7 Price Planning Tips to Maximize Retail Profits (2020)

7 Price Planning Tips to Maximize Retail Profits (2020)

Price planning is one of the most difficult (and often misunderstood) elements of retailing strategies. A relatively small change in price can have an enormous impact on product demand, and ultimately, your bottom line.

“Pricing right is the fastest and most effective way for managers to increase profits.” (McKinsey & Company).

That’s why the most successful retailers set pricing optimization as a critical priority.

But emulating successful retailers in price planning isn’t enough.

Standard price planning strategies (competition pricing, cost-plus, high-low, EDLP, etc.) may cover generic approaches to managing and optimizing price for any given product, but they don’t account for the most critical variables that determine your pricing success. These missing variables (like demand throughout product life cycles, pricing’s impact on demand, internal and external pricing constraints, etc.) ultimately determine how profitable your business is.

Retailers who don’t account for these considerations often struggle with profitability.

More sophisticated (and successful) retailers have built these variables into their price planning process to maximize their profits.

So, what are these considerations, and exactly how do they impact pricing strategy?

And moreover, how do you apply this knowledge to increase your profits?

1. Define Your Positioning

As any retailer will know, optimizing your prices isn’t as simple as changing a number on a spreadsheet.

There are many variables that come into play when choosing an appropriate price for your products. This may include:

  • Influencing factors on demand
  • Product life cycles
  • Inventory levels
  • Internal and external constraints

But before we can even start discussing these variables, there’s another, perhaps more fundamental consideration that retailers must always abide by.

We’re talking about your brand and positioning.

  • Are you positioned as a luxury brand?
  • A consistently affordable EDLP retailer?
  • A discount store for bargain hunters?
  • A purveyor of high-quality goods?

Depending on your brand messaging and your target market, your approach to pricing can be enormously different.

Here’s a very simplified example:

  • High-End & Low Volume Retailer
  • 10 units at $100  = $1,000 revenue
  • Economy & High Volume Retailer:
  • 100 units at $10 = $1,000 revenue

Despite generating the same revenue, these two imaginary retailers have to treat product pricing and inventory very differently.

  • If you are positioned in the market as a high-end retailer, constantly launching markdowns and clearances may hurt you in the long term by damaging your brand (this is why many high-end retailers never markdown products)
  • Conversely, if you are an affordable retailer, increasing prices may net more profit in the short term, but may drive away loyal bargain shoppers

Therefore, it’s vital to create your own pricing rules and thresholds (discounts limited to certain product classes, maximum clearance markdowns, etc.) to ensure that your brand and positioning aren’t tarnished in the long-term.

2. Forecast Your Product DemandNot Product Sales

There are no two greater forces for determining optimal pricing than the fundamental market forces of supply and demand.

In fact, most of the concepts we’ll discuss in this article stem from these two forces.

Supply is the total quantity of a specific product that’s available for purchase.

Demand is the total consumer desire to purchase a specific product.

The interplay of these two concepts is the single-most important element of pricing.

  • If a product has an exorbitant amount of demand (lots of consumers desire to purchase it) but very limited supply — prices can be high
  • If a product has very little demand but lots of supply — products will not sell at a high price

On a fundamental level, demand determines how much people will be willing to pay for a product. Consequently, it also determines how many units of a product you can sell at a certain price point.

And virtually all retailers understand this as a theory.

But many of them stop one step short of actually implementing this knowledge in their pricing strategy.

That’s because most retailers forecast their sales instead of forecasting their demandAnd most planners base their prices on sales forecasts instead of demand forecasts.

What’s the difference, and why does it matter?

The first big difference is that a sales forecast does not take factors that influence demand into account.

What does this mean?

At its core, a sales forecast can show you that, if all else was equal (location, inventory levels, price, assortment, consumer preference, competitors, weather, etc.), you can expect to sell X number of units.

The problem is that all things are not equal.

Just because you sold 100 units of Winter Jacket A at $100 in New York last December, does not mean you will sell 100 units of Winter Jacket A at $100 in New York this December.

