Stores already have their Christmas decorations out and holiday music playing to spread joy, lift spirits… and open pocketbooks. Yes, it’s that time of year. As consumers, we either love it or hate it. Retailers, though, pin their hopes on a good holiday season to bring in the most profit of the year.
2020 has seen its share of economic hardship, however, and retailers are showing signs of distress. Layoffs, reduced inventory, shops closing, and more signaling consumers are holding tight to money. We have also seen a shift in where people shop. E-commerce is booming while brick-and-mortar stores suffer, especially when cities are shut down to slow the spread of COVID-19.
The question is – how can retailers overcome a setback like the pandemic? Read on, because we have an answer that will not only overcome setbacks but increase revenue and cut costs even in the best of times.
Online shopping has now become even more popular due to social distancing. Brick-and-mortar stores that have not had to compete with other retailers online are feeling the pressure to maximize their online presence.
The challenge for brick-and-mortar stores is understanding how to get a shopper to still go to the website and buy something. Before the pandemic, shoppers were more inclined to drive to their favorite stores and buy something without driving to another store. However, online shopping is extremely competitive. Shoppers can easily click around to see if there is a better price.
Combating the competitive nature of online shopping is difficult to do on your own. But there are ways to encourage shoppers to stay on your website and buy right away. One of the best ways is to utilize machine learning capabilities.
You can achieve endless opportunities with machine learning. From discovering customer intent, how to mitigate churn, optimizing sales and promotions, enhancing the customer experience, the list goes on and is ever-growing.
Brick-and-mortar stores that have never depended on an online presence have a lot of catching up to do in the race to win customers. But even without a prominent digital footprint and tons of data to sift through, machine learning is still an excellent option.
For example, retailers know they have a captive audience once someone enters their store, and the ability to offer immediate gratification exceeds even the fastest shipping plan. Product placement within the store is often a trial-and-error effort that can be greatly enhanced by learning the patterns of consumers as they navigate throughout the aisles. Upsell is more likely by strategically placing impulse items.
Context is also key. The local weather can dictate what is stocked on the shelves and point-of-purchase displays. Last moment gift shopping belongs firmly in the hands of local shops, and the consumer will pay a convenience premium versus walking home empty handed or risking a late shipment. For each of these decisions, intuition can be replaced with data-driven decisions, and patterns will emerge that even the most sophisticated shop owner will miss. Machine learning doesn’t miss those patterns.
This example is also applicable to online shopping. Product placement and upsell by offering discounts on impulse items within the online store can lead to placing items in the shopping cart. Once a shopper has committed to buying a discount item, they are more likely to continue shopping to buy all the items they need from one store rather than checkout at multiple stores.
Reaching targeted customers who are likely to shop your online store is crucial to make the most sales. Determining who has the propensity to buy a certain product or to respond to an email is the first step to a great email campaign. You can create email campaigns manually but that puts all your potential customers into a bucket and hope that it draws in the right people. Manual email campaigns are also likely to have the unsubscribe button pressed many times.
Optimizing a direct email campaign is made possible through machine learning. Imagine being able to sift through thousands of customer profiles in a second and determine who is more likely to be receptive to an email and click on the link to your store versus someone who would be annoyed and press the unsubscribe button. Customer segmentation is an important part of many machine learning models because it helps you target the right customers, preventing the dreadful bucket approach. This is a twofold approach: optimizing the promotion to reach the target audience for who will take an offer and then simultaneously minimizing opt out – all machine learning driven.
Email fatigue opt out prevention which can reduce that chance of unsubscribing. For example, many retailers struggle to know when to send a coupon. There is a belief that the more a deal is communicated, the more likely you are to draw shoppers in. But that can produce the exact opposite intention and lose subscribers. Using machine learning allows you to know who has opened emails and followed the links on a very large scale. Those are the people who you should be sending the coupons to versus someone who you shouldn’t send too many offers.
Hyper-focused and targeted email campaigns are great to attract new customers but getting someone to shop your online store is even easier if they are loyal to your brand. And getting those new customers to keep coming back to your store is the key to continued success. Great email campaigns are a fantastic way to build brand loyalty.
For example, there are many households with the ability to spend money this holiday season. But there are also many who have lost the ability to spend due to an evaporated disposable income. Having creative ways to work with people facing financial difficulties and allowing them to perhaps get a gift for their child at a very discounted rate is something they will remember next year when the holidays come around again.
The websites offering the best prices usually win the most sales. Today, most people tend to use their phones to do online shopping. Apps that compare prices on the same items are frequently used and shoppers will go to the website with the most competitive pricing. This is especially important to get your voice heard above the crowd and beat the major online retailers.
Offering low prices is a good bet to win shoppers, but you also don’t want to leave money on the table. Especially for those shoppers who are brand loyal, starting high and ending low will keep profits secure. The question is, what price should you start and end at?
A reverse auction pricing strategy utilizing machine learning capabilities is the best way to take the guesswork out of the pricing dilemma. This strategy is to start the price high and dropping the price over time, letting those who don’t mind paying more for an item to get it faster versus those who are prepared to wait. The difference between those who use machine learning to extract the most value from customers versus those who attempt to do so manually is a successful company versus one that could get the least value.
Want to see how your data can work for you? Contact us today for a custom demo!
The pandemic is not going away any time soon; therefore, retailers must act now to find the solutions that will keep them from going under. Even with vaccines racing to the finish line, the pandemic is not going away any time soon; therefore, retailers must act now to find the solutions that will keep them from going under.
We hear what you’re thinking – why spend more money on a machine learning investment when companies are already struggling? Well, it’s the difference between investing and seeing amazing returns on that investment (ROI) or potentially going out of business if searching for solutions manually.
That’s where ElectrifAi shines. We have the domain expertise and experience to make your machine learning implementation easy, effective, and provide the best solutions to help your business recover from the economic hardship of 2020 and look forward to preparing you for a successful future! Don’t wait to until it’s too late! Reaching out to us is simple and we can help you determine the best path to take.