Artificial intelligence (AI) and machine learning are an increasingly integral part of many industries, including marketing, writes Rebecca Sentance from Ecoconsultancy. But while we often talk about using or incorporating AI in marketing, what do we really mean by that? What does it look like in practice? Read on to see 5 examples of AI and machine learning in action.
1. Product/content recommendations
The practice of clustering customer behaviours to predict future behaviours began in 1998, with a report on ‘digital bookshelves’ by Jussi Karlgren, a Swedish computational linguist at Columbia University. In the same year, Amazon began using “collaborative filtering” to enable recommendations for millions of customers.
Fast forward to 2019, and some of the most successful digital companies have built their product offerings around the ability to provide highly relevant and personalised product or content recommendations – including Amazon, Netflix and Spotify. Other major brands are now following suit with their own AI-powered recommendations. As customers become used to the level of personalised recommendations provided by services like Netflix and Spotify, they look for other brands to provide the same experience.
2. Data filtering & analysis
Marketing is becoming an increasingly data-driven discipline, and more effective use of data is the key to improving customer experience, personalisation, targeting and more. One of the great strengths of AI in the workplace is its ability to take on complex organisational and analysis tasks that would be difficult or impossible for humans to carry out, freeing up humans to do the more intuitive, creative work that they are better suited to.
For example, AI can be used to improve account selection in account-based marketing when ABM is carried out at scale. Targeting and personalisation company DemandBase found that it could use AI to filter out companies from its list of prospects that would ultimately lose the company money in the long run, as Jessica Fewless, the company’s VP of ABM Strategy, told the B2B Marketing Conference:
“We’re a stats-based company and if they churn from us in less than a year we lose money on them. So, we took the elements that made those customers churn and removed them from our model,” she said.
AI proved to be most useful for DemandBase in identifying ‘timely intent’: highlighting the accounts where there is a window of opportunity to approach before a commitment to a competitor is made.
3. Search engines
AI has had a profound impact on the way that we search, and the quality of the search experience, that we often tend to take for granted in 2019. Google first started innovating with AI in search in 2015 with the introduction of RankBrain, its machine learning-based algorithm. Since then, many ecommerce websites (including Amazon) have followed in Google’s footsteps and incorporated AI into their search engines to make product searching smarter.
4. Visual search & image recognition
Similarly, advances in AI image recognition and analysis are making it possible to achieve amazing things with visual search. Visual search – the act of using search to find results that are visually
similar to one another, in the same way that “traditional” text-based search finds results of a similar topic – is becoming more commonplace thanks to platforms like Pinterest, and technology like
Google Lens.
Visual search has a number of useful applications in marketing and retail. For example, it can be used to improve merchandising and personalise the shopping experience: instead of recommending products based on a shopper’s past behaviour or purchases, visual search technology can recommend relevant products based on how they look, helping shoppers to find items of a similar or complementary style.
Target and Asos are two retailers that have made a big commitment to visual search as part of their ecommerce experience. Target launched a partnership with Pinterest in 2017 that integrated Pinterest Lens, Pinterest’s visual search tool for the physical world, into Target’s app, allowing shoppers to snap a photo of a product while out and about and find similar items on Target’s website.
Asos’ Style Match visual search tool works in a similar way, allowing shoppers to take a picture or upload an image and search Asos’ product catalogue for items (or similar items) contained within that image. These tools encourage shoppers to treat retailers as go-to destinations for items that they might see in a magazine or while out and about, helping them to shop for the perfect product even if they don’t know what it is.
5. Social listening & sentiment analysis
Advances in natural language processing have proven extremely useful for marketers wanting to analyse their brand presence, and the conversations around their brand, on social media and use those to target campaigns.
AI allows brands to perform sentiment analysis on social conversations and understand the prevailing attitude towards their brand and products. This can allow them to spot potential issues and counteract them before they become too widespread. For example, Samsung – which works with AI consumer insights company Crimson Hexagon – was able to detect and counteract customer dissatisfaction with a red tint on the screen of its newly-released S8 smartphone model thanks to social listening.
Social listening and sentiment analysis can also be used to spot purchase intent by analysing the ways that consumers are talking about a product – for example “In the market for a new phone. Samsung S8 anyone? How does it hold up?” or “Borrowed my boyfriend’s iPad and now I’m thinking about getting one…” – which can enable marketers to target them with advertising or potentially a strategically-placed discount.
So, is there anything AI can’t do?
AI is transforming the ways that we work, shop, market and sell, allowing us to achieve things that would never have been possible without it. However, AI has yet to dramatically reshape most businesses to the extent that many expected it would. Brian Bergstein published an insightful recent write-up for the MIT Technology Review examining why this is. In it, he wrote,
“AI might eventually transform the economy—by making new products and new business models possible, by predicting things humans couldn’t have foreseen, and by relieving employees of drudgery. But that could take longer than hoped or feared, depending on where you sit. […]
“This doesn’t necessarily mean that AI is overhyped. It’s just that when it comes to reshaping how business gets done, pattern-recognition algorithms are a small part of what matters. Far more important are organizational elements that ripple from the IT department all the way to the front lines of a business. Pretty much everyone has to be attuned to how AI works and where its blind spots are, especially the people who will be expected to trust its judgments. All this requires not just money but also patience, meticulousness, and other quintessentially human skills that too often are in short supply.”
This article was originally published in February 2019 by Ecoconsultancy.
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