Transcript: A guide to AI retail pricing strategies

Notice my shift from data science to customer facing, because I realized I’m a lot better at chatting than I am coding! And also, I just love getting to know the intricacies of businesses and how they could benefit from artificial intelligence (AI). And I think that comes from the struggles and the stresses that I had in my merchandising career.

I was constantly putting out fires, constantly doing a lot of manual pulling that I had no time to even think about how to strategically grow my area. And I think if I actually had AI back in the day, for one, I would have worked a lot shorter hours. But two, I would have actually had a lot more love for it, and I hope that comes across today. So let’s get into it.

So, pricing: the amount of money for which something is sold. Seems pretty simple on this screen, but we all know it’s definitely not. Different businesses, different industries have different pricing decisions that they need to make, different levers that they want to pull with pricing, that even unpicking this for a business can seem quite complex.

So, ultimately what makes it into the boardroom, pricing allows you to be more profitable, drive more value and also attract and retain your customers. But that’s quite a lot.

Again, the world is still quite chaotic. But I’m not here to doom and gloom this session because we all know it. But the underlying message here is it was really chaotic and it’s getting less chaotic. So how do you really navigate the past three years? What about when the new normal is over the next year, when all of this is constantly changing, and that all of these things really play a role in pricing profitability.

You’ve got inflation and how that affects you and your consumer base. You’ve got cost pressures. Your cost pressures are lessening in certain industries in certain areas. So is there an opportunity to actually pass that into your customer base and give them lower pricing, or is there an opportunity to have higher margins again?

Also, shape-shifting consumer habits. The cost of living crisis, online transparency… as consumers we want to perceive value for money and also, in a lot of cases, we want more and more for less. So getting to know your customer who’s in market and what price they’re willing to pay is getting even more complex.

Also, complex networks; businesses have grown arms and legs. You might have acquired different businesses, gone into new sales channels, with new pricing models, complex networks of suppliers, factories, distribution centers and stores — all with their own costing. We speak to a lot of businesses that have grown so quickly that they actually didn’t have the processes underpinning it to be able to control prices across all of it.

And also, excess inventory everywhere. Over the last couple of years, it was hard to really gauge those demand and supply signals for a lot of businesses. And that means that they’ve ended up with a lot of aged stock and stock in the wrong places.

I wanted to drill into the challenge that your teams are facing when they’re trying to price optimally. As a business, you can have tens of thousands of SKUs all with different attributes and landed cost prices. You can have five million plus daily transactions, all with different basket sizes and discounts applied. You can have four different sales channels, all with different pricing models and strategies. You can have eight global markets and those global markets have different cost sensitivities and different cost bases. You can have hundreds of branches, all with different stock profiles and their nuances. You’re setting thousands of marketing campaigns trying to get your customers pointed towards different products. And you’re making tens of thousands of yearly price change decisions.

If you really tally all of that up, you’re really considering billions of permutations for a business to do this optimally. And that’s absolutely crazy to put that on a screen.

So businesses are having to do all these billions of permutations and navigate the chaoticness — still in Excel and legacy systems. I used to pride myself on my Excel skills, and love the complex Excel sheet. But we all know that it has its own limitations when it comes to data pulling and transformation. You’re limited in the time that it takes to pull it and transform it so you can make decisions.

Also, it’s limited to a million rows. And for big businesses today, that doesn’t just quite cut it. And still, as soon as you pull the data, it’s out of date.

So people are making generalized product decisions across maybe multiple categories because they can’t really get into the nitty gritty because they just don’t have time for it.

Also, to go into systems as well. This is a screenshot of a system that I had to use in Topshop back in the day. And you can see it’s a mashup of IBM and internal IT resources. And you can see it’s quite a jarring mashup of Pacman and a digital clock.

I was told I was going to love it. I never did. And we all know that ERPs are quite rigid and you can’t really quite get what you need from it. And you spend your days flicking between screens and copying and pasting and copying and pasting. And that is what everybody’s after. But just to caveat, I’ve met some rock star people that are able to do some crazy things in Excel and these systems, but we want to enable them a little bit more.

