Study: Cumbersome checkout alienates online shoppers
Complicated online checkout is taking a toll on digital sales — and long-term customer loyalty.
Eighty-seven percent (87%) of online shoppers will abandon their carts during the checkout process if it is too long or too complicated. In addition to abandoning their carts, 55% of consumers would never return to that retailer’s site, according to new data from Splitit.
Cart abandonment rates hover at about 70% overall, and older shoppers seem to have the least patience. For example, 90% of those aged 55 and older would not complete a long or complicated checkout process. This is compared to 83% of millennials, who said they would not follow through with a lengthy checkout.
Meanwhile, a mere 7% of those over the age of 55 would exit a lengthy checkout but return to the site later, compared to only 12% of millennials.
Excessive advertisements in the checkout process also make consumers less inclined to complete an online purchase, with 25% of respondents citing it as the reason for abandoning their carts. Millennials were less bothered by ads, with only 19% reporting that too many ads during the checkout process would cause them to abandon their cart. This contrasts with 28% of those aged 45 and older who would abandon their cart if they felt there were too many ads.
“With cart abandonment rates so high, retailers still have work to do in streamlining the online shopping experience,” said Gil Don, CEO and co-founder of Splitit.
“While consumers appreciate having options, it is essential that the checkout process is seamless, at the risk of permanently losing customers,” he said. “Online merchants must be sure to include clear and easy ways to enter customer details, choose delivery options and make payments, while ensuring that the process does not become cumbersome for the shopper.”
Amazon reportedly wants to hire new employees — but there’s a twist
An online giant is going on a hiring spree, but potential candidates won’t be working in an office.
Amazon wants to add more than 200 new full-time employees to its growing workforce of 575,000 full-time and part-time employees as of June 30. However, Amazon wants these new associates to work remotely, according to Fortune.
According to the report, Amazon’s job site wants to fill 235 full-time positions, and two part-time roles. The positions are defined as “virtual” or “work from home” opportunities, and they are open to applicants in the United States, the United Kingdom, Germany, and Costa Rica.
The jobs span a wide range of levels and specialties, including market manager for devices, senior regional logistics leader, head of business for Latin America Prime Video Direct, and customer service associate, the report revealed.
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Real-time modeling drives inventory optimization
Optimized inventory is a must in an omnichannel landscape. By leveraging real-time modeling to uncover customer patterns, retailers can adjust assortments — and amounts — of merchandise based on when customers are ready to make a purchase, according to Kevin Stadler, president and CEO of 4R Systems.
Chain Store Age recently spoke with Stadler regarding how forecasting, risk assessment, machine learning and predictive analytics are paramount in optimizing inventory and positioning retailers for maximum profitability.
What is profit optimization of inventory?
Most retailers today realize that inventory can be an asset (if it’s in the right place at the right time) or a liability (if it’s in the wrong place at the wrong time). Through a holistic model that combines forecasting, risk assessment, machine learning and predictive analytics, we can provide a future look at inventory productivity by item, by demand location and by fulfillment location to optimize inventory for maximum profitability.
Why is this important for retailers?
With the shifting dynamics of online versus in-store behaviors, it’s clear that inefficient retailers are closing locations and going bankrupt at an alarming rate. Wall Street is rewarding both growth and profitability, and punishing retailers that have neither of those. Today, in peer retailer investment, the leaders are getting rewarded with investment, so you must stay ahead of your peers to survive.
How has the game changed as the retail landscape becomes more digitally influenced?
Patterns shift faster than they used to. Consumers move in “packs of influence” today and change direction fluidly.
Older methods of target service level availability and slow supply chains are becoming antiquated because they are
expensive and slow to respond to a digitally influenced society.
What struggles do retailers have on the road to profitability?
The older perspective of inventory was very siloed and did not have a unified view that could provide profit and risk perspectives. It used to be that you would make a forecast, set a fairly high service level and then look at item profitability. An integrated model uses all of those factors simultaneously to goal seek rather than use a trial-and-error process that uses large staffs and a top-down category management approach. Today’s advanced retailers are going bottom up with big data and finding patterns using machine learning to quickly adjust on a very granular basis.
What tools do retailers need to solve these issues?
Having a real-time modeling environment that correctly weighs all factors is important. This takes a lot of data and a large, fast data structure. Then you need algorithms that can weigh risk and optimize against a number of variables all at once. To visualize all of this requires some data science and multidimensional techniques. As an example we use the Markowitz modeling formula, which is based on mean-variance analysis, to provide multiple variables to be integrated then visualized via risk curves.
What role do predictive analytics and machine learning play?
A common question we ask is “Do you remember what you had for lunch two weeks ago on Thursday?” The answer is that almost no one does.
To optimize the retail environment of thousands of items over many stores, and over a number of years, you have to use machine learning to find patterns. Then advanced predictive analytic algorithms reveal patterns to ensure you have the right product in the right place at the right time. To move to this proactive prescriptive technique, you have to have both machine learning and predictive analytics.
What results are possible?
The results vary widely from several percent improvement in revenue to double digits. Existing staffs can be more proactive and strategic. Profit improvement of course depends on the margins and turns of the product categories, but are significant as there is both an improvement in margin and turns at the same time.
How can 4R Systems help retailers in this journey?
4R has been a partner to retailers driving sales and profits for many years. We have a unique cloud optimization environment that uses modeling, predictive analytics, machine learning and goal seeking against very large data sets.
In addition, we can use our techniques to build the business case for change up front, not after you have invested. We reduce risk and maximize return for our partners at each step of the process.