A report by Edelman Earned Brand studied 14,000 consumers in 14 countries to find out how ‘belief-driven buyers would choose or avoid a brand based on their perception of the brand’s stance on a controversial issue.’ It found that 57% are buying or avoiding purchases based on the brand’s position on a social or political issue – which is 30% more than three years ago.

Millennials are shown to be the most likely demographic to buy based on their shared beliefs with a brand, and are also the most prolific social media users. The Edelman study also highlighted that belief-driven buyers won’t buy from a brand that stays silent on an issue it had an obligation to address, with 67% buying from a brand for the first time because of its position on a corporate social responsibility (CSR) issue.

As retail continues to move online, so has the conversation, with customers now more knowledgeable than ever about ethical practices, sustainability and CSR. While reports from surveys can reflect opinions from previous months, data can be captured from social media in real time.

In a study carried out by Juniper Research, the current spend on AI for retail will grow from an estimated $2bn to $7.3bn in 2022 – of which 54% will be spent on sentiment analytics and 30% on automated marketing. Both of these disciplines can work together to better understand and respond to the shared CSR issues being discussed on social media.

Most brands and retailers are already using social media analytics to respond quickly to individual cases, but with over 25 billion Twitter engagements every day, NLP and AI can recognise how large groups of users are collectively feeling about subjects surrounding brands, products and services.

Using social media data, sentiment analysis is being used to automatically categorise groups of posts by determining whether the writer’s attitudes were negative, positive or neutral towards a specific subject. However, with AI, the range of emotional intelligence for machines is widening to include nuances of negativity and positivity such as joy, thankfulness, anger and sadness.

The emotive data inherent in conversations around CSR subjects can be understood by NLP applications, but there will be challenges ahead to meet the AI adoption rate for sentiment analysis forecasted by Juniper Research. The context in which sentiment is expressed through words and understood by machines is key to assess the why, how and what of the sentiment.

The advancement of NLP can be clearly seen with conversational AI interfaces like Alexa and the three times winner of the Loebner prize, Mitsuku. A quick chat with the chatbot Mitsuku demonstrates the level at which AI can already understand and respond to words, and how quickly this technology is developing.

Amazon Echo utilises AI-powered assistant Alexa

Social sentiment analysis needs a system to follow up on its data, and businesses assess sentiment on CSR issues in order to respond with appropriate communications. Social media community manager at The Co-op, Sophie Newton, explains in an insightful blog that they are using social listening to make more informed policy decisions and to create content to clarify their messages.

AI is already at the forefront of NLP and sentiment analytics, and of course, it’s playing an increasingly significant role in marketing automation, too. Artificial intelligence is ideal to deliver on the model of social listening and marketing response when it comes to CSR policies and messaging. Improving the speed and accuracy of this process with AI can lead to meeting the increasing expectations of retail customers.

All together, as of 31 March, there are 2,724 community installations and 774,914 domestic installations selling their surplus renewable electricity back to the grid wholesale. Regulators are now looking into making the changes needed to allow small generators to provide their power directly to consumers peer to peer (P2P).

Volunteer and Director of GMCR, Andrew Hunt, explains: “The margin at which we currently sell surplus back to the grid would be certainly be improved if we could supply consumers directly, which would help us to continue building new solar installations for local schools and community centres.”

April saw the first P2P energy trade in the UK conducted via the Ofgem regulatory sandbox. This was done between residents of Hackney’s Banister House Estate, using a P2P energy trading platform and their solar panel installation as part of Ofgem’s Innovation Link.

This P2P model would make it possible to supply people in local neighbourhoods with electricity from their very own renewable source, all at a competitive price. With domestic consumers having access to multiple suppliers via smart meters, this new data flow will open up new possibilities for AI and greener energy.

The current system of Load Profiles used to keep costs down, whilst ensuring that the supply is as clean as possible, is used to classify users into groups depending on patterns of consumption.

By taking the usage data of customers, eight profile classes have been established to help forecast the amount of electricity required by the grid. The examples below represent the average consumption of Profile Class 1. Those in Profile Class 2 are offered electricity at a cheaper rate during off-peak times to help equalise supply and demand every 30 minutes throughout the year.