So how are businesses dealing with all this chaos? So we speak to a lot of businesses. We have a vast partner network. So how have people dealt with the chaos over the last couple of years? We’re finding a lot of people doing short term to stabilize their cash flow on their P&Ls. And our VP of strategy, Chris Ashley, has nicely penned these, the sticking plasters of profitability, which I’m going to take you through today.

So starting with reaction pricing, promotion and markdown. We’re seeing, with a lot of businesses, that they’re reducing their prices to counteract failing customer demand to try and meet their sales revenue targets or to clear through excess inventory.

Also, on the other hand, people are having to raise their prices due to their ever expanding cost bases. But we speak to a lot of businesses that are having to make this at a top level, based on assumptions and no scenario planning to know how that might affect the rest of their chain.

So you can see here from Mace and Pret that they’ve had to do this themselves. You can see the Pret headline is a little bit more sinister, but I’m that mug that’s still got a Pret subscription even though they’ve raised the prices — so they must be doing something right!

Also, you can see here, cheese prices — one we can rejoice about is that cheese prices have been slashed in latest supermarket cuts. But around nine months ago, we spoke to a supermarket that was spending millions of pounds on reducing their prices, but openly admitted that a lot of it was based on top-line assumptions and Excel transformations. And actually, they didn’t even have anything in place to be able to monitor if that was going to be effective and how to change it. Everybody is in this constant cycle of reaction pricing.

Also, there’s a known trade off curve between price and volume. It’s a really hard one to navigate for your business. And we’re finding that we’re seeing a lot of businesses having to put up their prices but not really understanding how that affects their volume. So people are going up against dwindling volumes, which is really hard for long-term profitability.

So now they’re going in and doing really big marketing campaigns to try and get that customer base back. Or they might be relying on reaction pricing that I just was talking about before. So now you’re in this constant cycle of putting up prices, putting down prices and not really knowing how to stabilize that.

Also, people are doing a lot of cost-cutting initiatives. We’ve all felt it, especially me when I was at the Titanic that was Topshop, that things go. So DCs go, stores go. Also, you cancel product orders because actually you can’t afford to bring it in, or it could be that you’re stopping investing in tech. But in order to run leaner businesses more effectively, you need to start investing in something that makes your life easier.

So if you take anything away from this speech, I’d like to take you to take away this. So McKinsey did a bit of research on 1,200 publicly owned companies and evaluated the effects of different strategies on either returning, maintaining or growing in profitability.

And they concluded that those that invested in pricing strategy, upped their margins whilst maintaining volumes, and had a longer term sustained ROI than any of those sticking plasters that I detailed before. And this is pretty massive. But they also said there was such a small number getting started on their pricing journey, and we find that a lot too. So, why aren’t businesses getting started on their pricing journey?

How can AI to improve retail pricing strategies?

So we find that a lot of people have a lot of complexities in the way that they work — chuck in all those things that I detailed on the chaotic slide — and it can be quite overwhelming to even start on this journey. And that’s why a lot of people were relying on the same way that they priced for a couple of years, or reverting to those reactionary pricing moments.

Or it could be organizational change. So we’ve all probably had to endure the implementation of a new ERP, a new warehouse management system or a new point solution, and the sweat that’s coming down your face trying to figure out whether you can run your day-to-day businesses on it.

Or, three, is an uncertain investment. Ultimately, when times get tough, you’ve got less money to spend on systems. You need to know it’s going to return on its investment. I’m going to try and use the next part of this to debunk all of these and show you why artificial intelligence (AI) is the answer.

AI can handle the complexity so you don’t have to. I said before that a lot of people are working in Excel sheets, and that has its limitations when it comes to data. AI in a productionized environment can have automated data feeds, a robust data pipeline joining it all together and advanced cleansing techniques.

