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. 

Michael Brennan|March 19, 2021

Last week’s Profusion webinar – Buy before you Try – featuring Profusion CEO, Natalie Cramp, in conversation with Majestic Wine, L’Oreal and Red Ant Retail Solutions, highlighted a number of the retail challenges and opportunities to emerge from the last 12 months. 

Of course, what we’d all like to know is how consumers are going to behave post lockdown. Is the Bank of England right to highlight a potential boom in ‘revenge’ spending? Are we ready to go back into retail stores? Are we happy with our ecommerce experience? Which behaviour changes are likely to stay, and which will be dropped (asap)? 

The ideal, as in so many fields, is to take the best of both worlds (the physical and the virtual) into the retail future, improving our quality of life, our customer experiences, and our well-being.  

Already it is fascinating to see some of the bets being placed by our leading retailers, with Marks & Spencer planning the demolition of their flagship 90-year-old Marble Arch store – which once made more money per square foot than any other shop in the world – while John Lewis is well advanced with store closures and property repurposing, including its own flagship Oxford St store, along with a pivot toward a more local proposition. 

Slightly ironic that these and other redevelopment plans are focused on converting retail space into office space – at exactly the time that the future of the office is being challenged as never before. The future shape of our city centres will be hugely influenced by the shape of demand for office space. 

Interestingly here we are already seeing Ipswich using the 15 minute city concept as the springboard for a new vision for their City – focused on residential and leisure rather than office or retail space.  

The pandemic has been a boon for local high streets relative to City Centres, with surveys showing that remote working has driven increased local engagement, enhanced community dynamics and renewed interest in supporting local, independent, retailers. 

The growth of ecommerce through the pandemic was driven by necessity and has had a profound impact on the grocery market especially. These changes are unlikely to be dramatically reversed. There is little pleasure to be had in doing the weekly shop, many of us are only too happy to shop the essentials online and will continue to do so. 

For ‘non-essential’ (as we’ve come to know it) retail the picture will be a little different, there is far more potential for a positive, enjoyable, customer experience in discretionary areas of spend, with surveys showing that a return to physical clothes shopping is most eagerly anticipated by consumers. 

What is certain is that consumers have, amid the trauma and disruption of the pandemic, proven (to themselves) the benefits of changing behaviours and habits. No longer will businesses be able to rely on inertia to retain customers, we all know we can take our business elsewhere and we are willing to try new ways of doing things. Customer Experience has never been more critical to success. 

As such and as discussed in our webinar it will be fascinating to see the extent to which new ecommerce audiences bring their appetite for digital interactivity into physical retail stores. How can we best connect the virtual and the physical environment to deliver truly personal omnichannel retail propositions? 

Can we integrate this with the demand for safe shopping, encouraging people to book appointments and consultations in advance – for example completing a beauty diagnostic online and then visiting a store for advice and treatment? 

Have we now reached the stage where we can transparently recommend that customers check-in on arrival at a store? Have consumers now seen enough of the advantages of a personalised service experience to sacrifice the perceived advantages of anonymity and privacy? 

An interesting area of discussion in the webinar was the impact of new audiences on the digital service proposition. For example, the ecommerce audience is significantly older than it was prior to the pandemic, and older users have different preferences and priorities compared to younger consumers. 

In virtual service for example, plain text live chat is seen to be particularly effective for older audiences compared to the more sophisticated interfaces that younger audiences may prefer. 

Self service is another interesting area to consider. Amazon have opened their first self-service store in the UK. The Ealing store allows you to grab, go and pay later with no need for any human interaction. At the same time critics of the John Lewis’ turnaround plans lampooned their ambition as creating the ‘Argos of the middle classes’. 

The tension between a high-quality in-store service experience, for which John Lewis was once rightly lauded, and the demand for speed and convenience exemplified by Amazon Go, lies at the heart of the challenge facing retailers today. It is probably fair to say that most people want both at different times and in different categories – and retailers must cater to those diverse demands. 

