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Digital commerce search framework - part two

Digital commerce search framework - part two

The first of our two part blog series delved into the future of search in an insight-led world. This part will consider and elaborate on a search framework suitable for all organizations.


The intent of such a framework is to provide a holistic view that will help derive a search strategy across different dimensions. A search strategy begins with a deep understanding of an enterprise’s digital commerce strategy. 


Adopting a ‘mobile first’, ‘content driven commerce’, or ‘CX design’ approach will shape an organization’s search vision and objectives. Also, the data and operations readiness of the enterprise in relation to data quality, business ownership and operations workflow will lay the foundation of future search. 


To effectively realize search-led ROI, search cannot be viewed in isolation but has to align with the overall content, product information and the organization’s front-end strategy roadmap.

Enterprise priorities

Digital commerce/content/CX strategy


Data and operations readiness


Alignment with transformation roadmap

Search scope dimensions


Business model

  • current model of operations
  • ex:  B2C / B2B2C,  D2C, marketplace  etc.
  • partner ecosystem
  • regulatory compliance

Business scope & growth

  • geo & market
  • business units/segments
  • product/category scope

Information architecture

  • product terminology/ synonyms/dictionary
  • faceted taxonomy
  • customer facing hierarchy


  • external - customer, partner, vendor
  • internal - employees, business
  • journey mapping

Data ecosystem

  • core commerce
  • ERP/data connectors
  • data lake
  • analytics
  • third party data sources

Enterprise search strategy approach


Vision & objectives

Align with business model evolution, scope, digital strategy and CX vision


Key strategic use cases & priorities

Derive from key personas and related pain points, with deep understanding of information architecture

Platform strategy

Choose right search solution – balancing vision, priorities and data/business readiness

Foundation setup (data, tech & infra)

Align with information architecture and data ecosystem needs


KPIs & metrics

Establish search-led commerce/enterprise growth metrics aligning with scope and priorities


An approach to search strategy


The five dimensions listed below guide an enterprise in crafting the right search strategy. 


Business model evolution: current digital maturity and an enterprise’s business model help formulate high-level strategic priorities. Search intent and capabilities vary depending on the existing and future evolution of the organization’s business model. 


For example, there are variations among direct-to-customer models such as B2C, D2C and B2B2C. While B2C is centered on promotions and a price-led search, B2B focuses on recurring seasonal search patterns. B2B marketplaces also face different regulatory challenges where the promotion of their own private brands require flexible search rules.  


Business scope and growth: the first part of this blog series discussed enterprise search – the scope of search determines its priorities and use cases. Geographies and markets determine language, customer demographics and behavior profiles power the scope of search engines. Depending on the format of retail (grocery, supermarket, fashion etc.), the scope of business units and product range could influence the search data scheme and processing logic. 


Information architecture: the nature of structured and unstructured product data alongside complexities related to volume, variations and associations demand a clear information architecture strategy, robust foundations and operations. Establishing a deep understanding of the current and future information architecture and the right taxonomy and hierarchy is important.


Personas: customer intent and search behaviors vary between personas with different motivations and search needs. Large retail enterprises with multiple business segments and categories need to understand the search and discovery journeys. Organizations must identify top personas contributing to the bottom line and map their pain points to focus on search priorities. 


Data ecosystem: defining clear data ingestion and connector boundaries are key to building the right foundation. Product, content, services, price, promotion, inventory, fulfillment and analytical feeds are critical for search engine optimization. Data ingestion could become complex considering the decentralized nature of data, disparate integration mechanisms and accuracy challenges.


An organization will have to define how it balances its vision for digital commerce versus enterprise search scope, set objectives to improve information architecture or enhance product findability and prioritize use cases for business units and personas in alignment with its business model. 


These objectives will have to be measured by key results or KPI/metrics to drive conversion and growth, including search led orders per month, average order value (AOV), click through rate (CTR) and conversion rate (CR).


Evaluating the right enterprise search solution


The recommended approach is to build digital commerce and content search capabilities and evolve to enterprise scale with advanced use cases, data modeling, configuration and analytics. 


The following are the key evaluation criteria that can be applied across the search platform solutions, depending on business priorities and weightage:

Strategic alignment with business
  • Business model coverage

  • Regional regulatory compliance

  • Balanced business and algorithm led configuration

  • Scale across group companies, business units and digital properties

  • Vision of seamless integration with merchandising, marketing, commerce and customer data insights/systems



Evolving business domain scope


  • Universal search beyond product, covering structured and unstructured data

  • Support for various stakeholder needs like merchandising, marketing and supply chain

  • Customizable algorithm and business logic



Alignment with domain driven architecture design
  • Thematic/semantic understanding of specific industry

  • Flexible taxonomy classifications

  • Configurable data schema

  • Indexing to support complex product associations and suggestions



Data interoperability led real-time query processing and qualification
  • Ease of ingestion and indexing

  • Attribute level configuration for precision, tolerance, weightage, boosting and synonyms

  • Word segmentation, synonyms and advanced syntax

  • Configuration of search results - no results, pagination, highlights, snippets

  • Intent based analysis - NLP, subjective

  • Support for non-product content search and diverse content types - videos, pdfs, logs



Analytics driven business configuration and customization
  • Relevance and ranking tuning based on analytics/personalization

  • Flexible A/B testing

  • Advanced rules/conflict management

  • Dynamic customization of facets/filters

  • Search analytics UI and dashboard to analyze query, clicks and results

  • Centralized analytics dashboard with data visualization

  • Analytics dashboard across total users, total searches, no result rate, Click Through Rate (CTR), Conversion Rate (CR), no click rate, top searches, top results, top searches without result



Digital commerce search transformation is a multi-dimensional, multi-year initiative that demands continued focus across people, processes, technology, operations and platforms. 


While it is not easy to build a search solution from the ground up, an enterprise needs to view this as a holistic ecosystem solution. It must avoid a myopic view focused on tactical relevance and ranking measures. 


The vendor landscape is quite fragmented and offers a multitude of solutions for the same capabilities such as core search, relevance/ranking and AI or ML models. The real differentiator lies in building a sustainable, integrated platform solution strategy based on a complete understanding of the enterprise business model, information landscape, customer intent and new search experiences.

Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.

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