In part one of this two-part blog, we looked at how algorithms are influencing our everyday lives. With more choice available to us, we are turning to algorithms for not only recommending the best product, but actually making a decision on our behalf. These algorithms exist in everyday objects, like the Amazon Echo or Smart Fridge.
In part two, we look at how algorithms will make automated brand choices on behalf of the consumer. We will discuss what impact these algorithmic gateways will have on marketers trying to communicate with consumers. And, how will marketers need to adopt more data and analytics approaches to promote their products and services.
So how are algorithms going to decide?
As marketers think about a new gateway of tools and technology, driven by algorithms that automate the consumers decision, it is natural to wonder how these algorithms will be coded to decide. How could the device (like the Amazon Echo or the Smart Fridge) choose the brand, make or model. You could classify the possibilities as:
The platform asks us to specify which brand we want to buy
The platform defaults to the brand we usually buy or last bought in that category
The platform selects a brand based on what our social connections buy
The platform chooses a brand for us, based on a variety of algorithmic inputs
The most likely answer is four – a combination of inputs that help the algorithm make an informed decision. But again, we already see this in action. Recommendation algorithms are already doing this. They rely on transactional and interactional information to understand what we want before we even know it. Case in point: 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendations based on such algorithms.
So how do we influence algorithms?
How do we make sure your products are at the top of the list? Ultimately, it comes back to really knowing your consumers, knowing where to find them, and what influences them:
Gather data on your consumers, across channels and across devices. Sophisticated cross-device tracking that informs desktop and mobile ad strategies has been shown to increase sales by 30%. For example, SilverPush embeds ultrasonic sounds into television commercials that can be picked up by standard mobile and tablet apps that are listening out. This means that technology can pair a single user to multiple devices, keeping track of the television commercials a consumer sees, and whether they act on ads by carrying out web searches or purchasing the product.
Build customer profiles based on lifestyles and preferences. With all of the data being collected today, we can generate huge insight into a consumer’s likes and dislikes. By building detailed consumer profiles, we massively increase the chances of success for any targeted marketing. Previously, companies had access to only the data they owned, i.e. consumers visiting their channels, sites and apps, however third-party data on your consumers is becoming much more readily available. For example, app measurement and ad company Flurry’s ‘Personas’ product is able to collect data on consumers across 400,000 apps from different companies on over 1.2 billion devices to identify detailed personas and segment groups for real-time ad targeting purposes. By building detailed customer profiles based on data from multiple apps, companies can improve sales by 2.5 times.
Anticipate customer needs and respond in real-time/at the right-time. While understanding consumer preferences and needs is valuable, it is even more profitable to anticipate these needs and be proactive. With reductions in delivery times, an emerging 24/7 culture and one-click ordering, it has never been easier for consumers to buy and expect instant gratification. To stay with the competition, it’s no longer good enough to wait for the consumer to make the buying decision, you must cultivate the need and be there at the right moment to collect (or even place) the order and fulfil. For example, Amazon has been ahead of the game with its anticipatory shipping patent. Amazon is focused on predicting intention based on previous searches and purchases, wish lists, and how long the user's cursor hovers over an item online. With the intention understood, Amazon can confidently ship products to nearby distribution centres, before the consumer has even clicked to buy, and potentially even to the customer’s doorstep in the future.
Recognise the huge brand influence of friends, family and peers. Research shows that we’re 10 times more likely to take recommendations from family, friends and peers, than we are business recommendations. Reviews and positive experiences are everything in today’s consumer-driven environment. This is why marketing budgets on social media are increasing, with some projections indicating 22% of marketing budgets may be spent on social media in the next five years. With platforms like Facebook and Instagram algorithmically prioritising peer content, it is important for marketers to consider how they will be part of the conversation and elicit active endorsement from key social media influencers and networks of connections.
Beware the ‘filter bubble’
Influencing algorithms can be very effective, but beware. There is a danger that we take this to an extreme and only look at the key characteristics of consumers, while ignoring the smaller signals that make consumers unique. We begin to market products in the same way to the same consumers on the same channels – becoming more and more irrelevant and feeding into the ad rejection culture. We’re in danger of getting to a point where we are commoditising products, with the only difference being price.
This causes the ‘filter bubble’ dilemma - where the algorithms become a negative influence, controlling and limiting what we see to be quite narrow. This can make marketing limited for the consumer, as they are not discovering new products. In 2010, The Guardian’s news had this exact issue, whereby a reader’s news feed was only providing a very narrow view of the news based on the specifics of what they had chosen to read in the past. To address this, The Guardian created a ‘serendipity maker’, introducing random news into a user’s feed so they could see more than just what they had selected to read in the past.
So how do we manage as marketers in this world of algorithms? Consumers are radically shifting and evolving their approach to purchasing decisions, putting algorithms in place as gateways to manage the marketing overload. How do we get past these algorithms? Well actually, algorithms are much more predictive than humans. If we can understand how they work, marketers can use this as an opportunity to get the right products or services in front of the consumer at the right time.
Consumers today are looking for new types of experiences. Yes, they may hand over the decision on what brand of toothpaste is bought on their behalf, but they are looking to leverage the time they get back from this low-level decision to spend on more experiential interactions. The brands who succeed in working with the machines will be the brands that take the opportunity to deliver tailored, personalised experiences that delight the consumer.
One thing is for certain, the success or failure of every brand is reliant on an analytics-aware marketing team, who are critical to the Future of Marketing.