Michael Brennan|September 8, 2021

We talked last year about why the time was right to develop a data strategy. We highlighted the fact that most organisations are not yet seeing the benefits of their data investments. Now we want to share some practical guidance on how to develop a data strategy that delivers real business value.

The essential  starting point

To be honest there is only one place to start and that is with the business strategy and priorities. Everything that we do with data must be clearly aligned with the needs of the business. Most will have short medium and long term business objectives in place – which is exactly what is needed in a data strategy.

By focusing on the business priorities the data leader will immediately need to open a dialogue with the leaders of the business. In this way we can avoid the danger of creating a Data Cult rather than a Data Culture. Too often we see  data teams and their leaders isolated from the mainstream business, regarded as outsiders, even as a threat to current employees and working practices.

To be clear. Before a single word is written about the data strategy it is vital that you have a strong understanding of the business context.

In developing this dialogue you should always be looking to bring the leadership team and CEO on-board with the possibilities enabled by a data driven approach. After all no business transformation has ever succeeded without the full support of the CEO and leadership team.

It is hard to imagine any business priorities or objectives that a data driven approach cannot support – including Finance, HR, NPD/Innovation, Sales and Marketing, and of course today’s key priority for many organisations – sustainability objectives.

In fact sustainability has been described as the new digital by the World Economic Forum – and there can be little doubt that they are the two biggest drivers of change across markets and industries today.

So, what is a Data Strategy?

A great definition of a data strategy comes from the Centre for Information Systems Research (CISR) at MIT, they could not be clearer about the relationship between data and the business –

‘A central, integrated, concept that articulates how data will enable and inspire the business strategy’

An alternative definition comes from leading management thinker Michael Porter, extending from his understanding of  business strategy (focused on unique value creation) he defines a data strategy as –

‘A question of capturing and using data,  across a system of activities, in a way that your competitors cannot’

Which many people might find a bit challenging or intimidating, but if we set it in the context of Porter’s thinking on business strategy – a question of creating unique value in a unique way – then we may be well served by interpreting it as a question of authenticity. Every organisation and business has a unique history, culture and identity. A bespoke data strategy should be an authentic data strategy – grounded firmly in the business context and priorities.

If we attempt to put the two definitions together we might end up with something like –

‘a data strategy is a central, integrated concept that  enables and inspires the business strategy in a unique and authentic way’

From which we might reflect that there is rarely anything unique about a business strategy or set of business priorities,  and that the key to differentiation in today’s global markets is the effective (and authentic) use of data, something at least partly seen in the current vogue for Customer Experience.

The Next Step

Having immersed ourselves in the business context and strategy and having held positive discussions with the CEO and leadership team, we are now in a position to think about your vision for data. Again this must be  a bespoke vision grounded in the history and reality of the individual organisation.

You should recognise that the Data Vision will become the preface to, and external face of, your data strategy. In practice your entire data strategy is likely to be judged on the credibility of this opening vision. Make it  too utopian, too radical, too abstract and you are highly likely to lose your audience and the  internal support that is vital to success.

With a draft Data Vision in place you should now engage the CEO and the leadership team with this output. Business leaders should be able to clearly recognise the value of their input, whilst appreciating the necessary translation to a data narrative. The Data Vision should encompass infrastructure, culture and (business) opportunity.

In dialogue with the business leaders you should also start to map out what success would look like in business terms and how you will measure the success of the vision and resulting strategy.

From Vision to Strategy

With a compelling data vision agreed, approved, and in the bank, we now have a clear rationale and purpose for our data strategy. For example –

“This Data Strategy provides the foundation for achieving our vision for data. It defines the relationships between data and the business context in which we operate, the outcomes we aim to achieve from successful implementation of the strategy, and the capabilities and culture we need to develop to realise these outcomes”

Leadership Checkpoint

Before we go further, and especially for a first time data strategy, it is critical that your CEO and the full Leadership team or Board is fully behind and committed to the approach. This needs to be more than lip-service, you need your leaders to walk the data walk, to change their approach where necessary, to allow data to drive decision making at all levels.

In some organisations and with some leaders this may require some difficult conversations. Many of today’s business leaders are not as au fait with statistics and data as they could be, they may feel threatened by the rise of this new way of working, and they may be embarrassed to admit that they don’t have certain skills.

Rest assured there are solutions available including through the soon-to-launch Profusion Data Academy, Leadership programme, which provides a 9 week immersion into the world of data specifically designed for business leaders.

Wrapping up

If you are able to successfully navigate the three steps outlined here; full business immersion, a compelling Data Vision, and appropriate upskilling for business leaders, you will have laid fantastic foundations for future success.

Next time we will move forward with the next steps in the development of an effective data strategy.

Natalie Cramp|August 26, 2021

First posted on Companies Digest.

Wide ranging survey into email and privacy preferences finds vast majority favour the ability to decide tracking on a ‘case-by-case’ basis.

Survey of 1,000 UK adults finds that 57% report online privacy concerns have risen in importance during the pandemic – now a major issue for 96% of respondents.

Growing awareness of online privacy controls with 67% reporting basic understanding of GDPR, 51% reading cookie notifications more closely, with 40% responding that this is new behaviour over the past three years, and 31% blocking most or all cookies (double the number from three years ago), and 86% unsubscribing from irrelevant marketing emails.

Email is still the supreme comms channel with 63% reporting it as their preferred channel – direct mail second on 14%. However, 58% say they receive too many emails.

