Four important questions that AI can help retailers answer

In my previous blog, I looked at how quickly Artificial Intelligence (AI) is rapidly becoming a part of the retail experience. Industry analysts agree that…

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Robin Gellerman

February 13, 20186 minute read

In my previous blog, I looked at how quickly Artificial Intelligence (AI) is rapidly becoming a part of the retail experience. Industry analysts agree that 2018 will be the year that AI brings top and bottom line benefits to innovative companies. So, where can AI most help retailers? Here’s four questions I think a combination of AI and analytics are perfectly suited to answer.

What do you think about being served by a robot? Is it cool or creepy? Early pilots with Softbank’s Pepper suggest that people love it. The Palo Alto tech shop, B8ta, recorded a 70% increase in footfall due to having robot assistants. This just one example of how AI is beginning to change the face of retail. Forbes suggests that deploying AI can return a 60% increase in net margin for US retailers.

Using AI – combined with analytics – systems can locate and interpret data, comprehend it and learn from it, and quickly and accurately present that data as easily understandable and actionable insight to drive decision-making.

This is all about the amount of data that a retailer has – and today’s retail has a great deal of data to work with. Retailers have a greater range of technologies from mobile and social channels to cameras and video to intelligent POS to Internet of Things and wearable devices – all producing a greater volume of useable data. It is increasingly affordable to store and process that data.

The power of AI is that it makes every piece of data – wherever it is and in whatever format – available to act as source for the learning and analysis. Deborah Weinswig, managing director, Fung Global Retail & Technology says: “Retailers have a significant amount of data to power AI and deliver personalized, customized and localized experiences to surprise and delight customers”.

So what questions should retailers answer by applying AI and analytics to that data?

1: How can I deliver a better customer experience?

Robots like Pepper and chatbots are growing in popularity to deliver seamless customer service across channels. However, the power of AI lies in its ability to apply machine learning to better understand the behaviors and preferences of every customer and how to personalize the experience to each customer. We are seeing more and more stores analyzing data from checkout lines, shelf refills, cameras and customer activity to tailor their product portfolios and store layouts to maximize customer satisfaction and sales opportunities.

Personalized promotion is an area of low hanging fruit for AI in retail. Online, AI enhances the personalization and customization of offers as it allow the retailer to mine far greater data sets and uncover more and more granular insights so that promotions and recommendations becomes truly personalized. In-store, companies are increasingly using cameras and Internet of Things (IoT) sensors and devices to track customer behaviors. The retailer can then target specific personalized offers – based on previous purchases and wider purchasing habits – to the customer’s cell phone or shelf display at the most appropriate time.

2: What exactly is it that my customer wants?

What a customer wants differs by customer and also changes for each individual customer over time. To accurately understand customer needs requires an intimate knowledge of the customer’s current and historical buying preferences and patterns. AI and analytics can take all that data and define customer want as well as identifying products that current meet the customer’s profile and assist in the creation of highly personalized promotions based on the behavioral and product criteria. It can automatically identify and exploit potential cross and upsell opportunities.

In an increasingly omnichannel world, AI can provide the visibility that retailers need into all customer preferences across touchpoints such as in-store, online and mobile to tailor experience and promotion to each specific channel.

3: Do I really know my customer?

Retailers are gaining more data online and in-store every day that is enabling them to build a clearer picture of each individual customer. However, there isn’t always a need to personalize the service to each customer. It’s more efficient and cost-effective if you can identify and market to defined segments of your customer base. AI and analytics helps companies to create micro-segments for the ‘anywhere, anytime, any device’ nature of omnichannel retail.

Using AI and analytics, you can look at the characteristics of larger customer segments to identity smaller groups of customers with extremely similar buying preferences and patterns. This allows retailers to apply more traditional marketing techniques – such as direct mail – to more targeted customer groups.

4: How can I make better use of my inventory?

AI and analytics has the potential to re-shape inventory management. It allows retailers to go beyond an analysis of current buying behaviors to include an evaluation of customer sentiment – using social media – and extended trends analysis. This enables far greater accuracy in planning inventory demand – especially around new product launches.

With showrooming and webrooming an established part of modern retail, AI allows retailers to adopt strategies such as endless aisle and flexible delivery options to reduce the amount of inventory within their supply chain. In addition, retailers are beginning to use data captured from POS systems and and IoT devices to track inventory in-store to ensure that replenishment is automatic and effective. For example, new solutions are available to monitor the status of perishable goods through the supply chain and in-store.

Retail is turning to AI-powered analytics

If 2018 is the year that AI becomes core to retail then companies require an enterprise-wide platform the combines AI and analytics. AI isn’t a replacement for traditional analytics, it’s an enhancement to add a deeper understanding of the data. The enterprise-wide platform should allow you to build AI into your other analytics activities to deliver what OpenText terms AI-powered analytics.

For example, OpenText™ Magellan can gain insight into all content and data to create a single source of the truth’ for each customer. It ensures that retailers release the value of all the data they are collecting on customers to create a more personalized and compelling experience. In addition, it provides the actionable insight to improve decision-making in key business areas such as operations, purchasing and inventory management. The area where retailers can quickly realize tangible, bottom-line benefits from their AI investments.

If you’d like to know more about what AI-powered analytics can offer your company, please fill in the contact form on this page and we’ll be delighted to start the conversation.

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Robin Gellerman

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.

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