Personalization has become an essential aspect of modern marketing, as consumers increasingly expect tailored experiences that speak directly to their individual needs and preferences. From personalized email campaigns to targeted social media ads, companies are striving to leverage data and technology to create hyper-relevant customer experiences to drive engagement and revenue.
We have seen personalization successfully deployed across many verticals in the B2C and B2B realms: retail, healthcare, insurance, telecom, manufacturing, non-profit, and travel & leisure to name a few recent cases. In this article, I will describe a personalization-maturity model, from nothing to the most innovative personalization systems. These algorithms build on each other and provide an actionable roadmap to building a personalized experience for every customer.
A definition of personalization to start with.
Personalization - the action of designing or producing something to meet someone's individual requirements
Individual requirements are a function of customers’ needs and desires, preferences, intents, and contexts at different scales.
The combination of all these together, at the particular moment, defines a personalized experience.
Presented here is a concise Personalization Maturity Model focused on algorithms. It helps organizations assess their personalization algorithm capabilities and create an improvement roadmap. The model highlights different maturity levels, ranging from basic segmentation to conversational personalization, and offers guidance for advancing through each stage.
There are certainly other aspects to personalization maturity, notably improving data collection capabilities, integrating personalization across all touch points, and facilitating a culture that prioritizes customer-centricity across the organization.
RFM and alike segmentation based on purchase behaviors. Typically, companies use a manageable number of segments for reporting and visualization; the so-called “9-blocker” is common wherein all customers belong to one of 9 segments based on recency and frequency. This type of segmentation is useful for rewarding high-value customers or growing low-value customers, but not a force multiplier in personalized customer experiences.
Lifecycle approach to track cohorts of customers defined by initial purchase or sign-up date. Common visualization are growth curves; where you can judge the health of your new customer vs. early adopters and so on. Often cohorts are differentiated by acquisition channel and/or promotion where the objective is to evaluate acquisition strategies and track acquisition costs. This approach is useful in the cohort tracking sense using acquisition date and can be used to grow new customers; it is not the entire picture when it comes to personalization.
Strategy to understand who your customers are by mapping key customer segments onto demographic data, usually from a 3rd party vendor. These persona approaches are useful at a high level in the case where companies have a diverse customer base who disambiguate based on demographics. We have mixed success using 3rd party demographic data; there are industries and situations where it is useful and others where it does not add any value. Persona mapping is useful for creating copy and creatives that relate to your customers and for identifying the new customers to pursue.
Micro segmentation is segments at a fine grain. Micro segmentation could be anything from 100 to 1000s of segments depending on use. Detailed RFM segmentation is a good example. A nested RFM approach with 5-levels of each RFM will have 125 segments. Micro-segmentation can be used as simple models wherein each segment receives a unique experience.
Segmentation based on multiple criteria. Could include value, engagement, platform, channel use, category preference, and so forth. May include 10s of segmentation attributes (dimensions). Multidimensional segmentation is used to be able to drill into different customer types and understand their behaviors. It is also used for targeting and micro-reporting. This is a great start to personalization, but often requires manually developing and testing experiences for each micro-segment.
Lifecycle approach to designed to understand customers’ needs at different stages of their lifecycle. This approach usually follows a logical growth pattern such as first-purchase, second-purchase, cross-category purchase, accessory-purchase, gifting, and so on. The approach is to identify key lifecycle events and monitor and incentivize your customers’ growth across these events.
Forward looking prediction of whether each customer will take certain actions. CX teams may run hundreds of propensity models such as churn-prediction and X-shopping propensity. Models can include many customer-features including purchase- and browsing-behavior, demographics, location, platform use and so forth. Models are usually used to refine targeting for specific actions. The degree of cardinality depends on how many features you have in the model.
Predicting future customer value. These models are rarely true lifetime in the traditional sense. Most practical uses look forward 1-5 years depending on the industry and use-case. Models can take many functional forms, personalized models are usually regression based and include 10s or 100s of customer features. Practically, once the LTV is predicted for each customer, the customers are segmented into groups based on ranges of LTV values; then the customer experience is defined and tested within segments.
Predicting customer’s short-term intent or purpose. Intent modeling is often used in service industries wherein customers may have multiple reasons for interest in the product. Intent models focus on short-term behavioral features; features such as search-queries, landing page and immediate browsing behavior, and so on. Personalization occurs by providing a unique experience for each intent.
These models are used to model the transitions between segments or lifestages. These models elicit transition probabilities that estimate the probability customers will move from their current state to a new (desirable) state. Customer-grain versions of these models can include 10s or 100s of customer features. Interpretative models can guide personalization.
