Employee Voice: How are you going to implement it?

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In July 2018 the Financial Reporting Council published its long-awaited update to the UK’s Corporate Governance Code. We’ve seen considerable coverage of this in the legal press but very little for HR. This seems strange given that those in HR are likely to have to implement it.

The new regulation applies to firms with over 250 employees in the UK.

What the regulation states and what it doesn’t

The new UK Corporate Governance Code states:

For engagement with the workforce, one or a combination of the following methods should be used:

  • a director appointed from the workforce;

  • a formal workforce advisory panel;

  • a designated non-executive director.

If the board has not chosen one or more of these methods, it should explain what alternative arrangements are in place and why it considers that they are effective.

The challenge with all of these methods is how to capture the views of the employees, to synthesise them and provide them to whichever party that has been designated to include them in the board decision process.

Of course the workforce is highly unlikely to have an uniform voice. Therefore it is essential to capture this diversity of ideas. The formal approach used to decide how to handle potentially conflicting ideas isn’t part of this article.

The challenge of upward communication

As an executive you’d like to hear about all the issues that need your attention. By ‘your attention’ I mean that either they’re things that you don’t want to be surprise with when presented from another source or things that for whatever reason need you to take action (maybe because they are cross-organisational or need your level of authority).

The problem that you’re faced with is twofold:

  1. Only a proportion of the topics you need to hear about will reach you ‘undistorted’

  2. Due to communication windows it will take time for these topics to reach you.

Let’s take the example of a business with 8 layers between the executive team and the folks who deal with the customers.

  • If a high 80% of topics that you need to hear about get through each level then this implies you’ll only hear about 21% of topics

  • If a more realistic 50% get through each level you’ll hear less than 1% of all issues

  • Of course you’ll probably hear about the the wrong topics. Because many of the issues might seem ‘too petty’ they won’t be raised. However if 500 people across the organization have this issue it probably demands your attention. Small and widespread often doesn’t get through, whereas big and niche often does. Often the real value is in fixing small and widespread issues.

The other key issue - one that we frequently see and one that is well-researched in the academic journals - is that employees often don’t raise issues - so called ‘Employee Silence’. It can be more difficult raising issues up through the chain, where it might threaten their managers than to raise it confidentially to a central point. The sort of issues here might be to do with cultural risks such as poor incentives or behaviours. 

Unlocking innovation

Executives often think about this as needing to facilitate ‘whistle-blowing’ or raising of issues. This is, of course, important but these instances are rare and high value - the ‘rare and niche’ that I mentioned earlier. In truth these cases need special channels and need to be treated differently than other forms of employee voice.

The real challenge is not how to find the rare event with a clear channel, it’s finding the diversity of opinions and ideas about something widespread.

Often the true value in integrating employee opinion into decision making is to understand the distribution of ideas and opinions from a large population. It’s about asking all of your employees about a decision and understand the breadth and distribution pattern of the responses.

Of course some of these ideas will be truly innovative and potentially super-interesting but often you’re trying to get a good grasp of the top ideas.

The need for qualitative information

There is a time for getting quantative information - mostly when you already know the problem and are looking to understand the scale of it. If you know all possible options, a closed question might be the best way of putting a number on it.

In most instances however you don’t know all aspects of the problem, or haven’t identified all the possible solutions. When you’re at this exploratory stage then it’s best to ask open questions.

Sufficient, reliable, relevant and useful

Adopting a concept that comes from internal audit:

  • Information is sufficient when there is enough of it to come to an unbiased and dependable view of the topic. If you’re asking all your employees about a subject and a meaningful number of employees raise an issue then it’s probably sufficient.

  • Information is reliable based on the source of the information. Customer perception data from people working day-in and day-out with customers is potentially more reliable than information that comes indirectly. Information that can be validated is more so.

  • Relevant information is information that relates directly to the question explored. When we ask open questions to employees relevant information is that which relates to the topic. We need to down-weight information that we’ll receive but doesn’t help us answer the question (some people have something that they will raise regardless of the question asked).

  • Useful information is information that helps an organization meet its goals. If the information was about an old reporting system which has since been phased out, it probably wouldn’t be useful (because the issue has already been dealt with.)

Employee feedback provided via free text can be all of these things, though it might gain extra reliability if linked to existing data. It’s important when reviewing the summarised feedback that managers assess it against these 4 lenses.

The need to synthesise

Once you’ve decided to ask your employees a series of open questions about a key topic or decision, what do you need to do?

For many of our clients if we ask a couple of open questions we’ll get on average 20 words per question per employee. This means for each question with 50,000 responses you’ll be faced with 1 million words. 

The problem is that you probably need to present less than 1,000 words to your executive team, ideally 1 side of A4 paper.

How do you do this?

Technology to the rescue

Historically, analysing a large amount of free text was a long, expensive process. The quality that you’d get out of this also was probably lower than you’d imagine. Getting 80% agreement between reviewers is pretty good. It’s really hard for reviewers to be consistent throughout a review. Identifying a new category means having to start again.

There are several different capabilities that you need to have, most importantly the need to understand themes - context rich descriptions - rather than topics. Historically this level of understanding has been hard yet with the progress of text analytics over the last couple of years the best machine learning approaches can match human performance in many tasks. 

What you will need your technology to do

When considering an algorithmic support to enable you to collect and understand Employee Voice you need to ensure that your text analysis tool can deliver several key capabilities

1) Ability to ask any question, and to analyse the responses

We do not believe that Employee Voice - the ability to let employees contribute to decision making - is possible without being able to ask and understand questions about the decision you need to make.

The first, and arguably the most important requirement for any Employee Voice technology is that you can ask any question, and the system can theme the answers with a decent level of accuracy. 

This might seem obvious but it’s not. The best text analytics approaches work on very narrow types of text. A system might be great at parsing a CV but couldn’t understand a shopping list for example. To get the level of accuracy that you need we think you probably need to have a model fine-tuned at a question level.

2) Themes not topics

As mentioned before it’s important to understand the themes - how people describe their views - not the topics. So ‘more transparent communication’ instead of simply ‘communication’.

The algorithms should provide summaries the answers. Only if you can understand the underlying meaning just from reading the theme label then it’s probably good enough.

3) Identify notable and unusual answers

Another key aspect is the overall pattern of themes, both in terms of overall distribution and ‘hot-spots’ of feeling across the organization and employee population. Often you’ll need to identify the rare comments that might bring genuine innovative ideas (or tell you about a problem you really need to deal with).

4 Track asking and answering of questions

For compliance purposes you will want to be able to show who was asked, when they were asked, how the feedback was analysed and how the information was integrated in the decision making process. Technology is well-suited to this task.

A process for working with Employee Voice

We do not believe that technology alone will enable firms to meet the requirements of using Employee Voice in decision making. However whichever way (or ways) businesses decide to use to bring voice into business decisions it’s clear that technology can significantly improve the cost, responsiveness and quality of the process needed to maximise benefits and demonstrate compliance.

What we constantly hear from Workometry clients is that when executives experience the benefits of being able to consult their organizations quickly and effectively they want to use it more and more. We hope that this new regulation helps elevate employee voice into a standard business practice.


Taking it further

Earlier this year we published a Guide to Employee Voice. It includes a set of useful resources and documents for anyone trying to understand best practice in this area.

Try Workometry

If you do have thousands (or even hundreds of thousands) of free text answers from your employees let us show you what is possible.



How to understand open-question employee feedback

Large organizations have vast amounts of employee text feedback that so-far they’ve done little with. Often this information is the most valuable part of a questionnaire or survey yet understanding it at scale has historically been hard and time consuming.