That means that no data point is out of reach and that means that you can reduce silos across different systems that you would want to feed into your pricing, but just can’t get to. And it also gives people an instant data source so they can start making decisions. And that’s insanely powerful in itself.

Also, we find a lot of businesses are having to make generalized pricing decisions across categories, but AI can handle granularity so it can look at every product location or customer group in isolation. This is AI prediction at scale. Also, AI gives the ability to understand the true effects of pricing on demand.

It can simulate hundreds of price changes on each of those products and also output the metrics that it would affect, so things like margin and volume. This is insanely powerful if you want to look at different pricing scenarios across your product range. I’m going to go into this in a little bit more detail in a couple of slides because this is the really cool bit.

Also, AI gives you the ability to optimize against your target metrics. Meeting your metrics has never been more important because you need to maintain that.

This just puts a lot more power in your team’s hands, and it also takes a lot of the risk out of actioning price changes as well. 

AI can also find relationships between products. What we hear from a lot of businesses is, “oh, I actually haven’t actioned a lot of price changes on certain products”, or “I have a lot of newness, how can I really truly understand how price will affect those?”

AI can find similar products through hierarchies attributes and then apply the learnings from that to them, which is some serious power. Also, it can see if you reduce a price in one area, how it might another — and that means that you’ll really start to balance pricing decisions across your range.

Also, dynamic agility. AI is an always-on application. It’s not a one time thing in an Excel sheet. AI learns in light of new data; it’s constantly adapting, it’s constantly learning. So if your consumer behavior changes, i.e. your customer base starts reacting to different products differently or different price points or different promotions.

Or it could be that those external factors are affecting your industry a little bit more. Or it could be your cost prices have decreased or increased in certain areas, or you could have a suboptimal product mix and you need to clear through it. AI can see all that and adapt accordingly, which gives you agility in uncertain times.

So I’m going to dig a little bit more into why understanding the effects of price is really quite difficult, and hopefully break that down here. So we’ve got quite a simple graph here that’s mapping demand volume against price.

In a simple world, you would think that a low price equals a high demand and a high price equals a low demand. But in reality, for most industries and product portfolios, it’s really hard to understand this relationship.

So here, the same axis, but we’ve plotted one product’s daily sales volume and the average selling price against it. And as you can see, there’s quite a loose correlation here. And this is what your team’s looking at in Excel sheets and trying to really figure out how you can pull the price in. And how you can pick a point on that line that is most profitable. This is because there are so many other factors that can contribute to demand, other than price.

So this is starting modeling those factors against price. So it could be stock levels that last year, that product, you had a severe supply chain delay on it. So it wasn’t optimally stocked across all your stores, and that equaled the lower demand. It could be that it’s a really seasonal product, it could be that your industry in certain areas is really struggling because of the economy. It could be that you were insanely promotionally-driven to clear through a lot of stock last year, but you’re trying to wean yourself off of that. It could be that there’s lots of product cannibalization across your range.

So when you really start to model this, this is when you can truly understand the pricing effect. But only really AI can do this for you — and this is AI-modeled price elasticity.

And it could be a case after you’ve done all this, and after you’ve factored in all of these factors, actually price isn’t seen to be a huge lever on demand — it could be that by discounting it, you’re needlessly eroding margin that you could save. Or it could be after you’ve stripped out all of this, pricing is a huge influencer to demand. So that gives you a lot of opportunity when it comes to pulling the strings of pricing.

I know this is quite abstract on this screen, so I wanted to delve into it a little bit more.

So this is one product, one location. This is a visualization about all the factors that AI have found to be contributable to demand. And it’s also applied a weighting of that factor as well. So you can see there’s other factors like marketing, seasonality and website placement. This is really hard for somebody to be able to unpick this themselves, especially when every single day that this product has a different combination of all these factors. It’s really hard to strip out the effects of pricing.