The bigger and more important aspect of the Amazon Go innovation, along with the Amazon purchase of Whole Foods is how this signals a movement by ecommerce leaders into physical retail. For too long we have assumed a one-way journey – from stores to online, now we are seeing leading brands recognise the power (and market share) of physical retail. 

Niche retailers will find it easier to define a position in the new retail ecosystem while mass market brands will need to segment their audiences effectively and provide solutions for their distinct needs. In this respect John Lewis is going in the right direction by diversifying their offer – the question is whether they have called the end of the department store too soon? 

In the UK shopping has long been core to our leisure repertoires, that is unlikely to change any time soon, but retailers need to step-up their focus on Customer Experience, always asking how new initiatives provide value for time, go beyond what is available online, how we engage customers, how we connect their virtual and physical interactions, how we entertain, surprise and delight. 

With thousands of stores closing across the UK this is a watershed moment for retail. It is an opportunity to emerge stronger and fitter for the challenges ahead. That will take imagination and commitment. Starting with an acceptance that the world has changed – and so must we.  

Diego Cordero|March 4, 2021

Video Transcript

Get the code here:

Hi, welcome back to the Corona Learning Series. My name is Diego Cordero, and I’m the BI lead and Data Strategist at Profusion. Today we’ll be looking at how to create a currency conversion switch using Sisense BloX, and some other Sisense features.


So, here’s what you’ll need, a data model with a currency table, which I’ll show you in a bit. And mine, in this case, is split by dates. You’ll need to add the currency rate to the formulas on the widgets that you want to change with the switch. You also need a filter for unique currency name column, so I’ll show you that in a bit as well. You’ll also need the BloX switch template that will be provided along with this tutorial, and the custom JavaScript code on switches script editor, these will be provided as well. Now I will show you how to edit for your use case. So, let’s jump to the demo now.


Here’s our welcome metrics dashboard with a currency converter switch. It displays sales data for avocados in the US, and it has three different types of avocados, small, large and extra large hass, and you can see here that we have split it by region, type, and date. But this is in US dollars. If I wanted to present this to our CEO, Natalie and convince her if it’s worth investing in avocados it needs to be in British Pounds, so I’ve added here a Great British Pound switch. And when we click it, all the prices, for sales are in British Pounds.


How do we achieve this? First of all, let’s look at the data model. Here we have our currency table, which is the most important one. These dates are covering the dates that are arranged here, which is early 2015, to early 2018, and we have four columns, the date, rate, current base currency, and the conversion currency. It is important to mention that the dates that you see here are duplicated. Again, after the Great British pounds entry, we have the US dollar. So, if I filter here for US dollars, we have the dates again. So basically, we have two sets of dates, with one for each currency. And you can see that the rate here is one because we’re using this as a multiplier in the formulas. The rest of the columns are a standard table that you would use in Sisense. So, this is our avocado table with all the data from the avocados, this another custom table that I created from this table just displayed by type and this is a unique date from this table, which I’m using as a filter. These are all optional and will probably depend on your use case, but it’s important to have your currency rates stable here. This was created in a Google spreadsheet, there is a formula out there that you can use to create these conversions and you can set a range date, and it will create those rates automatically for you.


So, let’s go back to our dashboard where we have our switch. By default, it is in US dollars and now we’re going to see how we’ve added these to our formulas here in our widgets. So we take, for example, our indicator, we have the average of the average price, I will never recommend to average and average. But in this case, we don’t have these already aggregated, the number probably is wrong, but just for the sake of it we’re averaging the average and we need to multiply that by the rate.

Now, in this case, I’m using a max rate and not the sum or the average, it would be the same to use the average but I’m using the max because here we have duplicated dates, as we have many dates, which basically make the right multiple by itself, or as many times as the dates appear. So, if we have one here for dollars in the database three times you will see by times three. So, we don’t want that, we just want the max and it’s worth mentioning that in my data model, all these numbers are filtered off with the date filter and the currency builder, I get the same number many times, so I can get a max, I can get an average and I can get the mean, because they’re all the same number, but not the sub, because it will just sum all of them. If I take this out, you can see that it’s the same number because it’s multiplying it by one, but I’m just going to cancel and not save that. That’s the same case for all of my charts, here, I’ve added the same rate to my formulas, and it’s the same for these two charts.