The process of understanding this feedback is known as ‘coding’. What a good qualitative researcher is trying to do is to identify a set of categories that both cover as many of the comments as possible yet not so many that they become hard to navigate and understand. 

The importance of the question and organization context

Feedback does not necessarily come in beautifully crafted sentences. The understanding of any answer to a question is highly dependent on the question asked. People write answers assuming that you know the question asked and view the feedback through the lens that this context provides.

Given this it makes sense that to develop a set of themes from the answer to a specific question it has to be done in the context of the question asked. General models are hard to make accurate. It’s why great text analytics models typically have narrow domain use. 

In organizations you also need to understand the context of the organization. Organizations develop their own language use. Many times this is internal jargon, project and technology names. Sometimes it’s re-purposing words to have a different meaning. The employees of one of our clients talks about ‘payroll’ when most people talk about ‘headcount’ (because if they have more money for pay they can afford to take on more staff). Really good models need to learn this language use.

Themes not topics

A good set of categories should be understandable without needing to read the underlying comments (though of course you will likely want to let users drill down to these if they wish). The theme labels need to be sensible answers to the question asked.

If we look at typical categories that survey tools are starting to provide in their text analytics, if the themes are simple, one-word nouns then users will almost certainly have to read the comments to understand what it is about that topic that they are discussing. Noun-only topics are good for navigation - guiding the user to read a subset of comments - but poor at providing insight.

For helping drive decision making what is needed is contextual understanding about what it is about that topic which is of interest. So instead of ‘Communication’ we need to describe that it is ‘more regular communication’, ‘less communication’, ‘more transparent communication’ etc.

It is quite common in our analyses that we’ll find conflicting themes within a particular topic. We’ll find that some employees want ‘more hours’, some will want ‘less hours’ and another group will talk about ‘more consistent hours’. If you’re just looking at the topics - ‘hours’ - all of these themes will be grouped together. To take action you need to understand the context.

Semantic understanding rather than keywords

Early text coding systems used keywords to identify topics. You can even do this with a simple spreadsheet application like Excel. Wordcloud applications typically use this approach.

 Wordclouds provide very limited insight.

Wordclouds provide very limited insight.

What is important when creating good themes is to use a semantic understanding of the comment, not just look for certain words. So an employee might ask for ‘more pay’ or ‘better wages’ or a ‘salary increase’. To a human reader these would all fall into the same theme. Our algorithms even recently allocated ‘ha ha, a raise’ to be about improving pay.

The bar for usefulness is quite high

When creating a good set of categories of it’s hard to be useful until you get to a stage where you’re able to identify most of the useful themes and that these themes are summarisations of the text they contain.

We’ve found from clients that the level of coding accuracy has to be high before it’s seen as valuable. In our instance, where we’re trying to replace quantitive, closed scale questions with open-questions it’s important to have good quality themes that accurately reflect the semantic and contextual meaning of the answers.

Whilst providing a list of topics might seem a good first-stage approach, the reality is that it doesn’t replace reading the comments and therefore doesn’t add much value. 

Deductive and Inductive coding

There are two general approaches to doing a coding - deductive and inductive.

Deductive coding starts with a predefined set of codes. It’s what most employee engagement apps and even consultants typically use. You start with a model of what people will likely say and then you allocate their topics to these categories. 

There are a few advantages with deductive coding:

  • You know that you will identify comments in the themes in which you are interested
  • It’s easier to compare results across different organizations

However there are several key issues:

  • You might miss key themes. From our experience, about 30% of topics from a typical employee question might not fit into a general model
  • The coding model has bias as the themes are based on an analyst’s model, not the views of the employees
  • It takes more time / resources up-front to decide what themes to look out for
  • A model really only performs well with one specific question

The alternative to deductive coding is inductive coding. With inductive coding you start from scratch, using the data as the source of the themes. With inductive coding you can understand the answers to any question.

The typical process for creating an inductive code manually is:

  • you read some of your responses
  • you identify themes (codes) that cover this sample
  • you code your all of your responses to these themes
  • you identify the comments you weren’t able to code and identify new themes
  • you then recode all your responses again against the new full set of codes
  • this whole process is repeated until it’s not possible to identify new, useful themes.

This is, at a high level, how we create the themes with Workometry, apart from instead of doing it all manually a set of algorithms helps with the coding.

Balancing accuracy and coverage

In any predictive model the analyst has a choice between false positives and false negatives. In the case of text classification, if you want to increase the accuracy of your coding (reduce false positives) you increase the risk that you’ll miss some comments in your themes (increasing false negatives).

Our experience of text coding is that readers are much more sensitive to a comment being misclassified than they are about being said that it hasn’t been coded. As a human we tend to be much more understanding if someone says ‘I’m sorry I don’t understand’ to them answering the wrong question. The inductive coding process is iterative to try and create the needed balance.

Whilst coding might seem to be a task which everyone can perform even humans find it difficult. Several studies have shown that when you get several skilled coders to theme the same piece of text you'll only get agreement about 80% of the time. Consistency is even problematic at the individual coder level as their opinions will change as they review more feedback. AI-based solutions will be more consistent than a skilled human.

With volume you get granularity

We often get asked ‘how many different topics will you find?’ The answer is almost always ‘it depends’.

However there are two key factors that it depends on: the question and how many answers that you receive.

With the question one of the big determinants is whether you’re asking a ‘what is good’ or ‘what could be improved’ type of question. Typically you’ll find less different themes in the former than that latter.

To understand the likely number of themes it’s worth understanding how the volume of responses matching a theme tend to be distributed.

If you plot the frequency of mentions for the different themes in your question and order the themes largest to smallest you tend to have an exponential-type distribution.

 How often a theme is mentioned will have an exponential shape when sorted most to least popular. 

How often a theme is mentioned will have an exponential shape when sorted most to least popular. 

If instead of plotting the number of mentions for each category you plot the logarithm of the number of mentions the curve becomes very close to a straight line. The area under the curve represents the total number of mentions.

 The relationship between the number of comments and the number of themes identified

The relationship between the number of comments and the number of themes identified

As long as the number of themes per answer stays the same as you increase the volume of answers then the effect of this is that the curve representing the relationship moves out as shown. The implication of this is that both the mentions of any theme increases and the number of themes identified increases.

Another way of thinking about this relationship is that as you get more comments you start being able to see further down the ‘long tail’ of theme groups.

Combining with other data

Understanding the themes within the data is important but where it starts to really come alive is when you start looking at where the use of certain themes is particularly high or low.

We have an automated detection approach which scans through groups looking for unusual incidents of particular themes.

One example of this would be to understand which themes were unusually likely to be used in various countries or functions. Often the theme might be expected (eg finance people talking about the finance system) but in other instances it might reveal areas for further exploration. We believe that managers should be managing exceptions.

Another use is to identify which themes are closely related to each other - ie co-occurrence of themes. For example you’d want to know that if the temperature in the stores was closely related to comments about customer complaints.

If you’ve been asking open question feedback as part of a mixed question type survey you might want to build a model which links the provision of your themes with the scores on various questions. This can help demonstrate what is linked to engagement, for example. 

Finally when text is structured it can be included in any other predictive model. You might want to understand the themes that people talk about which later drives attrition for example. Our experience is that these data points are often some of the most predictive variables, and the easiest to action.


