JUNE 2020CIOAPPLICATIONSEUROPE.COM9used to show live customer recommendations or tailored content marketing at each stage of the customer journey. This is likely to drive engagement and ultimately increase customer spending.From a broader network perspective, predictive analytics can be used to identify the need for enhanced resources on a given day. This can deliver tremendous value for retailers who offer regular seasonal sales which can put a strain on infrastructure. Predictive analytics means that retailers can predict and overcome this additional strain.On top of that, retailers can tap into the vast potential of anonymised data for further customer's insights. This can range from credit card usage to predict GDP and inflation to the economic potential of different locations. This makes it quite possible to go from delivering tens of use cases a year to multiple hundreds or thousands. Rather than designing a dozen or so predictive models, organisations should be looking to implement an end-to-end analytics factory capable of building hundreds of models in weeks.Maximising data's potentialDespite this, there has been widespread conventional understanding of AI for several years. This is especially true for use cases with a clear objective, such as reducing customer churn in long-tail organisations like Netflix, Amazon or traditional telcos, which target a large number of niche markets in a highly competitive sector.As digital adoption grows, so too does the data with it, and this provides an opportunity for organisations to maximise their customer interactions and monetise this momentum. There are five things that organisations need to achieve this:1. A clear business opportunity-There must be an actual opportunity to improve the business, as well as an understanding of its potential value2. Good data knowledge­This means an organisation has in place data governance, metadata, dictionaries, and processes to ensure good quality data is ready to be used3. A cloud platform ready to build and process large amounts of data at scale­AI requires the processing of large amounts of data, which an on-prem solution probably won't be able to handle4. The skills required to apply AI effectively­It is very difficult to find experts in this field, so in-sourcing this talent is a priority5. An agreement that AI adoption is for everyone­The outcomes generated must be integrated into all digital channels to maximise results in a seamless wayDriving real successRealising the true value of AI isn't easy and requires many teams collaborating and aligning on objectives. This means that internal transparency across all departments is essential. To get past just delivering demonstrations and case studies, businesses must approach AI with scalability and automation on top of mind right from the start. This will ensure any investment drives real change, and we will see big changes in the effectiveness and value realisation of AI and data analytics. With the modern customer more demanding than ever, big data and AI can help retailers deliver a personalised experienceDavid Gonzalez
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