Grace Russell, Marketing Manager, Celerity
As the physical and digital worlds become ever more intertwined through the Internet of Things (IOT), consumers are becoming more aware of their data. In particular, it’s commercial value to brands we engage with.
Within any type of conscious consumerism, be that eco-conscious, health-conscious or socially-conscious consumerism, transparency in how brands interact/ serve us is of the upmost importance. It’s a lack of transparency that fuels our sudden consciousness.
When it comes to data, the same process applies as most disasters/ annoyances that occur due to our data, such as data breaches and spam advertising, drive our desire to understand: ‘how are they using my data?’; ‘how much of my data do they have?’; ‘where are they storing it?’; ‘who are they selling it on to?’.
However, being a data-conscious consumer isn’t just about understanding data security or privacy, but an awareness of what is expected in exchange for personal data. Inaccurate and evasive marketing and advertising has led to three different types of data-conscious consumers, and the Direct Marketing Association (DMA), has defined these three groups: Data Unconcerned, Data Pragmatists and Data Fundamentalists.
Data unconcerned: These customers are generally disinterested in how their data is used. They will usually hand over their data easily and, as a result, end up on a lot of subscriber lists. According to the DMA, these customers make up around 25% of the population.It can appear great to a brand that these consumers want to hand over vital data, however this group is less likely to be affected by re-marketing, limiting any cross-sell/ up-sell or purchase acceleration opportunities.
Data pragmatists: These customers are willing to hand over their data, but only if they can see a benefit in doing so (e.g. a personalised discount or product recommendations), we call this a ‘value exchange’. Per the DMA, these customers make up 50% of the population. These customers may appear annoying for marketers because they demand a certain level of personalised, convenient services, but unlike the group above if you deliver the value exchange promised there can be huge opportunities for cross-sell and up-sell, as well as long term advocacy.
Data fundamentalists: These customers are tough nuts to crack – they’re ardent about protecting their data and are unlikely to share it, no matter how you tempt them. The DMA research suggests this group makes up 25% of the population. This group of consumers cause further challenges too, as they may input incorrect data to avoid the exchange and still get the product/ service. In a data-driven world this can be worrisome. As a marketer you may conclude that your main demographic are 25-year old females, when in fact it’s made up of 45+ year old men.
As time goes on with maybe data breaches continuing and the dream of personalised real-time experiences still not advancing, these numbers could change. But, at the moment, three quarters of the population are still open to personalised, data-driven marketing communications. So, if you can make your offering compelling enough, you’ve got a good chance of securing more sales for your company.
Here are three ways you can start to deliver a promising value exchange for your customer’s data:
- Use preference data
Preference centres are a great way of improving relevancy (with the ultimate aim of improving engagement). In practice, this means using data that a customer has already given about their different preferences, and putting it to good use. If a brand starts providing hyper-relevant information and offers, the idea is that consumers will interact with your platform more and might even switch back on their push notifications (if they’ve turned them off).
One company that does this well is Thread, a clothing manufacturer that asks customers what their favourite types of clothing, colours and fabrics are. They will then pair customers with a stylist, who will email their personal recommendations. This is useful for the consumer as they’ll already be aware that the choices aren’t generic, but have been specifically chosen for them, which helps move them down the sales funnel.
- Make it personal
Personalisation in marketing isn’t new, but it’s more sophisticated and potentially more powerful than ever before. Coinciding with the large proportion of consumers that are now more conscious of how much data companies are collecting on them, companies must be smarter about how they do it.
One of the most popular and effective elements of a value exchange is personalisation: from personalising the journey the customer takes, to the conversation we have with them.
Spotify is a great example of a brand that uses personalisation; it reports data-driven content back to the consumer: ‘Most played song of the year’, ‘Latest new artist you’ve discovered’, ‘New genres you’ve listened to’. Spotify create an air of transparency – openly showing how much data they have collected and in between the lines the awesome experience you’ve had in exchange.