Back to granularity; this is one product, one location. But it could be a product in a customer group. Every single product and customer group will be affected by these factors in different ways and it will be constantly changing. So this is constantly adapting, so you can pick the best price point that allows you to meet your metrics.

Also, the next one I want to debunk is that people believe there’s too much organizational change.

So, ultimately, your teams are still going to have to make pricing decisions based on data — but what if they could get an instant data set that had all the data points and the granularity that they always desired. They had access to AI demand forecasts, so they could start being more proactive than reactive. All of that price elasticity modeling, as well as price simulations across your whole product range and how that rolls up to your target metrics.

This just gives people a lot more fuel to feel more enabled to make those pricing decisions. And also, like I said before, every business is different. You’ll have different logic, different rules, different guardrails — and you are able to constrain AI so that when your team gets a pricing suggestion, they trust it every time. And also a big old wrapper of explainability. Every AI needs explainability so that your team starts to trust it. But then when they do, this is when the serious value arrives.

So the final one — it’s an uncertain investment. These are the metrics that we are consistently seeing when the AI pricing space. This is across a multitude of industries and different business models. So we’re seeing a one to five percent gross margin improvement and, for some businesses, that has meant multi, multi millions of pounds.

Also — for those businesses that needed to use pricing to pull their stock lever and reduce aged stock in that longtail — we’ve seen consistently an improvement of over five percent increase in volume.

You ultimately need your teams to love it and trust it; 100% pricing suggestions accepted. And I put a little asterisk here because, ultimately, it takes time to get to that point. It might be that we need to wrap it in more guardrails, more logic, also enabling teams to use it. And then you can get to this point where you’re really trusting the AI.

So the takeaway from this session, I hope it’s been useful, but you’re still going to have to navigate some chaos, and that’s undeniable. But you could stop relying on the sticking plasters and start utilizing AI pricing power.

I’ve never personally attempted to send a text message through a Microsoft Office product. But, as a former retail merchandiser, I have experienced many, many moments of personal anguish — one or two ‘dilemmas’ of my own — because of the limitations of spreadsheets.

Yes, just like Kelly Rowland couldn’t imagine her life without Nelly, there was a time where I couldn’t imagine my life without spreadsheets. Many merchandisers and planners today still feel this deep attachment. Spreadsheets are such an important part of our daily lives — but why do they hurt us so often?

In this article, I’m diving into the archives to run you through five of the most common spreadsheet headaches and heartbreaks that I endured as a merchandiser.

1. Unresponsive Excel? You’re not alone, Kelly

Workbooks weighed down with multiple tabs of data and complex recalculating formulas, with each sheet drawing data from another. This often makes workbooks unstable and unreliable.

Made a tiny formula error? Have you accidentally pasted data in the wrong format? Had the nerve to let several people work at the same time on a spreadsheet? Congratulations! You’ve won a one-way trip to Excel Hell.

Excel wasn’t made to handle the amount of data merchandisers need for their job, so we’d often push it past its limits. This can lead to the nightmare of every Excel user: an unclickable error message that says “Microsoft Excel is not responding”.

I often found myself in Kelly Rowland’s position, tearfully looking out the window as I tried to cope with unresponsiveness in the face of a spreadsheet’s limitations.

I remember one particularly challenging night. It was 10pm, the night before I was expected to submit a new weekly sales, stock, and intake (WSSI) budget and plan. With my attention focused on the task, I neglected to regularly save my workbook. Then I pasted some data into the sheet, and saw the dreaded error message.

There was no turning back. All I could do was terminate Excel, knowing in that moment in the late evening, I had lost every bit of my hard work.
Merchandisers, who spend hours carefully managing data, hate having to deal with software that breaks down without warning. They need stable software that won’t throw away hours of work for no reason.

2. Caught in a bad row-mance

Who needs more than 1,048,576 rows of data in a single sheet? A merchandiser, that’s who. Sadly, for Microsoft Excel 1,048,577 rows is one row too many. 

