Have you ever filled in a survey hoping to win a prize? If so, you have contributed to a brand’s Consumer Intelligence (CI) database. This basic data collection is alive and well in countries like Japan with legions of people filling in postcards of basic demographic questions, often with no prize at all. This information about customers and where they live are the building blocks of a data-led approach to understanding customer metrics.
In recent years, Customer Intelligence has augmented by better information gathering tools and more sophisticated statistical models to analyze this data. Click tracking may spring to mind as the tool of choice for data collection, but these days powerful AI tools allow us to use text analytics to mine natural language for hints on consumer preferences. These metrics then help make better recommendations and inform marketing decisions.
In the past, the use of this data was the realm of creative types who grouped customers into invented cohorts such as studious or athletic and attempted to match the products to the cohorts. These days are long gone (or should be!) and have been replaced by modeling that uses a core set of variables, including factors like demographics, website engagement, past purchases, interaction with marketing messages and in B2B marketing, webcast or event attendance.
Sophisticated companies use models such as logistic regression to predict consumer behavior and find the factors that have effects on purchase behavior of consumer for their products. These factors range from low prices, company reliability and product promotion – all aimed at predicting changes in consumer behavior from no purchasing to purchasing a product. However, these models are reaching their limit. Why? Because they rely solely on past behavior to predict or guide consumers through the funnel and the world has moved to faster gratification and faster feedback. Fast fashion? Customized products? Predictive analytics are good, but they have their limits and these limits can be broken through with the emotional connection of a good customer experience (CX).
As a brand, you may be collecting continuous data from customers but as the consumers become aware you are collecting their personal data, they will expect instant gratification and will want you to add value to their experience the more data they provide for you about them as a buyer. What do they want? InternetRetailing reports that 25% of shoppers actively search for photos or videos of real people using a specific product or brand. How can you predict this and how can you better respond to a quarter of your customers?
Yes, predictions will help but going beyond means providing individualized experiences within the frameworks you establish. This is where CX and Visual AI comes in. By emotionally connecting with customers, you can establish the fun within an increasingly commoditized arena and connect to the 25% of shoppers you are missing.
There is a growing wariness about the amount of personal data collected by tech companies and brands and retailers need to respond to this concern. Companies of any size may gather a plethora of personal data and many will try to sell you on the very personalized message as the key to unlocking riches. It does not have to be this way though.
A well-designed AI tool can deliver all the benefits of improved personalization with little of the need to collect and store hyper-personalized data on individuals. Most responsible companies are more interested in working with a brand to deliver better experiences and less interested in hoovering up lots of data for other projects.
Whereas, a well-designed eCommerce site does not need to know the past 10 purchases of a particular customer, their personal address and how much their annual spending is. Data analytics can aggregate data and discern patterns that will give you all the benefits of improved customer spend, retention and loyalty without the annoyance of collection and storing personal data.
Delvify delivers CI enhanced Visual AI to deliver customized experience to each customer.
How do we combine these features to make magic? Each customer is presented with Visually Similar items on their journey prompting more sticky engagement. That is not all, Delvify’s CI engine look deeper into the customer journey to enhance the type, frequency, and category of products shown to your customers. By working with brands we can boost or hold back items within the Visually Similar products to help create that unique and hyper-personalized journey.
How do we know that this works? Accenture tells us that 83% of consumers are willing to share their data to enable a personalized experience.
Get in touch with our team today if you would like to learn how you can increase the number of your brand ambassadors through CI!
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