Now we’ve seen the model and how to add the currency rate to the formulas, let’s talk about the filter. So, in the filter, here, we can see, the unique names, this column here, which has great British pounds and US dollars, is the filter that will be affected by our switch. We have also added the date filter, but that’s just for my use case and this will only depend on if you have a date as well. So, now let’s see the BloX template.


In our BloX template, we have one text BloX, which is the title and is optional, and then we have a container that has a column set with three columns, one for the US dollar title, one for British Pound title, and the middle column is our switch. Now, this text BloX is actually the container of the button, the one that moves, and the text within the text BloX is the actual switch that moves. So, you can see here the background colour is grey, and the height and the width correspond to that of the background, as well as the radius border, (radius of 10) which makes it rounder. So, if you wanted a square one, you could also do that, but in my case, I want a round switch. Now the text within, it’s an HTML that creates the circle which has a box-shadow, and a gradient, it has different styles to make it look better, but a very important bit here is the transition, which is what makes it move smoothly, so that’s the style for the switch, but we have more style here at the top, and this is so that when I hover over it, it changes colour. The other bit is for when I add a class active via JavaScript, so it changes the colour to green, and it also moves the circle to the right side. So, that’s the BloX template, and now we’re going to see the JavaScript behind this.


If we click ‘edit script’, we can see that it’s this function that gets triggered whenever I click the switch. So that is the ID switch, this is something important to add into your template because it triggers the function, and what it does, if we break it down here, is it toggles the class active in the switch and the switch button so in both elements, it adds a new class here that’s active like that and it adds the class active here as well. So in the end, it looks like this whenever you click it.


So, what the script does, is toggle the class active in the switch and the switch button, and then runs an if statement that checks whether the switch button is active or not. If it is, it goes through the filters in our dashboard, so as I showed you, these ones here, it goes through those filters, it finds the one corresponding to our column called current conversion currency in this case, and this is where you would change the name of the column and it changes the filter to GBP. It selects this one programmatically and then updates the filter with this function here, and this is also where you could change your desired currency. If you have euros or you have any other currency, you can change it there. Now, what it does, is it goes through all the widgets and checks if they have a title and because I was adding a title to those widgets that I want to be changed, it’s worth mentioning that, although you don’t see the title here, it does have a title, average value, and what I did was choose to hide the title bar, so you don’t see the title, but it does have it so the code actually gets it. You could add another function here so that the title is showing, it contains code to be converted, or if it contains price, then go through those widgets as well. You can also choose not to but I would encourage you to try to filter the widgets down because it makes the script a bit more efficient.


So, after getting those widgets that do have a title, it goes through the metadata of the widget, so what it does is check the panels here, so for each panel, it checks that the name has values, so it gets this here. Or values in some cases is just called value, it’s just being more robust. So, it’s either value or values. Then it checks if it’s undefined, so it’s checking that it actually has a value, and when finding the values, it goes through the values and checks if they have a mask of currency. The mask is this one that you see here, in this case, this one has and what it does is changes that mask to pounds in this case, and at the end, it just refreshes the widget. So, what we’re doing with the JavaScript is changing the filter, changing the symbol in our chart, and refreshing the widget. If it’s not active, what it does is the same thing, but with US dollars. So, it looks very complicated, but it’s not actually that complex. But these will be provided to you along with this tutorial.


So, that is how we create a custom currency converter switch. Thank you for watching and I’m looking forward to seeing all the use cases and how you use the template and the custom script, hopefully, you enjoyed the tutorial and thank you!