Excel’s row limit is bad news for merchandisers who have got portfolios of products, multiple channels, and more online data than most companies know what to do with. 

That means merchandisers find themselves crying over a patchwork quilt of sheets pulling data from other sheets. Some of the sheets are made by former employees, so when things go wrong, there’s no one around who knows how to fix the problem.

One of the best things about Excel is its versatility; it can be applied to many use cases, but that doesn’t mean it’s the optimal solution for every task.

I recently read an article about a Japanese artist called Tatsuo Horiuchi who created works of art using Excel — that’s amazing. But you expect art to take a long time and as much as I’d like to think there’s an art to merchandising, it’s an enterprise that needs speed and stability. 

Merchandisers need software that is made to handle the huge amounts of data they manage, not workarounds for software that doesn’t.

3. All dump, no pump

One of the things that used to bug me most about Excel was the fact that it couldn’t stream data automatically.

Excel’s all dump, no pump approach causes issues when you’ve got data across multiple sheets in a workbook too, as the linked data often isn’t live and needs to be refreshed — which itself can often lead to an “Excel is not responding” meltdown.

My life as a merchandiser was a disco of data dumps. Maintaining up-to-date data in spreadsheets took me away from the work where I could really add value.

By the time I’d updated my data across the myriad spreadsheets covering the products I managed, the data I’d put in would often already be out of date. I spent more time in cells than most criminals.

This was bad enough during periods of more predictable demand, but these days merchandisers are expected to manage demand against a backdrop of uncertainty, whether that’s a global pandemic or economic downturn.

It’s just not realistic to expect people to face these tactical challenges effectively when so much of their day is spent in spreadsheets, which are really only a small step forward from using a pen and paper.

In an age of cloud data and web apps, Excel fails on the fundamental need of merchandisers: to get a live view of their data.

4. Spreadsheets never forgive

We’re all going to make errors in our work. That’s because we’re human, but Excel isn’t, and it’s got no time for your mortal mistakes.

The problem is that Excel leaves the door to human error wide open. Every single cell is an opportunity for you to input the wrong data, format the data incorrectly — or worse, for Excel to go into one of its moods and shut down.

For merchandisers, spreadsheets aren’t designed to just keep records. They use them to make decisions on a daily basis. The scope for error in Excel is worrying for us, because all it takes is for one thing to go wrong and suddenly we’re making decisions using dodgy data.

For new merchandisers who are less familiar with Excel, the risk of error is even greater. The role is fast-paced and has a steep learning curve, so it leaves them vulnerable to little errors that can mess up their work and (possibly) their career.

As an example, a member of my team at another company once made a small mistake in Excel. The result? She sent 10,000 jumpers to the Middle East, where we weren’t able to sell them, and it was too expensive to bring them back to the UK. So the value of the stock was lost. It was a small error, but with big consequences.

People will always make mistakes. But merchandisers should expect a software solution for their role that minimizes the risk and potential damage of mistakes.

5. A very particular set of skills…

For all its failings, Excel is pretty powerful if you have a very particular set of skills. But most of us aren’t blessed with mad Excel skills.

Advanced formulas, pivot tables and conditional formatting are great ways to transform and visualize data. But not many people know how to use them.

Often, one person on a merchandising team knows exactly how to navigate the maze of Excel sheets, resulting in a single point of failure as a business. If that one person leaves the company, the rest of the team is left scratching their heads, trying to figure out what went wrong.

Sadly, this problem is only getting worse. Retailers are getting more complex, they’ve got more channels and more products to manage — that means more data and (yes) more spreadsheets. Merchandisers are expected to adapt this, but who has time to learn macros? Not merchandisers.

Merchandisers need a very particular set of skills, but extensive Excel knowledge shouldn’t have to be one of them.

6. Hide and sheet

Another Excel limitation that can hold merchandisers back is the way files are  stored.

While Microsoft’s Office 365 version of Excel has helped overcome some of these challenges by using cloud storage, many businesses are still using classic Excel, which stores files on a shared or local drive.