Incremental LTV models predict the change in LTV when customers take an action. Econometric causal modeling techniques are often used to estimate incremental values due to the cost of experimentation. These models can be at the customer grain, so that each customer by each action has an estimated value.
Uplift models are the incremental versions of propensity models. Propensity models are marginal models that predict the propensity a customer will take an action. Incremental models are conditional models that predict the propensity a customer will take an action if exposed to an incentive. Incremental propensity models are often used in pricing studies to learn the demand based on price.
High value actions are methodologies to identify and assess the value of customer actions. This can be thought of as being orthogonal to incremental LTV making estimates for actions rather than customers.
NBA systems identify the next best action for each customer. These combine incremental value and incremental propensities to rank the next action. Actions in this context can vary widely including an item, a price, creative, channel, and so forth. These algorithms follow a filter->score->rank paradigm.
Customer journeys are sequenced actions that lead to a desired outcome. Customer journeys could be highly targeted such as a cross-shopping journey or long-term such as a new- to hero-customer journey.
Recommendation engines recommend the next product at scale. Recommendation engines come in many forms. The most sophisticated engines include all customer purchases and/or their ranking and other data to make recommendations.
Real time learning occurs when both long- and short-term signals are included into decisioning, and models are reactive to context, intent, and historical signals. In this approach a recommendation engine may react to where the customer is at the moment, the weather, or what the customer is wearing.
With the launch of Generative Large Language Models (GPT-X) and Generative Image modeling (DALL*E), personalized copy and creative can be generated given prompts from customer behavior. Preferences, context, intent, and other signals will be included into a conversation with the customer. The generative models, which include a memory, can react to short- and long-term signals and unstructured behavior such as selfies or natural language (such as CS calls or product reviews).
The conversational large language models will include short term memory which will enable two-way conversations with customers. These omni-channel conversations will include elements such as asking questions, adding detail and examples, proposing ideas in a timely conversational manner.
At Blend360, we are committed to helping your company build a personalized experience for every customer. Contact us today to learn how our expertise in personalization algorithms and data-driven strategies can enhance your marketing efforts and drive meaningful customer engagement. Don't miss out on the opportunity to create hyper-relevant customer experiences and unlock the full potential of personalization.
Personalization has become an essential aspect of modern marketing, as consumers increasingly expect tailored experiences that speak directly to their individual needs and preferences. From personalized email campaigns to targeted social media ads, companies are striving to leverage data and technology to create hyper-relevant customer experiences to drive engagement and revenue.
We have seen personalization successfully deployed across many verticals in the B2C and B2B realms: retail, healthcare, insurance, telecom, manufacturing, non-profit, and travel & leisure to name a few recent cases. In this article, I will describe a personalization-maturity model, from nothing to the most innovative personalization systems. These algorithms build on each other and provide an actionable roadmap to building a personalized experience for every customer.
A definition of personalization to start with.
Personalization - the action of designing or producing something to meet someone's individual requirements
Individual requirements are a function of customers’ needs and desires, preferences, intents, and contexts at different scales.
The combination of all these together, at the particular moment, defines a personalized experience.
Presented here is a concise Personalization Maturity Model focused on algorithms. It helps organizations assess their personalization algorithm capabilities and create an improvement roadmap. The model highlights different maturity levels, ranging from basic segmentation to conversational personalization, and offers guidance for advancing through each stage.
There are certainly other aspects to personalization maturity, notably improving data collection capabilities, integrating personalization across all touch points, and facilitating a culture that prioritizes customer-centricity across the organization.
RFM and alike segmentation based on purchase behaviors. Typically, companies use a manageable number of segments for reporting and visualization; the so-called “9-blocker” is common wherein all customers belong to one of 9 segments based on recency and frequency. This type of segmentation is useful for rewarding high-value customers or growing low-value customers, but not a force multiplier in personalized customer experiences.
Lifecycle approach to track cohorts of customers defined by initial purchase or sign-up date. Common visualization are growth curves; where you can judge the health of your new customer vs. early adopters and so on. Often cohorts are differentiated by acquisition channel and/or promotion where the objective is to evaluate acquisition strategies and track acquisition costs. This approach is useful in the cohort tracking sense using acquisition date and can be used to grow new customers; it is not the entire picture when it comes to personalization.
Strategy to understand who your customers are by mapping key customer segments onto demographic data, usually from a 3rd party vendor. These persona approaches are useful at a high level in the case where companies have a diverse customer base who disambiguate based on demographics. We have mixed success using 3rd party demographic data; there are industries and situations where it is useful and others where it does not add any value. Persona mapping is useful for creating copy and creatives that relate to your customers and for identifying the new customers to pursue.