54% report little or no concern in the use of tracking pixels in emails with 65% reporting personalised offers and recommendations, their favourite function of marketing communications. 61% report understanding of tracking pixels role in personalising content and monitoring effectiveness.

London, UK; 6th August 2021: Research by data science company Profusion has revealed that 71% of adults would prefer personal control over whether to enable tracking pixels on emails rather than the blanket ban which will come into force on Apple devices in the autumn.

The finding is in line with a general trend towards people exercising greater control over their online privacy. A significant majority of people surveyed reported an understanding of GDPR, reading cookie notifications and modifying their access according to preference and unsubscribing from emails.

The research also highlighted the complex relationship people have with privacy in email communications. When asked to rank what they disliked about emails, the number one complaint was that they were ‘too frequent’ (58%) followed by ‘not personalised’ (39%) and generic (27%). However, when asked if they would accept more generic emails to protect their privacy 63% said definitely yes. In relation to tracking pixels – 61% of respondents understood their purpose in personalisation and monitoring open rates – and 54% reported having no issue with them being used.

Profusion believes these results indicate consumers accept limited monitoring if they are in control and if it is in the context of a clearly understood value exchange. In this instance, exchanging data on email behaviour for personalised offers and content.

Worryingly for marketers, 41% respondents say they believe they have never received an immediately relevant personalised offer in an email and 48% believe they have never received dynamic content and 41% rarely or never engage with marketing emails. 67% reported a dislike for retargeting ads and 43% believe social listening is a violation of their privacy.

Natalie Cramp, CEO of data science company Profusion, said: “Our research shows that the online privacy debate is becoming more nuanced. It is not as simple as saying more privacy is always popular. What people want is greater control. They want to better choose who they give their data to if it means a better service. The tracking pixel ban showcases this position. Many consumers love personalised emails and understand the trade off in giving some data to companies to make this happen. As a result, they don’t want a blanket ban but instead want to be able to tailor access on a case by case basis.

“This preference for personalisation does not extend to online advertising – a significant majority of people are put off  by retargeting. The lesson for marketers is that context and consent are crucial elements in how you personalise your messages. Giving your customers a clear and informed choice will get the best results. This means educating them on how their data is used and showcasing the benefits. Our results also highlight not only how important email as a channel is, but how much work marketers still have to do on getting this right – perhaps if we were we wouldn’t be facing blanket bans.”

The survey was conducted between 14-16th July 2021 in association with Alligator Digital.

Mia Shukri|August 25, 2021

Driven by Data: The Podcast

Kyle Winterbottom and Natalie Cramp.

In Episode 48 of Driven by Data: The Podcast. In this episode, Kyle Winterbottom is joined by our CEO, Natalie Cramp, where they discuss the role of HR in driving a data culture within an organisation and the benefits of putting data into HR.

Michael Brennan|August 4, 2021

In light of Apple’s high profile commitment to consumer privacy (now being followed by companies including Google) and especially the announcement of the Email Privacy Protection policy and App Tracking Transparency framework, we were keen to explore where consumer attitudes and behaviours are today. Are Apple and the wider industry meaningfully addressing genuine concerns or are they rather pursuing narrow commercial advantage and indulging in Privacy Theatre? 

As such Profusion commissioned a short, nationally representative, UK consumer survey (via Alligator Digital) to ask just these questions. In exploring the topic we also benefit from the additional context provided by the results of the annual ICO consumer tracker (as introduced in the Profusion blog here). 

Data protection and personal privacy have been on the agenda for some years now. The passage of the EU General Data Protection Regulation (GDPR) is credited with playing a key role, and with driving up data protection standards globally. The latest ICO research found that GDPR was indeed playing its intended role in promoting consumer confidence in the handling of their personal data.  

Our own proprietary research showed high levels of awareness, and understanding, of GDPR, with around two thirds of respondents at least somewhat aware, and with some level of understanding of the regulations. Interestingly we found a significant generational gap in the data, with 25-34 year olds 3x as likely to say they are ‘very much’ aware of GDPR compared to those aged 65+ and the same cohort 5x as likely as the over 65s to report ‘complete’ understanding of GDPR. That divergence is at least partly explained by a significant difference between London and the regions of the UK. 

When it comes to the importance of data protection and privacy to UK individuals today we found that 96% of respondents said it was very (66%) or quite (30%) important to them. Interestingly almost 60% of respondents reported that the importance of these issues to them had increased through the pandemic – perhaps as a result of last year’s furore over the test and trace app, and media coverage of a spike in fraudulent activities over the last 18 months. 

Equally we found that 84% of respondents said they welcomed moves to ‘increase choice, control and accountability’ over the use of their personal data in digital marketing (i.e., through regulatory or policy changes). This latter question reveals another generational gap in the data with only 27% of 18-24s saying that they definitely welcome such moves, compared to 67% of those aged 65+. 

Hardcore Privacy Advocates 

Combining the responses to those two questions we can create a cohort of Hardcore Privacy Advocates – those for whom data protection and privacy are very important AND who definitely welcome moves to increase ‘choice, control and accountability’. According to our representative survey this cohort represents almost 4 in 10 of all UK adults, clearly not an insignificant constituency for marketers to be aware of. We can look at this by age group as below: 

The Privacy Paradox? 

But what does this mean in practice, or to put it another way, how do these (claimed) attitudes translate into online behaviours? You’ll be familiar with the idea of a privacy paradox – the gap between our privacy concerns and stated attitudes, and our actual online behaviours. TO what extent do we see this own our own research? 