What if last December:

  • You sold out of Winter Jacket A in New York?
  • Winter Jacket B, C, D, E, and F cannibalized sales from A?
  • There was more foot traffic because you were selling popular Scarf X on promotion?
  • New York temperature was unseasonably cold, and won’t repeat this year?
  • A major competitor was closed down for renovations?
  • Etc.

Even taking one of these variables into account, you can see how a sales forecast can lead you astray.

If, for example, you completely sold out of WInter Jacket A (not satisfying the demand and losing out on potential sales) — by relying on a sales forecast, you’re actually setting your business up to make the same exact mistake again.

This is where demand forecasting shines.

An accurate demand forecast will predict how many units you could sell at different price points at different times and different locations, given dozens of influencing factors (i.e. product assortment, cannibalization, competitor prices, weather, etc.).

For example, demand forecasting can help you answer this question (and a million others):

If you sold 100 units of Winter Jacket A at $100 last December, how many units could you have sold at a price point of $120?

You might discover that you would have still sold all 100 units, but at a higher price point (because you underestimated demand) — increasing your revenue by 20%.  

This sort of process should be the basis of your price planning, and that simply can’t happen if you aren’t leveraging accurate demand forecasting.

What does this mean for price planning?

In short, if you base your retail decisions (including price) on sales forecasts:

  • You’ll be liable to make the same mistakes in pricing and inventory as you did last year
  • You’ll miss out on massive opportunities to increase profits

If you want to use your historical data as a basis to improve your performance, and leverage all of the opportunities at your disposal, you need to be using a demand forecast instead.

Retalon’s retail AI engine offers the most accurate demand forecast in the world. See it for yourself.

3. Get Granular With Unique Product / Locations

Every single retail product has its own unique life cycle (Product Life Cycle, or PLC for short).

Product Life Cycle describes the demand of a product across a timespan — from introduction to obsolescence (when a product is no longer desirable).

  • Commodities like milk have very stable life cycles, with small dips and peaks throughout the year
  • Some products, like winter jackets, may only have demand for a few short months
  • Others, like camping tents, can have longer seasonal peaks without dropping to zero
  • Others yet, like new video games, will peak at launch, quickly (and permanently) losing demand as the months go by

Here’s what the demand curves might look like in the absence of a variable like pricing:

And this isn’t just limited to general product categories.

Every single unique product variation (or SKU) will have its own Product Life Cycle as well.

For example, different color and size winter jackets from the same collection may have slightly different demand curves. A medium-sized, gray colored jacket will most likely be more popular than a hot-pink version in x-large.

But that won’t always hold true across every location.

Every single unique SKU will also have a different Product Life Cycle at each unique location where it’s sold.

That’s because demographics, socioeconomic factors, weather, and even personal tastes differ based on geography. A medium, gray, winter jacket will be in-season longer in Alaska than Kentucky.

Here’s what this might look like, visually mapped out:

If you sell 4 versions of a product in 3 different locations, you actually have to manage 12 different Product Life Cycles — each with their own unique demand curves.

You can imagine how quickly this number balloons when you’re dealing with thousands of products with hundreds of variations across dozens of stores.

If we combine this with knowledge from the previous section (that price and demand are inextricably linked) we reach an interesting conclusion:

A single product can have dozens (if not hundreds) of “optimal prices” depending on the SKU variation and location it’s being sold in.

What does this mean for price planning?

If you fail to account for the demand of UPL (Unique Product / Location) combinations, you will not be able to calculate truly optimal pricing for your products.

If your organization prefers to set initial prices consistently across all of your locations (I.e. Winter Jacket A will cost $100 in all stores) — this may not seem like a big issue.

But since demand will not be equal in all your locations, your inventory quantities must reflect this. And if your inventory quantities are not based on an accurate demand forecast for every store / SKU combination — then you will inevitably lose sales in some locations, and be forced to markdown inventory in others.

Discover how Retalon automatically forecasts Product Life Cycles for every store / SKU combination.

4. Optimize Prices for Each PLC Stage

Imagine you found a single, optimal price to maximize gross profit for a specific product in a particular location.

Your price / demand curve might look something like this:

  • Price remains flat
  • Product goes through a natural seasonality, unaffected by price

In a perfect world, pricing would work exactly this.

And for some companies, this is precisely the model they prefer. Luxury retailers and high-end brands prefer a single optimal price that they rarely deviate from (regardless of product life cycles).

