For most of advertising’s history, advertisers have produced their messaging primarily based on the gut instincts of their creative teams.

Sure, we’ve always had surveys and market research to provide loose guidance on the demographics and preferences of our potential customers. But until recently, the end result has always been a single, one-size-fits-all ad that succeeds or fails based on the creativity and the guesswork of the people who produced it.

With the development of digital advertising technology, this has all started to change. In recent years, advertisers have begun combining Human intuition with Artificial Intelligence to produce some of the most powerful ads we’ve seen yet. Today, many of the world’s leading brands are using customer data and powerful Machine Learning algorithms to inform smarter creative choices, and sometimes even to produce parts of the ad itself.

Far from removing creativity from advertising, AI is helping brands deliver effectively, evocative ads, tailored to the unique wants and needs of each customer. And its impact on digital advertising is only getting stronger.

The Future is Now

At its simplest, Artificial Intelligence is any technology that allows machines to perform Human Intelligence tasks as well as, or better than humans, at greater scale and lower cost. Through Machine Learning, these AI tools are often able to learn from their results, creating a feedback loop that allows them to make smarter decisions over time.

Already, AI is helping brands develop a number of creative, engaging advertising experiences. For instance, the fitness chain Orangetheory used an AI breakdown of its audience’s online browsing habits to learn that its customers frequently gravitated to the chain via ads on college sports content and music platforms, rather than health and wellness sites. Orangetheory then applied this information by producing video ads that linked the gym to the audience’s hobbies outside of it.

Meanwhile, other brands are using AI to edit and optimize their ads while their campaigns are ongoing. At VidMob, where I serve as Chief Product Officer, we use AI to evaluate how different aspects of an ad are driving advertising success (e.g. Is a nature scene more engaging than an urban landscape?), and then use the insights to optimize the campaign’s creative via our network of designers. Through creative versioning tools like ours, companies like AB InBev are using AI to pursue the most effective ad for every campaign.

The future may even see AI-powered ad messages inserted into entertainment content in real-time. The Chinese video site Tencent is testing a new feature that would allow advertisers to serve personalized ads as native product placements while people are watching its shows. For instance, if an on-screen character is waiting for the bus, a brand could cover the bus stop with an ad relevant to the viewer’s interests.

Through Our Powers Combined…

Too often, those of us in the ad industry get the wrong impression about the impact of AI on our field. While some see AI as eventually replacing human creatives and strategists, the truth is that this is unlikely to happen at any sort of scale.

Certainly, ad agencies have released a few pieces of video content created entirely by AI, but these have largely been experiments designed to push creative boundaries and impress awards show judges. A more likely outcome is that brands will use increasingly effective AI technology to free their human staff from the rote grunt work of data analysis and campaign optimization, giving them more time for creative conception, campaign strategy, and other more interesting tasks.

Indeed, the future of advertising isn’t a robot takeover—it’s a creative, efficient and effective collaboration between Artificial and Human Intelligence.

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NEW DELHI: Pacific Mall in West Delhi figured out through algorithms that 65% of the customers at its food-court preferred vegetarian food. That prompted the mall to add a Halidram outlet and sales at the food-court went up by Rs 50 lakh a month.

In Bengaluru, Orion Mall found that most of its customers are “trendier” young crowds who mostly purchased fashion and electronics, prompting it to ramp up those verticals.

Taking a leaf out of the ecommerce textbook, malls have started, albeit in a small way, mining customers data and using algorithms to drive sales. Prominent malls in India for years had revenue-sharing agreements with retailers and the shopping centres would receive daily or real-time sales data from brands through a common technological platform. Now such platforms are evolving to capture various other information on buying patterns and preferences of consumers to help malls drive sales and footfalls.

“We have built a platform which gives them insights into what are the areas they need to concentrate. We have an AI (artificial intelligence) platform and through data science we forecast their revenue and trends, and we tell them the buying habits of the consumers,” said AM Navail, an assistant vice president at tech firm Pathfinder. According to the company, it offers technology services to more than 100 malls and is helping dozens of them in mining consumer data to drive sales and footfalls.

Malls these days have a host of tech at their disposal to help them not only drive sales but also enhance the overall consumer experiences.

For example, high-definition CCTV cameras not only capture pictures but also generates heatmap of visitors around the mall that helps mall owners to assign facilities and manpower.

Such cameras are also used to analyses gender and age brackets of customers and the stores they are entering. “If the customers are thronging to the sports area, we can figure out with the heatmap technology and tally with the conversion rates with those retailers and realise we need more brands in that category,” said Deepak Zutshi, the centre head at New Delhi’s Select Citywalk Mall.

West Delhi’s Pacific Mall has installed a technology that can track the duration of cars parked in the parking lot. “That way we are getting average three hours of dwell time of cars that are coming into the mall,” said Abhishek Bansal, its executive director.