This often means spreadsheets are stored all over the place, and are often siloed in one drive away from the rest of the business. For instance a merchandising team might manage products in a spreadsheet saved to their shared drive, which other teams (e.g., buying) can’t access.

This siloing can can result in a duplication of effort, with two or more teams trying to maintain different views on the same products.

It also means that there’s no one version of the truth on that data. This can mean, because they are making decisions using different, teams can sometimes find their actions don’t line up, and sometimes work against one another. This is hardly a formula for success.

Merchandisers need a solution that centralizes all their data and gives teams the opportunity to collaborate using a single version of the truth.

There is another way (honestly)

If you’re a merchandiser or planner, I imagine that at least a few of the above headache-inducing moments sound all too familiar. For a number of years I found my brain was, on a daily basis, completely flooded by spreadsheets and spreadsheet-related information. Rows and rows, columns and columns. Formulas. Tabs. Error messages. Data.

So. Much. Data.

And I wasn’t alone. I’d go as far to say that spreadsheets have become a part of merchandising and planning in the same way that Adobe has become a part of design; they’re a fundamental part of how the job gets done, and a very useful (yet at times overwhelming) tool.

For years, I simply couldn’t imagine doing the job without Excel, and nor would I want to. Yes, spreadsheets have their limitations, so what is the alternative?

What’s the solution?

In recent years, I’ve been on a journey in my career, moving away from moments of spreadsheet-related merchandising madness and transitioning into a role as Product Intelligence Lead at Peak.

Here, I work really closely with our customers — merchandisers and planners at some of the world’s leading retailers — to help them rethink their approach and introduce them to the benefits of Products, one of our AI-powered applications.

Products acts as a single source of truth for merchandisers, planners and buyers, giving them a holistic view of product performance down to an individual SKU-level. From here, teams can use it as a base to action AI-driven, instant decisions around rebuy, markdown and replenishment to name a few.

The purpose of the Products application is to alleviate the strain on time-stretched teams who want to spend fewer hours with their heads number-crunching in spreadsheets and more time on strategy and creativity. It combines siloed data, surfaces key information and performance metrics about your current trade, and helps you review masses of lines effortlessly — in one handy, all-encompassing dashboard.

I don’t expect you to take my word for it, but I do encourage you to see the magic for yourself. Products is a game-changing proposition for merchandisers and planners and I wish it had been around during my time in the industry!

Pre-markdown: diving into the data

A common pain point we hear from our merchandising customers is a lack of time (and resource) to take into account all of the data needed to make informed trading decisions. This resonated with our roundtable attendees, with many claiming that data scattered across multiple systems leads to many hours spent number crunching.

One consumer goods key account manager claimed that “we have the data but we aren’t using it. Tracking the decisions and the results in the data is still a massive challenge”

“All of your time is spent on cleansing the data instead of thinking about what you should do with markdown. You can pick up on the slow sellers, but it’s difficult to be proactive,” said another.

A fashion start-up explained that they were favoring “clear as you go” promotional markdown strategies rather than four-times-a-year, big-hitting events. A retail consultant had observed that retailers had become reliant on these quick, fast promotional events – and that “almost nobody defines the objective of the promotion before they plan it. So it’s hard to tell whether it was a success or a failure.”

Markdown strategy

One particularly tough challenge highlighted was the initial planning behind a successful markdown campaign. One attendee cited difficulties around taking into account factors like shelf life, product lifecycles and the need to balance these with “respecting the brand along the way,” plus knock-on effects from the pandemic that continue to linger – such as drastically-altered customer buying behaviors and habits.

Lead times have increased significantly because of the ongoing supply chain crisis, and brands now need to find a way to “incorporate this nuance into their strategy.” Intake margins have risen substantially due to the increase of freight costs and soaring energy prices, which means that retailers now have a lot less margin to play with when marking down.

