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Welcome

Accelerate your growth with personalized customer experiences

The value of personalized customer experiences is readily apparent and intuitive to many retailers—tailoring messaging and creative to your customers makes them more likely to explore and buy your products. To deliver on this promise of personalization, retailers should periodically assess their current capabilities across four key dimensions that work together to deliver an outstanding personalization experience: data management, granularity, use cases, and omnichannel reach. For each of these dimensions, retailers should assess their company’s level of maturity as basic, foundational, intermediate, advanced, or differentiated.

As a first step in your process, take our Retail Personalization Maturity Self-assessment to determine your current stage of personalization maturity. By answering six multiple-choice questions, you can rate your maturity across the four dimensions to benchmark your progress to date and identify potential opportunities to close any gaps or challenges you might be facing. You’ll also receive a set of customized recommendations to help you move up the maturity model and accelerate your success in personalization.

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Questions

Data Management

What best describes your situation regarding product data?

Product data is used as delivered by manufacturers. There is no data enrichment with a digital commerce focus, including overriding technical vocabulary with customer-friendly vocabulary.

Product data is curated to ensure information is presented using customer-friendly vocabulary, avoiding technical terms typically found in manufacturing processes.

Product data is enriched in a way that is easy to build relationships among products, create consistent product catalogs, and facilitate product groupings based on different criteria.

Product data is enriched with information regarding environmental factors. Examples of environmental factors are weather conditions in which apparel is expected to be used and periods of the year in which certain products are expected to be used.

Product data is enriched with information that connects products with customers. Examples of consumer intent are "be physically more active" that could apply to a range of apparently unrelated products (e.g., sports apparel and cultural tourism), or "enjoy the company of friends and family" that could apply to board games and outdoor events.

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Questions

Data Management

What best describes your situation regarding customer and intent data?

Minimal customer data is managed beyond log-in and consent information.

Customer data is enriched with basic demographics, such as age, gender, and address.

First-party intent data, such as web analytics and order processing events, is captured and processed for both known and unknown customers.

Customer data is enriched with psychographics derived from intent data or captured directly via surveys.

A customer data platform coordinates first-, second-, and third-party data and takes care of identity resolution. Other intent data, like search history or interactions with customer service and support, is used to build rich customer profiles.

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Questions

Data Management

What best describes your situation regarding a data platform strategy?

There is no formal strategy. Systems act in isolation, and teams manage data ad hoc. The landscape is dominated by batch and point-to-point integrations.

Basic pipelines for data cleaning and enrichment are defined for product data, customer data, and order history. Those pipelines are still mostly batch-based and require manual intervention.

Product, customer, and order data are cleaned, enriched, and flow to a common data lake. There are controls in place to ensure data meets a minimum quality threshold.

More advanced data sets, like real-time analytics, search history, and shopping cart interactions, flow into the data lake. There is support for real-time data flows in a fully automated data pipeline.

A self-service data platform with proper governance, security, metadata management, and lineage is in place. MLops practices are embraced by the teams.

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Questions

Personalization Granularity

What best describes your situation regarding the granularity at which you offer personalization?

There is no granularity. All customers see the same experience.

Experience is curated at a country level or other high level geography, mostly for localization purposes. Hence, the same experience is delivered to all customers in a given country.

Experience is curated based on coarse-grained segments defined by basic demographics like gender, age, or past purchases. ML algorithms are used for segmentation and clustering, which is updated daily or weekly, but manual work is still required to take care of some aspects of the data pipeline.

Experience is curated based on fine-grained segments combining demographics, psychographics, and intent data such as web and mobile analytics, search history, and shopping cart interactions. Segmentation is almost fully automated and ML-based.

Personalization is happening at the individual level based on demographics, psychographics, intent data, and ancillary data sets such as weather forecasts, local events, or future promotions. A few or a single algorithm drive personalization.

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Questions

Digital Commerce Use Cases

What best describes the personalization use cases that you are delivering to your customers?

