- In an industry rocked by recent disruption, retailers are in the best position to remain agile and competitive if they embrace AI-enabled personalization.
- Data is the fuel for a personalization engine, and to do personalization right, a retailer must have the ability to understand how both product and transactional data can predict future shopping behavior.
- Tailoring experiences across channels and interactions over a shopper’s lifetime is practically impossible without AI to analyze data and bring automation to the mechanics.
- If evaluating a solution or adjusting current tools, retailers and brands need to ensure their technology is platform agnostic for integrating with other pieces of the ecommerce stack.
Consumers are emerging from their homes after three months of sheltering in place, as cities begin phased re-openings of brick-and-mortar businesses.
Despite having to pivot quickly at the onset of the pandemic, smart retailers recognize that consumers will still require a relevant and personalized shopping experience moving forward. Companies that prioritized personalized communication through the early stages of the pandemic have likely forged strong ties to their customers.
However, maintaining personalized communications was cited as a top operational challenge by 31% of respondents in a recent study by a retail trade publication.
While a recent study we conducted did not ask specifically about retailers’ response to COVID-19, the findings offer important insights for creating a path forward. By focusing on advanced personalization, retailers can set themselves up for meeting changing consumer demand with agility, and ultimately, achieve a higher return.
According to our research, 70% of retailers that used some measure of advanced AI-driven personalization achieved ROI of 200% or more. When that is taken a step further to be deployed across as many touchpoints as possible, ROI increases yet again to 300%.
Finally, ROI of 400% is attainable for retailers with a truly marketing-led, cross-channel personalization strategy, with nearly every touchpoint personalized to a shoppers’ history and preferences.
By embracing AI tools, retailers will be in the best position to achieve similar results.
Personalization requires data mastery to make an impression
Like any good engine, a personalization tool needs to tap into the right fuel source. That fuel is found in accurate data. Data is everywhere in retail, and customers are generating new data all the time.
Good data fuels memorable personalization – with this in mind, AI makes perfect sense as a main component of personalization engines. Through AI, data insights are made instantaneously more valuable, with automation kicking in to execute on the engine’s personalized recommendations.
There are two dimensions to a retailer or brand’s ability to harvest data. The first element is what knowledge a retailer has about a customer across different touchpoints or channels, and the second is what they know about a shopper based on every unique interaction or purchase.
Mirroring this, ecommerce platforms have two broad types of data available to inform personalization: product information and transactional data.
- Product – Data related to product categories and subcategories, gender-specific products, and product families; also includes product characteristics, such as size, style, color, cost, sales price, and margin, to name a few.
- Transactional – Data related to basket size and the items that make up each order, historically. Looking at past purchases for this particular demographic and region, what products are frequently bought together? How does this compare in shopping online versus in the store?
Data knows how a shopper has behaved in the past and understands to what extent it’s a predictor for future shopping behavior. Every action taken by a shopper, whether merely browsing or moving through to a final purchase, feeds into the ecommerce engine.
The power of AI is to then comb through that data – plus weather, location, time-of-day, device type or other environmental factors – to more efficiently “slice and dice,” analyzing and noticing counterintuitive demand patterns not obvious to the human eye.
Some data points are not all that significant while others are strong demand signals. AI sorts through all the noise to gain a comprehensive view of the shopper. The more data that’s put into the engine, the better the targeting gets, and the more possibilities arise for engaging with a shopper.
How AI delivers tailored experiences at scale
“Personalized commerce” is a three-part personalization strategy, prioritizing a one-to-one customer experience across every marketing, shopping and fulfillment channel.
When AI-driven personalization is done right, a customer should think, “Wow! How did they know I’d like that? It’s like they can read my mind.” That moment should feel authentic, and this emotional response should generate a natural sense of loyalty or affinity with that shopper.
But the ability to do this consistently and persistently over the lifetime of that individual shopper is a tremendous hurdle. Take a minute to imagine the endless possibilities – it becomes clear that these experiences could never be hand-stitched manually.
For example, a homepage can be personalized based on the current weather conditions for the web user. When a shopper enters the site, the engine can recognize their loyalty status and browsing history, showing specific sales or promotions that the shopper is most likely to act on.
As well, category listing pages and product search results can be customized from person to person. Even product reviews can be personalized, not defaulting to recency or rating, but instead showing a user feedback from someone most like them.
The opportunities continue. Cross-sell, up-sell and impulse purchases can be personalized at checkout, with the AI understanding a shopper’s propensity to buy even more at this stage.
Marketing delivered via email or social can be personalized as well, knowing exactly what offers to present to get a shopper re-engaged. The possibilities are endless.
Best practices for AI adoption in the ecommerce stack
The right personalization platform worth adopting today is one that is in itself platform agnostic. It must play well with other layers of the retailer’s tech stack – the retailer’s ecommerce platform, inventory management system, mobile, app, in-store POS, kiosks, email and so on.
In discussing the type of data that feeds into the personalization engine, it’s important to ensure the tool can interface with various data sources.
The ability to pass information back and forth through APIs or a microservices structure will enable a retailer to model all the data and create a single view of the shopper. The underlying algorithms must be robust, and the tool needs to activate quick reporting into key metrics and KPIs.
No matter the unpredictability of changes to the retail format, it is still incredibly important for organizations to work toward a shopping journey that feels fluid, frictionless and personalized, regardless of touchpoint or channel.
To become digitally agile and achieve the highest ROI for the technology spend, retailers need AI on their side, which will make personalized commerce a reality.
Meyar Sheik is the president and chief commerce officer at Kibo, which provides cloud commerce software and services that include ecommerce, Order Management, Certona Personalization, Monetate Personalization and Optimization, and Mobile Point of Sale for retailers, manufacturers and brands. Kibo acquired Certona in 2019, where Meyar served as CEO and founder.
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