Abercrombie and Fitch do a similar personalisation strategy to fuel transparency. It provides a single customer view within your ‘my account’ section. Whenever you have supplied a piece of data it’s added to a timeline you can see. Also, consumers are still less comfortable with marketers tracking and reporting on their engagement and digital movement, but A&F try to ease this concern by rewarding interactions as well as physical data input and purchases. For merely downloading the app after creating an account via the website you earn 100 points and even more points for completing your whole profile.
- Make it easy
Convenience in marketing is about removing friction for the consumer by making the buying journey easier, and you can do this in exchange for data. For example, pub retailer and brewer Greene King recently personalised and shortened the customer journey through social sign-which allows customers to like, share and comment more easily on content, and be surfaced more relevant advertising that actually benefits you. Tactics like these benefit all parties involved – they give customers a better experience and provide businesses with a better insight into customer behaviour via social channels.
Another popular option for hyper-convenience in exchange for data, is replenishment campaigns. This is where a consumer will input their product usage and frequency, letting the brand deliver this product to their schedule and skipping the re-order journey. Lancome provide this service for makeup foundation and it includes free shipping and the latest samples. For the consumer, they get the product they need, when they need it, and a few extra nice to haves; for Lancome, the customer is stickier. Achieving hyper-convenience using data can even remove the consumer’s ‘consideration phase’ entirely.
So what next? Today’s consumers are generally more conscious of how brands use their data, but that doesn’t mean that data needs to take a backseat in your marketing strategy. Use it wisely and with care – and most importantly USE IT during the customer experience – and you’ll stand a good chance of maximising your customers who remain data pragmatists, whilst winning over some consumers that fall into the data-unconcerned and data pragmatists pools.
RISK VS REWARD: IS AI TAKING OVER?
Xavier Fernandes, Analytics Director at Metapraxis
A study by Oxford University academics into “The Future of Employment” in 2013 prompted apocalyptic headlines which stated that in the future 40% of jobs will be automated thanks to advancing technology.
The researchers subsequently claimed that the truth was in fact a little more prosaic; rather than facing complete automation, the research found that 40% of jobs faced some aspect of automation in their activity. So with new ‘AI processes a likely reality for almost half us, what does that mean for our current roles and should we be worried?
The fourth revolution?
The first industrial revolution saw machines replacing muscle, both human and animal. The second and third saw electrical power, mass production and computerisation revolutionise the job market. Now, with daily headlines of AI as an employment superpower, there is some concern that AI is bringing a fourth revolution, and with it, unknown circumstances.
This ‘fourth industrial revolution’ is defined by replacing brain power with machines. Our thinking capacity is what inherently sets us apart from other species, so it’s not surprising that any encroachment on it triggers some existential angst.
Evolve to reap the rewards
While many businesses still don’t fully understand the capabilities of AI, those who fear its development are, instead of embracing it, missing all the benefits that it can bring to the workplace. Businesses that utilise AI appropriately are seeing vast improvements across their entire value chain; better customer experience, reduced costs, and more insightful analysis to support management decisions.
AI is particularly useful for supporting tasks with repetitive activity, for example, performing financial checks and assessing large sets of data within financial services firms. AI performs particularly well within this context, spotting outliers before a human expert would notice them, allowing impending problems to be flagged and avoiding costly mistakes.
There is also an increasing focus on maximising customer lifetime value through the use of AI. Being able to predict existing customers’ needs as well as track trends in their financial circumstances is supercharging the old cross-selling approach with testable, predictable outcomes.
With potential benefits like these on offer, management teams of innovative financial services are increasingly relying on AI to help them with some of the heavy-lifting of analysis. Using advanced data capabilities and learned behaviours, AI analyses market trends to provide predictions of future performance. This insight is invaluable and allows management teams to change direction and correct any problems accordingly. This offers a huge advantage over those that have not adopted such tools.
Supporting the workplace
Algorithms and AI are typically ‘smart’ at doing one, tightly-constrained task, but they can be less helpful with many of the activities that humans find straightforward. In most white-collar jobs, automation tends to replace certain tasks in the job, rather than the role in its entirety, as the need for human intelligence is still highly necessary. In particular, we still need human input to first challenge, and then synthesise, this information before taking action. Employees should therefore work with the business to proactively identify what areas of their role could be automated, so that they can focus on the areas that add real value to the business’ commercial goals.
