Businesses have spent tens of millions of dollars collecting data about the activities of their customers, but have struggled to put this data to good use. Machine learning systems are used to help computers identify patterns from big data sets and enable them to perform tasks such as predicting consumer behaviour and forecasting how people will react to different marketing strategies. This technology holds the key to unlocking the value from big data. It will help companies sharpen up their marketing and boost the effectiveness of their advertising.
US tech and internet giants, such as Google, Facebook and Amazon, have built their businesses by gaining access to huge amounts of data about customers and their online behaviour. They have deployed machine learning to analyse this data to generate new sales, make their advertising more effective and to make themselves more relevant to users. The more data they gain access to, the better their machine learning performs.
Other companies need to find different methods of accessing machine learning capabilities so they can put their own unique and valuable customer data assets to work. Importantly, they also need to be able to harness their own advertising footprint to access online behavioural information and other data to generate more effective insights on new and potential customers.
Banks, telecoms companies, retailers, insurers and travel businesses are just some of the companies that are all looking to apply their own in-house machine learning systems in pursuit of competitive advantage.
IPONWEB, a technology infrastructure business founded by theoretical physicist and entrepreneur Dr Boris Mouzykantskii, is offering companies bespoke machine learning applications to help them make the most of this opportunity.
A decade ago, IPONWEB first started using machine learning to automate digital advertising and helped pioneer a new field known as programmatic advertising. The company has already custom-developed programmatic systems for more than 100 companies in the advertising technology sector. Now it is applying this expertise to help large corporations to leverage and activate their data digitally. They offer highly customised, data-driven platforms built around a company’s unique data to harness the ability of machine learning to identify and predict user behaviour in their advertising.
IPONWEB can help businesses stride ahead of their competitors by applying machine learning to their unique data assets and digital footprint
Machine learning performs best when applied to massive data sets, so it is ideal for very large businesses with huge ad budgets. A really cool application is analysing buying patterns and online behaviour to predict the likely purchase behaviour for millions of individuals simultaneously. This is on a scale far beyond the very best planning and consideration by human minds. Machine learning is also useful for informing brands how they should then price (and make a decision) for each user on a digital advertising slot, whether on a website, mobile or video. Predictions such as these can help brands make huge performance gains by tightening up their targeting and cutting out advertising inefficiencies.
This technology has wide uses. For instance, an airline may have extensive data assets about previous customer purchases, website data, loyalty programmes and reservations. It could incorporate machine learning to identify numerous patterns of customer behaviour that lead to a purchase. By marrying this with digital advertising data, an airline can incorporate anonymous users’ browsing history, locations, devices, searches, weather and other such information. It would then be able to identify more accurately and predict each and every user’s unique potential travel needs at that exact point in time, context and location.
Over the next five to ten years, major competitive advantage will be created by those companies that use data the most effectively. IPONWEB can help businesses stride ahead of their competitors by applying machine learning to their unique data assets and digital footprint. They will then take their place at the forefront of the data revolution.