IT organizations that support e-commerce businesses will find that Machine Learning plays a big part in helping the shift towards hyper-personalization. In this chapter, we’ll find out how this trend, built on data, analytics, and ML, impacts IT organizations and benefits companies.
Content Summary
What is Hyper-Personalization?
How Customer Expectations Are Changing
Why Businesses Benefit from Hyper-Personalization
How IT Supports Hyper-Personalization
Conclusion
What is Hyper-Personalization?
ITOps teams typically have focused on supporting the back-office business (think corporate email, intranet, HR systems, and so on) and customer-facing offerings, like e-commerce sites and online support services. Now more than ever, ITOps is involved in improving the customer experience (CX), and a huge part of that is personalization efforts driven by the CMO and the marketing organization. But just what is “hyper-personalization,” anyway?
Modern marketing efforts began in the early 20th century with the concept of segmentation—defining who customers are and how to target customers using demographic and location data. It evolved to encompass customer details, including name, age, and purchase history once CRM technology arrived, which began the implementation of “personalized service” for the masses. As customers started leaving data crumbs all over the internet, companies began gathering that data to help design and deliver valuable offerings for those customers. For instance, Amazon’s famous personalization engine offers product recommendations, personalized product reranking, and customized direct marketing. Likewise, Netflix knows just what you want to watch (or what they want you to watch).
Hyper personalization is a massive leap forward. It moves beyond demographics and purchase history to consider browsing activity, purchasing habits, and other behavioral data to help companies determine what the consumer wants or needs.
Modern CMOs see the value of pivoting to a digitalfirst mindset and are interested in using ML and AI to create deeper, more authentic interactions with customers while effectively capitalizing on data gathered to drive insights-driven results.
How Customer Expectations Are Changing
Companies like Amazon and Netflix have effectively trained consumers to expect companies of any category to understand what they want and make it easy to get. As a result, a variety of features and functionalities are making their way across industries:
Relevant, Custom Advertising
Customers want to see meaningful advertisements, showcasing goods, services, or experiences that relate directly to them.
Recommendation Engines
Content, service, and product suggestions should be tailored to individual needs and preferences.
Omnichannel Service
Customers want to shop and connect with businesses through online and offline channels.
Self-service Assistance
Chatbots are becoming more prevalent and useful to both customers and businesses, making it easier to answer typical questions in a more personal way without the need for office hours or a contact center.
Dynamic Pricing and Offers
It’s easier to convert prospects when you can change the offer or price based on customer habits and preferences.
Next-generation Loyalty Programs
Re-engagement has previously centered on customer purchase history, but companies can now use sophisticated micro-segmentation and geospatial data to offer highly contextualized deals and suggestions.
Across industries, businesses are finding ways to bring their marketing efforts towards achieving hyper-personalization. We see it, of course, in retail and entertainment services, but it’s emerging across all industries, particularly those that come with lots of data. In healthcare, for instance, clinicians and medical facilities focus on remote patient monitoring and telehealth, nursing, and patient care in general and early detection of diseases, treatment, and research.
In manufacturing, the move towards automation is being accelerated by AI, robotics, and the public cloud. It shows up in nearly every aspect of the modern manufacturing business, even customer service and marketing, as companies implement customer service chatbots, order processing support, and logistical operations improvements. And in the financial services industry, ML algorithms are used in fraud detection, trading automation, and financial advisory services for investors.
Why Businesses Benefit from Hyper-Personalization
An often-cited study conducted by the University of Texas indicates that the need to personalize comes from the desire to control and simplify decision-making. Interestingly, companies that invest in personalizing products and offerings often end up with customers at the center of their corporate decisions. Instead of making a product and convincing people to buy it, companies can know what products and services consumers want and plan accordingly. This is a win-win for customers and companies alike: customers get what they want or need, and companies can be more successful in sales and support efforts.
This might explain why so many companies spend time and money developing hyper-personalization marketing plans. As noted by Forrester in a 2019 study commissioned by IBM: “Even with immature personalization strategies, firms see an almost 6% increase in sales revenue, a 33% increase in customer loyalty and engagement, and an 11% decrease in marketing costs.
How IT Supports Hyper-Personalization
Hyper-personalization is an art and science that necessitates both a strategy and the right technology. The strategy might be typically driven at the C-suite level but with a close connection to frontline workers (sales and support) and the IT teams who support it all. There are several ways IT teams will be supporting hyper-personalization efforts:
CX Platform Support
For many businesses, the first step forward is selecting a CX platform that supports omnichannel journeys and connects with the CRM system. IT personnel will be an integral part of deploying and maintaining CX platforms, whether they be outsourced or developed in-house—or a combination of the two.
Big Data Management
IT organizations may find themselves adding more data scientists to the team to help direct and manage efforts to gather, store, and process the vast amounts of data required to support the hyperpersonalization strategy. Compliance with data privacy regulations is also part of the equation that IT must support.
ML and AI Application
Part two of big data management is the use of ML and AI in CX and marketing solutions. Data scientists, ML and AI engineers, and product managers work together to create, maintain, and support personalized recommendation engines, chatbots, targeted ads, and so on.
Conclusion
Companies are increasingly personalizing along the customer journey, starting with product design and outreach and encompassing the end-to-end consumer experience. Data analytics, ML, and AI are indispensable tools for building a strategy to meet hyper-personalization goals. In our next chapter, we’ll look at emerging and forecasted trends in ML and AI that will transform IT organizations.