Peak continually finds that using AI-powered demand forecasting and price elasticities to optimize markdowns are often beyond the imagination of most retailers who are at earlier stages in their data journeys. A multi-channel manufacturer of consumer electricals explained that “we can look at current performance of lines using sell through and rate of sale metrics to highlight slow-sellers but it’s the decisions after that that are missing; it’s the planning ahead.”

One consultant explained that a lot of retailers were still favoring traditional blanket discounting “doing 20%, then 30%, then 70% off. The problem with this is you end up relying on that final hit of 70% – and then it’s too late and you’re selling for less than cost.” Another honestly explained their approach to markdown was more based on gut-feel and intuition; “there’s not much science in it, we just mark it down by 10% and see how it goes.”

Competitor analysis was also a popular discussion point amongst our attendees, with different brands ranking it at different levels of importance when it comes to their markdown strategy planning. One guest claimed that while they looked at what their competitors were doing markdown-wise, no decisions were taken purely based on competitor activity. 

Another, though, believed that competitor analysis was a key piece of the markdown puzzle. “Get your competitor analysis right in the planning stage so your initial pricing is right and the stock right for your competitive position – these are the big wins, everything else is tactical response.”

AI-driven demand forecasting

Another key talking point during the roundtable – and one that is obviously close to our hearts at Peak! – was the utilization of AI for demand forecasting. By leveraging all of your data, not just the top and bottom 20% of a spreadsheet, AI can empower teams with accurate, granular forecasts that allow you to see into the future and react to problems before they happen. 

We discussed the importance of…

1. 1. Data sources: Using AI to go beyond just looking at sales and stock levels, and start to bring in further datasets that give demand explainability. These include promotional/markdown calendars, events and even weather data. 
   2. Techniques and explainability: AI can quickly learn historical patterns and use the data that best explains the demand curves. It also brings granularity at scale, taking into account thousands of product-location combinations. 
   3. Automation and feedback loops: Data feeds AI applications automatically, so there’s no need to pull together data from different systems and spend hours wrangling the data in Excel. AI models constantly learn in light of new data, delivering continually-improving forecasts. 
   4. Customizing AI to your world: An off-the-shelf, one size fits all-approach to AI demand forecasting isn’t going to give you the best outcomes. The forecasting technique, guardrails and ways of consuming the forecast have to work for your business.

Price elasticity

Price elasticity is a measure of how much demand is affected when the price of a product changes. If demand is affected, the product is elastic. Understanding price elasticity allows you to simulate the effects of markdown percentages and choose the point that maximizes margin and sell-through.

Markdown success: the metrics that matter

One major talking point at the roundtable was the best way of measuring the success of a markdown. What metrics do teams consider to be the most important? What results should teams be prioritizing?

One retailer explained that they realized their markdown efforts were affecting gross margin, and that they needed to change their reporting to ensure they could monitor that before and after markdown periods.

When helping retailers with their markdown strategy at Peak, we find that there are four focus benefits of using AI:

• Profitability: Don’t needlessly erode margins by heavily marking down. Make promotional and markdown periods profitable every time

• Reduce terminal stock: Sell-through stock in the selling window and free up capital tied up in aged stock

• Never disappoint a customer: Markdown prices and promotions that entice in customers and keep them coming back for more without ruining your brand perception

• Improve productivity: Empower your teams to spend less time pulling together data and wrangling excel sheets, and more time making informed decisions

Sustainability

Sustainability continues to be a hot topic amongst all retailers, as well as businesses across other sectors. One question raised was around the relationship between markdowns and returns from a sustainability perspective. An attendee had seen that, if you markdown products close to full price, many people may end up buying the product again for the cheaper price – and returning the full-price item. To avoid this, should the markdown, perhaps, only be actioned once a 30-day return period has lapsed? 

This was a big talking point, with one guest claiming their brand actually analyzed why certain items are returned, but that the majority of others didn’t go into this level of detail. Peak’s Retail Director, Tom Summerfield, was of the opinion that markdown can actually help brands dispose of stock in a timely manner, rather than using potentially more carbon to move it around to store elsewhere – with AI also enabling teams to optimize markdown in the best way possible to minimize returns.

