Dynamic pricing doesn’t have to be incribly complex and confusing, but it does ne to be strategic and streamlin. Over the past year, as consumers shop online for everything from groceries and soap to yoga mats and laptops, many people have been remind of how easy it is to compare prices online. With just a few clicks, a shopper can find out which retailer is selling a specific item at the lowest price. And as the shift to e-commerce is expect to continue even in the post-pandemic era, pricing will become an increasingly important competitive tool for retailers. Dynamic pricing, in particular, is pois to become one of the key capabilities that will set apart the winners in the retail of the future.
Simply put, dynamic pricing is the fully or partially automat adjustment of prices. It’s a staple of the travel industry: dynamic pricing is the norm for airline tickets, hotel rooms, and car-sharing. In e-commerce, Amazon has long been a leader in dynamic pricing; the company changes the prices of millions of products every few minutes. But dynamic pricing isn’t just for travel companies or e-commerce giants, and it doesn’t necessarily require cutting-ge software that changes the price of every item multiple times a day. Even traditional retailers can benefit greatly from algorithms power by merchant data that recommend price changes on select products at regular intervals.
Pricing in Retail and E-Commerce: 5 Examples of Strategies
What to do:
Retailers who successfully implement dynamic buy telemarketing data pricing typically follow these rules: focus on the final price of the product, consider consumer expectations, test and refine your strategy, and plan your path.
1. Focus on the final cost of the product, not the retail price
Shoppers don’t just look at the price of the gambler data item they want to buy. Instead, they base their purchasing decisions on the total final price, which includes taxes, shipping, handling fees, and any additional fees add to the total final price. So your dynamic pricing strategy should reinforce the value proposition you’ve chosen. That means making smart choices not only about prices, but also about promotions, bundles, custom offers, and shipping times and additional fees.
For example, a furniture retailer test lower shipping prices and longer delivery times, bas on the hypothesis that shoppers don’t necessarily want their new furniture deliver right away, but would rather have a new dining table deliver on a Saturday than midweek. The retailer found that the longer wait didn’t significantly impact conversion rates and gave the company the opportunity to optimize deliveries bas on capacity and cost. Algorithmically offering the option to wait until the weekend for certain items, with the subsequent savings to the shopper, prov to be an effective way to drive additional sales.
2. Consider consumer expectations
Some products are better suit to frequent price changes than others. For example, in the apparel industry, prices for fashion items may change from week to week, but prices for basic items (like basic T-shirts or underwear) should generally remain more stable. Customers who have been buying white socks in your stores for years should ot experience price shock when they return for another pair. Carefully consider the length of the then, they can interact with the content directly purchase cycle, as well as consumer expectations for each set of products. Prices for big-ticket items that are typically heavily research by consumers, like televisions or sofas, should remain relatively stable, as frequent price changes may upset a potential buyer who has been researching for months.
In our view, all dynamic pricing algorithms should be review by retailers, and most price changes recommend by algorithms should be approv by the retailer before they are implement. This way, retailers can avoid the consumer backlash that comes with raising prices. For example, last year, retailers who rais prices on cleaning and disinfecting products were seen as taking advantage of the COVID-19 pandemic and thus lost customer trust and loyalty.
3. Test and refine your strategy
Dynamic pricing is both an art and a science, meaning a test-and-learn approach is critical to getting it right. To manage risk, consult with your CFO and agree on the direction of price changes during the initial testing phases. Start with pilots in just one product category or region. Assuming the first few price changes aren’t successful, develop an approach to track progress, measure impact, and make quick adjustments. Spend time with your salespeople during the initial tests and work with them to formulate next steps before moving forward with automat price changes.
For example, at a high-end accessories store, pricing analysts work with salespeople to emb the logic of their pricing strategy into algorithms. The retailer then conduct market testing to get two important inputs. The first was the limitations of substitutions between very similar items at different price points—for example, the retailer found that most customers interest in a $350 item switch to a similar item pric at $399, but not when the more expensive item was pric at $400.
4. Plan your route
=”yoast-text-mark”>=”font-weight: 400;”>As a basic step, understand your current competitive position in the marketplace and how consumers perceive your brand’s price. Then map out your path to dynamic pricing. Given the starting point of most retailers, reaching the end goal will almost certainly require a phas approach to building and assembling best-in-class data, infrastructure, tools, and people. Don’t expect to get there overnight. Set and manage internal company expectations, demonstrate quick wins, and help move the company forward.
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What not to do:
&amp;lt;span style=”font-weight: 400;”>When implementing dynamic pricing, retailers often make the following mistakes: introducing prices that alienate customers, changing prices too often, and using incorrect data.</span>
1. Don’t offend or push away the client</b>
Consumers expect airline prices to change constantly, but they expect the price of pasta or a bottle of shampoo to remain fairly consistent. Make sure any price changes recommend by the algorithm are consistent with your brand and the customer experience you desire. Set and enforce strict price caps. Your prices shouldn’t fluctuate so much that they confuse and alienate customers. If customers perceive price changes as random, unfair, or unrelat to your value proposition, they’ll simply shop elsewhere.
Prices should also be display consistently across all devices or channels. For example, many retailers ask online shoppers to enter a postcode before displaying prices on their sites. The goal is to ensure that customers who visit both online and offline stores see consistent pricing. Your customers may already have expectations about what good and best pricing options are (or your overall pricing architecture); in fact, your own pricing approach may have taught them this. Sudden changes that violate these norms can cause confusion and possibly customer loss.
2. Don’t change prices just for the sake of changing prices
Specific triggers for price adjustments can vary significantly among retailers and shoppers. In some categories, seasonal changes or upcoming comp stable because inputs generally did not change from week to week.
Another seemingly obvious tip: Don’t forget to let consumers know when you’re cutting prices. A discount retailer cut prices on a few key items but didn’t advertise the fact, so consumers barely notic and the price cuts were in vain.
3. Don’t let bad data dictate your prices</b>
Today’s technology enables accurate centraliz pricing management and rapid publication of price changes, but bad data will cripple even the best dynamic pricing strategy. For example, product cost, shipping costs, and customer service data are often amortiz across multiple SKUs, but in reality they impact the economics of individual products differently. Underestimating shipping costs for large items or fast shipping creates an artificially attractive margin profile, and in such cases, algorithms may recommend price cuts for products, but these price cuts, if implement, can result in large margin losses. Prioritizing cleaning of pricing inputs for high-value items, as well as greater granularity and precision in cost allocation, can greatly improve pricing recommendations.