Graphs courtesy of Elexon

• Profile Class 1: Domestic Unrestricted Customers
• Profile Class 2: Domestic Economy 7 Customers
• Profile Class 3: Non-Domestic Unrestricted Customers
• Profile Class 4: Non-Domestic Economy Customers
• Profile Class 5: Non-Domestic Maximum Demand Customers with a Peak Load Factor between 20-30%
• Profile Class 6: Non-Domestic Maximum Demand Customers with a Peak Load Factor between 30-40%
• Profile Class 7: Non-Domestic Maximum Demand Customers with a Peak Load Factor over 40%

As you can see, there are only two profile classes for domestic customers, segmenting a high number of consumers with variable patterns of use. With multiple suppliers for each meter, electricity supply will become more distributed. Together with the intermittent nature of renewable energy, future systems that include P2P will need to be able to automatically react to changing circumstances in real time – and that’s where AI comes in.

Individual usage data, cleverly combined with weather forecasting data, can build up enough information for a system of AI to understand when the best times are to switch a customer over from their default supplier to a cleaner energy source, all while getting the best price for both the buying and selling parties.

To help balance supply and demand with multiple power sources, including onsite renewable generators, battery storage company Powervault has trialled a system where a self-learning algorithm builds up a picture of a specific building’s consumption patterns.

Data scientist Ioanna Armouti explains: “SmartSTOR is a machine learning algorithm which uses historical house consumption data, metadata and weather data to best predict the power demand of the household, and therefore optimise Powervault’s behaviour to maximise energy efficiency and savings.”

If the plans to allow multiple suppliers to provide single residencies become a reality, the way homes receive their power will change, and we’ll see energy start to be provided in all round better and smarter ways. For example, when solar panels have fully-charged a battery in a home while the occupants are at work (and are therefore using little or no power at home), AI could facilitate the distribution and costs of supplying neighbouring buildings – eventually cutting down unused renewable power to almost zero.

Smart metering, multiple suppliers and storage all generating more detailed user data – more data than has ever been seen before in the retail energy market – will enable AI to create further substantial savings and a significantly greener environment.

As a supermarket customer chooses their grocery items, for them to know that the options available are authentic and sustainable is becoming easier with ‘100% traceable’ labels starting to appear more frequently on food packaging.

Some food brands, such as John West, have started to provide a traceable product service, where the barcode from an item can be entered by a customer into a website which will provide details on the product’s origin.

This growing demand is an opportunity for companies to maximise under-utilised data points in the supply chain, which can positively impact profit and loss, sustainability and customer satisfaction.

Technological advances such as blockchain have been successfully piloted in making supply chains more transparent, but the technology to collect and share data isn’t always available in farms that are sometimes thousands of miles away.

Photo courtesy of Evin Joyce

This picture was taken in a Zambian village, about 20km off tarmac roads and about 200km from the main markets in the capital city of Lusaka.

During the time the picture was taken, a trader bought their maize at 40 percent of its market value in Lusaka. The farmers cannot make informed decisions about who they should sell their crops to because of the lack of information available, and they are almost invisible to traders.

There are around 500,000 smallholder farms in Zambia who lack reliable, real-time market data and connectivity with other market players.

A solution to this problem could contribute to enabling hundreds of millions of people worldwide to break out of poverty and build better lives.

Evin Joyce has developed a non-profit mobile app, where smallholder farmers, who have previously had no access to smartphones or internet connectivity, can now receive market data and share their product data with a wider market.

So far, 1196 Zambian smallholder farmers can now use the ‘Maano’ app to register and sell their crops. During the 2017 pilot, 154 metric tons were traded on Maano, on which real time product data was collected at its origination.

The initial challenge of connecting smallholder farms further along the supply chain to get a better price for their crops can also add value to their product, by providing data on the provenance of their goods and a logistical advantage to the supply chain as a whole.

Research continues on new #Maano-5-bean stew.
How much would you pay for 1 kg of these beans + recipe + knowledge of exactly how much money went to the farmer who grew them? pic.twitter.com/z655qIH7xt

— Evin Joyce (@EvinJoyce) March 14, 2018

A detailed understanding of micro to international supply chains, and the price points within these supply chains at different scales of trade for each of the crops traded through Maano, would be needed to scale up to export markets.

Sharing accurate market data with all supply chain players, Maano makes food systems more sustainable by connecting smallholders to larger, more stable and equitable markets.

Could this be a blueprint for the 500 million smallholder farms worldwide? Potentially, yes. There are benefits to the whole supply chain for farmers to share product data long before it would normally become visible to the market.

This model can contribute to satisfying the demand of customers who want to know that a farmer has been given a fair price, and also create logistical efficiency and new market opportunities throughout the entire supply chain.

With product data coming directly from the source of food production, larger trade centres can forecast future availability to a higher degree of accuracy than the data received at a later stage in the supply chain, such as Lusaka, would allow.

Sustainability is built in to the Maano business model, which acts as a great example of how increased supply chain transparency can make commercial sense, from field to fork.