ThriveFNC Increased Client Consults By 15% Using New Website And Integrations
ThriveFNC is a Functional Health Consultancy provider. Established in 2011, ThriveFNC is focused on providing valuable health advice to clients. It has helped numerous clients in their quest for healthy lives. Their key offering involves functional nutrition consultancy that helps improve individual health.
ThriveFNC hired CodeInscribe to revamp their old website and create a brand new look. Their founder, Mugdha Pradhan was deeply involved with the redevelopment effort. The team is focused on building the website as the primary source to generate leads for their business.
The previous ThriveFNC website was old, unstructured, and was very slow to load. The design of the website was not in line with the latest design trends and it wasn't mobile responsive.
The previous website also made it very difficult to run any marketing campaigns since it wasn't designed from a lead generation point of view. It was also lacking integrations with a CRM to be able to track any leads coming in through.
Hence improving the website and make it suitable to be able to run lead-generation and marketing campaigns was the foremost objective.
The team at CodeInscribe understood the client's objectives and their intention to use the website as the primary source of lead generation. CodeInscribe redesigned the website with a fresh design in line with ThriveFNC's new brand identity. The website was hosted on a fast hosting account to ensure low page load times.
To allow ThriveFNC to accept payments from their clients, we integrated a payment gateway with the website. This formed a part of the overall funnel where the user would pay for online consultation and then be directed to schedule a time slot for the consultation. To enable that, CodeInscribe integrated a calendar scheduling program that would push the confirmed consultation time slots to a Trello board.
Since ThriveFNC publishes blog articles and recipes regularly, CodeInscribe created a template that would make it very easy to publish such articles easily and have them appear consistent. We created multiple custom content blocks which ThriveFNC could utilize to draft their articles.
CodeInscribe's implementation of the new website is already having a positive impact and delivering positive results to ThriveFNC. The number of new health consults has gone up by 15%. This has a direct business impact for ThriveFNC, since such online consults represent an important source of their revenue.
Upgraded Website Boosts XYZ's Overall Traffic By 34%
Today, we are releasing Query Rules, a new feature which enables you to modify, override, & enhance the behavior of the engine’s configured ranking for a subset of the queries. We wanted to share with you how we approached this key addition to our API, why we decided to build it, and explain the design steps leading to the release today.
Before we dive in, let’s look at a few examples of what you can do with Query Rules:
Transform descriptive text into a filter: for example, by automatically transforming a query word into its equivalent filter (“cheap” would become a filter price< 400; “red” a filter on the color…)
Merchandising: manually select a result for a specific query (e.g., decide that the latest iPhone 8 should be in the first position for the query iPhone)
Entities detection: detect entities (colors, brands…) in the queries and transform them into filters or boosts
Building upon our ranking formula
With Algolia, the same relevance algorithm is applied across the entire index. We expose features like Custom Ranking so that customers can customize the ranking strategy for their needs, and achieve a great relevance for the vast majority of queries. However, in the past few years, our customers started to bring to us examples of outlier queries.
We began compiling a list of these outlier situations. Here are a few examples:
When Apple releases a new iPhone, it should be in the first position of the list for the query “iPhone” (even though at this stage it has no views, no sales… no “logical” reason to be ranked high)
You need to get rid of a big stock of a specific model of a vacuum cleaner, so you’d like it to be promoted in the results (at least until enough units are sold)
The query “phone” naturally (according to the configured ranking strategy) retrieves feature phones instead of smartphones, but you’d like to highlight smartphones first
The query “red dress” matches with non-red results because the name of the brand begins by “red”
The query “cheap phone” doesn’t return any results (the records don’t contain the word cheap), and would behave better if “cheap” was transformed into a numeric filter on the price
Queries originating from users on mobile devices should highlight results that are mobile-friendly
There are hundreds of examples like this where having an exception to the general rule would make sense, either to improve the relevance, or to override the ranking for business reasons.
Thinking about our options
There are two main ways to address the types of exceptions we were seeing. The natural way to handle this would be to analyze the use cases one by one and add a configuration to the engine to handle each of them individually. We could, for example, develop a form of synonyms that would transform a word into a filter. Eventually, these settings would form a merchandising tool, allowing users to tweak and override the ranking logic.
We certainly had the experience on the team to execute on this approach. Several team members, including our founders, have used, or even built merchandising platforms prior to founding/working for Algolia. However, it is exactly because of this experience that we had doubts that this was the right approach:
Creating a new setting inside the engine for each exception quickly leads to a bloated solution: there are just too many varieties of merchandising strategies
Building a quality merchandising tool that would work for all of our users – each of whom have specific needs & exceptions – is virtually impossible
Multiplying the exceptions inevitably affects performance
More importantly, we wanted to do more than merchandising and address the needs of other industries. Media sites don’t use merchandising, but they still want to promote, for example, partner content. SaaS systems may want to improve the ranking by adding rules automatically based on the output of a machine learning tool.
To do this, we would need to be able to impact search results on a subset of queries in two distinct places:
Before the search query is processed, in order to override the query parameters
After the results are found, in order to modify the results list
What we came up with is a rules system for queries — or Query Rules — that sits inside the engine via two modules:
A query preprocessing module that will modify the search parameters before the search is processed by the engine
A results postprocessing module that will modify the results before they are sent back to our users
Each rule has the following pattern: IF the query contains X, THEN modify Y. A condition and a consequence:
If the query contains “iPhone”, add this result in the first position
If the query is “cheap phone”, replace the word “cheap” by a numeric filter price<200
If the query contains a color, filter by that color
This approach makes it simple to modify the behavior of a subset of the queries, without impacting the rest of the queries. We created two types of conditions, and seven types of consequences, which together allow us to handle a variety of exceptions, including:
Promote the latest iPhone on the query “iphone”
Filter on color white for the query “white dress”
Filter on price<400 for the query “cheap phone”
Display a promotional banner for the query Samsung
Remove the word “hotel” in a hotels search
With Query Rules, we’re bringing to Algolia the ability to handle queries on an individual basis by making exceptions on the regular ranking strategy.
We think the approach we took has a few interesting benefits:
It is low-level and flexible enough to adapt to a large variety of use-cases without requiring a lot of settings and configuration
It is exposed via an API, allowing both our customers and potential partners specialized on merchandising to build upon it
It doesn’t compromise performance for the sake of feature sets: you can add up to a million rules to an index, with little to no impact on search performance
In fact, we like to think of Query Rules as more than a feature: we like to think of it as an extension of our engine. We have designed it to be open-ended enough to allow our users to solve their own unique problems and push the boundaries of what is possible with search.
It’s been exciting to start sharing the feature with a few beta testers — we’ve been amazed at how easily they grasped its potential, and the combinations it allows.
The two conditions and seven consequences we are initially releasing are only the beginning — we look forward to getting your feedback and learning which ones you’d like us to add next!