Micro segmentation is segments at a fine grain. Micro segmentation could be anything from 100 to 1000s of segments depending on use. Detailed RFM segmentation is a good example. A nested RFM approach with 5-levels of each RFM will have 125 segments. Micro-segmentation can be used as simple models wherein each segment receives a unique experience.
Segmentation based on multiple criteria. Could include value, engagement, platform, channel use, category preference, and so forth. May include 10s of segmentation attributes (dimensions). Multidimensional segmentation is used to be able to drill into different customer types and understand their behaviors. It is also used for targeting and micro-reporting. This is a great start to personalization, but often requires manually developing and testing experiences for each micro-segment.
Lifecycle approach to designed to understand customers’ needs at different stages of their lifecycle. This approach usually follows a logical growth pattern such as first-purchase, second-purchase, cross-category purchase, accessory-purchase, gifting, and so on. The approach is to identify key lifecycle events and monitor and incentivize your customers’ growth across these events.
Forward looking prediction of whether each customer will take certain actions. CX teams may run hundreds of propensity models such as churn-prediction and X-shopping propensity. Models can include many customer-features including purchase- and browsing-behavior, demographics, location, platform use and so forth. Models are usually used to refine targeting for specific actions. The degree of cardinality depends on how many features you have in the model.
Predicting future customer value. These models are rarely true lifetime in the traditional sense. Most practical uses look forward 1-5 years depending on the industry and use-case. Models can take many functional forms, personalized models are usually regression based and include 10s or 100s of customer features. Practically, once the LTV is predicted for each customer, the customers are segmented into groups based on ranges of LTV values; then the customer experience is defined and tested within segments.
Predicting customer’s short-term intent or purpose. Intent modeling is often used in service industries wherein customers may have multiple reasons for interest in the product. Intent models focus on short-term behavioral features; features such as search-queries, landing page and immediate browsing behavior, and so on. Personalization occurs by providing a unique experience for each intent.
These models are used to model the transitions between segments or lifestages. These models elicit transition probabilities that estimate the probability customers will move from their current state to a new (desirable) state. Customer-grain versions of these models can include 10s or 100s of customer features. Interpretative models can guide personalization.
Incremental LTV models predict the change in LTV when customers take an action. Econometric causal modeling techniques are often used to estimate incremental values due to the cost of experimentation. These models can be at the customer grain, so that each customer by each action has an estimated value.
Uplift models are the incremental versions of propensity models. Propensity models are marginal models that predict the propensity a customer will take an action. Incremental models are conditional models that predict the propensity a customer will take an action if exposed to an incentive. Incremental propensity models are often used in pricing studies to learn the demand based on price.
High value actions are methodologies to identify and assess the value of customer actions. This can be thought of as being orthogonal to incremental LTV making estimates for actions rather than customers.
NBA systems identify the next best action for each customer. These combine incremental value and incremental propensities to rank the next action. Actions in this context can vary widely including an item, a price, creative, channel, and so forth. These algorithms follow a filter->score->rank paradigm.
Customer journeys are sequenced actions that lead to a desired outcome. Customer journeys could be highly targeted such as a cross-shopping journey or long-term such as a new- to hero-customer journey.
Recommendation engines recommend the next product at scale. Recommendation engines come in many forms. The most sophisticated engines include all customer purchases and/or their ranking and other data to make recommendations.
Real time learning occurs when both long- and short-term signals are included into decisioning, and models are reactive to context, intent, and historical signals. In this approach a recommendation engine may react to where the customer is at the moment, the weather, or what the customer is wearing.
With the launch of Generative Large Language Models (GPT-X) and Generative Image modeling (DALL*E), personalized copy and creative can be generated given prompts from customer behavior. Preferences, context, intent, and other signals will be included into a conversation with the customer. The generative models, which include a memory, can react to short- and long-term signals and unstructured behavior such as selfies or natural language (such as CS calls or product reviews).
The conversational large language models will include short term memory which will enable two-way conversations with customers. These omni-channel conversations will include elements such as asking questions, adding detail and examples, proposing ideas in a timely conversational manner.
At Blend360, we are committed to helping your company build a personalized experience for every customer. Contact us today to learn how our expertise in personalization algorithms and data-driven strategies can enhance your marketing efforts and drive meaningful customer engagement. Don't miss out on the opportunity to create hyper-relevant customer experiences and unlock the full potential of personalization.
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