If you’re in the pricing game, you’ve probably figured out that price is the biggest influence on a company’s profits. While there’s no one-size-fits-all strategy, combining a pricing strategy (or two) with data can help you optimise your prices while meeting your goals.
While you’ve probably come across examples of dynamic pricing in action - with airline tickets, Uber’s “surge pricing”, and Amazon’s countless daily price changes at the top of the list - you might have found that examples of price optimisation more broadly are surprisingly lacking.
What is price optimisation?
Price optimisation describes any pricing strategy that uses analytics (and often machine learning) to consider multiple factors and predict an optimal price or price range for a given product. This price will generally maximise a measure of customer value while ensuring the company turns a profit.
How does this look in action?
Sink your teeth into these examples
Pricing scenarios are far from uniform and, dependent on your market, can be prone to abrupt change. Setting multiple prices for a single product can be useful - when coupled with data from historical sales, competitor sales, similar products, seasonality and demand. While changes in demand will be easier to predict for some products - think about how many customers will buy jumpers and jackets in the leadup to winter versus when summer approaches - others will see demand that is pretty myopic. Lucky for you, there are ways to optimise prices for both types.
Let’s consider an online retailer selling bouquets. In general, the best price would be highest when demand peaks, such as in the leadup to Mothers’ Day and Valentine’s Day, and would be lower between these periods. In addition to this, a customer could pick from several different pricing tiers, where the more expensive options may be larger and come with additional products - think vases, chocolates or other gifts as examples - with the cheapest options being the most basic. Prices are set to change slightly according to different levels of site traffic: priced higher when there are lots of visitors to the site, lower when site traffic decreases. These three strategies optimise prices in several ways:
Charging higher prices when demand peaks (such as during holidays) works since the majority of customers are not aware of the standard price and thus tend to be more willing to pay higher prices than usual, or they accept this is simply how the market works.
Offering pricing tiers also caters to customers that are more and less price-sensitive
Dynamically setting prices according to site traffic assumes that the likelihood that a meaningful subsection of customers will be willing to pay a higher price. Thus, when traffic is high, we can sell less stock at a greater price and generate more revenue and margin.
Optimising prices for seasonal products
Next up, let’s look at how an online fashion retailer might optimise their prices. While the strategies discussed above can be applied here, there are additional ways to optimise prices that suit these retailers particularly well. For instance, the initial price for new products can be set higher, then adjusted over time to account for dips in demand over the season (such as when new styles are introduced). This works for several reasons, where higher initial prices:
Capitalise on increased demand at the start of a new season (both in fashion and weather) in the same way as the site traffic example above.
Can help establish the position of the brand in relation to its competitors, customers will be more willing to pay higher prices for products that they perceive to be of higher quality or come with additional or unique features when compared with competitor products.
Discount prices can then be applied to stimulate demand for particular products. These prices can also be optimised to determine which products should be discounted, the degree of discounting, the length of time and how often the new price is applied for. This ensures that the new price is:
Low enough to attract customers and high enough that the increased number of sales counteracts the reduced revenue for individual products,
Applied for a limited time and infrequently to capitalise on a customer’s fear of missing out, which can inspire customers to return more often to catch a sale, without affecting customer perception of the product.
While frequent or large discounts can attract customers in the short-term, this strategy should be used with caution to avoid potential knock-on effects for long-term revenue. In some cases, heavy discounting can trigger a reduction in customer perception of a products quality, which can lead to less price-sensitive customers going elsewhere (since product quality is their priority over price) and restrictions on how high your prices can go (to cater to the more price-sensitive customers that have stuck around).
And, similar to a florist offering tiered products (and prices), larger fashion retailers can also separate products by category. In this case, though, prices can be tiered by frequency of use. Again, this ties into price elasticity where customers will generally expect to pay less for products used on an everyday basis and will be willing to pay more for items worn less frequently or for special occasions - think designer items and evening wear.
Divide and conquer your customer base
As a final example, consider a small to mid-sized online retailer selling wine. On top of incorporating price changes based on site traffic and upcoming peak demand periods, promotional prices can also be particularly effective. This could take the form of product bundles, such as mixed dozens or cases of particular varieties, or a buy-one-get-one promotion. This works by identifying different customers segments:
Price sensitive customers that respond to the promotion (even if they pay more or purchase more items than they might have otherwise).
Price insensitive customers that are not as responsive to the promotion and may be more loyal to particular brands.
And, armed with this and other data, the retailer can identify the proportion of price sensitive and price insensitive customers within their customer base. Prices can be raised or lowered if a majority of customers belong to one segment, or additional pricing strategies can be implemented to cater to both segments (such as discount competitor pricing).
While these are just three examples, price optimisation techniques can be combined and adjusted to suit the needs and goals of your business. Whether that means profit margins increase, customer retention improves or your market share grows, optimising prices can have plenty of benefits.
When it comes to helping your business succeed, there are few things with the same impact as optimising your prices. Successfully implementing price optimisation means your prices should achieve three core objectives:
Satisfy customers - both in their perception of your products value and in their willingness to pay
Cover your bottom line (and make a profit)
Help (not hinder) your company achieve its goals
Since there are plenty of pricing strategies out there to choose from (which you can read more about here), a price optimisation platform can be vital in executing a strategy (or combination of strategies). Coincidentally, Remi AI has a powerful Price Optimisation module that can help you:
Automate millions of pricing decisions on a daily basis
Make intelligent pricing decisions that fit the constraints and chosen pricing strategy of your business
Increase revenue by up to 20%
Want more info about optimising your prices? Drop us a line here. Or, if you’re looking to learn more, have a gander at our case studies or head to our blog, where you can find more interesting AI reads and stay up-to-date with our monthly Recommended Reading lists.