Admin Profusion|March 3, 2021

First published by Open Banking Reporting

OBR, a data-driven risk management fintech, has teamed up with data science company Profusion and AI-driven platform for infusing analytics everywhere Sisense to launch a new platform to help lenders provide greater support to SMEs.The platform, called OpenRep, uses Open Banking Data and predictive modelling to provide financial institutions with a much more detailed, near real-time assessment of the viability and future growth of a business. As OpenRep enables lenders to go beyond basic financial performance it will be invaluable help for viable SMEs, which have been hit hard by the Pandemic, to secure funding.

OpenRep provides visibility on a range of factors including a company’s client base by industry and geography, P&L, supply chain, balance sheet, targets and performance, liquidity and cash flow management. Using data science the platform can predict future cash flow, performance and monitor for fraud.

By automating the collection and analysis of data, OpenRep will allow lenders to make more timely decisions, while at the same time reducing their risk. OpenRep uses Sisense’s data visualisation tools to enable all the information on an SME to be presented in a clear and accessible dashboard. Profusion, whose clients include HSBC, first direct and Coty, worked with OBR to integrate Sisense’s platform and build OpenRep’s predictive models.

Eddie Curran, CEO of Open Banking Reporting said. “We are very proud of our flagship product OpenRep. By linking real time financial data with advanced analytics and robotic automation we have developed an early-warning system that can help SME owners and Lenders identify risks and opportunities. As we enter a period of economic uncertainty it is even more important that SMEs and Lenders access not just the most up-to-date data but the tools and technology to turn it into actionable insight.

“OpenRep helps business owners achieve their strategic goals and allows Lenders to provide a more proactive and personalised service.”

Natalie Cramp, CEO of Profusion, said: “Combining the data generated through Open Banking with the power of data science opens the door to a host of potentially game-changing new products and services.

“OpenRep is a brilliant example of how deeper analysis and predictive modelling can help lenders make better and faster decisions. This should really help SMEs because it takes into account their real world, individual circumstances alongside their future potential. With such serious economic volatility, it’s more important than ever that every business gets a fair opportunity when applying for a loan.”

Shelly Landsmann, VP of Cloud Alliances for Sisense, said: “We’re committed to helping businesses of all sizes infuse analytics everywhere, whether that is to drive more personalized value to their customers, optimize their businesses or innovate new products and revenue streams. In the case of OpenRep, banks, not only the large FSIs but the SMEs, can now service existing customers better, identify opps and risk, make better decisions, and then ultimately service more people. It has been exciting teaming up with Profusion and OBR to unlock the value of actionable intelligence.”

OBR, in partnership with Profusion and Sisense, will release OpenRep™ in Q2 of 2021 via a limited pilot scheme with selected Lenders and SMEs.

samuel obafemi|February 22, 2021

2020 has seen immeasurable business disruption, defining a new economic era, and forcing organisations to adopt digital transformation rapidly in order to survive. In a matter of days, companies scurried to implement new systems and tools to adjust to the new normal, and online businesses instantly needed to create compelling and effective customer experience across digital channels.

If we fast forward to 2021, focusing our attention on technology, we can start to see the power dynamic balance shifting to the workforce rather than the employer. Technology is the empowering force to the new social-economic destiny powered by the development of technologies including Automation and Artificial Intelligence. The shift in normal behaviours such as, early morning gym sessions before work and boarding a packed train twice a day is the story of yesterday, and we now can use some of that energy to do what we choose with an opportunity to spend time focusing on personal interests and projects. The parallel between social and technological productivity is one that may propel through 2021.

Organisations are developing their current technological offering, investing and acquiring new capabilities to become more resilient. The advancements are increasing in most cases the workforce productivity, benefiting the employee immensely. The widespread adoption of technology across a number of business verticals may aim to avoid being burned out at the keyboard, but is this conducive to long term human interaction with technology? This being said, legacy business models are not out the window, and the new economic revolution will for sure still incorporate the story of yesterday.

The shift in the adoption will come with its effects, and underpinning digital transformation is the powerful digital technologies which include some renowned cloud computing arms such as Amazon, AWS, Google, and Microsoft. It’s no secret with a small group of companies operating in trillion-dollar markets we could start to question the diversification of software. In connecting disparate workforces, partners, suppliers, and customers there will be an acceleration of software-as-a-service offerings and with the development of advanced applications, we may see a monopoly with these solutions as they are a major component of digital transformation. Technology is changing the landscape however it’s not apparently clear yet if it will fully migrate from the cloud from on-premises, but a shape is being carved to a hybrid model.

