1. Recommendations/content curation

Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

IBM explained:

A 32-year-old woman who is training for a 5km race could use the app to create a personalized training and meal plan based on her size, goals, lifestyle.

The app could map routes near her home/office, taking into account the weather and time of day. It can watch what she eats and offer suggestions on how to improve her diet to improve performance.

Under-Armor-Record-app

Under Armour’s Record app

  1. Search engines

In late October 2015, Google announced it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries.

RankBrain should mean better natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

  1. Preventing fraud and data breaches

Analysing credit/debit card usage patterns and device access allows security specialists to identify points of compromise.

The relevance of AI is not just for card issuers, though.

Retailers, for example, have been subject to high profile data breaches (e.g. Neiman Marcus) as a result of a system based solely on usernames and passwords (without any stronger type of authentication).

This area of security analytics has been around for years but is becoming more sophisticated. Solutions have to react quickly to new fraudster tactics and analyse unstructured data, too. And this is where AI can help.

Natural language processing (NLP) can be used to look at text within transactions, for example, transforming it into structured data.

Newer AI implementations, such as that used by the United Services Automobile Association (USAA, which provides financial services for ex-military), will identify anomalies in behaviour even on the first instance.

 

  1. Social semantics

Deep learning has plenty to get its teeth into on social.

Sentiment analysis, product recommendations, image and voice recognition – there are many areas where AI has the potential to allow social networks to improve at scale.

 

 

  1. Website design

The Grid is an ‘AI’ website design platform.

Intelligent image recognition and cropping, algorithmic pallette and typography selection – The Grid is using AI in certain areas to effectively automate web design.

 

  1. Product pricing

With thousands of products and many factors that impact sales, an estimate of the price to sales ratio or price elasticity is difficult.

Dynamic price optimisation using machine learning can help in this regard – correlating pricing trends with sales trends by using an algorithm, then aligning with other factors such as category management and inventory levels.

 

  1. Predictive customer service

Knowing how a customer might get in touch and for what reason is valuable information.

Not only does it allow for planning of resource (e.g. do we have enough people on the phones?) but also allows personalisation of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analysing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

The AI has so far helped USAA improve its guess rate from 50% to 88%, increasingly knowing how users will next contact and for what products.

Saffron-Naturally-Smarter-image crop

Saffron’s mantra

 

  1. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research says: “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimising bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimising what product mix to display when retargeting, or what ad copy to use for what demographics.

  1. Speech recognition

Skype Translator now supports Arabic, English, French, German, Italian, Mandarin, Brazilian Portuguese, and Spanish.

Translation of speech has come so far due to progress in neural networks over the past five years.

Siri and Cortana and other personal assistants also use speech recognition of course, so many consumers are aware of how accurate they are.

Speech recognition is particularly important in the Chinese market, where using a keyboard to type small and intricate characters can be laborious. Baidu is making big strides on this front with voice search.

 

  1. Language recognition

Behind speech recognition sits the challenge of language recognition: what language means in relation to other things and concepts.

However, language recognition may be increasingly used by brands to digest unstructured information from customers and prospects.

WayBlazer is a so-called ‘cognitive travel platform’, a B2B service using IBM’s Watson AI to power consumer applications from third parties such as hotel chains and airlines.

So, for example, images, recommendations and travel insight are personalised depending on customer data, which might be unstructured text, e.g. ‘We want a family beach holiday with plenty of kids activities but also culture’.

 

  1. Customer Segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or custom experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimise email and website comms, continually learning from user behaviour.

 

  1. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analysed and appropriate action taken based on past behaviours and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

 

  1. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognises faces, as does Snapchat Face Swap.

Baidu have also designed DuLight for the visually impaired. The early prototype recognises what is in front of the wearer and then describes it back to them.

Of course, for marketers the uses could be manyfold, from content searching to innovative customer experiences.

 

  1. Content generation

At the moment, content generation is chiefly done using structured data. Wordsmith is a platform created by Automated Insights that allows the automatic generation of news articles – for example, from financial reports.

This relies on the reports being fed into a CSV in the right way – it’s essentially automation.

However, in the not-too-distant future, the plan is to do this sort of content generation with unstructured data using AI.

 

  1. Bots, PAs and messengers

Chatbots are thought by many to be the future of user input on mobile, replacing apps.

Simply talking or typing to a chatbot will allow a service to be delivered through the analysis of natural language combined with understanding of a brand’s datasets.

Facebook’s platform, previewed at F8, could conservatively soon lead to chatbots replacing ‘1-800 numbers, offering more comfortable customer support experiences without the hassle of synchronous phone conversations, hold times and annoying phone trees.’

 

 

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