BEING ABLE TO SATISFY THE CONSUMER IN REAL-TIME MAKES FOR BETTER CUSTOMER EXPERIENCE AND BOTTOM-LINE RESULTS
- Post-covid, ecommerce is expected to grow 25-40% across categories becoming the driving force of shopper’s experiences.
- There’s a difference between personalization and prediction: personalization knows some things about you and prediction knows what you’re going to do because it knows the RIGHT things about you.
- Not all predictions are created equal. To get the most accurate predictions you need volumes of data so you can show the right message to the consumer at the right time.
- Cart abandonment is becoming a standardized metric in ecommerce.
- To satisfy today’s digital shopper you need to provide them with the RIGHT information, at the RIGHT time, in real-time; you can’t wait to send them something after they leave your site or hope they come back later.
- AI powered ecommerce can increase profits because it gives customers the best experience. The digital shopping journey is mapped out in real-time, predicting what and when the shopper needs to see.
Want to learn more about how Metrical’s predictive AI can get you more carts, more revenue and less abandonment? READ OUR JCPENNEY CASE STUDY
Ecommerce is the revenue driver for retailers, but consumers will make decisions faster, stay or go, if you don’t provide them with the right information; and that’s where AI powered ecommerce comes into play
The pandemic kept us home and switched bricks and mortar shopping to online. And that’s here to stay. Ecommerce will keep growing.
McKinsey’s recent benchmarking survey, in partnership with the Retail Leader’s Association (RLA), reported that the ecommerce portion of retail revenue is projected to grow 25-40% across categories.
The pandemic accelerated the growth of ecommerce and it also made it easier to switch between brands, impacting brand loyalty. The accessibility of choice, combined with the importance to consumers to buy from brands that share their social values, resulted in 76% of people switching stores, brands or channels.
Retailers need to shift their mindset and toolset to realize the potential that ecommerce has to their bottom-line. Speed, accessibility and discovery are especially important to the online shopper, and that’s reflected in low conversion rates and high abandonment rates. Consumers have less patience online and can access endless options online. This is unlike physical, in-store behavior where shoppers will spend time browsing, waiting in line or getting to the physical store.
Personalization isn’t enough, you need to anticipate and predict
Ecommerce faces a myriad of challenges in its efforts to shift to a digital-first strategy. Retailers need to decide between a never-ending list of vendors, how to keep their tech effective yet not complex and unwieldy, what key things they should measure to better estimate down-river actions, and how, where and when to use AI.
Personalization has long been considered the solution that will most help retailers online. But it’s only part of the digital solution and most often, not enough. Ecommerce retailers need to anticipate and predict what the consumer wants and when they want it. This will drive better conversions from visitors to shoppers.
Personalization is knowing some things about the consumer. Prediction is about knowing the right things at the right time so you can avoid abandonment and bounce, and increase conversions.
Retailers need to personalize the experience for their digital shoppers, but the revenue inflection point will be when you can anticipate what the buyer is going to do and predict what will work best to result in a checkout transaction.
Prediction is about the right data and a lot of it
If you’re considering AI powered ecommerce you want to make sure your vendor:
- Digests data from your site and external sources
- has access to significant volumes of data
If your vendor limits their predictive intelligence on data from your site it’s incomplete. You want deep psychographic analysis across different situations that only comes from obtaining data from different and external sources, demographic, geographic, income, weather, etc. These sources also provide different contextual information so you’re able to have a more complete view of your digital consumers.
However, having data from external sources and your site isn’t sufficient. Your vendor needs significant volumes of data to enable the most accurate predictions. Data that covers everything from where the customer is physically, to which price ranges they’re shopping in, to what the weather is going to be like next week if they’re buying certain types of apparel. Significant volume equates to billions of data sets across many external sources.
Your vendor needs to be able to understand the why of what people do to make changes that will impact AOV, cart creation, cart abandonment and bounce. Your vendor should model behavior with data that is most relevant to the shopper, use data to understand what matters to the customer, and the relative influence of how that matters to the customer. The vendor’s models can then learn what AI triggers would most resonate to the customer, test different approaches and measure results to make sure they’re moving the needle in the right direction.
Almost 100% of carts are lost
We’ve seen up to 97% of visitors leave a site before checking out. Cart abandonment rates vary greatly by site, product being sold, or audience being marketed to. Here’s a sampling from a Dec 2020 sampling Baymard did.
The bottom line is, most of your digital shoppers don’t complete checkout. That’s even true for most people that go so far as to fill a cart. There are many reasons for this. A big one is they’re not ready, they’re just browsing. But a reason that’s almost as common is the digital shopper isn’t finding what they want, it may be the right information or the information at the right time.
So here’s what is key:
- there are many reasons for consumers abandoning carts
- most people leave your site before they buy
What’s needed to increase checkouts? What we’ve found is if you give people the right information, at the right time, you can create amazing results. By combining data across multiple customer segments, referral sources, marketing campaign effectiveness and dozens of other critical journey data elements, AI can engage shoppers to provide them the experience they need.
Real-time information, at the right time
It’s a no-brainer that if you get the right information at the right time, it helps. What’s harder to compute is knowing what the right information is, and when you need to see it.
The only way to do that is with AI powered ecommerce, and that requires a lot of data that comes from a variety of sources and is continuously being updated and kept relevant. AI powered ecommerce can boost your bottom-line results because it gives customers the best experience, and provides them with what they’re looking for at the right time.