AI solutions precisely tailored for every industry

Reduced Churn by 33% and saved key customers for a CRM Platform.

Problem

We've worked with Salesforce's major client, a UK-based platform that simplifies customer relationship management and retention for SMEs.

They were concerned that the high-value customer churn rate was increasing and wanted to discover how AI could help them fix this.

Stage 1: Understand the Churn Problem

Our business, market and product analysis teams worked hand in hand to identify the key drivers of the churn. What we found:

Customer disengagement: High-value clients were using fewer features, signalling a decline in perceived value.

Inefficient retention efforts: The customer success team spent a disproportionate amount of time manually chasing at-risk clients, often without clarity on who needed attention or how to engage them effectively.

So we together decided to implement customer behavioural monitoring to detect early signs of disengagement and launch an AI-powered personalised messaging workflow to reactivate at-risk accounts automatically.

Stage 2: AI-Driven Churn Prevention

The Outter team built a headless API integration between the client analytics systems and our AI engine.

Using this data, we trained and deployed a churn forecasting model that auto-flags the accounts at risk.

Further, we created an automated messaging pipeline feature to send personalised, multi-step messaging that helped customers regain value and re-engage with the product.

Stage 3: Real-Time Customer Success Insights

Thinking future-wise, we provided the client's team with a flight-control dashboard showcasing live updates on accounts and integrated this with the AI reactivation system to empower their customer success team to focus on high-value interventions while the AI handled routine re-engagement.

How Fast?

5 weeks to train the AI model and build the personalised messaging pipeline.

7 additional weeks to integrate analytics, deploy the dashboard, and ensure the system was fully operational.

Here's what we've achieved

Multiplied User Engagement and increased new clients Conversion Rate by 57% for an EdTech platform.

Problem

The client was feeling a severe bottleneck within their acquisition pipeline. Their platform was receiving fantastic feedback, but they just weren't converting potential clients to paying customers through the final onboarding stages of their product.

Stage 1: Product Analysis

In the in-depth audit, we identified key challenges.

The onboarding process was a nightmare: clients had heaps of legacy content in different formats and did not want to have it migrated to yet another platform.

Also, tutors could hardly analyse student progress and, therefore, could barely work on improving it. A large number of students weren’t finishing their courses on the platform.

Stage 2: AI Content Transformation

The team of Outter developed an AI that could create ready courses from any course material: PDFs, Word documents, presentations and everything else a learning institution could think of.

They then integrated that service directly into the client's platform via a very straightforward API, so onboarding was quick and painless.

Making Learning Smarter

We added a layer of behavioural analytics for the client's platform to dynamically build "learning profiles" for each student.

By using personal learning profiles, we deployed a recommendation engine with the best-fit courses for every student to help them stay engaged and complete their programs.

Implementation timeline

4 weeks to nail the content transformation process, training the AI with both client's data and extra data sourced ourselves.

6 more weeks to roll out insights for tutors and the recommendation engine for students.

Metrics we've made possible

Added 32% in Average Order Value for a global mobile ecommerce.

Problem

Shopify's top-tier customer, a UK-based gourmet service, wanted intelligently curated, personalised boxes to increase the sale of premium products.

The problem was that manual personalisation is cost-y, but without this personalisation – premium products are not performing well.

Step 1: Analysis

We kicked this off by doing deep insights into the company's operations and customer feedback. In such regard, we pointed out that:

Almost no personalisation is done while curating boxes, making it very difficult to upsell any premium item.

A lack of tools for tracking customer preferences and optimising offers.

The high manual effort required for curation strained their team and limited scalability.

Stage 2: Personalised AI Recommendations

We integrated the client's data into Outter's recommendation engine, which then allowed the platform to craft personalised boxes based on the preferences and past orders of each customer.

Introduced intelligent upselling features that would offer premium items tailored to each individual customer, increasing the likelihood of added purchases.

Stage 3: Actionable Insights for the Team

We created user-friendly dashboards for the client's team to get insight into customer preferences, behaviour, and trends.

These dashboards allowed them to adjust upsell strategies effortlessly, increasing effectiveness while saving time.

How Fast?

4 weeks to implement a recommendation engine, train AI with client's data, and deploy upselling features.

5 additional weeks to roll out dashboards and ensure the team can seamlessly manage and optimise customer interactions.

Metrics we've made possible