One way of addressing that question is to see how people are responding to the cookie notices that have proliferated in response to the requirements of GDPR.  

Here we see that 46% of all UK adults will click through a cookie notice as quickly as possible, with a similar share (44%) stating that this hasn’t changed over the three years since GDPR was introduced. Of most interest when it comes to the privacy paradox is the fact that our Hardcore Privacy Advocates are no different to the general population in this regard – despite their stated attitudes. 

The apparent paradox deepens when we see that almost half of our privacy advocates strongly dislike retargeted advertisements, regarding them as creepy. This is a significantly higher share than among the general population (37%) – and yet their cookie notice behaviours are no different. 

Overall then 70% of respondents reported disliking (strongly/slightly) retargeted advertising (but note that more than 1 in 5 18-34 year olds said they really like them).  

These findings are broadly consistent with other research in the field including this recent survey commissioned by Cheetah Digital, that asked international consumers which digital advertising practices were cool and which were creepy

We also asked our respondents about their attitudes to the widespread practice of social listening, something practiced and valued by marketers for at least a decade. Here we found that only around a third of respondents had any awareness of the practice – suggesting a transparency gap – and also that 44% believe the practice to be a violation of their privacy, rising to 63% of our privacy advocates. 

As such we start to see a picture of passive anxiety emerge. Despite their significant concerns around digital practices, consumers are not willing to (take the time to) mitigate the risks to their privacy and personal data.  

This is consistent with other UK research including from the DMA, which in 2018 reported that the Data Unconcerned were the fastest growing audience segment while the Data Pragmatists were the largest segment overall. Together they accounted for 75% of the UK audience.  

Equally research from Which? The Consumers Association showed that while the Data Concerned and Data Anxious were their top two attitudinal segments, there was a significant misalignment with behaviours – such that the Anxious Maximisers were the fastest growing audience overall. 

For Robert Solove, a seminal thinker on contemporary issues of privacy, this is all perfectly rational, and therefore the privacy paradox is an illusion.  Solove’s argument rests on recognition of the futility of privacy self-management. What is required instead is to shift the onus away from the individual consumer and toward the organisations collecting our data 

And yet in our individualised age, DMA research showed that 48% of UK consumers believe they should have the ultimate responsibility for their data security – compared to 10% for government an 7% business. Which is perhaps an implicit recognition of the value exchange involved and the personal judgements that we all have to make. 

Email privacy protection 

Returning to our survey, we went on to discuss the issue of marketing emails, as directly addressed by the new Email Privacy Protection policy. 

Our first finding was that only a minority of respondents had a clear read on how many marketing emails they received, with only around 1 in 3 confident that the majority were from organisations they currently dealt with. In this space the action required to manage emails could not be simpler – hit unsubscribe when you no longer want to receive them. So are UK consumers doing that regularly? 

Here we find a relatively high level of positive action with over 80% claiming to unsubscribe ‘from emails that are no longer of interest’ at least sometimes. And the most frequent reasons for unsubscribing?  

Perhaps unsurprisingly the number one reason is the volume of marketing emails received, which goes some way to explaining the difficulty in identifying how many emails are being received (despite 60% receiving emails from less than 20 organisations).  

In fact the top two reasons for unsubscribing from marketing emails are exactly the same as the two things that UK respondents dislike most about all marketing communications – too high a frequency and too little personal relevance. 

When we asked consumers what they thought of the established practice of tracking email opens we found that a significant minority saw this as a violation 

And yet when we went on to ask to what extent respondents understood why marketers might value this information, or in what ways they would reasonably expect marketers to use this information we found a relatively high level of understanding, especially among older respondents. 

This reasonable expectation point is important as it mirrors the language of GDPR, specifically in the context of the balancing test between individual rights and an organisation’s legitimate interests. 

It is also important when we consider the nature of the value exchange that is implicit in so much of this discussion. UK consumers told us that what they most like about marketing communications are personalised offers/discounts (53%). 

When evaluating the possible impact of the Apple policy proposal, some commentators have suggested that marketers will increase the frequency of messaging and default to generic messaging. Given what we learnt above about the reasons to unsubscribe from marketing emails we were keen to hear what respondents thought of those outcomes. 

Here we can see a significant difference with a significant majority willing to accept standardised, generic, messages, if only reluctantly, while only a minority would be willing to receive a higher volume of emails. 

As such it is clear that email marketers are going to have raise their game in response to these changes. Our data makes it clear that simply doubling down on email volumes is not a sustainable approach. We would argue that marketers will have to adopt more meaningful, and less vanity, metrics while working to improve the personalisation of content and promotions. 

In this way we can drive up the share of consumers who are regularly reading our emails – our survey showed only 8% saying they read their marketing emails ‘most times’ with a further 26% reading them ‘sometimes’ and 22% ‘occasionally’ – and maximise click throughs and of course revenues. 

A final point on the Apple policy. We asked respondents if they would rather a more granular, case-by-case, approach that that being proposed by Apple. 31% said yes definitely and a further 41% said ‘yes, that sounds better’. We know that this is unlikely to slow the Apple privacy juggernaut, and also in light of Robert Solove’s argument may simply add to the consumer burden while offering little in the way of data protection. 

And to close this article on an upbeat tone, it’s important to remember that email remains consumers preferred channel for marketing communications – across all age groups. For all the frequent criticism of email marketing it offers advantages to consumers that are simply not available elsewhere. 