But for the vast majority of retailers, this approach won’t work.

Firstly, there’s no guarantee that you’ll have the exact amount of inventory to satisfy demand perfectly. And this defeats the purpose of your pricing strategy.

Too much or too little inventory, and your price is no longer optimal. (More on this later.)

Secondly (and perhaps most importantly to planners), static prices remove a very useful lever for controlling demand. Without control over pricing, we can’t:

  • Throw a promotion to spike the demand of a new product
  • Run a sale to increase foot traffic to your stores
  • Put deadstock on markdown to recoup some costs

That’s why price planning isn’t about finding a single optimal price for every SKU in every location.

Price planning is about finding an optimal price for every SKU, at every location, and at every Product Life Cycle stage.

Instead of the graph above, the ideal Price / Demand Curve for each Product / Location should look more like this:

  • An initial price is set for product introduction
  • Temporary promotions are used to boost demand during the season
  • A regular price is maintained until end-of-season
  • As people no longer want to purchase the product at full price, markdowns are used to extend demand and get rid of stock

Each of these stages is an opportunity to optimize prices and increase profits. Let’s take a look at them individually:

Product Introduction (Initial Pricing)

In some cases, setting initial product prices isn’t very difficult — especially if you have historical data from similar products (older models, for example). You can simply analyze your costs and your sales from similar products to find a price point that can maximize profits.

But in many cases, setting an initial price may be a challenge, because the product may be completely new with little historical data to reply on.

Either way, you need to consider

  • The impact of of initial pricing on projected demand (in many cases, a small increase to price can massively dampen demand)
  • The amount of inventory you need to initially stock to satisfy demand at the chosen price

Both considerations are vital for initial price, because if you set the price too high and bring in too much inventory, you may lose money on the product. But if you set the price too low, and don’t have enough inventory to meet demand, you might lose out on massive profit.

In-Season

A product is “in-season” when its demand is peaking.

Most of the time, an in-season product will be offered at the regular price (set during the product introduction).

Finding the optimal price depends on several factors, including:

  • Cost from a vendor
  • Target gross margins
  • Competitor prices
  • Price zones
  • Etc.

Promotions

Simply put, the goal of promotions is to temporarily increase the demand of a product by decreasing its price. This can be done with multiple goals in mind:

  • Generating extra foot traffic to retail stores
  • Winning market share from competitors
  • Making consumers aware of a product
  • Expanding brand awareness in collaboration with vendors
  • Simply selling more units

Promotions come in many shapes and sizes (including percentage discounts, dollar discounts, Buy-One-Get-One, bundles, free samples, financing, etc.).

You can learn more about promotion types here.

When and how to run a promotion is a science in-of-itself. And to do them particularly well, you need to be able to predict their subsequent demand lift (and impact on other products). This is vital because improperly planned promotions can actually harm your business:

  • Promoting products with low instock percentage may not increase total sales, but may decrease profit margins (and anger your customers)
  • Promoting products in a saturated category might cannibalize sales from higher margin items
  • Discounting by too little may not result in a significant promotional lift
  • Discounting by too much might increase demand beyond what you have in stock

As with setting an initial price, running promotions without an accurate demand forecast can leave you losing revenue or profits.

End of Product Life Cycle

At the end of a product’s “season” (or life cycle), demand shrinks and fewer people want to buy it at standard price.

If your inventory and price are perfectly aligned to demand, you’ll sell the last unit just as demand reaches zero.

But it never works this way, for many reasons.

  • Retailers prefer to maintain safety stock on popular products, and overstock on purpose
  • Stores and warehouses often have left-over sizes / product variations for many products
  • Late shipments can cause large amounts of inventory to arrive past peak demand
  • Failed promotions can leave too much inventory on shelves

And that’s where markdowns become vital.

Only a handful of retailers can effectively avoid markdowns. Some luxury brands, for example, will avoid markdowns completely by carefully controlling supply (either by strategically “understocking” or destroying out-of-season overstock). As you can imagine, this approach comes at a massive short-term opportunity cost — one that most retailers can’t afford.

So while many retailers wish they never had to mark down products, most have to.

