Last Sunday, my husband and I went to visit our daughter. As we drove, my cell informed me that we were 30 minutes from our destination. How did it know? I hadn’t told it where we were going – there wasn’t an appointment on my calendar. The cell had worked out this was a trip we regularly take on a Sunday and was able to provide us with useful information based on that knowledge. This is just an everyday example of how quickly Artificial Intelligence (AI) is becoming a normal part of our lives. It’s something that’s beginning to shape retail customer experience. In this blog, I want to look at how AI and analytics together can deliver the highly targeted and personalized experience that customers demand.
The holiday season has just passed and, if you’re like me, you’ll be giving thanks to Amazon (other online shopping services are available!). Going online is quick and convenient. Personally, I like shopping in the mall but our busy lives often make this practically impossible. What’s more, the personalization and recommendations engines of services such as Amazon are now so sophisticated that it really does feel that I’m receiving an individual service that understands my wants and preferences. This level of personal service is something that every retailer must aspire to.
Everyone wants to feel special
Most research makes it clear that personalization helps retailers drive conversions, boost engagement, and improve loyalty. Accenture suggests that 75% of consumers are more likely to buy from a retailer that recognizes them by name and can recommend options based on past purchases. This means being able to understand each customer at a granular level. AI can help retailers achieve this as well as going further to understand how the customer feels and how they are likely to react. In fact, Gartner says that, by 2020, smart personalization engines will be able to recognize customer intent.
Insight squared: AI and analytics
Neither analytics nor AI are new technologies. They are technologies whose time has come. The exponential explosion in the velocity and volume of data along with a comparable growth in computing power and storage means that organizations can gain much more from the information they hold.
You can think of AI as the ability of computers to display ‘human-like’ logic and thinking. Machine Learning – and Deep Learning – describes the way that computers can learn from the data they process over time – the more data they have, the better they become. It’s an on-going process of continuous improvement needing no human involvement. As every organization deals with Big Data, AI has excellent source materials to learn and improve. It is perfectly positioned to automate many of the repetitive tasks within retail processes.
It’s easy to see the benefits of AI for retail customer experience. If your computers do more than process data but learn and interpret it, you have a system that can truly produce the real-time, actionable insight needed to improve decision-making.
Improving decision-making has always been the province of analytics and some commentators have suggested that AI will replace analytics as it’s traditionally understood. This is unlikely to be the case. Rather the real power arises when AI is combined with analytics. It provides a complete platform that can take data from all sources and begin to really release the value in data and content that has previously been inaccessible – and, most often, lost.
Introducing AI-enhanced analytics to Retail
OpenText calls this AI-enhanced analytics. It allows retailers to gain a laser focus on the customer by having access all data from all source to create a single view of the customer. You can understand your customer at a transactional and an emotional level. Importantly, AI-enhanced analytics can help break down the barriers between data silos and also analyze the information that has been previously trapped in unstructured data and other forms of content.
OpenText partner, SAP, believes that retailers will quickly look to integrate AI with their analytics capabilities. The company suggests the following examples:
- Retailers will include machine learning algorithms as an additional factor in analyzing and monitoring business outcomes in relation to machine learning algorithms.
- Retailers will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends and have accurate answers before competitors do.
- Retailers will use real-time analytics to bring different business areas together, facilitate collaboration and improve productivity
- Core to delivering the capabilities of AI-enhanced analytics will be a central platform that can connect to and access all data sources. Modern platforms – such as OpenText Magellan – combine open source machine learning with advanced analytics, enterprise-grade BI, and capabilities to acquire, merge, manage and analyze Big Data and Big Content stored across your organization.
In my next blog, I’ll take a look at some of the questions you need to ask when considering AI. In the meantime, if you’d like to know more about how AI-enhanced analytics can offer your company, please fill in the contact form on this page and we’ll be delighted to start the conversation.
Robin Gellerman is the Product Marketing Manager for Life Sciences Enterprise Content Management solutions at OpenText. With over 20 years in the enterprise content management industry, Robin has held a variety of product and industry marketing positions supporting document management, capture and customer communications technologies at OpenText, the Enterprise Content Division of EMC, Captiva and Document Sciences. Most recently, Robin was the Industry Strategist for retail, and has previously worked with energy & engineering and healthcare solutions.