Thank you.

Michael Brennan|July 22, 2021

Over three years since GDPR was written into UK law as the DPA (2018) and following a year like no other, the ICO published the results of their latest consumer tracking survey last month (fieldwork conducted in May 2021).

To put things into context the number one objective for the ICO as per their 2017-2021 Information Rights Strategic Plan is to ‘increase the trust the public has in government, public bodies and the private sector in terms of how personal information is used and made available’. Drivers of public trust include increased transparency and creating a culture of accountability for the use of personal data in the digital economy and across public service delivery.

The 2020 survey results showed a significant shift to the middle ground among consumers, with a fall in the share of both high and low trust and confidence in the use of their personal data. In 2021 we see no significant change in the overall figures as shown below.

When we break the data down we can see that men are more likely to say that they have a high level of trust and confidence compared to women (33% v 22%) and also that parents are more likely to say the same than non-parents (37% v 23%).

The latter is perhaps particularly important given the role of online services in supporting children’s education through the pandemic and comes despite the many stories from the early days of lockdown of Zoom bombing incidents and the like.

The analysis also shows a higher level of trust and confidence among BAME respondents than non-BAME (34% v 27%), between urban and rural respondents (38% v 21%) and, less surprisingly, between 18-24 year olds and 55+ respondents (39% v 18%).

Overall therefore there are a number of confidence gaps to be aware of – by age, ethnicity, gender, geography and parental status – which can be important for brands and organisations to be aware of.

Unsurprisingly given the widely reported spike in scams and fraud through the pandemic the public’s key concern when it comes to their personal data is it being used for fraudulent purposes.

Conversely, the number one reason given for a having a high level of trust & confidence is the role of legislation, in 2021 this supplanted the previous number one – good previous experience – which dropped to number three in 2021 below trust (I trust them or their policies).

This a major positive for the regulatory approach and suggests that GDPR is having a positive impact on consumer confidence in the digital economy – one of the primary objectives of the regulations. That said there is a relatively small number (c10%) who have chosen to exercise any of their data rights under GDPR to date. Interestingly BAME, Parent and Urban respondents are more likely to have exercised any of their data rights than their peers – suggesting a positive correlation between the exercise of rights and high confidence in personal data processing for these audiences.

There is an important lesson for brands and organisations here. Embracing and responding positively to rights requests can be an important driver of confidence and trust in their approach to personal data processing. We should therefore be including this within the scope of our CX focus.

It is also worth being aware of which sectors the public trust most when it comes to personal data processing as shown below:

Here we can see that financial services, despite a dip in confidence between 2020-2021 continue to outperform any other business sector and are trusted more than local government for example.

This is only a brief introduction to the ICO research, we will pick up on a little more of the analysis in our August 2021 newsletter where we will introduce the results of an important piece of proprietary consumer research undertaken by Profusion in July 2021.

And to leave you with a little teaser, the ICO found that, despite all of their expressed concerns, 53% of respondents will click through a cookie notice as quickly as possible. This is consistent with our own findings and with consumers continued willingness to engage in a value exchange when it comes to personal data and online services.

Until next time.

Michael Brennan|June 7, 2021

As the UK steadily emerges from lockdown, we can see signs of our old, normal, life returning – including a notable shift in consumer spending away from ecommerce and toward stores, along with a resurgence in out-of-home leisure spending.  

The end of inertia 

As so many businesses across the UK continue to grapple with the negative impacts of the pandemic, they are also attempting to plot a course for the future, to understand where their customers are at, to think about what changes are here to stay and which will soon fade away.  

They will rapidly realise that one thing has changed forever – consumers have lost their fear of change. 

We have all had to try doing things differently. For many this will have meant trying ecommerce for the first time, for others new delivery options, for others transforming habitual shopping behaviours. For others it has been a period of reflection resulting in greater support for local businesses, or healthier, and more sustainable, consumption habits. Some will have simply got used to spending less. 

Little wonder then that Customer Experience (CX) is even more firmly in the spotlight. Those who were demanding, impatient, and hyper-critical before March 2020 are a whole lot more so now, and they have been joined by many more, newly empowered, consumers demanding the very best experience. 

Retail strategy must now be explicitly focused on the idea that every dissatisfied customer will leave. 

Enable pre-emptive action 

There are multiple implications arising from this approach. One of the most important is that we can’t afford to wait until the customer has left us before we act. Therefore we need to develop a joined up view of the customer such that every brand touchpoint, service interaction and sales transaction is captured and added to the individual profile – the single customer view. This is especially important as customers start to combine store visits with digital interactions. 

Without this holistic view of the customer our approach risks being seen as impersonal or irrelevant 

With the right data on individual customers we can then develop predictive intelligence in order to identify those with the greatest propensity to churn (i.e. your biggest risks) – allowing us to tailor and target interventions in good time. 

Predictive intelligence empowers you to devise and take action to retain your most valuable customers 

Closely related to the need to identify customers at risk of churn is the need to identify your most valuable customers, those who spend the most with you and who would be the most damaging to lose. If you can profile your most valuable customers you can then develop appropriate propositions to meet their needs. You can also use this information to support the attraction and acquisition of lookalike customers – and so increase your average revenue per customer over time. 

Maximise the impact 

With this level of customer insight, it is vital that we now maximise our chances of success by ensuring that we are communicating with our customers at the right time and with the right messages. 