And one of the reasons most retailers hate markdowns is because they’re difficult to plan effectively.

In some cases, products have to be marked down multiple times over a period of weeks (or months) to sell remaining stock — indicating that the markdown price was too high initially (and some sales were lost over a span of weeks).

In other cases, marked down products will disappear from shelves almost instantly — indicating that the markdown price was too low (and you missed out on some profit).

These issues can be easily addressed with a dynamic demand forecast and simulating “what-if” scenarios for different markdown prices.

What does this mean for price planning?

You need to not only have a strategy for each product, but you should also be planning in advance how each product’s price will change throughout its life cycle stages.

Unfortunately, the prices of products at different stages are often determined by different retail departments and decision makers. For example, these departments may have a hand in your company’s pricing strategy:

  • Buyers set the initial price
  • Merchandisers and Inventory Analysts determine regular prices
  • Marketing sets promotional and markdown prices

This fragmented approach to pricing makes it nearly impossible to have truly optimal prices throughout your product life cycle.

Competing priorities, mandates, and KPIs from different departments are one issue. But the bigger issue is a lack of centralized data and assumptions (like forecasts). Each department analyzes its own data in its own way.

The most sophisticated retailers have started implementing a solution called UPPMO (Unified Price, Promotion, and Markdown Optimization).

The chart above maps out the maturity level of a retailer vs. their effectiveness of their pricing strategies. According to Gartner, there are 5 specific stages all retailers go through on the road to price management and optimization.

Taking a UPPMO approach to pricing centralizes the planning process, eliminates noise and inconsistency in pricing data, and allows retailers to easily manage and optimize pricing across the entire product life cycle.

But if you’re on a lower rung of this ladder, you don’t need to jump all the way to the top. Taking even one step towards unified pricing has massive benefits.

Get our FREE Starter’s Guide to Unified Pricing

5. Always Plan With Inventory in Mind

You can have the most advanced analytics for planning “optimal prices” around all product variations at all possible locations during every life cycle stage…

But it’s all moot if your pricing plan isn’t aligned with your inventory plan.

Inventory is not just a limiting factor — it can also make a perfect plan entirely unprofitable.

There are many scenarios where “optimal prices” and planned discounts can actually decrease your profits if you aren’t optimizing your inventory levels at the same time. Let’s take a look at a few different scenarios where this could happen.

First, let’s start with a few assumptions:

  • We have a reliable demand forecast
  • We have a perfect plan to maximize profits at every life cycle stage
  • Our cost is $50 per unit, and remains the same throughout the timeframe

Here’s a hypothetical price plan for a winter jacket — following the patterns we discussed in the previous section:

Scenario #1: Inventory Shortage

What happens if we run out of stock in our peak month, restock late, and with too few units?

Even with the best-laid price plan, an oversight on inventory and a delayed shipment causes us to lose more than 50% of our profit.

Had we known that we would only have 400 units for the season, we could’ve adjusted our price plan to maximize profits. Here’s a hypothetical case with the same inventory levels:


It’s not as good as our initial plan (because there, we optimized perfectly for product demand and gross profit) — but it’s much more profitable than the alternative.

Scenario #2: Overstock

What happens if we stick to our original pricing strategy, but our inventory analysts order way too much stock?

What happens if we order 1,800 units instead of the 900 that we have demand for?

Without any additional markdowns, we would lose $10,800 on this product.

In order to sell through the entire stock and hit profitability, we would need to sell at completely different price points — making our initial price plan worthless.

What does this mean for price planning?

Although we used extreme examples to illustrate a point — it’s easy to see how even minor discrepancies between your price plan and real-world inventory levels can have an impact on your profitability.

The critical takeaway is this:

A price point cannot be optimal unless it takes inventory into account.

This presents a fairly big challenge for retailers that operate with functional silos. If initial prices are set by buyers, inventory is maintained by inventory analysts, and promotional prices are dictated by marketing — it can be very difficult to optimize pricing for your products.

A lo-fi solution to this problem could be simply improving communication and planning across departments. Sharing information, working from a single set of assumptions, and aligning your processes can help substantially in avoiding these issues.

But realistically, there are too many SKU / Store combinations, too many product life cycles, and too many variables for anyone to take into account. An intelligent, automated retailing system is necessary to truly bridge the gap between inventory and pricing.