How Workometry codes answers

Our Workometry solution uses an inductive approach to coding feedback themes meaning that it can be applied to the answers of any question. We learn the themes for a specific question in a specific organization and can understand multi-language feedback. Regular clients benefit from our ability to learn organization-specific language and embed earlier knowledge in themes.

As an augmented intelligence solution, Workometry scales very well and many clients are dealing with tens, or even hundreds of thousands of comments to any question. Our key differentiator is to be able to build highly accurate, inductive models in a very short time.

Learn more 

Using Employee Voice in Open Innovation

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One of the key trends that we’ve seen with Workometry clients over the last 18 months is the increasing use of Employee Voice as a contributor to enable innovation across global organizations. Leading firms are increasingly actively involving their employees in contributing ideas and identifying issues that need addressing, effectively using the huge cumulative insight as large-scale collaboration.

To understanding this trend in more detail I interviewed Yves Zieba, a prominent advocate of open innovation here in Switzerland. I hope that you enjoy it.

 Yves Zieba

Yves Zieba

Hi Yves, could you please give me us an introduction to who you are?

I am a typical ‘slasher‘. I share my time between co-animating an open innovation space, facilitating workshops on strategic agility, design thinking or fablab experience. I am also coaching successful SMEs in the European Union Horizon 2020 program. We apply leading edge practices in facilitation with Seethrough and with business models for the open economy workshop from Without Model. It is our way to combine open innovation principles, facilitation skills on emerging HR trends or peopletech topics.

What is ‘open innovation’? How are firms using it?

 It is primarily a mindset. It is often confused with open source. There are similarities, although it is not exactly the same thing. The idea is to co-create with the right customers, partners and providers. It is the exact opposite of what is happening in closed and highly secretive R&D departments. It can take several formats. What I see most these days are idea box, hackatons, public private partnership, problem solving contest, UX beta testing communities, or corporate venturing (i.e. large group working with small startups in different partnership formats).

What is the employees role in open innovation ?

It is to adopt this open mind, this equal-to-equal relationship, and to develop their ability to co-create, to work in flexible teams co-develop modular solutions with external stakeholders in a similar way than with internal stakeholders. Most of the time the open innovation transformation is taking place at the same time as the digital transformation. So the employees role is to drive this change, or at least to understand and contribute positively to this change. To some extent, taking some risks is also part of the game. Employees can now take advantage of the emergence of new management technics and latest organisation design, such as holacracy.

When we facilitate open innovation or buisness models for the open economy workshop, I am surprised to realise how little most people know about these principles or about creative commons licences.

What have been the challenges to using employee voice in this manner ?

In open innovation as well as design thinking the prerequisite is to adopt an equal to equal relationship. I typically ask everyone to adopt a beginner ‘s mindset and to start to « think together ». In reality, it takes time as existing hierarchies continue to carry their weight, and are making use of their business stamina in many organisations. So when we are using employee voice, typically in an « idea box » context, it is crucial to have transparent criteria for selection. We can also introduce elements of collaborative and participative approach to project selection and investment decision making. We plan ahead to be clear about the rules of the game (which idea will be funded, how, what will happen to the team, who owns the IP…). Companies sometimes fail, it typically occurs when they ask their employees to contribute and then fail short when it comes to implementing great ideas.

Who would you say has done this well? What benefits did they realise ?

There is a long list of organisations who have been using open innovation extremely well and successfully for years. To name a few, Quirky, for collaborative inventions, SamsungLegoGE with their ergonomic design challenge, Philips, on crowdsourcing in the healthcare and well being industry, Baiersdorf with the Nivea campaign, Procter & Gamble Connect & Develop campaign or Mozilla toolkit are some of my favorite examples. The University of Geneva Quality of Life Lab  and the Geneva Lab togther prepare a great program with EnoLL, there should be lots of best practices being displayed at OpenLivingLabDays2018 during the week of August 23rd

These players have adopted what they like about open innovation to meet their needs.

 

Sometimes the best solutions aren’t the most sophisticated

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Last week I was at Tucana’s People Analytics World 2018 in London. I have a special affinity for this conference as I co-chaired the first three years from when a hundred or so people came together in a small space in Canary Wharf (and when the Tucana team was tiny) to today with several hundred of Europe’s top practitioners met in the QEII Conference Centre in Westminster.

Over the five years that the conference has run we’ve arguably seen three phrases in the analytics approaches presented:

  • The decision whether to do People Analytics
  • The academic – an approach to People Analytics where the objective was to run a project like academic research
  • The value where the time / effort concentrated on identifying business value.

This year was the first where I had seen value being cited as the prime driver.

I’ve written about this before. In 2016 I wrote “The greatest mistake for many in people analytics” where I discuss the need for a loss function to convert the statistical model to business value.

In the past too many presentations discussed finding statistical relevant findings but didn’t extend this analysis to the business impact of the change. At People Analytics 2018 two presentations especially made the necessary leap from this, rather academic approach to one centred on identifying value.

Michael Tocci of P&G even joked that his previous academic peers would challenge him because of his ‘less-rigourous’ approach. However, through his excellent presentation he showed how P&G analysed the value created through the global mobility program and refocused it to ensure it was being used effectively in a way which was highly mature. It was always value-led.

Pressed with a challenge, presented to investors of pulling cost out of employment costs P&G used analysis to understand how to do this in a way which minimised the long-term disruption to the organization. Make no mistake, this was not cost-cutting for the sake of cost-cutting, but an economically-led approach that ensured that mobility assignments were being used effectively, with the right people going on the right assignments to the right locations to maximise value. 

The techniques that Michael described were not what we would describe as ‘advanced’ but the clarity of thinking, and understanding of the business showed a maturity that we wouldn’t have seen even 2 years ago.

Similarly Swati Chawla of Syngenta described how they used People Analytics to analyse sales force effectiveness in APAC. Again, the analytic techniques weren’t advanced but the focus on identifying value as well as (the usual) cost enabled them to optimise on the correct variables for the business.

As with P&G, the techniques that Syngenta used aren’t ‘advanced’ but the maturity was achieved via a carefully selected and balanced set of measures. She demonstrated a strong understanding of the business trade-offs between trying to minimise cost-per-employee and balancing this with productivity.

The final characteristic that both presenters demonstrated was a strong understanding of the need to build wide support for action through a robust change management approach. It might (possibly) be easier to build a convincing case with simpler analytic techniques due to the ease of comprehension. However the focus always has to be on action, not clever analysis on a PowerPoint presentation.

As I flew back to Switzerland on Thursday evening I reflected on the lessons any CHRO could learn from these presentations.

  • Focus People Analytics efforts on the net value of the topic that is being analysed.
  • Business understanding is the most important factor, both to ensure the right thing is being optimised but also that analytics teams are able to motive leaders to change
  • Sophisticated analytics doesn’t necessarily mean sophisticated results. Pick the right tools and techniques for what you want to achieve.
  • These approaches can be used regardless of the size and complexity of the organizations. Whereas some sophisticated approaches require a decent size population to find meaningful trends, clear thinking is applicable to all
  • People Analytics, at least in some firms, feels like it is becoming mature. I hope People Analytics World 2019 shows this to be true.

AI in HR – how to understand what is happening

AI in HR – how to understand what is happening

There is a considerable buzz these days about so-called ‘AI in HR’. Most vendors are claiming to have some sort of machine-learning within their products and some are making claims that, from the perspective of someone who has been doing this for the last 15 years, seem unlikely.

Understanding what these new technologies can (and can’t) do is vital if HR is able to evaluate purchasing them, or work with their internal teams in designing, developing and deploying their own approaches. Analytical literacy is rapidly becoming a core skill of HR.