Personally optimised messaging and send times can transform brand communications engagement 

There are two elements to this layer of personalisation, one analyses the propensity to purchase particular products across your database, the other seeks to send marketing communications at the optimal time for each individual, maximising the likelihood of opening, engaging and acting. 

Put together your communications will then be promoting the right products to the right audience at the right time. You will also be cementing a personal relationship with your customers who know they can trust you to only send relevant messaging and promotions to them.  

Important to note that this is a dynamic concept, our propensity to purchase different products and services changes over time as does the optimal time of day or week to receive promotional communications. It is important that we are able to spot the signals of change and respond accordingly. 

Don’t be shy 

As we explore such a data driven, personalised, approach to customer engagement and retention we will first need to examine the health and quality of our customer database. And that may be daunting! 

It has become a cliche to suggest that 80% of a data scientist’s time is spent cleaning data but there can be no doubt that data quality must be the number one priority when it comes to deploying analytics, machine learning and more. 

One lesson of the early stages of (the first) lockdown was that too many retailers didn’t hold basic demographic data on their customers, presumably sacrificing such detail on the altar of efficiency and frictionless commerce. This needs to change. Personalisation should be built on freely given personal data – not on inference alone. This is wholly consistent with the direction of personal data regulations.  

Now is a great time to, transparently, initiate customer surveys, enabling you to better target communications and customers based on the details they freely provide. 

Connect the store 

A key question many retailers will be thinking about is how best to connect the store visit to the individual customer and their historic relationship with the brand. 

It feels as though there will never be a better time to launch store check-ins, people have become familiar with the need to use the NHS app to check in to locations and this can be positioned, at least initially, as an extension of that safety first approach. 

For high value, committed, regular, customers, selling benefits of a retail check-in shouldn’t be too difficult – what’s in it for me? How about personalised pricing, automated updates on product availability, free home delivery, or more simply an informed shop assistant who knows a little about you and can provide relevant and informed guidance and advice. 

For the rest, you’ll need to develop your own value exchange with the most obvious route involving spot prizes or instant discounts based on in-store location and behaviours. For the time poor shopper there is the assurance that they can continue their journey online at a time that suits them. 

Leave no-one behind 

As we look to digitalise the store experience itself it is vital that we don’t leave anyone behind, especially those older consumers who may be less familiar with new technologies. Many will have seen the heart tugging social post about the old man in the Wetherspoons who didn’t have their app and so couldn’t order a pint. 

We can skip the stuff about his newly polished shoes, and ask instead why it took another customer to see what was going on – where were the staff to help? Why had no-one apparently given this any thought (especially given the (daytime) age profile of Wetherspoons customers)?  

Closing comments 

The bottom line is that retailers are going to need to be brave and to be prepared to test new approaches and tactics with their customers. Ideally much of the initial exploration and ideation can be developed and co-created through brand communities, surveys, workshops or similar. 

But the real magic involves the effective capture, analysis and application of customer data, starting with a single customer view that connects on and offline behaviours. And that’s where we come in! 

The Ai Marketer (AiM) from Profusion provides you with simple and effective access to a range of modules covering all the elements mentioned above; propensity to churn, customer lifetime value, propensity to buy and send time optimisation.  

With AiM plugged into your customer database you can quickly and easily identify your most valuable customers, identify those with the highest propensity to churn, understand the propensity to buy different products, and optimal communication times.  

With all of that analysis automated, you can now use your time to get creative and brainstorm new ideas to enhance customer engagement and to improve the customer experience in order to grow customer loyalty, advocacy and sales.  

Doing nothing is not an option. Your customers have lost their fear of change.  

Now you must lose yours. 

Natalie Cramp|May 4, 2021

An article by Natalie Cramp, first posted by HRZone, 29 April.

Creating more diverse and inclusive organisations is a key priority for many HR and business leaders today. But without using data science to support decision making, bias will creep in and hamper any well-intentioned efforts.

The past 12 months have been challenging for everybody, not least HR professionals. Not only has the complete disruption of office life made supporting, monitoring and assessing teams much more difficult, the Black Lives Matter protests last year have placed issues around diversity and inclusion near the top of the corporate agenda. 

Add to this the difficult economic situation that has necessitated rapid changes in hiring and retention policies as well as new furloughing and return-to-work processes, and the average HR function’s to-do list can be eye watering.

Despite this, every period of change offers an opportunity to challenge the status quo and tackle long-standing problems. In this instance, the increased prevalence of data science combined with remote working offers a way to revolutionise the HR function within most businesses. 

If applied well to a data-driven HR function, data science has the capacity to help HR professionals and line managers identify unconscious bias.

What exactly is data science?

Let’s start by drilling down into what we mean by data science. For our purposes, it’s best to think about it as the extraction of knowledge and insights from data. It is not just about looking backwards at what has happened and analysing it, it’s also about making predictions about what will happen in the future. 

The crucial factor is that data can be both structured and unstructured. Structured is the information you usually think of in the context of analysis – neatly arranged rows of numbers. In comparison, unstructured data is unorganised and often includes rows of text, videos and audio files. For HR, think about the structured data of an individual’s sales numbers contrasted with the ‘unstructured’ nature of written feedback, performances in meetings or on Zoom calls. 

In practice, this means that data science can take into account nearly every stream of relevant information to not only provide a more complete picture of what is going on but also cast a more scientific and empirical light on factors that you might have considered ‘unquantifiable’. For example, how an individual contributes to morale, their potential or personal ambitions. 