Challenging AI is certainly still important. We know that algorithms can be much better than humans on certain, bounded tasks. However, many algorithms rely on existing data sets to build their understanding. As a result, when a business unit has ‘symptoms’ that fall outside of that body of knowledge, the algorithm may suggest the wrong course of action with costly results.
Indeed, even with plenty of data, algorithms will reflect any biases the data set contains. We’re seeing this with some legal sentencing algorithms where there is evidence that they are treating disadvantaged people more harshly. Getting the answers to why and how far we should trust our algorithms should therefore become an everyday part of any job affected by AI.
Rather than depending entirely on AI for all decisions, workers should be taking all these new, AI-generated insights and using them to complement the human decision-making process. No manager of a complex business ever has enough time to sieve through all the analysis available, but with AI driven algorithms able to flag up any issues and indicate where action needs to be taken, we may find that we have some AI ’colleagues’ who will cover our backs and suggest innovative options. Yes, there will be times when the algorithms get it wrong, but as long as we’re watching out for those, the future is bright.
HOW TECHNOLOGY IS FUTUREPROOFING STOCK MARKET TRADING
Tony Shaw, Executive Director, London Office and Head Sales UK & Ireland at the Swiss Stock Exchange
Markets are shifting, there’s no doubt. Amid all the disruption and volatility from the past year, the Swiss Stock Exchange asked traders about what they expected in 2020 and beyond in our industry survey. The findings point to a rise in digital to help traders content with external forces.
First and foremost, traders are enthusiastic about what digital assets can offer.
Two thirds of traders polled said they’d had a marked rise in interest from their clients for digital assets and crypto-products. Given the interest, traders are increasingly bullish about the potential of these products – so much so that 80% have predicted an increase in overall demand in the long term. Market users believe these assets will help generate cost synergies and streamlining trading and settlement processes by creating efficiencies and ultimately reducing costs.
Our 2019 results reflect what traders have told us when it comes to digital assets and products. Last year, we saw significantly higher trading volumes from products with crypto currencies as underlyings. Overall volumes grew by +8.5% over 2018, but the increase in crypto products alone was +17%, reaching CHF 518.2 million ($534.54 m). There was a year-on-year increase in the number of transactions, as well (+21%): 19,636 trades in total.
The potential digital assets hold is clear – evidenced by the building of the SIX Digital Exchange (SDX), a fully integrated issuance, trading, settlement and custody infrastructure for digital assets.
According to traders, artificial intelligence (AI) is expected to bring further benefits to market operations.
Two thirds of our survey respondents anticipate AI will create more opportunities for the traditional equities business, while a similar number expect it to reduce the cost of trading. Innovation in AI is already – and will continue to be – a key driver in making our industry more effective at withstanding future risks and challenges both within and beyond the market itself.
In Europe, there is growing momentum behind calls for shorter trading hours – this trend was reflected in our survey as well.
Industry groups such as the Investment Association are advocating for stock market trading hours to be cut from 8.5 to 6.5 hours to open the industry to working parents and women who cannot commit to such long workdays. We found traders were largely supportive of this, with many saying that it could even facilitate operational benefits. The roll of AI is clear here in improving efficiency while minimising time wastage: 36% of traders said the introduction of shorter trading hours would prompt greater market liquidity.
Beyond the market itself, geopolitics continue to shape wider market sentiment.
It comes as no surprise that four fifths of traders said their strategies have been – to some extent – influenced by Donald Trump’s tweets. Interestingly, only 39% of those polled viewed Brexit as an influencing factor in trading activity, while three quarters believe the US election will drive trading activity in 2020 and 65% acknowledged trade wars would also have an impact.
More broadly, traders are split on the state of the global economy – 58% are bracing for a global recession while 42% predict stable macro-economic conditions over the next three years. What seems clear is that whatever happens in the wider economy, traders are making headway with new technologies that can improve their strategy, efficiency, and overall market health.
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