Algorithms are free

Many of the technology vendors will market their products as having some amazing, unique algorithms. In almost all instances this is unlikely.

One of the remarkable trends that we’ve seen over the last years is that the big technology companies have acquired large teams of the best data scientists and have been publishing new algorithms in journals, often open-sourcing code at the same time.

Pretty much everyone is using these algorithms – and in many instances much earlier developed ones – as the basis of what they’re doing. They will almost certainly be combining them and changing settings but at the heart we should assume the same freely available building blocks.

What is needed is great training data

In contrast to algorithms being free, data is not and as such is what differentiates decent from great analytics efforts. This matches the message that we always tell clients – to improve early analytic results there is usually a need for better data, not better algorithms. It’s why we built Workometry – to make the collection of the great-quality data as easy as possible.

In 2014 I wrote an article describing the 5 types of HR Analytics vendors. In it I described a category which I called the ‘data aggregator’. This was a firm who, by collecting vast amount of cross-firm, individual-level data were able to build valuable analytics offerings.

In 2018 pretty much every SaaS HR offering is trying this model. In many instances the data doesn’t really have enough value (there is a lot of it, but it’s not really that rich – most survey providers could be put in this category). However some vendors will find true value in this approach.

This data becomes a barrier to entry for new firms wanting to enter the industry – it’s hard and costly to acquire. It’s a good reason why many of the most innovative HR analytics start-ups are in recruitment. In recruitment far more data exists outside the firm in public data sources.

General AI is a long way off

When vendors talk about AI in their product to the lay-person they often conjure-up images of technology that has near-human levels of reasoning. Most data scientists would tell you that this reality is a long way off.

One of the interesting aspects of machine learning techniques is that it can solve some tasks that we humans might find difficult (playing chess for example) yet it might struggle with tasks that even a 4 year old could achieve easily. I suspect that we’re close to developing an autonomous van which can take a parcel from the depot to your house but it might be harder for a robot to take the parcel from the van, up the stairs, enter the building and find the correct letter box.

What today’s current approaches can do is solve certain, well defined problems, usually with lots of available data with extraordinary levels of accuracy. Often, the narrower the problem and the greater the data size to learn from, the more accurate the prediction. These narrow problems are often described as ‘Specific AI’.

Benefiting from Specific AI

Take the example of text analytics. Even within text analysis there are different firms in the HR space doing wonderful things. TextKernel has developed very good approaches to understand CVs and Job Descriptions. We, through our Workometry technology, have probably the leading approach to understanding the answers to open questions (for example in employee suggestions or feedback). We even go so far as building specific models on the organization / question level (arguably our key differentiator is how quickly we can build these models). With such specific models we can out-perform skilled humans at this task in a fraction of the time / cost.

We can think of the implication on work of AI / robots therefore not as automation taking away whole jobs – as most jobs require a variety of tasks, but of AI automating specific tasks. These will be the ones with a lot of repetition or where large volumes of data need to be acquired and synthesised.

When thinking of how to apply AI it’s important to therefore break a job down to tasks, ideally the smallest, most specific tasks possible and identify which are candidates for AI. At the same time we need to identify the value / cost of these tasks to identify which are worth developing solutions to automate.

When doing so we shouldn’t constrain ourselves to tasks that we’re currently doing. Many tasks are possible without AI, but prohibitively expensive. For many firms the sort of text coding Workometry does has been too expensive and time-consuming to perform. For many of our clients Workometry is 10x cheaper & 200x quicker than the alternative solutions and is of higher quality. What was difficult to justify therefore becomes attractive.

Benefits from AI

There are 2 key drivers of benefits from using so called ‘AI’ in HR:

  • To improve a business driver (eg productivity, customer experience) and by doing so enable the business to achieve better results
  • To reduce cost of of delivering HR.

In many instances the first is likely to provide opportunities to realise a greater return to the business, however it is also likely to require greater & more wide-spread buy in to results. Implementation costs and risks are likely to be higher with a greater number of uncertainties influencing the end deliverable.

With this type of analysis it’s highly unlikely that the data needed will be residing in one system or database. Given this we can expect fewer instances where a single system provider will have enough data-coverage to be able to build a complete model. The best work in this area will remain the preserve of data-science teams within a firm who can identify, process and join the necessary data sources into a reliable model.

Cost reduction for HR will ultimately be easier for predicted results to be achieved. In many instances there will be a smaller number of decision-makers (the HR leader) and it’s likely that cost reduction will be a core part of their objectives. Data for this type of analysis will be easily available and more likely to be of high quality / have less measurement error / to be more complete. It will also be more likely to reside in one system. In the medium term we can expect system providers to deliver such capability.

Some points getting the most out of AI for your HR team

  • A little knowledge will go a long way. Think about up-skilling your team so that they have a good understanding of where AI can be deployed in its current state and what the likely benefits are. Several providers (including us) can help here
  • Don’t expect system providers to provide complete solutions where they don’t have access to all the data. There will be a need for the foreseeable future to build good People Analytics capability
  • People Analytics technology won’t solve all your problems, but it might remove routine tasks from the People Analytics team, thereby enabling them to focus on higher-value tasks. Think of these solutions as complements to building capability, not a replacement
  • Challenge your technology vendors (especially if you’re a key client) to develop solutions that can identify cost improvements. With all the transaction data they should be identifying efficiencies. This will soon be a hygiene factor for systems providers
  • Often simple models can be built quickly. In a drive for accuracy you hit decreasing marginal returns pretty quickly. How much more valuable is this solution than what your team could build in 10 days?
  • General models, built on other firms data is unlikely to perform as well as specific models built on your data.

How to start a People Analytics project

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As one of the earliest People Analytics practices we have extensive experience of working with clients to help build great People Analytics organizations, either by helping them work through pilot projects or through our regular analytics trainings.

In most instances, to improve the quality of analysis it’s likely that you’ll need to acquire better quality data, not use better algorithms. Our Workometry product was initially built to meet the need of providing high-quality perception data to use in predictive models. Our experience is that this data is often the most valuable sources of insight and most predictive variables when model-building.

There are a few simple things to consider when starting analytics projects in HR. The most important thing is to do this in a systematic manner, not just grab the easiest-to-get dataset and start modelling.

What is your business objective? Really??

Possibly the hardest challenge of any analytics project is to accurately define what you want to analyse.

This might come as a shock but with so many things in employee management commonly used concepts are poorly defined. A good example is employee engagement – there is no common definition of engagement and therefore statistical analysis is made more difficult.

One of the ways that we recommend clarifying such topics is to add the words ‘as measure by X’ to the end. So if the project is defined as improving employee experience then the definition could be ‘to improve employee experience as measured by the employee experience rating on this survey.’ Socialising such definitions is important to ensure that all the key stakeholders agree with your definition.

Another useful technique is to use a version of a technique ‘5 Whys’. Here the purpose is to challenge the initial problem description by repeatedly asking ‘why?’ until the actual causal issue is identified. To give a simple example:

Manager: We need to understand how to reduce employee attrition?

Analyst: Why do you want to do that?

Manager: Because it is causing us a lot of unnecessary cost.

Analyst: Why is it so costly?

Manager: Because it is disruptive to our business and we have to pay recruitment costs

Analyst: Why is it disruptive?

Manager: because it takes so much management time both in recruitment and also bringing people up to speed

Analyst: Why do you think we need to focus on reducing attrition rather than reducing the management time it takes for each hire?

