Delivering results. A practical guide to AI search with Algolia & BigCommerce.

But not all search is created equal:

• Any basic search solution must be able to efficiently and effectively facilitate discovery of your product catalogue and related content through user-provided queries
• Better solutions allow for refinement and personalisation of results based on user input
• Excellent solutions do all of these things plus offer AI features such as natural language processing, context-based recommendations and ranking and real-time personalisation

A modern search solution should offer all of these features and be able to meet your customers wherever they are by providing an omnichannel offering that delivers a consistent experience no matter the device or interface being used to interact with your business.

The more of these features your search solution can provide, the more likely it will be that your search delivers a positive customer experience and helps drive conversions. You may not need all of these features immediately, but your search solution should be able to scale along with your business.

Algolia is a modern search solution that we have been using here at Ridgeway. In this article I’ll break down how we approach incorporating Algolia into our eCommerce projects, how it integrates with other platforms such as Storyblok, BigCommerce and Akeneo, and the journey from design to implementation to optimisation.

The challenge of product discovery

Most traditional CMS’s and eCommerce platforms offer some form of search experience. The problem is that these solutions:

• Typically only provide the most basic of features – no or limited options for refinement and personalisation, no AI features, and no support for omnichannel experiences
• Can be rigid in their design – difficult to add or surface custom fields or to combine with data from other sources e.g. marketing content, reviews or data from PIMs or ERPs
• Are not a dedicated solution – they are part of the wider product offering and performance, innovation and optimisation of search features are not the priority

This can leave you in a tough spot when you outgrow the built-in capabilities of your chosen platform.

What does Algolia do differently?

What separates Algolia from built-in eCommerce search is its API-first AI powered search and retrieval platform:

• Dedicated solution – search is Algolia’s primary product focus and they are continuously optimising their product and adding innovative new features to enhance or complement the search experience.
• Flexible design – design of search indexes and fields are entirely in your control and because of the API-first architecture data can be sourced from anywhere and combined as needed, plus support for omnichannel experiences are easily facilitated.
• Scalability in mind – Algolia offers different plans that allow you to scale features and capabilities as needed, and even the simplest plan supports features you wouldn’t typically see in traditional CMS’s and eCommerce platforms such as natural language processing and recommendations.

Algolia is also part of the MACH Alliance, which means it is designed to fit into any well-architected solution, such as BigCommerce and Storyblok, and it’s flexible design means it can scale to meet the demands of your customers and your business.

With great power…

Flexibility is great, but requires planning to make sure you get the most out of the product now and identify opportunities for growth in the future.

The best place to start in our experience is with the core feature, search and how data needs to be surfaced through your channels to your customers.

For example, a typical eCommerce app will require product information to be surfaced through free-text search and refined through filters, but you will also need to ask:

• Does my product catalogue include variants and bundles and do I want them surfaced as individual products or just as the “base” product?
• What information do I need to surface e.g. Name, SKU, Price, URL, Dimensions, Description and which attributes are searchable?
• What information do I need to include to setup up facets for filtering, ranking or sorting?
• Where is the source of my product information e.g. a PIM like Akeneo, a CMS like Storyblok or eCommerce platform like BigCommerce, and do I have multiple sources that need to be combined?
• Do I need to surface other types of information through search e.g. supporting content like news or blog articles, marketing content?
• Can Algolia drive other features such as Product Listing Pages (PLP) or Store/Stockist location?

All of these questions help you to think about, plan, create and configure indexes in Algolia that hold the records that represent your data. Getting this right is the key to a successful implementation and poor planning to lead to performance or indexing issues such as exceeding the maximum record size.

After you have successfully structured your records you can start to think about how you might layer on and take advantage of other features such as analytics and insights, personalisation, and recommendations.

Of course, cost is always a factor that needs to be considered at every stage to ensure that you stay within your budget. You will need to consider the features you wish to use so that you can select the most appropriate plan, but also the volume of search requests per month factor heavily into the overall cost, and the total number of records. Failing to plan appropriately for usage can result in a much higher than expected bill!

How we integrate Algolia with BigCommerce

Ridgeway does not take a “one size fits all” approach to any integration, there are always multiple options and the right one heavily depends on the requirements of our clients and any constraints imposed by their business or customers.

Thankfully Algolia’s flexibility lends itself to many different approaches from off-the-shelf to fully bespoke.

