How to Avoid the Shopping Cart Blues: Analytics Corner
KPIs Big Data Analysis Rabbit
Most people will only want the sad kind of blues that comes with music. For retailers, that sad blues feeling comes with poor shopping cart experience.
Shopping online is not going away – in fact eMarketer announced that for the first time holiday shoppers will budget more for shopping online than offline. So having a tip-tip shopping cart is essential. The question is how to maintain one?
The key question retailers should be asking is about what hampers a purchase online. For years, the analysis focused on observational metrics – what could be observed from webpage activity. Even today that approach still works. But the introduction of predictive analytics has added another perspective – determining causal activity, or relating what the customer does to previous behavior. Causal analysis has to be handled with care if it's to be helpful, but not intrusive.
Here are a few tips for improving the online shopping experience.
1. Check how a page appears on mobile...
By now, every marketer knows we are in a mobile-first world. But webpages consist of different elements, so it's possible for one or more to not render well in mobile. It's good to verify a page's appearance – are button large enough to be clicked, or have the font and images shrunk to ridiculous levels? Google provides a free checker as a starting point.
2. ….And check page speed while you are at it
Just like checking oil on a car, checking page load speed is essential. Many of the issues that tie into mobile accessibility lie with poor page load performance. Use a browser developer tool to view a cascade report of how website elements load into the browser. Doing so permits you to know where to focus on improving page elements.
3. Remove links that send customers away from the checkout
Keeping a check out page simple in presentation is essential. The customer has shown intention to buy, so any enticements to leave that page should be kept to a minimum: That include links to other pages.
4. Use alerts to know where the issues are
Alerts in an analytics solution guide your team to where increases and decreases in website or app activity is occurring, saving analysis time. The key is to set alerts that relate to sales activity.
5. Set automated cart abandonment emails
eMarketer highlighted that email abandoned cart notifications have a 40.5% open rate, twice the rate of other typical emails. To set, look for webhooks in your email service. Webhooks are a short line of code that developers use to configure the callbacks, so that events on a website cause an email to be sent with media based on those events.
6. Help customers to shop simple
Marketers should also streamline product images when possible in cart abandonment emails to show consumers what they have left behind, as well as providing no more than three suggestions for other products.
The goal in this instance is to reduce content bloat on a page. Describing too much can increase time on a page and can confuse buyers out of making the purchase.
7. Use chatbots to answer common questions
Sometimes a landing page is too static to really give customers a feeling that they are receiving help with their questions. Rather than drive potential users to landing pages, retailers are creating and driving users to chatbots for better engagement. Chatbots can be deployed on a cart page or on a platform customers use regularly, like Facebook Messenger.
8. Review reporting to make sure tracking includes cross-device and user ID
Many analytics solutions have a cross device ID and user ID schematic to tell when the same visitor comes to a website or app through different devices. You can then filter analytics reports such as visitor flow navigation with metrics that cover multi-channel visitors. or visitors who engage your brand with different devices.
Setting these identifiers reveal if the shopping cart issues happen when users are seeing something while one the go (mobile), then completing a decision process to purchase when at home (laptop).
9. A/B test variations on content
Choose to test a set of changes rather than “microtesting” one change at a time. For example, test the location of a different button location or checkout description against the current setup, and determine if there is a significant change in the conversion rate.
Bear in mind that many test platforms have introduced conditional settings, creating an opportunity for evaluating the effectiveness of predictive strategies like re-marketing based on other shopper activity. Read my earlier post on Google Optimize to learn more about what kinds of features are available.
Creating conditions in an A/B test that emulate the customer experience online opens the door for advanced analysis, and offers marketers new ways to ensure that content entices a sale and a satisfied customer.