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.
Digital commerce/content/CX strategy
Data and operations readiness
|Alignment with transformation roadmap
Search scope dimensions
Business scope & growth
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
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
Evolving business domain scope
|Alignment with domain driven architecture design
|Data interoperability led real-time query processing and qualification
|Analytics driven business configuration and customization
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.