Feature
The Gist
- AI Progression. AI and generative AI advancements fuel predictive analytics in ecommerce capabilities.
- Consumer understanding. Customer insights and personalization elevate overall customer experience.
- Equitable opportunity. Predictive capabilities democratize competition between large and small brands.
In the dynamic digital marketplace, predictive analytics in ecommerce is becoming the cornerstone of ecommerce evolution. With the power to anticipate customer behavior and preferences, this disruptive technology arms businesses with tools that reengineer their interactions with customers. Creating a more engaging, personalized shopping experience, predictive analytics is catalyzing a significant shift in ecommerce landscapes worldwide. This article delves into three transformative ways predictive analytics, enriched by customer insights, is reshaping the ecommerce industry — honing targeted customer offers, enhancing knowledge of customers and improving predictive capabilities. Uncover how this potent blend of AI technology and data analysis is redefining the ecommerce playbook, equipping both large and small brands with new ways to grow, scale and compete effectively.
Below are three important ways that predictive analytics is impacting ecommerce:
1. AI-Powered Predictive Analytics Means More Personalization
“Predictive personalization has been on the rise with the advent of more sophisticated AI and the ability to quickly harvest and analyze the resulting data,” said Sharad Varshney, co-founder and CEO of OvalEdge. “Machine learning models are analyzing customers’ past searches, buying patterns and demographic details, and real-time activity, enabling companies to target personalized content to individual shoppers.”
When displaying a product description to the potential shopper, product content can be adapted to match demographic and psychographic understanding in terms of language and culture, which will greatly enhance the level of personalization and the shopper’s CX, Varshney said. With all of these factors, many online retailers’ algorithms are also adjusting prices to create a more competitive experience for the individual shoppers.
In the last year, a typical shopper, via search and rich product imagery, could come very close to finding products matching their tastes and preferences, Varshney said. “But in today’s competitive, fast-paced world of shopping, attention spans are at a premium. Retailers now can display exactly the kind of product the shopper would like to buy on the very top of the results.”
As consumers browse a product, examining its specifications and description, they are also presented with product suggestions that align with their preferences. These recommendations are derived from insights gathered from their previous shopping history, including the average expenditure on similar items, increasing the likelihood of a purchase, Varshney added.
Related Articles: The 5 Stages of Predictive Analytics for CX Success
2. How Predictive Analytics Improves Knowledge of Customers
“If everything looks like ‘me dot com,’ then every brand is simply competing on price,” said Michelle Bacharach, FindMine CEO. “To maintain the brand identity and position they’ve cultivated in the minds of consumers consciousness, staying true to the brand’s editorial vision is an important differentiation and closes the gap between consumers and the aspirational vision of themselves.”
Personalization shouldn’t be the end all be all, but rather an opportunity to learn something about your customer, Bacharach added. “A new mom trying to get back in shape is a different shopper than a marathon runner, even though demographically they might be the same. We train our AI to split the ‘running’ campaign into all the different flavors — marathon runner, your first postpartum run, etc. They each contain the unique expertise of the brand but didn’t have to be manually set up, so the brand can create every flavor of the running campaign, rather than just one generic campaign.”
By doing this the brand can see who's clicking on what and store that information to their CDP so they know that a certain person should be in the "new mom" segment and tailor a more custom experience to that shopper in the future, according to Bacharach. “In this way, personalization and aspiration combine to get a better view of the consumer, while helping her become a better version of herself at the same time.”
Incorporating customer insights from unstructured data sources such as comments, reviews and social media platforms offers ecommerce businesses a deeper understanding of the overall customer experience, added Alana Dell, Thematic vice president of marketing. “Analyzing sentiment, recurring themes, and specific feedback gives companies the insights to pinpoint pain points, identify bottlenecks, and address areas of friction in the customer journey.”
With these insights at their disposal, ecommerce platforms can focus on key interaction points, such as menu navigation, the checkout process, and procedures for refunds or cancellations. By improving these essential areas, they can elevate the overall customer experience, build enduring loyalty, and boost customer satisfaction as well as retention, Dell added.
Related Article: Predictive Analytics: Overcoming Data Swamps in Tech's Dynamic Landscape
3. Improved Predictive Capability Through Real-Time Data
Though predictive analytics isn’t new, improved access to real-time data is making it easier than ever for brands to use, said Ronald Grisha Bornsztein associate director of Performance Media at Collective Measures.
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SaaS companies use predictive analytics to predict seasonality, automate budgets and detect fraud, Bornsztein added. Built-in functionality within advertising platforms enables brands to leverage advanced predictive models and reach consumers more efficiently than ever before.
“For example, one of our ecommerce advertisers saw revenue increase after launching Google’s new Performance Max campaign type,” Bornsztein related. “This campaign leverages machine learning and predictive analytics to analyze consumer behavior and reach potential customers wherever they may be in the purchase funnel. It can adjust for each user in real time, adjusting bids and messaging to ensure users are shown the right product, at the right time."
By making this technology available directly within the advertising platform, Google and other advertisers have taken steps to level the playing field between big and small brands, ensuring CX is the priority, Bornsztein said. While big brands will continue to outspend and capture the majority of market share, small brands can take advantage of these tools to improve their efficiency, scale their campaigns and grow their businesses.
Final Thoughts on AI-Powered Predictive Analytics in Ecommerce
The transformative power of predictive analytics and customer insights cannot be understated in the rapidly evolving ecommerce landscape. From sharpening customer offers to amplifying understanding of customers and enhancing predictive capabilities, these game-changing technologies are redefining ecommerce strategies for businesses of all sizes. As AI continues to improve and generative AI becomes more pervasive, ecommerce firms will further enhance their capabilities, harnessing deeper insights and reaping greater benefits from predictive analytics. This, in turn, will stimulate continual innovation, ultimately propelling the ecommerce industry toward unprecedented growth and success in the future.