No personalization use cases are delivered. Customers interact with essential digital commerce functionality that allows them to browse products, add to cart, and check out.

Customers are presented with basic use cases like “similar products” or “customers who bought this also bought that” in product detail pages. Static, rule-based relationships among products and order histories are built to enable those use cases.

More ad-hoc product relationships are built either manually or via ML to deliver use cases around complementary items, like “shop-the-look,” “complete the recipe,” or “this might also interest you.”

A combination of search listings and facets, category listings, and featured products are influenced by customer segmentation. Advanced use cases, like “cold-start recommendations” or “item exploration,” start to appear.

Customers enjoy a fully personalized experience at every step of the journey. Business users have the ability to influence the personalization engine in real time. For example, they can define business rules to exclude products purchased in the past or only offer products with one-day delivery to a certain segment of customers.

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Questions

Omnichannel Personalization

What channels are benefiting from personalization?

No front-end experiences are delivering personalization use cases.

Personalization use cases have a strong commerce focus and can only be experienced on the organization’s website.

Personalization capabilities are productized via APIs and can be leveraged in multiple channels, although still focused on commerce front ends.

Personalization capabilities are leveraged across multiple channels. Content in marketing and transactional emails are personalized, social interactions with customers take advantage of personalization input, and retail stores leverage personalization in situations like self-service kiosks and smart fitting rooms.

Personalization is leveraged across channels by customers and business users. Store associates, social media managers, and customer service agents receive input from the personalization engine to tailor their interactions to specific customers.

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Results

To generate a more prescriptive evaluation of your retail business’s personalization maturity as it relates to the four key dimensions— data management, granularity, use cases, and omnichannel reach—download your customized report using the link below.

Differentiated

Congratulations! Based on your selections, your organization presents a Differentiated level of maturity across the dimensions of data management, personalization granularity, use cases implemented, and omnichannel reach.

You are very close to offering a unique, individualized experience to every customer across all touchpoints. When successful, a range of KPIs are positively impacted: quantity and quality of traffic, conversion rate, average order value, customer lifetime value, and email open rate, among others.

Advanced

Congratulations! Based on your selections, your organization presents an Advanced level of maturity across the dimensions of data management, personalization granularity, use cases implemented, and omnichannel reach. A truly differentiated customer experience is within your grasp.

To measure the impact of your personalization initiative, focus on traffic, conversion rate, average order value, and customer lifetime value as the key KPIs to assess progress.

Intermediate

Based on your selections, your organization presents an Intermediate level of maturity across the dimensions of data management, personalization granularity, use cases implemented, and omnichannel reach. You have done an excellent job establishing foundational components and are ready for specific actions within each dimension to take your business to the next level.

To measure the impact of your personalization initiative, focus on traffic, conversion rate, and average order value as the key KPIs to assess progress.

Foundational

Based on your selections, your organization presents a Foundational level of maturity across the dimensions of data management, personalization granularity, use cases implemented, and omnichannel reach. You have established the basics and are ready for specific actions within each dimension to take your business to the next level.

Before further increasing omnichannel reach, measure the impact of your newly deployed use cases. At this stage, conversion rate, average order value, and traffic are the key KPIs to assess progress in the right direction.

Basic

Based on your selections, your organization presents a Basic level of maturity across the dimensions of data management, personalization granularity, use cases implemented, and omnichannel reach. Your are offering the same user experience to all customers across channels, and your existing customer and product data attributes are not yet enriched.

Before increasing omnichannel reach, measure the impact of your newly deployed use cases. At this stage, conversion rate and average order value are the key KPIs to assess progress in the right direction.

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Next Steps

For more information about how AWS works with leading retailers to create the most advanced customer experiences and how we can help you improve your own personalization efforts, read Room to Grow: How to Supercharge Your Retail Personalization Capabilities for Boosted Sales and Deeper Insights.

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