One of the core use cases for AI in marketing is deep personalization, whether it manifests through website customization, customer purchase journey mapping, collaborative filtering (“customers who bought this item also bought”), or recommendation engines, to name a few.
In an effort to wade through the noise, the onus falls on organizations’ marketing executives and data team leaders to identify bespoke personalization strategies that will positively impact their engagement metrics rather than have them remain stagnant or, worse, cause a prospective customer or user to abandon their experience altogether.
With the example of recommendation systems, many companies rely on the transactional data that they receive directly from customers. They utilize machine learning algorithms that understand an individual consumer’s buying history, as well as his present behaviors and motivations, to properly evaluate his needs.
These systems are built on a myriad of different data sources and machine learning techniques in order to drive users to continue “basket building” (like on Amazon) or consuming incremental content (like Netflix). The two aforementioned enterprises, though, are considered the exception, rather than the rule — using AI isn’t as natural or scalable for many organizations looking to level up their personalization efforts.
Starting With "Tailored Help"
According to the Gartner Tailored Help Personalization Showcase for Marketing Leaders, “Gartner research indicates that marketing leaders can meet the dual goals of ensuring consumer privacy and relevance in their personalization efforts by delivering 'Tailored Help' to their consumers. 'Tailored Help' personalization refers to messages designed to provide valuable assistance or support while using a limited number of data dimensions to balance consumer relevance against privacy concerns.”
By tailoring the help they provide to customers by leveraging consumer data such as patterns and preferences (think Stitch Fix), organizations will more effectively be able to optimize and scale their AI efforts and, in conjunction, achieve their business objectives. The framework gives companies the chance to engage with customers in the ways that they are proactively pursuing as they organically move through the purchase funnel. Gartner also provides the “Help Me” Framework to identify opportunities for help:
1. Direct Me
- Guide me to a product/service that would solve a problem for me
- Help me decide between multiple products or services I have been considering
2. Teach Me Something New
- Give me a better idea of how I could use a product or service
- Provide me with information I didn’t have before
- Make me aware of new products/ services I didn’t know the company offered
- Help me explore relevant options
3. Make It Easy/Quicker
- Save me time
- Help me get through the purchase process
- Make the purchase process less confusing
- Help me organize an overwhelming amount of information
- Remind me to stock back up on something I use frequently
4. Reassure Me
- Reduce my anxiety about making the wrong decision
- Introduce me to other users of this product/service
5. Reward Me
- Help me get a better deal
- Provide me with exclusive benefits
It is clear that there are a myriad of ways for a consumer to interpret a personalized message as helpful, which brands can use as fodder to inform their own personalization strategies. For organizations with a strong emphasis on personalized choices and recommendations, there should be a renewed focus on the holistic data collection and deployment process to mitigate the risk of losing consumers’ trust.
While basic recommendation engines and traditional marketing engagement tactics are not new, they will continue to evolve and become more complex as new technologies continue to be introduced, experiences are increasingly digital and uniquely catered to the individual, and the consumer consumption ecosystem continues to transform.
Source: Gartner “Tailored Help Personalization Showcase for Marketing Leaders,” Marketing Research Team, 25 February 2020