Do you often feel confused by the redundancy of different pricing strategies that you come across every day? Finding the most efficacious strategy can be challenging. But it’s also necessary.
Most of our clients ask us, what’s the most effective and up to date pricing strategy for e-commerce businesses?
Unfortunately, there is no straightforward answer to this question. We tell them each business has different needs and objectives, therefore, their marketing strategy must be unique. The most logical approach would be to incorporate the convenient aspects of the tactics you’ve learned into your unique strategy.
But then, how can you decide which tactics/strategies to add to your marketing mix?
Here in this post, we’ll talk about the latest pricing trends in the e-commerce industry to keep you on track. Hopefully, knowing these trends will help you develop an up to date pricing strategy and avoid outdated practices.
Trends in e-commerce pricing
Nowadays, shoppers have easy access to pricing information. That’s why they easily detect fake original prices. What’s a fake original price?
Look at the product below.
A £210 discount sounds too good to be true, right? Well, you guessed right.
As you can see, the original price has never been £439 elsewhere. In fact, this vacuum cleaner’s market average is much below that amount. The £439 offer is called a fake original price.
Today’s shoppers not only find the best deal they can get in seconds, but they are also able to compare prices offerings from different cities, states, countries, and regions.
A shopper from San Francisco and another from Orlando are now able to see Walmart’s offers from around the country. The giant has to operate knowing the fact that an unreasonable price difference will be noticed immediately, resulting in dissent.
Although there is greater price transparency between geographical boundaries, marketers found ways to personalize pricing.
Businesses often collect consumer data with consent, but they don’t always ask for it. Banks also collect data regarding shoppers buying behavior via customers’ credit card expenses, paychecks, monthly expenses, etc., and later sell it to businesses to be used in many decisions including pricing strategy.
Firms try to make use of that data to offer their customers personalized shopping experience. In recent years, big companies like Amazon took personalization to another level.
The company uses historical data to reveal individual buying patterns and offer each buyer different discounts and bundles with the help of their dynamic pricing engines.
Say, a shopper buys ink and toners for her company. Every four months, she looks for a good deal to buy in bulk. When online retailers have such valuable data, they can offer her a personal discount in times she’s looking for a bargain.
Instead of constantly offering mass discounts targeting every single shopper, knowing their buying patterns allow retailers to offer personalized prices. It has two significant advantages.
First, mass-marketing efforts are necessary, but they can also push people away. Sometimes marketers overexpose their audience to endless promotions and commercials.
Rather, knowing their needs and offering them what they need at the right time will help you establish a loyalty relationship with your customers.
Moreover, even though big data analysis is not exactly cheap, it can be more effective than campaigns targeting a mass audience.
Dynamic pricing refers to a strategy where businesses continuously adjust prices according to market conditions. The factors affecting the changes vary for each company, but they are usually based on demand and supply forces, competitor prices, seasonal shopping events, etc.
The most famous example of a successful dynamic pricing strategy is Amazon. So surprised, right?
The company developed an internal dynamic pricing engine that tracks competitor prices, calculates many factors and continuously adjusts prices according to optimal price/demand point based on its calculations.
Furthermore, a former Amazon employee states that the company’s pricing engine detects the lowest per-unit price offer for a product, even if it’s in a bulk-sized pack, then applies that price to the same products on Amazon, no matter the size of the pack. So, if Walmart sells a pack of 20 chocolates for $20, Amazon sells one unit of the same chocolate for $1.
The giant doesn’t profit from all of its products. Even when they do, the profit margins are very small. But Amazon has so many loyal customers that start their price search from Amazon’s marketplace, assuming that they’ll find the best deal. The company’s pricing engine counts for a great portion of its success.
However, SMBs don’t have the resources to build such an engine. Even if they did, they would still lack big data that is necessary to obtain statistically meaningful results. However, pricing software solutions provide a similar service to SMBs. The software automates the price tracking process and constantly adjusts prices against changes in competitor prices.
Artificial Intelligence infiltrated the e-commerce market from several channels, and predictive pricing is one of them. AI processes historical sales data to determine the optimal price/demand ratio. Looking at the past data, to some extent, it’s able to predict future sales volume.
Of course, predicting the future sales volume can give you significant leverage over competitors. You’d have a better understanding of how many units you’d need to have in stock, you’d have an approximate idea of your shipping costs, etc.
Furthermore, it allows business owners to segment their market and personalize their operations. However, even if there is growing concern around data privacy, businesses still lack the necessary data for AI to operate on.
Big data also helps in micromanagement. Marketers use big data analysis to identify hyperlocalized price zones. Suppose there is a significant income disparity between two neighboring areas.
Offering them the same products at the same prices is not a very reasonable strategy.
Instead, you can differentiate your product assortment to target different buyer personas.
Let’s think of an example.
Suppose you want to sell smartphones in these neighborhoods. You have iPhones and Xiaomi smartphones in your assortment. They both have very similar product features. iPhones symbolize prestige, whereas Xiaomi phones triumph in a cost-benefit analysis.
Now, in a neighborhood where people have low discretionary incomes, pushing people to buy a $799 smartphone is not the smartest plan, and it can cause negative feelings toward your brand. On the other hand, promoting Xiaomi phones in the same neighborhood can help you boost your sales.
You can also variate your prices. But there is one thing you need to be aware of. As we mentioned above, shoppers have easy access to price information from all around the globe. Even if it’s not illegal to offer different prices to different market segments, it disturbs shoppers.
As we mentioned above, personal offers overcome this problem.
Technological advancements in the 21st century have reshaped the nature of retail. Now that online retailing grows at an unprecedented pace, e-commerce businesses try to make use of the latest technologies to outshine their competitors. Retail giants like Amazon pioneers the integration of the latest technologies into e-commerce practices.
Many pricing trends are now shaped by the use of artificial intelligence, machine learning, software developed for a variety of purposes, etc. Although big data analysis has been utilized only by a few, it is still shaping today and the future. For example, Amazon’s dynamic pricing strategy has forced all e-commerce businesses to track competitor prices and make frequent price adjustments.
No matter the size of your company, you can level up your game with the help of software services that are more affordable than in-house software.