Manager: You’re right. We need to investigate both and understand what possibilities would yield more value from the investment.

How are you going to realise value from this analysis?

It’s important at an early stage considering what you’re going to do to implement the results of any analysis. Will changes be influencing policy change? Will you be creating a predictive model to give you an individual risk score on each employee?

The reason why this is important is that it has implications on what data you can bring into your model. As data needs to be captured for a predefined purpose then providing a personal ‘score’ has implications on what data can be used in your model. Working with anonymous data sets and at an aggregate level may enable you to do far more with the data and give you far more flexibility in your model building.

How you will realise value will also drive which types of modelling you want to do. Does your model need to be easily interpretable (needed for policy, process or training changes) or could a black-box model be sufficient. If a black-box model how are you going to determine you’re not at risk due to discrimination regulations (hint, just not adding a gender variable to your model won’t prevent your model being discriminatory).

What could be could be driving this behaviour?

The next important action is to identify a serious of explanations which could be causing / influencing what you’re studying. There are 3 main sources that we tend to use:

  1. Desk research: What has been identified by others as causes / correlations
  2. Brainstorm: Get together a group of key stakeholders to identify their view on what are the causes. This also helps socialise the problem
  3. Ask employees: Short open-question questionnaires (like Workometry) to as wide a population as you can will help you get an extensive list of possible causes. (We want to do an exploratory analysis at this stage). Our experience is that there will be a significant difference between this list and the stakeholder list.

What data do you need to test each possible explanation?

Now that you’ve identified the potential causes you need to identify the possible variables could you use in your model which would enable you to test each potential relationship. Again, this is another instance where being clear in what you are actually trying to measure it’s critical.

Some of the information that you need you will have in traditional employee systems, but it’s not likely to be enough. You may have data in other business systems but you might need to acquire new data.

Lots of data is available online from various credible data sources. Numerous governments and organizations like the UN publish great databases which can help you understand what is going on outside the organization with things such as the labour market or populations.

What new data will you need to capture?

It’s highly likely that you will need to capture new information to validate some of your ideas. In many instances you’ll have to ask people directly.

There are numerous data capture methods that you can use, however the process of how you solicit information is often at least as important as the questions you ask. You need to identify approaches which require low input from both the organization and the individuals concerned. If you will need to understand this on an ongoing basis you need to make sure it’s sustainable.

How will you measure the success of any changes?

Finally, before implementing any changes it’s important to identify how you are going to measure the impact of your changes.

It’s likely in most situations that this will have to be a hybrid approach – some measurements will need to be quantitive. Others are likely to be perception-based.

What is unfortunate is that all changes within an organization are likely to have unintended consequences. Also, given the complexity of organizations it’s unlikely that your model will be stable over time so you need to identify when the model will need reviewing.

The use of exploratory, open-text questions on a regular basis will enable you to monitor when new reasons emerge.

When to ask open vs closed questions in employee surveys

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The traditional employee survey has a selection of closed or scale-based questions. These might be asked as a statement which the respondent has to agree to – usually on a Strongly Disagree to Strongly Agree scale, or a numerical score such as the 0–10 scale of an employee Net Promoter Score question.

We believe that in the vast majority of times such closed questions are used not because they are the most effective or appropriate way of gathering information, but because they historically have been the easiest to analyse.

With a scale-based question the respondent provides a response by selecting an option from a list. With most questions they are able to select a single answer however it could be possible to select several items from a list of alternatives. Because of the way the way the surveys are administered it is easy to count the number of people who answer “Strongly agree”, “Agree” etc and calculate a percentage.

Open questions however provide text data. This is typically described as ‘unstructured’ and requires effort to transform it into ‘structured’ data before it could be analysed. Until recently the only reasonable way of doing this was for a skilled researcher to do it manually. It is a long and slow process and is subject to a number of biases and issues with consistency (both between researchers and over time).

We have now reached a stage where computers are able to perform the coding task. The cutting edge algorithms, such as the ones we use in Workometry, are able to out-perform humans at coding text responses to open questions. They’re also able to do it at a tiny fraction of the cost of doing it manually meaning that cost and time becomes less of a deciding factor between using open questions and closed questions.

When you do this you get ‘count’ data plus the option of various other elements of metadata (data about data) such as whether the comment is offensive, is an outlier (some of the most important comments are often outliers) or even something simple like word count.

The choice of which question type to use therefore depends much more on why you are asking the question and what you want to do with the answers

Objective

The first question to ask yourself is why are you collecting the data.

Scale questions are best for providing a quantitive response from all respondents about a particular topic. They are therefore best for things such as KPIs which you need to track accurately on an ongoing basis.

The other way you might use a closed scale question is to test the strength of feeling on a particular topic, for example to provide as accurate value as possible for use in a business case.

If you need to identify how strong a feeling is you should use a scale question. However if your aim is to understand what people are thinking, why they are thinking it or how it affects them (or your business) you probably want to use an open question which provides text.

Open questions provide information which enable you to understand how to act. Closed questions might indicate you might need to act but won’t tell you how to do that.

MECE

A key principle when thinking about collecting information via closed questions is that you’re ensuring that the topics or reasons are Mutually Exclusive and Completely Exhaustive. Of this I think the most important is that the categories are Completely Exhaustive. Mutually Exclusive is important but having a hierarchical structure – ie that a category can be a sub-category of another – can be useful.

In some instances having completely exhaustive categories is easy. I could ask people which is their favourite Shakespear play as the number of plays is finite and reasonably small. My list could quite easily be Completely Exhaustive.

An alternative way of thinking of categories is at the question level. With an engagement survey historically we’ve asked a set of questions that are used to create an engagement index and then a large number of questions that are used to understand which factors, or categories, correlate with engagement. You can think of all those questions – ‘is communication good?’, ‘does your manager support you?’ etc. – as covering all the categories. The reason these surveys are so long is that there are so many possible categories.

If I want to know why someone did something it is impossible to build a Completely Exhaustive list. Some closed questions on survey might have an ‘other’ choice where the respondent then writes in their answer. Alternatively there might be a single open question at the end for the user to add anything that hasn’t been asked. Really this is saying ‘we don’t have a completely exhaustive list’. Unfortunately we see that these uses of ‘other’ will provide different (lower quality) responses than if you just ask an open question.

Open questions are, by their nature, exploratory in nature. This means that when you ask them you’re open to the possibility that the answers are outside the group of categories you could initially identify. When we ask open questions in an engagement type survey we find that about 30% of categories that employees mention are ones that we’ve never seen on a commercial engagement survey. We see a difference between two companies, even in the same sector. The reasons are very personal and business specific.

Another way of thinking about closed vs open questions is that with closed questions you have to ensure you’re Completely Exhaustive before asking the questions; with open questions your answers are Completely Exhaustive automatically. This makes survey design much simpler and removes many issues with ‘validation’.

How much signal is enough?

Fortunately the topics and their frequency identified during a coding are not randomly distributed. With our clients, a typical open question will generate about 250,000 words which result in the region of 100 different themes. The most common theme might appear 10% of the time where the smaller themes might appear less than 1%.

As the data size increases two things happen: first, the number of statements where we can identify a meaningful topic increases. The first or second time the algorithm spots something could be an outlier but after a few more instances we start to have enough signal to determine that this is a meaningful topic.

The second is as you get more and more data the confidence that you can safely assign to any ‘answer’ increases. You can start to consider tracking usage of topics over time. You can start to see which parts of your employee population are far more or less likely to talk about a particular topic.

