Survey data has long been the go-to for companies seeking direct feedback from their people on topics of significant importance. But for far too long, they’ve had to rely solely on – and make do with – this approach to track employee experience, EVP, engagement, culture and behavioural health, in order to inform people strategy and to understand how best to optimise performance.
By taking their frontline workforce offline for 15 minutes to complete a survey and still be paid, they face a bill running to over €3 million.
There is no love affair between people and their answering surveys. But there is a love by people in business wanting rapid answers to questions. With a survey, insight can only be gained within the scope of the questions asked. It’s dependent on the quantity and quality of the responses provided. Add to this the time, cost, effort, friction and sometimes complex vendor onboarding and onerous info security requirements, and it’s clear why surveys have their limitations.
Take this example. We have a client who knows that by taking their frontline workforce offline for 15 minutes to complete a survey and still be paid, they face a bill running to over €3 million. However, using our methods to take pre-existing public domain data and upcycling readily available in-house data, they are able to reach a better outcome at a fraction of the time and cost – and with a better UX for all.
Our mission is to use the power of Gen AI to evolve this space in the same spirit that post, telegrams, phones and wireless internet gradually and spectacularly transformed communications between people.
We are, however, working to a much tighter deadline to bring a North Star triumvirate of improved efficiency, UX and productivity to life for our clients!
80-to-90% of information about companies across the internet is created by people who are working – or have worked for – that company.
AI deals with some of the inherent problems of a survey. It gives us the opportunity to combine already-collected data from multiple sources. We can reuse and upcycle, unveil multiple perspectives and extract more nuanced insights. It becomes a strong complement to the specifically-generated, internal data gained from a survey alone.
Passive data – that is, data previously generated or shared – shared exists both outside of an organisation in the public domain and internally within a company (albeit in separate tech platforms with often little integration!). In fact, we find that 80-to-90% of information about companies across the internet is created by people who are working – or have worked for – that company. Imagine combining social media chatter, company reviews, and analyst reports with the internal data you already have such as the rosters, psychometrics, exit interviews, absence rates and customer satisfaction ratings. It’s all there: it’s pre-existing data.
There is little need to design a new survey, trial it, build a comms plan, cajole people into completing it or chase up submissions, only to realise the analysis doesn’t give the information or insight needed. Perhaps a crucial question was omitted, or time has moved on and there’s a more relevant area to explore?
Bringing together this pre-existing or passive data unlocks new insights.
Accessing external, passive data from workforce reviews, recruitment sites or customer platforms lets you do this. You will see where you are strong and it will signal when action is needed.
No amount of internal data will enable you to benchmark your company with the competition. Accessing external, passive data from workforce reviews, recruitment sites or customer platforms lets you do this. You will see where you are strong and it will signal where action is needed. And, because you are accessing already-available data, it is superfast, tends to cost less than other projects and requires no effort from your wider workforce.
This lack of friction frees up your time. Time you can spend digging deeper into the data and being far more strategic. And it enables you to be far more responsive to those additional, not-yet-thought-about questions asked of you by your board or senior team.
As an example, one client wanted to understand and improve their measurement of psychosocial risk. Previous efforts had focused on manager training to identify potential harassment or bullying, with no clear link made between mitigating this risk and business performance. By leveraging the existing internal data (performance, customer success ratings and product sales), we demonstrated this link. We identified those key characteristics in the model relating to psychosocial risk. Interestingly, the strong behaviours needed to eliminate psychosocial risk showed a strong correlation with increased revenue and positive customer success metrics. It meant that the business no longer saw mitigating the risk as a must-do compliance activity but as an amplifier of product sales.
Our psychology, data and technology teams have built the capability to combine pre-existing, real-time data taken from multiple sources with the scientific, legal, data privacy and security rigour required.
We know how to combine data, and to check for validity, reliability and relevance, and we understand the ethical considerations and the legal frameworks to navigate.
Our system looks at patterns, themes and indicators and then flags the most important or high-impact actions to take. We work with our clients from the outset to learn about their business and develop a model, against which we examine the data to reveal what it shows.
We know how important it is for our customers to feel confident in what is being done and to understand the analysis, the provenance of which is critical. We openly share every source of every data set accessed.
Our primary driver is to help our customers find how AI can work for them to improve what they already do, and to do things differently in order to transform their company.
Companies are currently losing out. They cannot get the information they need about the topics that are critical for them to manage. We want them to get productivity and efficiency gains, and an improved employee experience.
We believe the use of AI within HR has been, to date, quite limited: ‘Write a job description’, ‘Optimise a job posting for search engines’ – AI offers so much more.
We know we are groundbreakers, market leaders and possibility challengers. We are transforming the way science and technology can do good stuff for people – and for organisations. We are not copycatting something invented a decade ago, but making something that will become the norm in five years’ time. It is a journey for us all.
No doubt you have questions about generative AI; we can share what we know. Our team is scientifically and technically strong and curious. But more than this, we are keen to help organisations access what they need to get the outcomes they require.