Personalisation of communications and websites – whether recommending products, search engine results, news or advertisements – are becoming increasingly prevalent. Employing software that does it autonomously for you is now a necessity because of the vast amounts of data involved. While this suggests benefits for both individuals and businesses, but to understand why requires an appreciation of its value to the retailer and consumer alike.
The literary definition of ‘autonomous’ is self-governing – the ability to be one’s own person and to live life according to our own reasoning and motives without external manipulative or distorting forces – and that this is something we strive for, we need to question whether modern business-to-consumer interactions are supporting this aim.
Marketing initiatives have long been present in commercial practices; it always mattered how products are displayed physically or virtually or how they are advertised and represented. However, big data and AI is disrupting the ways in which this is being done and will continue to do so. Businesses have not only developed the capacities to determine consumers’ specific wills, they have also increased capabilities and resources to shape those wills. Depending on the degree of personalisation that companies implement, it becomes possible to match the right consumer to the ‘right’ outcome.
Any information – interests, hobbies, behaviours, habits, goals, intentions, demographic features, education, skills and employment – can become valuable input in the hands of a firm trying to adjust information to the consumer. Likewise the consumer’s own actions on a website commonly demonstrate preferences and criteria for selection, far and away greater than a prima facie understanding of our own tendencies. This complex process is called predictive personalisation software (PPS) and machine learning algorithms function is such depth, that the accuracy of product selection is on a par with galactic mathematical equations. Recently some organisations, for example IBM, have started to try to increase transparency of such algorithms.
Autonomous solutions incur no human cost
Autonomy often equates to the ability to make choices and fulfil one’s interests, and computer science recognises this connection between autonomy and choice. However, irrespective of whether we adopt a legal, philosophical or practical approach, it is sometimes assumed that any reduction or manipulation of choice is negatively related to personal autonomy. Whereas reality is that the burden of choice can emancipate the consumer from that burden; the retailer’s use of PPS selection being perpetual and so accurate that the consumer’s satisfaction is stimulated.
While time or cognitive limitations to processing information make some content moderation reasonable for the customers, there is no longer any concern of who decides which content is ‘relevant’, ‘optimal’ or ‘best’. The algorithm has used the consumer’s data to calculate this before presenting the appropriate and optimal product selection already.
Even though content is personalised with the primary aim of increasing the bottom line rather than facilitating consumer decisions, it is not the aim to exploit. Far from it, the purpose is to eliminate the need for the consumer spending precious time, and avoid distractions from reaching the products they most want to see and buy.
That this in turn negates their likelihood of looking at competitors sites as well obviously has a huge financial advantage over automatic, triggered or segmented alternative solutions. Autonomy might be assumed to have diminished when our actions are explicitly guided by the technology, but the reality is very different, as it has employed greater affinity with the consumer, and learnt a better understanding with every purchase and site visit impressions as they happen.
It might be argued that systems do not explicitly diminish personal autonomy, but they might be perceived as creating environments where it becomes harder to change course of action even if one has reasons to do so. However, the reality is that it identifies a change of preferences and desires before it even becomes conscious to the consumer, so has amplified the autonomy not diminished it.
Is personalisation right for your site?
In our fascination with big data, these problems should not be forgotten. The quality of the data is critical as predictive analytics is based on correlations, not casual relationships. Personalisation is a statistical likelihood of a myriad of events occurring together, all of which are actual consumer preferences. There is also no guarantee that the algorithms are good enough to correctly determine consumer preferences and send these selections of the most perfectly relevant content to them.
But machine learning algorithms means that over a short time it perpetuates personalisation and enables it to become phenomenally close. It ranks every SKU in order of greatest buying propensity for the individual consumer, and ensures the top ones are in their inbox at exactly the right moment. It achieves 20x the ROI that email marketing and omni-channel marketing combined, because it is what the consumers wants. Further the RoR falls through the floor because they don’t want to send it back, once ordered.
There is a quid pro quo on the gathering and use of individual consumer data, regardless that it is on their behalf. This consideration is tightly governed by GDPR laws and regulations. Should the consumer feel they are being exploited, they are legally entitled to take back control of their information, removing themselves. However the reality is that the opposite is demonstrated. Such is the enjoyment of personalisation, and the autonomy PPS enables the retailer to enjoy, that average order value grows and in turn customer lifetime value increases. It might be more worrying for retailers not using PPS, to consider that building such depths of loyalty that competition might and will become more difficult to secure than without it.
Transparency in terms of AI, is becoming increasingly important to the business itself, rather than on the relationship between the business and consumer. Transparency ensures that each individual consumer understands and appreciates how the AI working for them operates, as well as the effort and investment being made on their behalf. Enterprises, that recognise the high importance of autonomy self-regulating their ethical codes to set responsibility standards, and increase risk assessment and foresight capabilities will reap addition financial rewards as well as longevity of those relationships.
SwiftERM is a Microsoft Partner company. Further articles are available here.
You can enjoy a free trial of autonomous predictive personalisation software for your website here.