Rajneesh Mahajan, the CEO InOrbit Malls that operates shopping centres in several cities, said churning consumer data was in its infancy in India due to limited and non-uninform data available from retailers to mall owners.

“We are still at an initial stage and this will evolve and people will get unified platforms to get the data,” he said. “Unless everybody comes on board and data in a certain manner and the KPIs (key performance indicators) are defined, it won’t be that meaningful.”

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Accenture suggests that the core of retail strategy is a 720-degree view of customers — reaching digital natives with rapid focus shift, high expectations, and growing demand for personalization and perks. Successful examples such as Amazon, Macy’s, and Walmart prove that the way to reflect and interpret this view goes through experiment and innovation. In particular, the use of AI and its integrals includes data mining, machine learning, natural language processing (NLP), and bots. But how is AI a good match for retail? Here are three advantages.

1. AI can extract value from massive data sets

Industries have been struggling to build data-driven strategies for a while. According to the numbers from the last year McKinsey research put out, retailers were second in this race. Retailers are lucky enough to collect and own massive data about customers and buyer behavior. However, they have been unable to transcript this data properly.

According to the research, retailers extract value only from 30-40 percent of existing data. That leaves two thirds of data wasted due to a lack of process, technology, and analytical talent. Besides, most of the data stays “siloed within companies.”

Improvements in machine learning and, most importantly, data availability help retailers unlock the full potential of customer data. On one hand, a regression model allows a retailer to leverage legacy data and reuse it effectively. On the other hand, predictive capabilities of machine learning let retailers not only learn from experience, but also apply those insights in order to model and predict future buyer behavior. It’s a true virtue to know what customers want before they want it.

Example: Walmart uses machine learning to predict what its ecommerce shoppers are likely to buy. Thus, the retailer provides focused recommendations based on past customer behavior. This is what the VP of customer experience engineering at WalmartLabs calls “the bridge to enhance online shopping experience.”

2. Customers need to be understood

The market of in-messenger and voice assistants is growing. The results of the 2017 Prime Day sale let Amazon claim Echo Dot as its best-selling product.

At last, people gain the power to speak with digital systems using natural language thanks to chatbots — in particular, the bots enhanced by NLP engines by Google, Amazon, Microsoft, IBM, Facebook, and soon also Apple. And people like it.

NLP engineers, in turn, finally get access to real-time data in natural language pumping from messengers, web, and voice assistants. This data is the key to maturing AI, and therefore to truly intelligent and helpful systems.

Meanwhile, retailers, especially big ones — which remain the driving force of NLP development — already use the opportunity to understand their customers. They sell and upsell via AI-driven chatbots and Alexa skills.

Growing attention and use cases feed both business and technology, proportionally. The more customer queries are parsed, the better NLP systems understand natural language. The better NLP engines work, the more customer needs are met and products are sold.

Example: The range of Amazon Alexa skills is already pretty impressive, from recommendations on books (Pan Macmillan), wine (MySomm), and music (Spotify) to full automation of cab ordering (Uber), pizza delivery (Domino’s), and household services (Laundrapp). These skills literally sell using voice.

If voice assistants don’t do direct sales, they at least make them smooth. Macy’s On Call, based on IBM’s Watson engine, enhances customer experience and helps shoppers find items in a retail store with no human involved.

3. NLP and machine learning provide real value

We’ve learned how to understand real-time customer queries via NLP and extract value from legacy data using machine learning methodology. The challenge of making use of ongoing customer feedback is bigger, but so are its benefits.

This challenge requires joint forces. First, an NLP engine needs to extract sense from a query in natural language. After, machine learning steps in to extract value from this sense.

Using classification, intelligent machines assign meaning to data, relying on their background and existing knowledge.

In practice, the system classifies certain products, say “books,” by categories, say “popular among women over 65.” For retail, this means more focused recommendation and upselling.

Using clustering for new information, in turn, opens totally new horizons. This method allows the system to find patterns and build connections between the bits of information with no set criteria and thus without prejudice.

In practice, it means that machine can find unlabeled, non-standard connections between customer buying habits. It can understand why people who have read X books by author A will most likely to go for a book by author B despite a whole range of other authors in their category. For retail it means more than a focused recommendation. It means more intuitive recommendation, better service, and higher customer satisfaction in the long run.

Machine intelligence market has reached more than a hundred billion dollars in value and keeps growing. It doesn’t seem likely to fade anytime soon, either, since all the cool kids are in it (Google, Amazon, Apple, and the like). What’s more, AI giants are striving to make it both available and affordable.

In this context, the retail industry doesn’t have much choice but to embrace AI. Directly connected to and dependent on customers and data, retailers start using AI as an experiment. But soon, application of intelligent machines will become a competitive advantage. Then, it will turn into a necessity and a part of every retailer’s business strategy.

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