• Off-the-shelf integrations exist for:
  • eCommerce platforms such as BigCommerce
  • CMS such as Storyblok, Contentful
  • And many more
• Bespoke integrations are possible by interfacing directly with Algolia via its Ingestion or Search APIs

Off-the-shelf integrations are great when you only have a single source of data to ingest into Algolia. They are usually relatively quick to setup and manage although we do advise some caution because not all integrations are created equal and it’s possible that if rough edges exist you may not be able to get the support that you need!

Bespoke integrations can cost more, but the result is a solution that is tailored directly to your needs that can be further developed over time to stay in line with changing requirements.

If you have more than one source of data it may also be your only choice because, for example, the Algolia Storyblok integration won’t also be able to source data from BigCommerce.

As well as integrations between other products and platforms, Algolia also provide SDKs, API clients and component libraries for popular programming languages and application frameworks. These all help to reduce development cost and reduce time to market when implementing high-quality search experiences for end users.

The team here at Ridgeway are experts at designing, delivering and working with composable architectures and will be able to help guide you to the optimal solution.

Worked example. Integrating Algolia with BigCommerce & Storyblok

Your eCommerce store has a web app and you want to implement Product search and product listing page features using Algolia. Within your catalogue you have music products e.g. CDs and Vinyl and alongside basic product information you want to be able to surface relationships between artists and products e.g. “this artist appears on…”.

You are using Akeneo as your PIM, BigCommerce is your eCommerce platform, and Storyblok is your CMS. Here’s the high-level overview:

• BigCommerce and Storyblok both have off-the-shelf integrations with Algolia
• Akeneo does not, but it does have an integration with BigCommerce that you are already using as well as an API
• There is product data held in Storyblok that you need in your index e.g. Product names, URLs
• There is also data held in Akeneo that you need in your index e.g. Product SKU information, price, taxonomy, artist and contributor data
• Some of the data in Akeneo is synced with BigCommerce through an off-the-shelf integration. However, not all of the data you need in Algolia is available in BigCommerce e.g. artist and contributor data
• What you need is a combination of the data in Storyblok and Akeneo

In this case, a bespoke integration would be a good choice as it allows you to retrieve and combine data from multiple sources and take complete control of how the data is structured in the index to drive the features that you need.

Our first step is to plan the required record structure required. The record structure would utilise relationships to represent artists and contributors, while facets (attributes) and filters enable cross-referencing between products. Facets and filters would also drive refinement of product search and product listings using taxonomy data.

The next step we would take is to design and develop the custom integration using built-in APIs to retrieve the relevant data to populate the index. Webhook notifications would be used to subscribe to events such as products being created, updated and deleted to ensure that the data in the Algolia index is kept in sync with minimum delay.

Algolia provides features such as typo-tolerance, synonyms, and natural language processing as part of the base plan. Analytics, personalisation, recommendations, and AI features such as dynamic re-ranking to provide contextual and more relevant search results can then all be configured to take the search experience from basic to best-in-class.

Analytics & optimisation

Once your new enhanced search experience is live, you should see marked improvements in product discovery and customer experience. However, ongoing optimisation is where the real opportunity resides.

Algolia's analytics give you a clear window into how customers are actually using search, what they're looking for, where they're not finding it, and how results are performing over time.

Query analytics are a natural starting point. Understanding which searches return no results, which terms are driving clicks, and where users are dropping off gives you the insight to make meaningful improvements. You can refine synonyms and query rules to close the gaps, keep your index fresh as products and content change, and gradually layer on more sophisticated capabilities like personalisation and dynamic re-ranking as your confidence in the platform grows.

The result

The brands we work with that invest properly in their search experience, with the right data architecture, thoughtful integration, and ongoing refinement, consistently see the difference where it matters most. Customers finding what they're looking for faster, spending more time engaging with the right products, and converting at a higher rate.

Algolia provides the foundation to make that happen. Its flexibility allows it to grow with your brand, from initial implementation through to personalised, AI-powered experiences as your needs and ambitions evolve. And because it's built to work within composable architectures alongside platforms like BigCommerce, Storyblok and Akeneo, it can fit right into your existing ecommerce stack

If you're thinking about your search experience and want to understand what the right approach might look like for your business, we'd love to talk. Get in touch with the team at Ridgeway.