Rebuilding loyalty with Loki
Loki enhances articles with auto-generated interactive visuals allowing publishers to gain insights about readers, and in turn allowing more effective ad-targeting. Loki was part of the 16 semi-finalists of our Startups for News competition. We asked the team a few questions about their startup.
What did you do prior to launching Loki?
I led product and data science at a small Singapore startup till late 2015. I created analytics dashboards and recommendation engines for media and e-commerce companies, and learnt a fair bit about how users behave online.
After quitting the startup, I spent the next 20 months freelancing for a number of media companies in the Middle-East, South Asia, and South-East Asia — including Khaleej Times, the Times of India, and Hindustan Times. This gave me an insider’s view into how media companies work and validated the hypotheses that are central to Loki.ai.
What problem are you trying to address for newsrooms?
Newsrooms have entirely ceded their relationship with readers and advertisers to aggregators like Facebook and Google. As print has died, the owned and operated reach of most outlets has monotonically declined.
This has meant that — despite having record audiences — newsrooms now have far less revenues compared to two decades ago. They are also vulnerable to the whims of Facebook and Google, and remain one algorithm change away from becoming another Zynga.
How are you attempting to solve the problems described above?
Loki.ai helps newsrooms rebuild their relationships with readers and advertisers, by helping them create and monetise local content that:
- helps local newsrooms become ‘need’ publishers that users come back to every day;
- helps newsrooms monetise their offering at a much higher rate than ineffective display ads.
We do this by automating much of the grunt work in gathering and presenting information, as well as crowdsourcing information issues like corruption and disrepair.
Concurrently, we have a CMS for native ads that lets local advertisers use a combination of geolocation, context, and user data to target users in a way that they are unable to when using Google and Facebook. This drives healthy RoI for advertisers, and up to 40x revenue (compared to display ads) for publishers — albeit with a somewhat lower reach.
Watch our video for more details!
What sets you apart from your competitors? List three elements.
- We have spent two years building tools that automatically scrape and parse thousands of government documents every day. Replicating this requires significant time investment.
- We have spent a lot of time building relationships with major newsrooms in South-Asia and to build a solution that integrates well with their existing workflows. This is difficult for a newcomer to replicate.
- We have created profiles of over 50,000 neighbourhoods over 300 cities using government data that is no longer publicly available. This means that the data that powers our ads algorithm is immensely difficult to acquire, which is a huge moat for us.
What is your business model?
We take a cut of the native ad revenue that publishers get from advertisers on our platform.
What are your next steps?
We will expand outside India to Asian countries with a similar profile in the next 6 months (Malaysia, Indonesia, Thailand, and the Philippines). Next, we will attempt to internationalise our platform and expand globally.