A simple example of data science in action is a recommendation engine. On sites like Netflix or Amazon your behaviour and preferences combined with demographic information is all used to generate predictions on what shows or products you will be interested in. The more you use the recommendation engine, the more data is collected and the accuracy of the algorithms that underpin it should improve.  

What does data science mean for HR?

Well, as I’ve touched upon, people professionals use a lot of structured and unstructured data to make important decisions. Each decision is intertwined and has to be set in the wider context of the company they work in. The average HR professional is actually performing a series of incredibly complex calculations. Even with the best skills, intentions and processes, mistakes are going to happen. 

The most obvious way that these mistakes have manifested themselves is in the dismal levels of diversity in UK businesses. Naturally, a lot of research has taken place to identify just what is going wrong, and one of the issues that comes up time and time again is unconscious bias.

Everyone, even with the best intentions, is being unduly influenced in their decision making in a way that can disadvantage underrepresented groups. To make matters worse, such is the nature of unconscious bias that these decisions are often self perpetuating and a direct contributor to systemic discrimination. 

What I’m advocating is not the removal of the human from HR, but rather the application of the latest tools to empower HR professionals.

So how do we break this cycle? This is where data science can play a powerful role. If applied well, to a data-driven HR function, data science has the capacity to help HR professionals and line managers identify unconscious bias by providing more scientific rigor and insights on which decisions can be made. 

Getting started on this approach does not mean tearing down your current HR processes and starting from scratch. What is required is first collecting and storing information in a systematic way that enables it to be continually and automatically updated. Then, data scientists can work with your HR team to build algorithms that will ingest this data and generate the insights that are needed to inform decisions.

For example, improving succession planning by identifying a broader pool of potential, or identifying a broader pool of top talent in an organisation to consider for progression.

Empowering, not removing, HR

It is very important to note that what I’m advocating is not the removal of the human from HR, but rather the application of the latest tools to empower HR professionals. Indeed, it is absolutely essential that the HR function works closely with data scientists to build these systems. This is because an algorithm is only as good as the data that is inputted and parameters within which it operates. This means that HR needs to ensure that the factors assessed provide a level playing field and do not actually perpetuate discrimination. 

A natural consequence of this is the need for HR practitioners to upskill and educate themselves on the basics of data analysis. This will help to create safeguards that will ensure that HR and line managers are not blindly led by the data but can verify outputs and, crucially, recognise how certain data points, or lack thereof, can inadvertently discriminate against certain groups.  

Make data science a key part of your diversity strategy

Data-driven HR will not solve all the problems linked to diversity and inclusion in the workplace. However, it will provide a whole host of new tools and techniques that will help to identify discriminatory or biased practices and ultimately enable fairer decisions to be made. 

Our current period of upheaval, where new ideas and systems are needed to help businesses adapt, presents an unparalleled opportunity for companies to take their HR function down this route. My advice to every HR professional is to actively start learning about data science so they can see how data can help them do their jobs now and in the future. 

Michael Brennan|April 26, 2021

“beyond the term artificial intelligence there are popular beliefs and fears that have long been conveyed by the film industry…. we must navigate between all of this and not stigmatise technology” 

Thierry Breton,  EU Commissioner, Internal Markets 

In the week that the EU unveiled its plans for AI regulation it was great to join the Profusion Technical Reading Group to discuss the concept of the Technological Singularity – the ambition to achieve (human level) Artificial General Intelligence, and the related prospect of a Superintelligence that far exceeds human capabilities. 

Heady stuff for a Spring Tuesday evening, as the news swelled with a remarkable display of Super Stupidity from some of European football’s biggest, and richest, clubs. Yet the two events may not be as far apart as they appear.   

The football clubs, like so many AI evangelists, demonstrated a remarkable lack of understanding or appreciation of the context and heritage surrounding their ambitions. In their financially driven and enabled hubris they mirror the excesses of Big Tech, forever justifying their behaviour with reference to historic inevitability, rather than an unprecedented level of corporate greed.  

These very same Big Tech companies have hoovered up the best and brightest brains from across the world to support their ambitions and to complement the most powerful computing architectures ever built. In the same way the self-styled Super Clubs hoover up the best talent to train in the best facilities and to play in the best stadiums. So far so capitalist you might say – a football oligopoly to match the Big Tech oligopoly. 

But one of the key questions raised in our group was the misallocation of resources to support the vaulting ambition to achieve (human like) Artificial General Intelligence, rather than using those same resources to address urgent real world problems and challenges (not the least of which is sustainability) – as someone pointed out, AI hasn’t helped much with the pandemic, has it?  

Similarly, how much good could be done with the billions promised by JP Morgan to the nascent European Super League, to support grassroots, youth and women’s football in local communities across Europe, fighting the scourge of obesity while addressing racism, misogyny and other forms of prejudice?  

A reality check 

Just as the European Super League plans faced a rapid reality check as soon as they were unveiled so now is the time for the AI industry to dial back its own hyperbole and face reality. 

Our reading group proved to be a remarkably sceptical bunch. While there is a profound intellectual and academic interest in understanding the human brain and attempting to recreate its capabilities, this is a world away from the realities of today’s practice of Artificial Intelligence. 

Key techniques deployed under the umbrella of AI include Machine Learning, Deep Learning and Reinforcement Learning (not mutually exclusive). All have made great progress in recent years, much of it driven by enhanced computing power (enabling the analysis of vast datasets).   

