buy online pick-up in store (BOPIS) will evolve from here.       

How Do Retailers Overcome System Integration Hurdles?

Retailers need a solution now to provide contactless applications in the store. There is an immediate need to enable simple and reliable ordering for critical consumer goods, which means stores must overcome complicated integrations and quickly deploy the basic functionalities needed today. 

To do this, system integrations can be bypassed in the short term to make critical service improvements that can be addressed over time. Retailers can then enable more interfaces, such as pricing and promotions features, later down the road.  

That’s why we’ve created GetMyGooods so that retailers don’t have to spend time and energy integrating with current systems such as merchandise management or point of sale. Customers simply download the app on their smartphone and start placing orders. 

What is the Future of BOPIS after COVID-19?

With social distancing measures in place, BOPIS has become increasingly popular, with one in 10 respondents having tried this fulfillment option for the first time during the pandemic, and 13% having increased the frequency in which they’re using it. 

New shopping habits will likely stick after the crisis has passed, and the increased familiarity with and adoption of online grocery will have a long-term impact on the acceleration of BOPIS – or curbside pickup to limit in-store traffic – and contactless payments, even for items collected at the store. 

It will be up to retailers to drive a change in processes as consumers adopt these new fulfillment and checkout options. By allowing customers to do everything from browsing to payment on their personal device, retailers can ensure the safety and health of both employees and shoppers. 

The Right Technology at the Right Time

COVID-19 has taught everyone how important it is to future-proof the retail industry to survive in a tough and fast-changing climate. The retailers who succeed post-pandemic will be the ones who are already implementing innovative solutions at speed and scale and who continue to look for customer-focused technology that enhances safety and convenience at an even greater level. 

To get started, visit the GetMyGooods website and let us help you rapidly deploy this critical new service today, protecting your business now and in the future.

GK Software, the international market leader for store solutions, today announced availability of its GetMyGooods app, which allows shoppers to order essential items online and pick-up in store. GetMyGooods helps limit in-store contact between employees and shoppers and restricts the number of stores shoppers visit when looking for out-of-stock products.

After downloading the app, shoppers can order the products they need from their mobile device. Their shopping list is sent directly from the app to the retailer, where the order is prepared in store by associates. Once ready, the customer is notified and can pick up their items immediately from the store, confirming their order through a QR code and paying on-site. This eliminates the need for shoppers to spend time in the store searching for essential products that may be out of stock. At the same time, the number of interactions between sales staff and customers is greatly reduced.

“GK launched GetMyGooods in order to quickly provide the critical service of grocery pickup while ensuring the protection of everyone involved,” said Michael Jaszczyk, CEO, GK Software USA. “Buy online, pick up in store had been gaining momentum in retail, but rapid adoption is now critical as retailers look to provide another level of convenience as safety requirements take precedence.”

The app is designed for immediate use and is easy to operate. By eliminating the need to integrate with existing systems such as merchandise management or the point of sale, retailers are able to focus on ensuring their customers can safely get the products they need, while also delivering exceptional service and protecting customers and employees alike.

The GetMyGooods App is available for download from the Apple App Store and Google Play Store today. Retailers can also register and find more information at www.getmygooods.com.

In my recent posts I have shown which insights you can gain with the help of price elasticity and how this helps you to better understand your entire product range and to price it accordingly. The splitting of your product portfolio into article roles, such as common and focus articles as well as basic and skimming items, was particularly significant. It provides a valid basis for a holistic, AI-supported pricing strategy by answering the following questions:

• Which product segments can best serve which of your target KPIs?
• Which product groups are particularly important for your customers and therefore particularly effective in marketing campaigns?
• Which degree of automation should be chosen when using a dynamic pricing software?

We will now answer all these questions step by step. Let’s start with the basic and skimming articles. These article groups are usually not very price-sensitive and therefore less competitive relevant. This is mainly due to the fact that consumers have only a low price awareness for these products – either because they rarely buy these articles and/or because they do not have a high investment value associated with them. (You may recall the examples: pots and spices.) Basic and skimming items can therefore be pretty useful to increase your target KPI Gross Profit. For these article groups, my recommendation is: The pricing shall be done fully automated by an AI-supported dynamic pricing software to effectively capture the existing gross profit potential. Especially because basic and skimming products are not in the focus of your customers, it makes little sense to invest a lot of work in these articles. Especially since manual pricing is also hardly to be done in a meaningful way anymore, since on the one hand too many influencing factors have to be taken into account (such as daily stock, daily demand, weather, holidays, seasonal characteristics etc.) and on the other hand the sheer quantity of such articles is too large. Basic and skimming items make up about 60-80% of a total assortment – in principle, the broader and deeper the assortment, the higher the proportion. Therefore, automating the price calculation and price setting for these assortment parts is particularly effective.

Different rules apply to common and focus articles. These articles are highly price-sensitive and therefore enormously relevant for your competition. Your customers are highly likely to notice price changes for these articles and they will react to them. The good news is: You can use this to your advantage! By increasingly integrating common and focus products into your marketing campaigns and by setting prices in a way that is factually valid and strategically well-founded. In this way, you can control the KPIs turnover, sales, frequency and market share in a very targeted manner. If you offer these articles at special prices, you can attract your customers to your store in a plannable way. In this way, you can generate corresponding turnover and sales – and at the same time increase the frequency of your store. In the medium and long term, you will secure your position in the market and gain additional market share.

