Six Steps for building a Hyper Personalization Engine

Why unified hyper personalized engine?

Investment in hyper personalization is a must as customers expect personalized experiences across multiple channels.

The COVID-19 pandemic has demonstrated a radical shift in customer behaviour towards digital channels. Today, customers expect personalized experiences across multiple channels and in every interaction with a brand. When it comes to personalization, traditional brick-and-mortar organizations are facing tough competition from non-traditional, digital-first, tech-savvy companies that place hyper personalization at the heart of their business strategy. 

Recommender systems have been in existence for a long time now and almost all organizations have invested in some sort of recommender system to boost their sales. However, in the era of digitalization, the business value of traditional recommender systems is not clear. 

To drive business value and continue to remain competitive in today's marketplace, organizations will need to invest in building hyper personalized recommendation strategies, which focus on improving customer experiences and building brand loyalty. Hyper personalization uses knowledge of individual customers to provide them with personalized recommendations, in the moments that matters, based on their position in the purchase funnel. It adds value to the customer experience and helps build a bond between a customer and the brand.

Strategy for hyper personalization

Defining a common strategy for hyper personalization across all customer touchpoints and channels is key to building a unified hyper personalization engine. 

This will require organizations to work with key business stakeholders to first identify the business objectives and goals that the hyper personalization engine is intended to achieve. It will involve building agreement and alignment on critical aspects such as: 

• Defining the business case for hyper personalization including:

1) Vision

2) Gaps in the current siloed solution

3) Capex and OpEx investments

4) Benefits and return on investment (RoI)

• Creating customer segments that should be targeted using hyper personalization.

• Defining personalization strategies to be used across various customer segments.

• Defining key customer journeys and moments in the customer’s journey that should be hyper personalized.

• Charting a roadmap and project plan for deploying a unified hyper personalization engine within the organization.

• Identifying the metrics that would be used to track the success of the unified hyper personalization engine.

6 steps to building a hyper personalized engine 

Once the strategies are defined, it is important to define the steps for building a hyper personalized engine.

We recommend six key steps for building a hyper personalized engine: 

• Step 1: Build a customer data platform. Build a customer data platform that creates a rich persona for each customer and provides input to the hyper personalization engine.

• Step 2: Build a feature store that enables reuse, reproducibility, and better performance. The feature store is an integral part of the unified hyper personalization engine. It provides the data inputs to the hyper personalization engine for generating recommendations. Building the Feature Store requires choosing the “Best Features for Personalization”, which includes various types of data, such as user profiles, preferences, behaviors, interactions, and other relevant features. They enable higher reuse across use cases, consistent data quality, and better performance of personalization models.

• Step 3: Generate customer insights to be used for recommendation. Customer data platform enables companies to draw insights about the customer in terms of dynamic customer segmentation, customer churn, customer lifetime value, and customer sentiment analysis. These insights provide an enriched data input for the unified hyper personalization engine. The unified hyper personalization engine will use this data to offer recommendations that are relevant, engaging, and timely for each customer. 

• Step 4: Build global and contextual recommendations for unknown and infrequent customers

The following strategies can be implemented for unknown and infrequent customers for whom there is less information to create personalized recommendations: 

1) Provide global recommendations based on trending categories or frequently purchased products.

2) Leverage in-session behavioral data to decipher customer intent and use industry-leading contextual recommendation algorithms to provide tailored, real-time recommendations based on the customers’ context

• Step 5: Build hyper personalized journey aware recommendations To ensure customers are always served relevant content, use filtering rules combined with machine learning models to identify and match the right experience to the right customer based on the knowledge about the customer. The filtering rules can use various criteria, such as customer preferences, behavior, context, location, device, and time. By using these criteria, the hyper personalization engine can generate recommendations that are aware of the customer journey and tailor them to each stage and goal. For example, a new customer who is browsing for a product may receive recommendations for similar or complementary products, while a frequent high valued customer who is ready to make a purchase may receive additional curated product recommendations based on their purchase history, in-session browsing behavior, preferences, and location. These recommendations may also be clubbed with additional discounts or free shipping.

• Step 6: Build personalized customer experience Build & deploy personalized and relevant UI across all channels to provide a delightful, customized experience for each customer. Leverage comprehensive testing experiments, to tailor the best experience for every customer based on their context and history. These tests can be used for testing new ideas or hypotheses, learning from customer feedback and improving individual customer experience over time.