Reinforcement Learning, behind the development of AlphaGo Zero and the understanding of protein folding, is slightly different and potentially more exciting as it does not involve vast training datasets and has the potential to display genuine creativity (e.g. when playing Go).  

Yet, this exciting example bears little relation to the commercial realities of AI, which remain profoundly human, and subject to the many failings, foibles, prejudices and biases of human actors.  

The hyperbole surrounding AI, part of the wider tech narrative we’ve been imbibing for the last 50 years, has dulled our critical faculties to this essential truth.  

Language is of course a key part of the problem, with the use of the term Artificial intelligence directly related to a confrontational view of the relationship between human and machine intelligence. From the very earliest representations of AI in film there has always been a threat scenario, an existential danger, a fight to the death, the man (always a man) versus the machine. But why?  

And why can’t it be different? Why must we pursue our God Delusion (nice turn of phrase from our group)?  Why are we so determined to disembody intelligence, to strip it from its corporeal and environmental context? Why don’t we step back down to earth and look at how we actually live? 

Augmenting our intelligence 

Shifting our language and so our cognitive frame from Artificial Intelligence to Augmented Intelligence would be a significant step forward, it would be more honest, more modest and more realistic. It would speak to our historic use of technologies and (other) intelligence (e.g., animals), and it would immediately present a more collaborative approach to that garnered from sci-fi and film representations. 

The mundanity of much so-called AI was exemplified in our group when the humble thermostat was posited as one of the simplest technologies that matches a working definition of an AI as “any device that perceives its environment and takes actions that maximise its chances of achieving its goals” (Poole, Mackworth, Goebel, 1998). 

Thinking this was may also help us to think differently about the relationship between AI and employment. The western world seems to be obsessed with robots and AI coming to take our jobs. The situation is very different in Japan and South Korea for example.  The reality is that AI will automate tasks not jobs and we have always used technology to mitigate the dull, dangerous and dirty work that us humans would prefer not to do.  

The question we should be asking is ‘how can we invest in technology to help our people to do their jobs better?’ rather than ‘how do we invest in technology to replace people?’. 

Equally we should be celebrating the differences between human and machine intelligence, it is remarkable what machine intelligence can achieve in so many fields. Indeed, we can reasonably argue that the singularity has already been achieved in myriad vertical fields – including image recognition, medical diagnostics, natural language processing, drone flying and even flying helicopters on Mars. 

There is so much that we can and are doing with technology and machine intelligence that we should celebrate that we don’t need the distraction of worrying about a Super Intelligence. As (leading AI researcher and tech entrepreneur) Andrew Ng put it; worrying about a Super Intelligence is akin to worrying about overpopulation on Mars! 

Let a thousand algorithms bloom  

In attempting to wrap up a vast subject (and over ambitious blog post) the key message from our reading group was that we shouldn’t allow ourselves to be distracted by the search for the Holy Grail of Artificial General Intelligence, let alone the prospect of a Superintelligence. 

By contrast we should accelerate our commitment to using the latest data science techniques to address real world issues and challenges in academia, business and society. We should champion social responsibility and inclusive purpose.  

We should build on the successes we have already achieved in key fields like image recognition and language processing, we should celebrate successes in medical applications, robotics and more. 

And we need to embed all of this work much more firmly back into our real world. We need to demystify the discipline and to be humble and honest about its human nature – with all of our strengths but also all of our weaknesses. 

We need to be very clear that we are the masters, and that technology is our servant, we need to celebrate the rich diversity of humanity, and the complexity and mystery of the human brain, rather than dumbing ourselves down to meet spurious AI tests. 

Whether the fantasy is about a malevolent or a benevolent Super Intelligence, it remains just that, a fantasy, nothing is going to save us from ourselves, we are the masters of our destiny and we must take (back) control of our futures! 

Emma Woodward|April 22, 2021

Video Transcript

Hi, and welcome to a new email essentials blog with me Emma Woodward, Senior Strategy Director at Profusion and today I’m going to talk about the two most important things when it comes to creating a really good email and an overriding approach that you could take on how to achieve it.

It comes as no surprise when I say that personalisation is at the heart of creating a great customer experience, and email is a fabulous channel for delivering personalised experiences. I think you might also agree that a poor customer experience is when we make the customer do the work when there are too many options and they have to hunt and scroll to seek out that one thing, choice is demotivating.

According to many significant studies over the years, customers, when presented with fewer options were more likely to make a purchase and be happier with that purchase thereafter. So, personalisation isn’t about thinking about what we can include, a clutter of things to reduce the value to the customer, but rather what we can actually exclude less noise. Viewed this way we could approach personalisation as a really effective filter. We filter out the noise and we concentrate on enabling the data to deliver these two key things in email.

One, present only your best items at an individual customer level, of course, and recommend what’s next, again, at an individual customer level so they can continue that journey with you. If your overall quote goal is to drive lifetime value, then make it your mission to enable the data to deliver on those two key things. This is a great approach, customer experience hinges on personalisation, so it’s worth valuing it as a filter as your starting point.

If you want some guidance on how to use personalisation as an effective filter within your emails them do get in touch emmaw@profusion.com

Thanks very much and see you again next time. Take care stay safe.

Natalie Cramp|April 6, 2021

Written by Natalie Cramp

First posted on HRreview

With the dust still settling on 2020, it’s difficult to know what all the long term impacts on the business world will be. What we do know is that remote working is likely to remain widespread and, following last summer’s protest regarding equalitydiversity and inclusion will stay near the top of the corporate culture agenda.

