eCommerce brands around the world are stepping into the new age of customer experience, using marketing’s hottest technology – artificial intelligence – to go from reactive to proactive, boost conversions, and predict revenue.
We’ve assembled a detailed list of 25 of the most impactful use cases for AI in eCommerce to help you get a grip on where to focus now and in the future.
Intel & Analysis
Artificial intelligence is changing how marketers can collect customer data and drive business intelligence. This is evident in the way data is being used to inform marketing outputs in ways that marketers hadn’t been able to access (or scale) before.
Descriptive analytics is the foundation of all analytics — it’s concerned with “what happened” and the basic analytics infrastructure including Google Analytics. Descriptive analytics help ensure your house is in order before tackling more advanced kinds of analysis.
Diagnostic analytics helps explain “why” things happen. Let’s say that you know your website was up 40% yesterday but you want to know why. This subset of data analysis can be helpful in explaining to the CMO/CFO/CEO why numbers are up or down without having to do significant backend work.
Predictive analytics leverages data mining, data modeling, and statistical models to make predictions about future outcomes. Historical and behavioral data sets plus rules allow algorithms to determine likely user responses before they actually happen.
Coupled with AI, predictive analytics identifies trends and launches campaigns likely to inspire action. Proactive marketing depends on predictive analytics – and predictive analytics has arguably the most utility and potential among any AI-related technologies today. We’ll dig into predictive marketing further down this list.
- Driver analysis
Driver analysis is a subset of predictive analytics. It’s concerned with what makes a result happen. If you were to take all the analytics you have access to and you have an outcome you want – form fills, leads, shopping carts, checkouts, revenue, repeat customers, etc. – machine learning can help you understand what combination of variables lead to that desired outcome.
Driver analysis is the epitome of data-driven marketing because you can understand which methods ultimately lead to conversions, sales, customers retained, and the like.
Software exists today that helps us with these questions.
Time series forecast
Time-series forecasting can help determine what’s likely to happen in the weeks, months, or years ahead. With this method, the machine takes any data series which can be organized by time and forecasts it forward.
Last-touch attribution helps identify the last link a customer clicked on and attributes previous actions to an ultimate sale. The Google Marketing Platform – formerly known as Google DoubleClick – is one example.
But, attribution models are changing, and even though last-touch attribution is a good use case today, it will eventually become obsolete as we’ll more effectively be able to attribute actions in more granular ways. For instance, position-based attribution allows us to take all touchpoints into account as opposed to just the first or last.
- Channel selection
AI is helping marketers become much smarter in terms of channels. We’re able to find out what channel people want and don’t want to communicate on. Then, we can use channel suppression, channel selection, and channel promotion which are being automated on a 1-to-1 level.
This means, for example, you can suppress contacts from email if they don’t want to receive messages from your brand there. Self-learning machines are learning how to handle all of this automatically.
Voice, VR/AR, & Customer Service
By 2020, more than 3/4 of customer interactions with brands may be handled by AI. Let’s look at a few ways that next-gen tech is impacting retail.
- Virtual assistants
The possibilities and potential for VAs in marketing are virtually – pun intended – limitless.
As Terry Tateossian of Forbes writes, VAs will “automate and streamline tasks [and] add a kind of appendage to our abilities to help usher us into the next phase of digital evolution.”
VAs could add a completely new dimension to online shopping by enabling consumers to speak aloud their desire and then be brought to that page, category, or product immediately.
The North Face, a popular outdoor clothing retailer, currently offers an app that uses a virtual assistant with natural language capabilities to guide the customer through the shopping experience.
Google Assistant, Amazon (Alexa), Microsoft (Cortana) and Apple (Siri) have paved the way, but why can’t e-commerce retailers follow suit? By 2020, 7 billion devices will have voice-powered assistants. And they’re getting better.
Okay, so chatbots are sort of a subset of VAs. But they’re making a huge impact already on the CX. Retailers and consumer brands like Domino’s Pizza are automating repeatable processes (like ordering products) by leveraging chatbots.
Chatbots, VR/AR, voice assistants, and other kinds of technologies are improving by the day (the more experience they get, the better they perform). In fact, 2020 could be the year that bots can outwit humans (they’re already able to more accurately predict outcomes)!
- Voice search
Similarly, voice-enabled search is booming in popularity among today’s consumers. By 2020, 30% of web browsing and searches will be done without a screen (Gartner).
Auditory technologies like Amazon Echo, Siri, and others can recognize spoken language and syntax, derive meaning, and not only deliver, but personalize, results. According to Gartner, the voice-first revolution will gain prominence in no time.
AI-enabled voice recognition technology can help retail brands serve the needs of customers on a 1-to-1 level, providing custom product information, delivery/shipping options, and even payment instructions.