Artificial
Intelligence

Opportunities in Retail

By 2020, 85% of customer interactions will be managed by Artificial Intelligence. Learn more about the origins of AI and it’s applications to the retail industry in this report, independently prepared by EIQ.

Introduction

Today’s retail industry is far more fragmented and competitive than ever. Multiple store formats and an arsenal of digital tools are making shoppers more educated about choices. Digital channels also continue growing. This is particularly true in grocery, where heavy hitters like Amazon and Walmart continue to eat into the market share of traditional chains.

The landscape has also become more diverse, with a variety of household types and lifestyles having very different needs than the mom-dad with-kids target that dominated generations past. This is compounded by a burgeoning ethnic population, with each group having a distinct profile in every area from language and food to shopping style and economic status. Add to this revitalized inner cities, which are attracting young Millennials in droves, and the result is a seismic melting pot that never stands still.

Retailers and their suppliers need real-time, in-depth knowledge to attract diverse shoppers. But the advent of distinct devices, sensors, and machine-to-machine communications has made data sets so large that timely manipulation, management and analysis present significant logistical challenges for companies using on hand data management tools or traditional data processing applications. Other entities have incorrect or outdated legacy data.

This is compounded by the difficulties many companies face in recruiting the talent needed to implement complex technology tools, analyze the data and make effective recommendations. Many successful high volume retailers and consumer packaged goods (CPG) organizations have turned to artificial intelligence (AI) to navigate the muddle.

At the simplest level, AI machines or systems imitate human behavior in intelligent ways that can augment productivity and optimize business performance. AI applications include machine learning, natural language processing (NLP) and robotics.

AI allows retailers and manufacturers to gather customer insights in an automated fashion and predict next actions based on previous patterns or images. AI uses predictive patterns to help understand desires, motivations and actions across both physical and digital channels. This lets retailers and suppliers enhance many functions, such as executing more targeted and personalized marketing campaigns and improve trade promotion efforts. AI can also automate forecasting of inventory needs, more accurately predict out-of-stock incidences and ultimately help optimize supply chains.

Types of Artificial Intelligence

With artificial intelligence (AI), machines mimic or replace intelligent human behavior, like problem solving or learning. They “sense,” “comprehend” and “act” in accordance with the real world. In essence, machines learn from experience and make recommendations, learning and improving over time.

AI applications fall under three key areas

Machine learning

Machines automatically analyze large amounts of data and “learn” using rule-based algorithms that identify patterns and trends. As an example, this could mean combining 100,000+ data points from 75 million customers regarding shopping patterns and other habits.

Natural language processing (NLP)

NLP is a machine’s ability to understand, analyze and generate human speech. A computer listens to a natural language spoken (or written) by a person, understands its meaning and responds by generating natural language to communicate back (as opposed to a computer language like Java or SQL).

Robotics

Involves full-scale automation of tasks traditionally performed by humans. Warehouse picking and packing, for example, can be performed by robots.

AI’s Growth

Machine learning first became a scientific discipline in the late 1990s. But it did not seriously take off until the 2000s. Growth was fueled by access to huge amounts of real time Big Data and the emergence of algorithms that make sense of that data for productive output. AI is continuing to grow, touching more industries and functions every day.

To date, much AI retail activity has revolved around machine learning in e-commerce, particularly for search analysis, product recommendations, promotions and analyzing consumer sentiments. Amazon is regarded as a pioneer here, and it is widely estimated that 25% of its sales are generated through recommendation-based product views and previous purchases. Today, Amazon is even marketing its easy-to-use, highly scalable search and other machine learning technologies to outside parties.

Other e-commerce companies have used search and recommendation tools for some time. But in recent years, e-commerce has reached new heights by using machine learning to make functions more comprehensive and specific. User choices and information can be cross referenced in numerous ways. Customers can locate merchandise faster, more products are sold per transaction and there are fewer abandoned carts.

Retail and consumer goods are among the top five industries in which AI is being applied1.

In 2017, the global AI market was estimated at $2.4 billion. It is expected to grow at a CAGR of 50% to over $59 billion by 20252.

64% of CIOs plan to invest significantly in cognitive or AI technologies over the next two years3.

By 2030, AI will drive Global GDP gains of $15.7 trillion (14% higher) through productivity and personalization improvements4.

Now, retailers and suppliers are applying AI to areas outside e-commerce. Demand forecasting that incorporates machine learning, for example, allows online and offline retailers to generate more precise forecasts than traditional time series approaches. Machine learning also facilitates warehouse management by helping to alleviate the over- and understocking scenarios that can erode a retailer’s bottom line. When applied to trade promotions management, AI could help suppliers improve timing, tracking and other aspects of retail marketing investments.

NLP is also making inroads by providing conversational answers in areas including category management through deep analytics, data mining and visualization at department, planogram and product levels.

1 PWC 2017 data | 2 Statista | 3 Deloitte’s 2016 Global CIO survey | 4 PWC 2017 data

AI is continuing to grow, touching more industries and functions every day.

Top AI Applications in Retail

AI is gaining an important place in retail with growth expected to reach $40 billion by 2025, up from an estimated $6.46 billion today. It is being driven by an increase in customer-centric initiatives, more social media advertising and heightened demand for virtual assistants.

Among retailers, 16% already use some form of AI, while 20% plan to add it over the next 12 months; another 18% hope to implement it more than a year from now6. Following are the leading AI, machine learning and NLP application areas in retail…

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Symphony RetailAI is the leading global provider of Artificial Intelligence-enabled decision platforms, solutions and customer-centric insights that drive validated growth for retailers and CPG manufacturers, from customer intelligence to personalized marketing, and merchandising and category management, to supply chain and retail operations.

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