Sometimes the topics are tightly distributed. With one client we saw a few people raising issues about bad contracts. Whilst in many organizations this might be ‘noise’ in this organisation the topics were all from one group and about one contract. By highlighting this statistically the management team could investigate and de-risk the situation.

What open questions don’t do is find a quantitive score against all potential categories – they don’t allow you to understand what each person thinks about each category. Instead they identify what is ‘top of mind’.

Respondent burden

As I’ve written about before, with data and analytics you need to think about what’s in it for the employee. Surveys and questionnaires are just the same.

There are three aspects for improving the respondent experience that I think you need to consciously try to improve:

  • The technology interface of the survey tool – how easy is it to provide the answers, especially across different devices including mobile
  • How long the respondent will have to devote to providing feedback
  • Whether the respondent will be able to tell you exactly what is on their mind.

On the first of these points we did worry about whether respondents would find it difficult to respond to open questions on a mobile device or whether responses would be shorter. At the moment we’ve found little evidence (though the 500+ word answers are mostly done with a proper keyboard).

For the second, to collect the richness of data a questionnaire which is based on closed questions inevitably needs to ask at least one question for each topic. Hence we either have the traditional long surveys, or we are forced to abandon data quality to provide a shorter experience. With a 4 question open question survey we find the average time to complete is less than 5 minutes.

Finally, open questions are the only way of ensuring that all issues are captured. Ideally with closed questions you’d want to ask about each category ‘how do you rate this’ and ‘is this important to you’. For example you might ask all employees about whether they think the firm offers family-friendly policies or benefits, but if a respondent doesn’t have a family they might not care (but could rate it as true). Many surveys assume that each category is equally weighted where this assumption is highly unlikely to hold.

As previously noted, when we’ve used open questions instead of scale questions in firms we’ve found that only about 70% of the important topics were typically part of a traditional employee survey.

Conclusion

Although we’re very strong believers in the importance of open questions linked with AI-linked analysis it’s clear that the best solution is a balance between open and closed questions, using both for their strengths.

In terms of information-gathering the vast majority of questions should be through open questions as the principle aim should be to identify issues that need fixing, or ideas that will be implemented. However, it’s important to have a quantitive measurement to capture your KPI on each survey. This data is very useful not only for tracking trends, but also for analysis.

The key point is that if your aim is to take effective action you should only use closed questions where absolutely essential. After all, if you want to really understand what people think you don’t ask a series of closed questions.

Focussing on the causes not the symptoms of employee engagement

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Depending on which data you look at employee engagement has tended to be flat or declining over the last 15 years. There is obviously something wrong with the way that firms have been attempting to manage engagement within their organizations. Our view is that the core issue is that engagement typically suffers flawed analysis leading to ineffective changes.

A simple way of thinking about the way most traditional surveys are designed is by considering the questions in two groups: a set of questions that make up the engagement index and another set, usually larger, which is used to identify factors that are correlated to that engagement index.

If we consider how this works in more detail we can see that what we’re doing is looking at the differences in views between engaged and disengaged employees.

Most engagement survey analysis is static – i.e. no knowledge of each respondents’ previous states or answers are considered when conducting the analysis. Where history is included it is usually at the sub-organization (eg team, department) level where there can significant differences in the underlying respondents from survey to survey.

During the analysis stage of a survey typically variables that are most closely linked with engaged / disengaged employees are identified. Unhelpfully these are often called ‘drivers’ implying, at least to the lay-person, that they are causal. They are not, they’re simply correlations.

What these analyses do is identify symptoms of being engaged rather than what causes the employee to become engaged in the first place.

There are several reasons this matters. The first and most obvious is that any post-survey action is likely to be sub-optimal. I can take a flu-remedy which prevents my nose running and tempers my headache but I still have the flu. As soon as the medicine wears off the headache returns.

The second reasons is that our sensitivity to certain issues changes as a result of our overall view. We probably all know people who cited reasons such as ‘they left the toothpaste lid off’ when explaining why they changed partners. When love disappears these things become more grating, but overall they aren’t the reasons why love is lost.

How to fix this

To understand the issues that are linked to employees moving from an engaged to a disengaged state (or vice versa) we need to include how any individual changes over time in our model.

There are two key ways of doing this. My view is that the ‘gold standard’ is to do both:

  • We can look at the transition between engaged & disengaged at an individual-level and understand what happened to them. One way of doing this is to link employee background (education level, function, tenure etc.) and event data (eg pay rises, job changes, performance reviews) and build models which identify which combinations are linked to changing individual-level engagement. We can look at which responses change and explore whether there were underlying reasons why this might have occurred.
  • We can ask people if they feel more / less engaged (or even identify it from their history) and if they feel a change what events, experiences or factors have influenced their changes in feeling.

What we learn

When analysing data in this way we see significantly different issues that are cited as reasons for a change in engagement (or recommendation when using eNPS).

Our employee feedback tool, Workometry, uses open questions instead of traditional scale questions. It then uses highly sophisticated text analytics approaches to summarise the statement and group them into meaningful groups (with meaningful descriptions such as ‘More clarity on the future direction’). The summaries and groups the algorithms find will depend on the answers to the question asked (and often be organization-specific).

Many of the reasons cited for changes in engagement would not be part of a typical engagement survey, meaning that there is no way they’d be able to identify them, and by implication that you wouldn’t be able to act upon them.

Reasons may seem at first glance far more personal but there are a few key themes that we typically find:

  • Environmental issues are often the largest group. These will include the team dynamics, toxic colleagues and ineffective management. I’d suggest that the single most effective way of improving a disengaged employee’s engagement level is changing their role
  • Barriers to getting the job done is a significant issue, especially amongst high performers
  • Measurement and incentive related issues are common. These could include targeting at the general level or where targets are badly aligned to things that are important to employees values, such as delivering great customer experiences.
  • Various events that could all be categorised in a general ‘Employee Experience’ group can have significant changes. For individuals where we see sharp drops in engagement over two, close time periods there is usually an event which has caused the change.
  • New senior leadership and refocussed strategies can increase engagement for certain groups.

Changes to action planning

Given the reasons we see most of the action that is likely to be effective would be bottom-up rather than the typical long, top-down approach. There will be some systematic issues that will need to be addressed but in general a principle of ‘decisions / action should be made at the lowest possible level’ is probably correct.

For organizations which really want to get this right will be building teams to address more structural issues. I’d imagine such teams would have experience in areas such as process and job design, technology enablement and incentives. Such changes would need to be formally costed / valued and prioritised.

As an extension of this organizations need to have a serious debate about how to ‘close the loop’ with employee issues in a timely manner. Whilst customer feedback can trigger this easily, in organizations employee feedback is usually provided with a promise of confidentially. Technology offers opportunities here.

‘Low hanging issues’ need to be fixed quickly. We always see issues such as staff facilities that are broken or policies, designed for one situation which are inappropriate in another area. Fixing these things quickly will likely not address fundamental reasons (they’re the toothpaste-lid sort of reasons mentioned above) but rapidly taking action sends a signal to employees that feedback is being listened to and providing it is worthwhile.

Overall we believe that much of the failure to improve engagement in organizations is due to using static analysis to understand dynamic systems issues. Businesses address the causes rather than the symptoms will realise significant benefits.

HR Analytics doesn’t mean abandoning intuition

HR Analytics doesn’t mean abandoning intuition

There’s a belief, that I hear frequently in the HR analytics community, that HR Analytics means a move away from intuition. This isn’t true.

