Improving customer experience & content recommendations using AI/ML

Leading Biotechnology Company

An illustration depicting the use of artificial intelligence & machine learning to improve customer experience
The client

Leading Biotechnology Company

Our client is a market-leading biotechnology company headquartered in the UK that sells to more than 100 markets across the globe. They have a proven track record of delivering disruptive technologies to the markets that they serve and are focused on automation and increasing ease of use through the provision of more cost effective solutions and an increasing number of products that can be used without internet connectivity. Our clients’ technologies are used by research scientists worldwide as well as to support ‘real-life’ decision making in healthcare, industry and other applied settings.

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Key Outcomes

We helped a leading biotechnology company to improve their customer experience, and automatically provide website visitors with valuable content recommendations through the use of artificial intelligence and machine learning (AI/ML). This has resulted in them being able to effectively nurture prospective customers through automation.

  • Click-thru rate (CTR) 200% increase in CTR per article resulting in double the amount of time and views on articles
The Challenge

Content recommendations weren't engaging customers

Our client was using a third-party software-as-a-service (SaaS) product to personalise the content seen by visitors across three of their websites. The existing solution was frustrating for their marketing team as it would recommend articles to users without the marketing team being able to verify whether those articles were actually relevant. This issue was compounded by the fact that the software used a generic prediction model to recommend content rather than one that was specifically tailored to the biosciences industry.

The limitations of the existing solution were exacerbated by the fact that the software being used did not allow the clients’ marketing team to sequence articles to website visitors based upon where they were in their buying journey, nor did it make full use of the clients’ customer data to enhance the recommendations. Lastly, there were inefficiencies as the client had to manually upload and process data in their customer relationship management (CRM) system once a month, which was provided to them manually in an excel spreadsheet.

The client challenged us to build a solution that would improve their engagement with customers visiting their websites through presonalised user journeys and the creation of sequenced recommendations that were tailored to their interests. This needed to be achieved through a solution that would present content articles one after another in a defined order based on user data and make concrete, helpful recommendations as to what a user could do next.

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AI-powered recommendations

We worked with the client to build an initial 2-week proof of concept (PoC) using AWS Personalize - a cloud-based technology that enabled our developers to quickly build and deploy curated recommendations and intelligent user segmentation at scale using machine learning (ML).

This PoC was later developed into a full product following validation that it could achieve the results that the client was looking for.

The user recommendation action that we built collects data from the clients’ community, store and website properties then uses artificial intelligence/machine learning (AI/ML) to analyse the data and make recommendations depending on the actions that a user has taken. These recommendations are then published via API allowing HubSpot - the clients’ CRM system - to feed real-time next article suggestions to visitors on each of their websites.

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Curated recommendations and intelligent segmentation at scale using AI

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The Results

Nurturing a consistent, regular pipeline of new potential customers

The new user recommendation engine has enabled our client to provide their website visitors with relevant, personalised recommendations for content that is tailored to both the content they’ve already consumed and their current readiness to buy.

This means that their customers are able to rely on the clients’ websites as trusted sources of information that help them to increase their understanding of biosciences, as well as increase their awareness of how the clients’ products will help them to solve their organisational challenges. In turn, this means that they’re effectively nurturing a consistent, regular pipeline of new potential customers.

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