CIOs should consider harnessing Artificial Intelligence to deliver personalization in every facet of interactions and transactions with customers.

Before the advent of industrial production and mass advertising systems for developing and bringing goods to market and enticing consumers to make purchases, there was more room for business owners to provide individual attention to the customer.

For example, a grocer might recommend a new product because he knew the likes and dislikes of a particular family. And customers often would rely on salespeople to help guide them in purchases if any product education was needed before the transaction.

Or consider the position of the owner of a woman’s clothing boutique. She would make it a priority to get to know the personality, style and other factors influencing her regular customers. The idea is to make it easier to give them what they needed most: individualized attention instead of relying on generic sales prompts based on mass-marketing theories.

But when you are gearing up to produce or sell items on a large scale, in the thousands or millions, it’s impossible to get to know customers as discrete individuals with their own particular tastes, motivations and backgrounds.

For processing power above the ability of humans, artificial intelligence is used. This is where AI-driven personalization comes into the picture. It’s within any company’s reach today to use technology and software solutions with artificial intelligence underpinnings. Enterprises starting now will use AI-driven personalization to increase their return on investment by 200% or more, as reported by ClickZ.

It’s natural that more businesses will start using artificial intelligence to customize their approach to customers when they see the efficiencies it brings. As the U.S. National Science Foundation put it, “the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI research and development to create new sectors of the economy and revitalize industries.”

Companies from any industry in need of boost, especially those now making efforts to turn around sales numbers that declined during the early days of the coronavirus pandemic lockdowns can now look to artificial intelligence to reinvigorate their operations.

Harnessing Artificial Intelligence to Deliver Personalization

What Data is necessary for Personalization?

To simplify matters, one can separate data used in AI-driven personalization into two categories: Product and Transactional.

Data collected on products has to do with how it is categorized, such as by age or gender and whether it is part of a family of products. Attributes from color to weight to the margin on sales versus ordinary prices also figure here.

Transactional data has to do with customers’ sales history, from how much they buy and how often, to what gets put into their shopping cart and then is abandoned. The information is also viewed in the context of demographics as companies try to figure out who is buying what, when they tend to make these purchases and why. There’s also room to explore linkages, such as what kinds of products are most typically purchased together.

When this data feeds into the enterprise’s database, machine learning processes the transactions, refining the data and identifying patterns, trends and areas where the company can focus its efforts. These efforts can occur in marketing, through recommendations and even in details to drive future product development in response to all of the signals generated by shoppers and captured by the AI.

It’s crucial to keep in mind common challenges to obtaining useful data when creating your own program, so you can ensure you’re gathering and manipulating the information needed to build your bottom line.

One challenge has to do with identifying users and their location. According to TOPBOTS, the majority of companies can only recognize about 10% if that many of their consumers. Lacking timely/real-time access to inventory updates is another bottleneck your AI-driven personalization program will need to address.

It may not be obvious what items your AI should recommend to shoppers according to data you’ve been collecting on what they viewed and what they’ve purchased in the past. One response is to develop user profiles based on semantic attributes instead of data on specific products.

For example, you link consumers to an affiliation for sweaters made out of natural fibers rather than tying them to the name of a particular brand or type of sweater. Here, a holistic view is desired into customers, built over the long haul of data collection rather than focusing on analysis of individual transactions as they occur.

Leveraging Artificial Intelligence to Deliver Personalization

When building an AI-driven personalization process in-house, a range of talent is needed, noted INSEAD. For example, you’ll need business managers, data scientists, economists, operators and statisticians to manage the entire effort.

And employees will need to adopt the perspective that artificial intelligence efforts are more than just basic data analytics.

Of course, enterprises that lack this kind of in-house expertise can work with outside, third-party experts to consult and develop AI-driven personalization tools for them.

Enterprises preparing for their own AI-personalization efforts will want to examine how other organizations got involved with artificial intelligence to turn sales around.

Examples of How Companies Use Artificial Intelligence to Deliver Personalization

For example, a need to boost its bottom line prompted the Olay soap brand to start using artificial intelligence to hone in on its customers, noted Meltwater, citing Venturebeat data. Inside stores, customers were faced with so many options, they were paralyzed and unable to make a decision about what to buy. In many cases, customers couldn’t or wouldn’t consult with experts inside the stores about the right product for their type of skin.

Olay responded by releasing its Skin Advisor mobile app about two years ago. The app is doubling customer conversion rates with machine learning. Olay’s technology analyzes customers’ skin tone based on photos they take of themselves.

Skin Advisor is built on an enormous database of images of people’s skin and faces, reflecting a wide demographic range. The database also gave Olay access to customer shopping behavior and preferences to help make very specialized recommendations thanks to this deployment of AI-driven personalization.

Product and shade recommendations based on aggregated machine learning data in the beauty and skin care industry are nothing new, as Maybelline and Bare Minerals have also entered the fray. Olay stands out because it works with much more details in the image data, with facial recognition algorithms helping to personalize skin care product recommendations even further.

Helping customers find a product based on images of items they’ve seen and liked already is an AI-driven personalization strategy used by Wayfair to great effect. Wayfair made a visual search engine from machine learning technology and allowed customers to upload previously saved images of things that caught their interest while browsing the web. The AI examined the images to see if it could find similar offerings in the Wayfair catalog, which it could then recommend as a personalized sales suggestion to new customers.

AI should become an integral part of Enterprise IT Strategy

It’s clear that going forward, more organizations will find it productive to harness AI-enabled personalization when reaching out to consumers, for better engagement. Crunching through the huge amounts of data to do proper analytics helps to further hone in on consumer desire and needs. It’s also useful for predicting customer responses to current marketing campaigns and how their shopping patterns might change.