Successful e-commerce retailers stay competitive in ever-changing environments. They respond to changes in customer preferences, economic conditions, regional trends, and more. Predictive analytics help decision-makers know which products to buy based on projected demand. Here are some specific ways to use predictive analytics to make online stores run more smoothly.
1. Identify Where Customers Live
Many of today’s online shoppers expect to receive their purchases quickly. However, it’s more challenging for e-commerce retailers to meet that need when they lack a clear understanding of those customers’ locations.
A 2022 study indicated that 62% of internet shoppers expected to receive their purchases in less than three business days, even when choosing a free shipping option. The research also indicated that a lack of fast, convenient shipping was a significant driver of cart abandonment.
Fortunately, predictive analytics can help e-commerce decision-makers see where the highest percentages of customers live. That way, they can plan when or where to open new distribution centers or other hubs in those places.
Similarly, predictive analytics tools can help leaders determine if it makes good business sense to open a new facility in a place that doesn’t yet have a large customer base. Perhaps sales team members often receive queries from potential purchasers about whether orders could be shipped to Ireland. An executive could feed various pieces of relevant data into a platform’s algorithms, then get a return on investment estimates about opening a fulfillment center in the island nation.
2. Respond to Seasonal Trends
One of the difficult things about running an e-commerce site is that people must indicate consumerism triggers before they happen. A freak snowstorm in October could make an outdoor goods store sell out of sidewalk salt and shovels, even though those things don’t usually become hot sellers until December.
School terms and academic semesters also create periodic demand. Educational administrators use centralized dashboards to track a school’s performance and efficiency. They can also work with cloud-based inventory platforms that alert staff that they’re about to run out of printer ink or reams of paper.
E-commerce retailers use algorithms to get ready for incoming students, too. They may use social monitoring tools to learn about the most desirable laptops or determine how much parents have budgeted for school supplies.
Algorithms also alert e-commerce retailers to specific seasonal items selling faster than expected — nationwide or in particular regions. Then, individuals with purchasing authority can restock proactively before websites show goods as sold out.
3. Provide Personalized Content
E-commerce sites are more likely to get consistently high traffic levels when the people working behind the scenes understand search engine optimization principles. Knowing which phrases customers search for often and providing helpful, relevant content to match those needs creates customer loyalty and fosters appreciation.
Predictive analytics could reveal the content type and topic that will resonate with the most customers. For example, would pet lovers shopping for a new dog food prefer an infographic or a feature article to help them decide which variety to choose?
Additionally, once shoppers register with the websites where they love shopping, algorithms working in the background could provide them with personalized content, such as coupons, discount codes, or product suggestions.
A company’s representatives could also look at which type of content performs best to determine which products customers will likely buy or from which regions, states, or countries they’ll place their orders. Customers who see e-commerce site content reflecting their needs, backgrounds, and preferences will likely linger and stay interested rather than go elsewhere.
4. Justify New Fulfillment Options
Customers may order an item online, but that doesn’t necessarily mean it will be shipped to their doorstep. Many companies operate websites that allow people to order and pay for products online and then retrieve them at a store, in a parcel locker, or through a curbside pickup service.
Predictive algorithms can identify whether now is a good time to launch a new order fulfillment option. What are the chances that someone who typically orders from a grocery website once per week would rather pick up their goods on the way home from work rather than wait for delivery? Which consumer groups are the most or least likely to take advantage of such options?
Predictive algorithms are also ideal for showing which parts of the country or world have the best-existing infrastructure for alternative fulfillment options. Suppose a large segment of a city’s population uses public transit. In that case, they may not find it convenient enough to pick up their items in a physical location after buying them online.
However, college campuses, large apartment buildings, and office complexes are ideal parcel locker locations. People may need to visit those sites daily for work or school, so they’d probably appreciate it if their online orders were there when they arrived. Predictive algorithms can pinpoint heightened demand within communities, giving e-commerce executives the confidence to approve and plan new fulfillment strategies.
Predictive Analytics Improve E-Commerce Competitiveness
These are some of the many ways e-commerce leaders can and should use predictive analytics to understand what customers want and how to get those products to them as quickly as possible. All predictive analytics initiatives require careful planning, but the results can quickly become worthwhile and apparent.