The Client
We created the system for a marketing company that caters to some of
the biggest names in American publishing. Thanks to our reputation, we were approached to build a system
that addresses some of the key challenges faced by publishers as they digitize their operations.
Business challenge
As publishers digitize they must find new ways of reaching out to
readers, and focus on building a revenue model based on subscriptions in a media marketplace that has
never been more crowded and competitive.
Revenue model
Publishers are shifting back to a model powered by their
readers, rejecting the advertising-based model of the early digital years. This requires
a new approach to readers and to acquiring and keeping subscriptions.
Converting subscribers
Converting casual readers to subscribers in the digital
world is both more necessary and more difficult than signing up regular newspaper
readers. Publishers must source the tech and skills to convert and retain a new
generation of consumers.
Consumer expectations
Customer expectations have been conditioned by other
digital media platforms such as Netflix. Readers now expect personalization as standard,
and are no longer satisfied with a one-size-fits-all approach.
Content responsiveness
Publishers need to know which content is successful with
which readers, in order to plan future content creation and distribution strategies.
Without attribution and analysis, they cannot build the publication their most
profitable readers want.
When they attempt to address these concerns, publishers typically encounter technical issues including:
Persistent user identification
Identifying anonymous, non-logged in users on the site
across sessions. These are conversion targets, but while they remain anonymous there can
be no data-driven conversion rate optimization (CRO) aimed at them.
Support for different clients and structures
When building tools for publishers, agencies encounter
difficulties in creating a tool that supports multiple website structures and client
backend logic flows.
Data acquisition granularity and volume
To be effective, the system must collect and analyse
massive tranches of granular pageview data for large numbers of users, including
anonymous users and subscribers.
Inbound sales
Integration across the stack is a vital component of
integration across departments; in particular, inbound sales needs a responsive,
real-time window into the insights generated by the system.
The goal was to
create a system that addressed these concerns, equipping publishers for digital success.
In particular, it was necessary to deliver:
- Increased sales efficiency
- Transparency of data and decision-making facilitation based on analysis
- Automated suggestions to readers and campaign running
- Calculation of likelihood to subscribe
- Personalized content consumption patterns
Personalizing publishing CRO at scale with machine learning
Lightpoint created a ML-based tool that matches users with content based
on analysis of their previous behavior, and creates proposals designed to appeal to individual users,
programmatically and at scale.
1. A reader visits the publisher’s site.
2. The backend matches that reader with a persistent identity
inside the tool and an analysis of their previous onsite behavior.
3. Based on this, the system offers that reader content with a
higher probability to encourage conversion.
4. The system also generates tailored calls to action and
personalized proposals emphasizing the content most likely to generate conversions.
5. Post-subscription, the process continues, with relevant offers
displayed to segmented subscribers to encourage retention and upgrade, and sales involved in
upselling and cross-selling where appropriate.
Key features of the system
Single user identity
Even anonymous users have a single, persistent identity
across sessions, which survives cache clearance and enables long-term data collection.
Rapid sales response
The system is built to help increase the speed of sales
reps to warm customers, as well as putting customer information at their disposal to
improve closure rate.
Marketing campaign automation
Offers and analysis are built into marketing campaign
design, allowing automated launch and monitoring.
Report generation and dashboarding
The system converts analysis into reports delivered via a
dashboard, providing the insights necessary for data-driven strategy.
Lightpoint built the system with:
2 Front -end
Developers, 1 Back-end Golang Developers, 1 Data Scientist / Python developer, 1 Manual QA
Tech stack:
Database:
Google bigquery, Mongo, Postgres
Back-end:
Go (REST WebAPI), Python, NodeJs
UI:
TypeScript
Other:
GCP, GA, Google Data studio
Results
The product has become one of the most popular in the US media market,
and continues to develop and expand its functionality. Our customers became the largest players in the
American media market with a combined market share of around 60%. Customers of all sizes, not just
enterprise clients, saw sharp increases in revenue and subscriber numbers.
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