Discover how Retalon’s AI driven retailing solution unifies inventory, merchandising, pricing, and promotions — using the world’s most accurate demand forecast.

6. Account For Your Pricing Constraints

So far, the prices we’ve talked about were only optimal in a theoretical sense.

Any retailer reading this will know that prices can’t just be set based on a mathematical model or a suggestion made by AI. The reality is much more complicated than that.

Even if you know that the most optimal price for a product is $10 — an MSRP may require you to price it at no less than $12. Or maybe, if the product was priced at $10, it would take away sales from your more profitable private-label version. Or maybe, a competitor just started offering a similar product for $8.

All of these real-world limitations are pricing constraints.

There are dozens of potential pricing constraints and business preferences that will will influence pricing Taken together, all of these constraints will define the upper and lower bounds of your pricing, and create a band of possible prices for each product:

This graph is a visual representation of some common pricing restrictions, and how they might interact with each other to form a “container” (or a set of price-bands) within which you can set prices without breaking a restriction.

As you can see, these pricing constraints can’t be optimized for individually.

Although one constraint may set a maximum boundary for your price, another equally important constraint might have a slightly lower maximum price. If you only looked at the first constraint, you might build out a pricing plan that breaks a critical constraint (like a legal restriction to pricing).

As such, all constraints must be accounted for at the same time.

And depending on your industry and product, this can be relatively simple (unregulated consumer products where you manufacture your own goods) to dreadfully complicated (highly regulated pharmaceuticals with complicated supply chains).

You can accomplish this with:

  1. A manual process with Excel and traditional business intelligence tools
  2. A dynamic and automated process powered by predictive analytics, integrated with the rest of your business units (e.g. inventory, promotions, etc.)

Either way, it’s vital that your price planning process can account for all of your pricing constraints at the same time — lest you put your company into a position of legal exposure.

Here are some ways pricing constraints can have an impact on your pricing plan:

Store Types

Many retailers don’t just have a single type of store.

In reality, they have some combination of:

  • Small convenience (or express) shops
  • Mall locations
  • Large stand alone stores
  • Outlet locations
  • Pop-up kiosks
  • Booths in department stores
  • Ecommerce platforms

Each of these store types will have slightly different constraints to pricing.

For example, larger stores may want to offer cheaper prices, while express airport locations may want to set higher price points.

Price Zones

You may want to change product pricing based on where your store is located geographically, based on the demographics of your shoppers, based on municipal / state jurisdictions, based on the logistics costs of each store, or even based by proximity to distribution centers.

For example, if you have stores in more remote regions, where transportation costs are much higher — it might make sense to increase prices for products in that location.

Legal Restrictions

Prices may be restricted and controlled by the law. This is very common in verticals such as pharmacy, liquor, and grocery.

And with the recent COVID-19 pandemic, many constituencies are also implementing anti-price-gouging laws for regular retailers.

Manufacturer or Vendor Prices

Retailers may be forced to stay within a price band set by their vendor or original manufacturer (MSRPs). Violating these pricing constraints could result in losing product lines, or even legal action.

Competitor Prices

In general, starting a price war against a competitor is a lose-lose scenario that will ultimately drive margins down significantly for all parties involved.

However, it is still a good idea to keep an eye on comparable competitors (local and online) to understand where your products stand. This is because competitor promotions can hit your bottom line, especially if you offer price match guarantees.

If you are, on average, more expensive than the competition, you may still be able to retain customers by offering more value (better shopping experience, loyalty programs, brands, warranties, expertise, etc.). But if the price disparity becomes too big, you will likely lose business.

Alternatively, if you’re much cheaper than your competitors, you’re probably enticing a larger volume of shoppers to do business with you — but you may be leaving profits on the table.

Product Families

This is a similar concept to product cannibalization except it refers to the immediate “family members” of a product.

The pricing relationship between different products within the same “family” can be a serious constraint.

For example, if you promote a regular size cereal box with a significant price reduction, how will this affect the sales of the jumbo size of the same product, or the small version? If two medium boxes are cheaper than one large box, you may tank sales for the latter.

To address this, prices need to be changed for the entire family group proportionally.