For HR teams, these issues pose a range of complex challenges. How do you accurately monitor, assess and safeguard the wellbeing of employees who are no longer in the same office, and may not even be in the same country? Similarly, how do you devise a recruitment, retention and promotion system that ensures a level playing field and provides more opportunities to underrepresented groups? The answer could be provided by another trend that emerged in 2020: the rise in prominence of data science. 

For the uninitiated, data science is all about extracting knowledge and insights from structured and unstructured data. You may not realise it but you experience the outputs of data science everyday. The Netflix recommendation engine being a prime example. It takes complex behavioural data e.g. the types of shows you watch, how long you watch them for and runs it through an algorithm to predict what else you might be interested in. Throughout 2020 data science played a key role in predicting the spread of the coronavirus and the public’s potential response to it. It also was less illustriously involved in helping to determine A-Level and GCSE grades (more on that later). 

So what does any of this have to do with HR? Well, HR is a profession that uses a lot of structured and unstructured data to hire and assess the performance and morale of staff. A huge number of factors are taken into account, from dry statistics on productivity and qualifications to the views of colleagues and managers.

All of this information is applied and contextualised by the current business situation and commercial needs – not to mention the individual preferences, ambitions and expectations of the staff member or candidate. Perhaps without realising it, HR professionals are performing incredibly complex analysis to make decisions or recommendations. Naturally, with so many moving parts, and let’s be honest, a lot of subjectivity at play, it is difficult to always make the right and fair decision. A more data-driven approach offers a way to break this influence by creating a more objective, fair and all-encompassing approach to HR. 

Many of the above assessment factors are predicated on HR people, team members and managers interacting with people in person on a daily basis. With remote working, the HR function and line managers are essentially shorn of a huge number of data points. It is much harder to know how well someone gets on with their colleagues, presents at a meeting, adds to the culture of a company or provides useful insights if you are left to observations via Slack or Zoom. The only real answer is to gather every data point that is available and apply a consistent, subjective methodology from which these traditional factors can be predicted or inferred. In short, the insights generated by using data science techniques can provide the answer. And this is where these seemingly disparate trends of remote working and inclusion intertwine. 

Numerous studies and reports have been conducted into unconscious bias in HR and recruitment. One of the most common research pieces, which I’m sure many of you are aware of, showcases how changing BAME sounding names on a CV to those more commonly associated with white people resulted in a higher success rate.

However, bias is encountered in a huge range of forms. For example, according to the CIPD, 51 per cent of HR professionals were found to be biased against overweight women – and were unaware of it. Another form of bias is the so-called “beer test” – the tendency to like and reward people who seem in the ‘in crowd’. ‘Confirmation’, ‘affinity’, ‘similarity’ and ‘attribution’ bias, along with the ‘horn’ and ‘halo’ effects are all revealed and analysed in a huge tranche of reports into why diversity and inclusion is a challenge for HR. We do not need to delve into each type of bias to know that what unites them is the subjective, human factor of HR decision making. This is not to say that HR workers are themselves prejudiced, just that we all have unconscious bias and the structure and culture in which they work can inadvertently influence decisions, and these seem to disproportionately affect underrepresented groups. 

The commercial imperative of giving HR teams more data tools to manage a workforce, presents an opportunity to remove this unconscious bias by applying a more analytical approach to HR. This will mean creating systematic processes for collecting data points on individual workers or candidates and then using data science to create algorithms that will provide the answers needed to make decisions. Excitingly, data science isn’t restricted by numerical inputs, 360 reviews or other assessments can also be analysed and incorporated. Wider, big picture factors such as the commercial considerations of the business can be included. Not only will this make decision making fairer – it will also make them faster. 

At this point, you may baulk at the thought of removing the ‘human’ aspect from HR. However, to be clear this is not what I’m advocating at all. Indeed, HR workers and line managers need to play an essential role. This is because, although an algorithm is itself unbiased, it is only as good as the data that is inputted and parameters within which it operates. This means that HR professionals need to ensure that the factors they assess provide a level playing field and do not actually perpetuate discrimination. A very good example of this in action involved Amazon using an algorithm to both speed up recruitment and address its gender imbalance. Unfortunately, because the algorithm was trained using historic data – i.e. mostly white, male candidates who went on to perform well at Amazon – it actually overly favoured this group and continued to discriminate against women. Because Amazon had total faith in the objectivity of its algorithm it failed to adequately monitor it, resulting in the algorithm operating in this way for an inordinate amount of time. Similarly, we saw with the fiasco around exam results how taking into account factors such as the size of school classes or the location of the school, inadvertently discriminated against students from poorer backgrounds. 

Therefore, for a truly data-driven HR function to work, HR professionals need to be at its heart, working hand-in-hand with data scientists to design it. Naturally, this does mean that they need to upskill and educate themselves on the basics of data analysis. This will help to create guardrails that will ensure that HR workers do not have to rely on black box solutions, can question and verify outputs, and crucially recognise how certain data points can inadvertently discriminate against certain groups.  

Whether wholesale data-driven HR will provide the silver bullet that tackles the inclusion crisis remains to be seen. What we do know is that it has the capacity to provide HR professionals with a raft of new tools to provide fairer assessment in recruiting, monitoring, developing and supporting workers. With the workplace in one of its greatest periods of transition in living memory, now is the perfect time to embrace innovation to build a more equal and hopefully happier world of work.