2021 will open up collaboration with workplace technologies across teams and customers, reorganising organisations and defining new business processes. The scale at which we manage complex data in 2021 will force automation and AI to be smarter, faster, and more powerful. The acceleration has created a trend for smaller and medium-sized business to make use of intelligent platforms that operate a software-as-a-service rather than reliant solely on IT departments. This will demand a change mindset with an “always on” mentality, as we pivot workforce practices to run more efficiently.

Michael Brennan|February 17, 2021

Amid the gloom surrounding UK retail we’re all aware that there have been winners and losers; that some retail categories have grown through the last 12 months while others have seen revenues plummet in the face of store closures and lifestyles put on hold. 

Cycling is one of the growth categories, aided and abetted by an unprecedented explosion in cycle lanes, low emission zones, and government encouragement to avoid public transport wherever possible. Cycling and walking are seen as critical to the future of urban mobility, with ebikes especially seen as a game changer in terms of the audiences they can attract to cycling – and the commuting distances they enable. 

My local store has had a good pandemic experience and appears to be riding the crest of the wave with new store openings expanding their profile, reach, customer base, and growth potential. And yet as a long-standing, relatively high value, customer I’m left feeling like I’ve lost some love in my life.  

More accurately I was left feeling that all my custom counted for nothing when I needed a quick brake repair in advance of a long-distance journey. I got the job done by another shop where I have a far less valuable history. 

Discussing a recent communications failure relating to a bike on order (first Covid then Brexit, still no bike), I tried to articulate my feelings, explaining that I really do sympathise with the difficulty of scaling a highly personal proposition, but also that data and systems are central to the way forward. 

In response, it was clear that my argument was lost amid the recognition that personnel changes (good staff going to support new openings) meant that they are slower to recognise local, regular, customers than they may have been in the past, but that in time the new staff would be able to offer the same quality of service (so bear with us). 

A couple of days after that unsatisfactory conversation I saw that Econsultancy had published a guide to CRM. The resource includes the following lines within the introduction: 

as your business [grows], it becomes a lot more difficult and less practical to try and memorise all the individual details about each customer. There are simply too many of them, and you can’t always have a direct and personal relationship with each . So, either you simply lose those details and that nuance in your interactions with each customer – or you develop some kind of centralised system for managing those relationships. This is CRM – customer relationship management 

And that strikes me as the perfect starting point for building a data led business. What could be more important and vital to your future than being able to provide the highest quality customer service across every channel and touchpoint, maximising the amount of repeat business you generate and wrapping services around your product proposition? 

In addressing this starting point, you will rapidly realise that you need visibility and integration across your customer service, sales, and marketing functions (at the very least) with each feeding into your single view of the customer. As the Econsultancy piece suggests this really is Retail 101 today. 

By way of simple example, my own cycle retailer has an attractive weekly newsletter, that suffers from a total absence of any personalisation – not even a personal salutation, let alone any relationship with my recent purchase history or products owned. Even more basic is the failure to clean the database such that I receive the newsletter to two separate email addresses and had multiple store records.  

Resolving these issues doesn’t require huge investment or skills, it should be at the forefront of any retailers’ mind in 2021, particularly in those sectors where you’ve benefited from an influx of new customers as a result of the pandemic. 

Many of these people may be new to cycling (or your category), not just to your shop – how are you going to keep them engaged with you, how are you going to make sure their next service is through you, or their next children’s bike comes from you, or that they recommend you to their friends and family? 

You have no control without a CRM approach, and as a result, while you may be winning in the immediate short-term, I suspect you may lose a lot more than you gained over the medium term. 

To make CRM the first piece in your data business jigsaw, speak to Profusion today…. 

 Contact us: hello@profusion.com