Analytics using your own data is just one tool needed to conduct empirical decision making. Doing analysis – however sophisticated – can only be part of what you need to make great decisions. ‘Numbers are just another voice at the table’ as the saying goes.

As Sam Hill mentioned in his post on this blog ‘People Analytics – It’s a mug’s game. Isn’t it?’:

‘The People Analyst will keep formal and informal channels of communication open with HR process owners, line managers, senior managers, HR Business Partners and potentially external stakeholders to measure the pulse of their organisation and to identify emerging workforce issues or opportunities.’

As HR professionals there is usually a good reason that we hold the beliefs that we do. Many of us have built up knowledge and experience over many years of seeing similar situations, reading case studies and books or speaking to peers.

Managers too have built valuable experience. Many tend to have a good knowledge about what is happening in their organisations. They will have seen similar situations or even studied organisation theory on a general business course.

Discounting this experience and knowledge would be like starting with your hands tied behind your back, but unfortunately it’s common with some HR analytics teams.

Let me illustrate this with an example.

Suppose we are asked by a friend whether a coin is fair (ie as likely to come up heads as tails). They then toss the coin 10 times and it comes up heads 7 times. The chance that this will happen is in the region of 12%. This would be unusual but certainly not impossible. Do you tell the friend it’s fair? I suspect you might.

Now let’s change the situation slightly. Let’s say your friend then tells you he was given the coin by a magician at his daughter’s birthday party. My guess is that this bit of information would make you think that the coin probably isn’t fair. Maybe you’d think you were lucky to see 3 tails.

In both instances you’re updating your view based on the information that you had before. In the first instance you start with an expectation that the coin is fair as most coins have an equal chance of coming up heads or tails. You probably have some doubt but not enough for you to switch your view built upon a lot of previous experience.

In the second instance the knowledge the coin came from a magician changes everything. Given the source you’re now comfortable declaring the coin isn’t fair. Background information makes a big difference!

Inexperienced analysts rely too much on their data. They’d look at their data only, without any context, and say there isn’t enough evidence to say the coin isn’t fair. For them the data is everything. HR Analytics without incorporating experience or intuition is like this.

Good analysts, as Sam previously mentioned, start by collecting as much evidence as they can. They’ll ask managers and HR colleagues, they’ll probably do some desk research and see what others have found. Then they’ll take their data and update their view. The more data they have the more their recommendation will be based on the data. Conversely the less data they have the stronger will be their weighting towards intuition and experience.

Analytics models are only as good as the information they have. Intuition and experience are valuable sources of information. It’s crazy to ignore them as we move to using analytics in HR.

Engagement is necessary but not sufficient

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Andrew Marritt reflects on a People Analytics Podcast episode, a fascinating conversation between Laurie Bassi and Max Blumberg about the myths of Employee Enegagement, and looks into the real impact of Employee Engagement in business performance and value. 

A large part of economics is concerned with understanding and modelling individuals’ behaviours when faced with various incentives. Whilst some believe that economists are only interested in money as an incentive, in truth incentives can take many forms.

George Akerlof co-wrote a fascinating book – with Rachel Kranton – on how identity is an incentive (Identity Economics: How our identities shape our work, wages and well-being). There is even a sub-discipline of economics called ‘Personnel Economics’ which is dedicated to understanding how organisations work.

Ten years ago, when I was part of the HR Analytics team at UBS, I was the only economist in a team of psychologists. Looking at People Analytics from the perspective of an economist forms a significant part of this podcast between Laurie Bassi and Max Blumberg.

One of the things economists spend a huge amount of time learning at university is calculus. Calculus is useful to tackle problems involving optimisation. Given we are always dealing with limited resources almost all problems in organisations are optimisation problems. I would argue that one of the key historical basis of modern people analytics in organizations comes from the discipline of operations research, like economics an applied mathematics discipline concerned with optimisation.

The reason calculus is useful is that rarely is there a straight-line relationship between a variable and its desirability. For many firms it would be beneficial to reduce employee turnover, however I’ve worked with a couple of organisations over the last 18 months where more attrition would be beneficial. There is an optimal level, past this the benefits decreases. You can have too much of a good thing.

Our organisations work as systems. There is rarely a clear, unique, optimal solution. Invest money in one area and – given limited resources – you can’t invest the same money elsewhere. Much of management is about prioritising resources to maximise returns.

This brings us onto a key point in Max & Laurie’s conversation – how much resource should one allocate to improving engagement. As the resources are limited what should we reduce spend or effort on? New production investments? Training?

In a system, many inputs depend on each other. In a dynamic system they often depend on the levels of the previous state. Rarely is it true that you provide the optimal solution for the system by optimising each individual input. Furthermore, optimisation doesn’t mean maximisation.

Unfortunately this is often the belief of many in the HR profession when considering engagement. Grand action plans are built without considering the alternative uses of the needed time or money. As Laurie so eloquently mentioned ‘Engagement is a necessary but not sufficient condition’.

Surveys should be mostly open text

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Tom H. C. Anderson of OdinText has been writing about some research that he has run comparing Likert and Open Text survey responses.

Tom used Google Surveys to ask two samples of 1500 people a question about perceptions to Donald Trump’s controversial executive order banning people from certain muslim countries. To one group he asked the question as a typical 5 point Likert Question.

 

To the second group he asked the exact same question but instead let them respond in open text.

 

As you can see the answers are remarkably similar – within 2% of each other. According to his experiment an open question would be as suitable to gauge the level of agreement as a traditional quantitive scale question.

Tom’s findings are remarkably similar to what we see when using open questions as a replacement to traditional quantitive questions. We tend to use a combination of structured and unstructured questions in employee feedback requests but by far the most important are the open text questions. Open questions can achieve most of the aims of a scale question but provide some notable advantages.

In his post, Tom later highlights the difference between the Likert and the open text where the latter provided much richer data on the long-tail of responses (he describes them as low-incidence insights). As he notes:

“While there is nothing more to be done with the Likert scale data, the unstructured question data analysis has just begun…”

Recently a client asked us why we didn’t have a sorting question available in Workometry. Our answer was that for that type of question we’d prefer to use open text.

Their proposed sorting question had 8 different choices for the employee to sort. I could show an almost identical question asked as open text by another client where we had identified just under 30 different options. Whilst we hadn’t done a controlled test like Tom did given our experience we’d expect pretty much identical results. A sorting would be right only if you want to limit the potential options to a subsection of the person’s true opinions

In a recent FT Tech Tonic Podcast with Astro Teller, the head of Alphabet’s ‘X’ lab Astro notes: (at about 14:45)

“If you are serious about learning you set up your test in a way you don’t know the answer to the question you’re asking and you’re indifferent to the answer. You have to do that or you’re not really learning you’re biasing either the test or how you read the data.”

However good your survey design is, if you’re using a structured, choice-based question you’re asking a question where you’re by design limiting what the answer could be.

Open questions on the other hand give you the option of not knowing what you’re going to learn, before you ask. In our experience, if we’re doing a question like the ‘most important’ / sorting question above it would be common to find 2 or 3 of the top 10 answers that you wouldn’t have included in a similar structured question.

The other aspect that text provides is context. A structured question might identify the strength of feeling (though the example above shows that text can do this equally well) and who holds which feeling but it can’t show why they feel it. It’s why when companies do large engagement surveys often the immediate action is to do a series of focus groups on particular topics to understand the context.

When would we recommend structured question?