That’s why it’s important to have business rules that take into account the pricing relationship of products within the same family.

Private-Label Products

Many supermarkets, grocery stores, pharmacies, and department stores sell generic versions of brand name products with their own labels.

The relationship between private-label prices and brand name prices needs to be formalized and added as a business rule.

For example, if the price of a branded product is changed, how will this impact the sales of your private label substitutes?

What does this mean for price planning?

There are many more factors and constraints that need to be accounted for when setting a pricing plan for a product — and we can’t possibly discuss all of them in one guide.

To add to this complexity, you also need to be responsive to any number of external factors that change your fundamental pricing calculations.

For example, imagine one of your vendors increased prices on one of your most popular products by 5%. This, of course, has an impact on your margins.

Should you change prices and make the consumer pay for the increase?

  • If so, how much of a price change will you be rolling out?
  • If not, should you switch vendors? Eat the cost?

These are not easy questions to answer without data and powerful analysis.

You need to decide at what point a vendor cost change will trigger a price change, and what this change will mean for different store types, prize zones, and related products. Plus, you have to consider the impact this change will have on customer demand for the product.

Thankfully, all of these decisions, preferences, and constraints can be completely automated.

A retail analytics engine like Retalon will automatically account for these considerations and business rules when determining the optimal prices. This allows retailers to dynamically optimize and manage prices from the introduction of a product until the end of its life.

Retalon’s AI and predictive analytics solution automatically optimizes all Store/ SKU prices within your unique constraints and business rules. Book a demo to see how.

7. Unify Your Planning Process

Here’s what we learned so far:

  • Your pricing strategy should be framed by your brand positioning
  • Pricing decisions should start with an accurate demand forecast
  • One product can have multiple optimal prices if it’s sold in multiple locations
  • Profits can be increased by changing prices of individual products in different life cycle stages
  • Pricing cannot be optimized without taking inventory into account
  • Every price change needs to be limited within a set of pricing constraints

You can imagine that, if you knew (and could act on) all of these considerations, you would be able to:

  • Optimize stock levels for all variations across all stores
  • Maximize in-season sales
  • Maximize the sales impact of promotions
  • Minimize unnecessary markdowns
  • Maximize GMROI
  • All while adhering your brand and business rules

But the reality is that most retailers don’t do this, because they can’t do this.

This is partly because their structure doesn’t allow for it.

Different departments (with their own priorities and mandates) will handle budgets, inventory purchasing, allocation, analytics, and promotions. And they all work from different assumptions and data sources. And those data sources often don’t integrate cleanly with each other.

In other words, most retailers don’t have a unified approach to setting product prices.

Price planning for most retailers is therefore necessarily reactive, instead of proactive, because the inventory has already been ordered and allocated to stores by the time promotions and markdowns are planned.

But largely, retailers don’t take advantage of demand forecasts, granular store / SKU pricing, and PLC stages because they don’t have the tools to do so.

Many large retailers still calculate their prices manually using tools like Excel and some basic formulas.

Calculating optimal prices for every SKU / Store combination across their life cycle (which can range in the billions, depending on your organization) can become virtually impossible using this method.

Imagine trying to use Excel to:

  • Map out Product Life Cycles for 10,000 SKUs
  • Predict weekly demand for 10,000 SKUs across 100 ZIP codes
  • Manage and optimize millions of units of inventory across 100 stores
  • Apply dozens of pricing constraints to every price change across all combinations
  • Use all of these variables to find the most profitable configuration of inventory and pricing every month / quarter / year

That’s why more sophisticated retail organizations use predictive analytics and AI to optimize product pricing on the fly.

Apart from simply reducing your manual labor and resource costs by automating the majority of these decisions, a good retail predictive analytics solution will be able to account for all constraints and find the optimal price that aligns to your business and gross margin goals.

If you’re already using an advanced analytics platform for your pricing, and need help implementing some of these steps, feel free to get in touch with us at discover@retalon.com.

But if you’re missing the analytical capability required to set optimal prices for every product across every store, click the button below to book a demo of Retalon’s Price Management and Optimization software.

Forecast true demand, set optimal prices, and maximize your profits with Retalon’s AI-driven pricing software.