What is important is the strategic interaction of the two article groups when setting prices. If you have the basic and skimming articles priced by an AI, up to 80% of your assortment will be priced fully automatically. This saves you a lot of time for manual price maintenance and also allows you to make the most of your gross profit potential. Conversely, this means that your Category and Pricing Management has gained time which it can use for planning the strategic use of the common and focus articles (e.g. in promotions or marketing campaigns). It also means that you can use a portion of the additional gross profit you generated to price those products very attractively, that are in the price focus of your customers. Here, an AI pricing software will support you and provide you with appropriate price suggestions that take into account all important general conditions. In addition, a truly powerful dynamic pricing solution automatically provides a forecast of KPI effects. This gives your category and pricing managers a differentiated basis for each pricing decision and allows them to set prices manually. For me, this form of man-machine cooperation is particularly remarkable, because here the algorithm with complex mathematics and statistics complements the competence and proven gut feeling of the assortment professionals.

Are you now asking yourself what you need to use an AI solution for price optimization? Then look forward to my next post! Or just ask me directly!

Mrs Pricing

In my last blog post I introduced the mathematical concept of price elasticity and the benefits it brings to AI-driven price optimization. As promised, in my post today I explain how you can use price elasticity to create a strategic basis for the use of an AI pricing solution.

With the help of price elasticity you can determine exactly how price sensitive and competitive your products are – ergo: which roles your individual articles play within your overall assortment and what this should mean for the pricing of these articles. In principle, the following differentiation has proven to be useful:

• Common and Focus Articles and
• Basic and Skimming Items.

Customers generally have only a few product prices in mind when shopping. Typically, they remember the prices of the products they buy often and, above all, regularly. In a supermarket, for example, these are basic foods such as bread, butter or eggs. Based on these items, consumers judge whether a retailer is expensive or not because they have corresponding comparison prices in their minds. Consumers therefore react very strongly (both positively and negatively) to price changes for these items, which makes them highly relevant to competition for you as a retailer: We are talking here about common and focus articles. Products that take on such a role are crucial for your image. They enable you to proactively shape your price image and then sustainably underpin it. In the course of AI-controlled price optimization, you will therefore pay particular attention to these articles and apply a dedicated strategy for the specific requirements of these assortment components in order to

• actively increase your customer and purchase frequency,
• to strengthen and expand your brand in competition and
• optimize target KPIs such as turnover and sales.

Basic and skimming items, on the other hand, will serve a different purpose for you and will primarily serve KPIs such as gross profit and margin. Basic and skimming items are hardly or not at all in the focus of your consumers and are therefore hardly price sensitive and just as little relevant to competition. Why is this so? Skimming articles are often longtail products. In our supermarket example, these would be pots or irons. Customers buy these items very rarely, sothey have hardly any comparison prices in mind. The situation is similar with basic articles. These are also rarely bought, but regularly – spices are a good example here. Because consumers are less price-conscious, all these articles are very well suited for optimizing gross profit and margins. It is particularly noteworthy that on average up to 80% of a product range consists of basic and skimming items – so the leverage for your optimisation potential is enormous!

Now the question remains how to use the knowledge gained to control your price optimization by means of an AI. You already suspect it, don’t you? Exactly! That’s what my next article is about. For all those who are rather impatient like me, I will of course provide a shortcut ;-)

Mrs Pricing

In my blog post today, I would like to present THE all-rounder for dynamic price optimization: The Price elasticity. Mathematically as well as strategically, it is capable of great things! I am convinced that there is no other approach that works as efficiently, economically and above all sustainably as the price elasticity.What is particularly remarkable is that it not only provides an ideal basis for price differentiation, but can also decisively support your entire pricing process chain, including:

• The strategic planning of your pricing policy,- the determination of your most competition-relevant assortment components,
• the detection of the products that are particularly important for your customers and thus for your price image,
• the simulation of the earning potentials of various strategic approaches to pricing,
• up to the AI-controlled calculation of market-driven, demand-oriented prices for your entire product range.

Let’s first have a look at how price elasticity is determined and what it means in concrete terms: Price elasticity is determined via the relationship between price and sales. At the individual product level, price-sales functions are thus formed, which represent in the form of a mathematical curve at which price a product was sold and how often. From such a price-sales function, it can now be derived how the demand for a product changes when the price is changed. Strictly speaking, we are therefore actually talking about the elasticity of demand rather than that of the price. Depending on how elastic the demand for a product is, this provides information about:

• How sensitive your customers are to a price change,
• how relevant this product is in your competitive environment and
• how you should treat this product within your pricing strategy.

How do you now use this information for price optimization? If the demand for a product is highly elastic, the sales of this product will change significantly if you change the price (positive or negative). This means that your customers will reactvery sensitively to a price change for such a product. At the same time, in most cases this will mean that this product is also highly relevant for your competitive environment. For this reason, price optimization should be carried out with particular attention for products that are very price-elastic. The situation is different for products whose demand is not very elastic. Here, price changes havea comparatively small effect on the sales. This means that your customers
tolerate price changes very well or do not even notice them at all. This also means that the competitive relevance of such products is significantly lower. In terms of price optimization, this means that you should use these products to operate effectively and sustainably.

What is the next step?

As a first step, you should use price elasticity to analyze your assortment and derive the various functions of your articles. From this you will draw very important, strategic conclusions that will form the basis for AI-controlled price optimization. But more about this in my next post!

You don’t have that much patience? Then reach out to us for a demo.

Mrs Pricing