Another viewpoint was that in-store markdowns are more likely to be kept rather than returned, as people still love the thrill of visiting a store and finding a bargain!

Conclusion

Our markdown virtual roundtable was a great opportunity for Peak to hear first-hand, from a wide range of retailers, some of the problems they face around markdown strategy – and for them to learn more about the applications of AI for markdown and how technology can alleviate some of these concerns.

Thanks to everyone who joined us to chat all things markdown – and keep your eyes peeled for the next one! If you’re keen to learn more about Peak’s AI-driven markdown application, you can see it in action in our short demo below.

Blanket markdowns are not the answer

They might act as a good ‘quick fix’, but too much focus on flash sales, promotions and markdown events to drive sales means unsteady cash flow. 

With costs soaring and margins squeezed, the ability to pull back from the cycle of longer sales periods and deeper discounts that many retailers have fallen into offers a competitive advantage. We talk regularly to retailers keen to reposition as a ‘premium’ brand that doesn’t heavily discount or promote. 

So, how do you compete without marking down excessively? Artificial intelligence (AI).

This data technology can ingest huge volumes of data from within your organization, as well as external sources, to provide predictive analysis and recommendations in real-time – simplifying your forecasting, planning, execution and demand planning. 

Peak’s Markdown application (app), for example, recommends markdowns for each individual SKU that will increase sell through and save margin – ensuring that you don’t leave profit on the table and out-of-season inventory in the stockroom.

AI markdown with Peak

Markdown helps you set the right price to maximize profit throughout the markdown period while taking into account business constraints and priorities (e.g. target sell through of all close out units, or target weeks of cover for each product). It can be for a specific location, such as a store, or channel (web/retail), or it can be applied at a national or international level. 

Markdown is all about understanding how demand changes with price; data scientists would call this price elasticity. How you set price impacts demand, and price elasticity is finding the sweet spot between increasing demand and maintaining gross margin and profitability. Markdown simulates hundreds of thousands of SKU/price combinations to find the ones that will maximize the metrics that matter most to your business.

Every business is different, and Peak bakes business nuances and your way of working into everything we do. We work closely with you to understand the rules, logic and guardrails unique to your business – such as embedding pricing rules or not marking down a certain brand – and ensure they are represented in your Markdown app. 

But the app will only drive outcomes and value if it’s accessible. Peak provides a number of options to enable teams to interact with models.

Making AI accessible to everyone

We’re not aiming to replace your existing systems – Peak is designed to work with them. We have a specialist integration team, focused on scoping out how we push the price recommendations to your ERP, POS or price execution systems. 

For the more technically-advanced business, we provide markdown scenario planning dashboards. These enable you to explore different markdown strategies and how they will affect your key metrics, deep dive into different locations and product-specific suggestions and ultimately export the prices that will work best for you. For those with less experience, we can also enable full markdown automation with no human intervention at all.

This isn’t an out of the box solution. Your business will change and evolve – and Peak will grow alongside it. You can extend your use of Markdown, integrate complimentary apps, or expand your use of Peak to other areas of the business.

Markdown results

Peak’s Markdown application is already being used by leading retail businesses, helping to power greater levels of profitability with SKU-level markdown suggestions in a range of situations. 

Implemented in a single department, one omnichannel retailer identified an opportunity to drive $3 million in additional margin – equivalent to 1% of its overall turnover.

Another retailer calculated that the application saved their 50-person merchandising team 30 days per year. This is time they can now spend growing their departments and focusing on other high-priority tasks.

With Peak’s Markdown application, retailers can – at the click of a button – get intelligence-driven markdown recommendations in real-time, so they know which products to markdown, when and by how much to meet business objectives. This frees up entire teams to spend more time on strategic work and important tasks that are often otherwise neglected!