Before the iPhone, mobile network operators ruled the market for downloadable games, images, ringtones and other paid-for content. Their own-brand stores created millions of revenue, but could always work harder. The problem was that with so much content, small screens and slow connections, it was difficult for consumers to discover content they were interested in.
So we looked to solve that problem. And the answer was our IntentPredictionServer, a high-speed and flexible recommendation engine. But it wasn’t just any old recommendation engine – it could understand things that other technology couldn’t; content and context: what and why a visitor is looking at, and why they are there. It can adapt any content – banners, product recommendations, text – to ensure that the most relevant is presented to the individual user at the right time, creating a completely personalised experience.
And then the iPhone arrived. Quickly, network operators stopped investing in their downloadable content stores, whilst manufacturer-run app stores blossomed. So we looked at other markets where the issue of too much content was significant.
We arrived in the online retail space. Amazon championed recommendation engine technology in 1999 with their patented “collaborative filtering” technology. “We have 6.2 million customers,” Jeff Bezos claimed. “We should have 6.2 million stores.” But this wasn’t smart technology. Other companies were playing in the online retail recommendation space, but none really pushing the boundaries.
We took a gamble. We pivoted; we left our comfort zone. Were online retailers ready for one-to-one onsite personalisation?
Today, our PersonalMerchant technology is powering millions of unique online shopping experiences – increasing retailer revenues with relevant product recommendations and swapping generic content for more effective, persuasive banners and text.
But at the heart of it, our technology has evolved without forgetting our roots. With zero-latency, carrier grade architecture, we’re computing Big Data in milliseconds to decide the right content to show to each visitor at every step of their journey. Our simple and well documented APIs mean any developer can jump right in and explore the possibilities, hack together prototypes in days, and going from initial brief to live system as quickly as they can.
- Retailers looking to embark upon Big Data products, can – with our technology connecting stock and ERP systems, loyalty systems and CRM data into an automated “single view of the customer”.
- Companies looking to use a highly tuned, automated and personalised decisioning system to provide relevant experiences, can.
- Content providers who want to help visitors discover content, can.
- Businesses who want to take social employment data and match it up to the most relevant job openings in real, can.
- And retailers who simply want to sell more with personalised product recommendations, can.
From today, we’re moving away from just talking about ecommerce personalisation technology. We’ll be embarking on a journey to spread the word about how automated discovery, decisioning, personalisation and recommendation technology can help all businesses.
The digital world has moved on tremendously in recent years, but we’re only at the beginning of a huge movement in the digital world. It’s exciting. So, why not come along for the journey?
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