Even though we believe that in most instances the right option is to ask an open question there are a limited number of occasions when a structured question might be better. We find ourselves using them:

  • when we want to report a simple metric over time, e.g. engagement or eNPS
  • when our objective is to build a model and you need a score on a target variable for each individual

In both of these instances it’s because you want to have a score on something simple and purposely constrain the answer. We might be using such a score to determine what topics those who are engaged (or disengaged) are more likely to be discussing. It’s important to note, however, that for any feedback request we might ask 2 or 3 scale questions.

Why are scale questions so popular?

If open questions hold so many advantages why are most surveys mostly scale questions?

A very big factor is that it’s easy to report and analyse scale questions. Before tools like OdinText for customer feedback or Workometry for employee feedback analysing large volumes of unstructured data was hard.

Now, however that is not the case. Given the rapid progress of text analytics I suspect we’ll start to see the gradual death of the traditional survey. If you’re serious about learning it can’t come too soon.

New ways to run engagement surveys

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A version of this post originally appeared in EFMD’s ‘Global Focus’ Magazine.

“A new market has emerged: Employee feedback apps for the corporate marketplace. These tools are powerful and disruptive, and they have the potential to redefine how we manage our organizations.” Josh Bersin, Forbes August 26 2015

The measurement of employee engagement is changing. Businesses have been measuring engagement for about 15 years, the market is currently worth USD1bn per annum yet most reports suggest engagement is trending flat if not actually decreasing. Something is obviously not working.

There are many reasons businesses are growing frustrated with current methods. Slow, expensive and resource-intensive are some of the more common ones we hear. In many businesses the only things that are now measured on an annual cycle are engagement & performance management – both run by HR. Business leaders are demanding more real-time insight.

During the same time HR has been emphasising understanding employee engagement the measurement of customer engagement and feedback has been changing remarkably. Today many firms are capturing an always-on stream of customer data from a wide variety of channels from short surveys to social media.

Often called Voice of the Customer, the emphasis has switched to continual listening, rapid resolution and bringing deep insights of customers needs into everything from product development to service provision.

Business leaders are asking why there is a disconnect. Why have customer teams adapted whilst HR has stood still? Many of the trends that we’re seeing in engagement measurement could be viewed as an application of a Voice of the Customer philosophy to employees.

At the same time that there is this shift in the demand side we’re also seeing a shift in the supply caused by technology.

The technology changes can be classified into four categories.

Automation

Technology-led automation is something that is happening across society and it should be no surprise that it’s surfacing in our area.

Firms, especially new entrants, are automating parts of the engagement measurement & analysis process that typically was done by analysts. Whilst it started with relatively simple report automation – the production of thousands of template-based pdf reports moments after a survey closed – we’re seeing the level of sophistication increasing.

Whilst this might, and should, have been utilised by the traditional firms for some time their incentive was to increase margin. Most of the new entrants are using it to radically reduce prices and complexity. The new business models are disruptive.

Real time reporting via dashboards is becoming the norm. Production of large numbers of PDFs is possible. We’re seeing a shift from the multi-page result presentation to one page infographic style reports. Ultimately there is a shift from seeing the provision of a large numbers of individual reports as complexity to seeing it as a commodity solution.

The ability to automate however can blind the user to really question whether they’re addressing the real issue, or merely creating a faster, cheaper broken process.

 

 Rethinking the end-to-end feedback process

Rethinking the end-to-end feedback process

For Workometry we took the full end-to-end feedback process back to first principles. At the beginning and end of this process there are likely to be two time consuming and expensive periods of qualitative research – designing a great survey at the beginning and running workshops to understand context regarding the issues at the end. Only by addressing these long, expensive activities can you make feedback truly agile whilst preserving the richness.

Mobile & User Interface changes

The second technology-led shift is to do with the way that employees are able to take surveys.

For several of our clients mobile has become the dominant channel for employees to take surveys. We see respondents taking surveys just before the working day, during lunchtime and even in the evening. They’re interacting on the edges of their working days and grabbing a mobile device to do it.

Consumer web technologies have changed the way we expect to interact with our devices & engagement surveys can’t escape this trend. Many of the question types we used were the same as we used on paper but digitalised. We used these methods often because they were easy to score. Digital-only surveys aren’t bound by these constraints.

Research in user interfaces is reinforcing these methods. In a world where people expect to touch, slide and scroll through long-form sites surveys have needed to adapt.

Big data technology

The majority of the new entrants are focussing on the previous two technologies. Whilst this is right for medium sized businesses, enterprise organizations typically have a set of needs that extend these simple use cases.

One shift that has occured during the last few years in a number of firms has been the building of sophisticated People Analytics capability. Firms in this position are increasingly wanting to combine and analyse employees’ demographic, behaviour and perception data to answer key, strategic business questions.

Whereas employee survey data has historically been treated as an island – analysed with the context of the perception data or a predefined limited set of demographic information – survey data is now used to give critical insight into the reasons why.

To do this type of analysis requires that the survey data can be linked on an individual basis to both an extended set of demographic data, and to behaviour data, either from HR or business systems.

Furthermore it’s often useful not just to analyse the result of one survey with the extended data set, but to also include all other survey data belonging to an individual. Such requirements quickly dictate the sizes of data processing systems.

As well as the ability to handle large data sets increasingly analysts are using non-table data structures to better answer questions. One alternative that offers great potential are so called ‘graph databases’ where data is stored in a network. Such data structures allow us to ask very different questions.

With network data we can more easily answer questions not only about the individual employees but also the relationships between the employees. We see early promise in a network perspective at looking at contagion of engagement – ie how changes in employee engagement can spread across an organization.

Network survey technology such as that produced by Australian start-up Polinode allow businesses to capture not only traditional survey questions but also ask questions about an individual’s working relationships. Alternatively it is possible to understand communication patterns through the data trail left by emails, telephone calls or participation in internal social channels.

Machine learning

The final technology trend which is starting to disrupt the survey world is the application of machine learning – the use of algorithms to search for and learn patterns in large quantities of data. Machine learning is also the basis of much so called ‘predictive analytics’.

With employee survey data we’re seeing great success with three applications of machine learning: using text analytics to make sense of vast amounts of open text answers, using pattern-spotting techniques to make probabilistic assessments of which populations are most likely to raise certain topics and finally to use survey data to answer business questions.

Historically it’s often been acknowledged that open text is the most valuable part of a survey, however it’s been very difficult and resource-consuming to deal with it at scale. Text analytics can solve this problem and therefore provide new opportunities to capture this richer form of information.

Our experience is that with these techniques we’re able to analyse open text responses in almost any language, categorise a comment against a continually evolving set of categories & score against things such as sentiment and to do so in near real time. With this capability it’s possible to radically rethink how and what data is captured.

The second use of machine learning is to identify groups most likely to be discussing certain topics. Whereas traditional surveys might show differences between one function’s scores against their peers, with machine learning it’s possible to segment the population a much more granular manner. For example you might discover that those complaining about a shortage of career development opportunities are much more likely to be women, gen ’Y’ers who are in the upper performance grades.

Finally survey data is increasingly important to answer strategically important business questions that involve the workforce. For example you might link the survey data to sales data from a CRM system to try and optimise sales performance. In some cases it’s possible to use existing survey data. In others surveys need to be used to collect new data.

So with all these opportunities where to get started? We typically advise our clients to do three things:

1) Make sure that the legal and other agreements are in place to use data in this new manner. Be open with your employees about how their data is being used and how the new approaches don’t need to mean lower levels of confidentiality

2) Pilot some approaches with new use cases or in discrete populations.

3) Consider those pilots as supplementing existing work. From our experience you might replace old approaches but there is often significant political capital invested in the established approaches.