Hyper-personalisation is a proven way to achieve customer centricity, but the choice of methods to achieve it can be overwhelming.

Customer Data Platforms can feel like an easy solution, but many organisations struggle to deploy them effectively. An alternative option is using bespoke AI tools.

Which one is right for you depends on what you want and need from hyper-personalisation.

No customer-centric business or digital marketeer is immune to the allure of hyper-personalisation. The benefits it can bring are tempting, but the route to effective deployment is littered with pitfalls.

One such pitfall is the rapid proliferation of marketing technology tools that promise to achieve hyper-personalisation. The cost and benefits of these tools have to be weighed up alongside the growing limitations on access to consumer data due to increasingly stringent privacy regulations, web-browser and in-app restrictions, consumer actions, and the walled and closed ecosystems of some ad tech platforms.

This doesn’t mean that companies should give up on looking to understand their customers. The challenge resides in how to make it happen.

The true value of hyper-personalisation comes from a better understanding of where a differentiator offering becomes an advantage over competitors. This means having a clear purpose for it. Are you personalising product features? Are you choosing to create personalised marketing communications and sales offers? Are your retention offers based on individual customer needs? All of the above?

Once you’re clear about what you want to achieve with hyper-personalisation, you can plan an effective route to success.

Customer Data Platforms are well known to be great tools for driving personalisation, but they are not the only viable option.

Simply put, CDPs are software tools that take data from many sources, organise it and send it somewhere to be put to use. Data sources are primarily first party and can include websites, mobile apps, and email platforms – anything from the buying cycle that helps to offer a single view of your customer’s interactions with your products, services and marketing activity.

The data stored in a CDP can help you to predict the optimal next move for individual customers and customer groups. This enables the creation of innovative use cases at scale to, for example, re-target a group of customers who are close to conversion or orchestrate a single, personalised customer journey across multiple channels or devices. 

Choosing the right CDP isn’t straightforward and the market is growing fast: as of July 2022, there were 161 vendors in the market(1). They come in all shapes and flavours so having the right framework for determining requirements and evaluating vendors is essential, but not simple. 

Once past the choice hurdle, getting the best value from your CDP investment will be affected by two things often overlooked: the quality and availability of your data and whether you have the ability to deploy and embed the solution. Getting the multiplicity of technical and business stakeholders aligned behind the idea and working together to deliver a new solution is an unenviable task. 

CDPs rely on first-party data for things such as identity resolution and personalisation, and this comes with significant privacy and compliance requirements. CDP programmes must consider user consent, what the data is being used for, how long it is retained for and so forth. Robust data governance and security protocols are essential in successfully deploying a CDP.

Delivered effectively, CDPs can be massively valuable. However, there is an alternative route…

(1) Source: CDP Institute, accessed in July 22, https://cdp.com/articles/basics/customer-data-platform-market-size/

Another route to hyper-personalisation is to start small and focus on select outcomes and rapid time-to-value with a targeted application or use case.

Some organisations are leveraging bespoke AI tools to hyper-personalise critical steps of the customer journey. The decision to do this starts with understanding the way your business creates value. You can then set about exploring the most strategic challenges and opportunities across the value chain and how AI and data could be leveraged in each case. This leads to a collection of AI use cases aimed at targeted interventions, allowing the business to prioritise and focus on what is most critical first, then leveraging those capabilities and learnings in the following use cases. 

Even though these bespoke AI solutions can break straight into uncharted territory, their experimental nature means they can be riskier and not always as scalable as off-the-shelf solutions. 

As with CDPs, AI models rely heavily on data, so the same privacy considerations apply. In this field, federated learning is emerging as an exciting privacy-preserving way of implementing smart and personalised solutions to customers.

In reality, CDPs and AI use cases are not mutually exclusive. They tap into personalisation opportunities from slightly different – and sometimes complementary – angles. Either one can reap huge benefits when applied to the right business problem or opportunity. 

On the one hand, a CDP enables experimentation and personalised interventions at scale which is difficult to replicate with bespoke AI (or Machine Learning) applications. On the other, the simplicity of solving technology problems in an agile fashion is a winning recipe, especially when off the shelf market solutions don't fit.

Hyper-personalisation is a proven way to achieve customer centricity, but the choice of methods to achieve it can be overwhelming

Hyper-personalisation: what, how and why?

Our client had a familiar problem, but wanted and needed an atypical solution. Arca Blanca helped them implement improved cost controls at the same time as increasing staff engagement and satisfaction.

Revenues were rising, but costs were rising a lot faster. Inflation was a factor, but other things were contributing. And with each branch manager empowered, the autonomy meant internal data didn’t support like-for-like comparison for informed, centralised decision-making.

Empowering people, not disempowering staff

The traditional options were unattractive: they’d crash staff morale and hurt long-term prospects. 

Standardising the local operating models would disempower employees, and likely wouldn’t make sense in local market conditions. Centrally imposed cost cuts would alienate branch managers. And the sale of underperforming branches would hand market share to well-capitalised competitors.

Together, we chose the option of providing local decision-makers with quality insight. They had the context, but lacked the detail to understand which decisions to make. So, by supporting these key people to make and own the necessary choices, this would drive results and build engagement. 

Delivering results at speed required an innovative use of data. Reforming in-house data would take time that wasn’t available. So instead we supplemented internal data with several publicly available external sources. This new ‘bespoke big data set’ allowed us to:

Understand branch performance relative to local market conditions: including competitor footprint, population demographics, and internal factors such as staffing model. Statistically relevant clustering allowed new insights across the existing portfolio – unlocking new choices in stocking and targeted customer offers.

Make sense of internal data: by providing 'outside in' context, we could quickly show which parts of internal data made sense and which related to data quality problems. This didn’t fix internal data, but meant we could start to rely on key elements. It also demonstrated why data was valuable: helping local teams value data entry and process software. This has started a 'virtuous circle' towards improved data quality, without big central investment.

Put power back in the hands of those needing to act: simple, intuitive insights delivered to branch and regional management helped people make local decisions. This worked with the culture of delegation and empowerment. 

Cost is under better control. Teams are much clearer on what they need to do, and why. People across the company are better aligned, pulling together across functions and silos to deliver results. A new vocabulary has emerged: much deeper insight over local market conditions and customer needs. 

This is helping the company to become more data-led: thinking through new operational improvements, growth opportunities, and (at senior level) working out how to increase the company’s valuation as part of exit planning with their private equity owner.

And this provides a unique big data set to power the company’s first use of Artificial Intelligence – using the dataset to segment customers and forecast pricing.

Building a company’s future through innovative cost control

We are excited to announce that this month marks the start of our year-long partnership with the British Neuroscience Association (BNA).  During this time, we will be hosting events, supporting knowledge sharing opportunities and working collaboratively to provide insights between industry and research.  

We are kicking off this new partnership by co-organising an event discussing ‘Careers for Neuroscientists in Artificial Intelligence’ on 17th June. If you would like to be part of the conversation, you can get your tickets here.

Arca Blanca is an integrated AI and consulting company. We help resolve complex growth and profitability challenges using the transformative power of data, experienced consulting and industry teams, and creative business design. We are trusted partners to market-leading firms across a range of sectors. Our results set us apart, with clients and their stakeholders realising measurable growth, profitability and value-creation.

The BNA is the largest UK organisation representing and promoting neuroscience and neuroscientists. The BNA now has around 2,000 members and looks forward to the next 50 years being even more transformative and exciting than the first. 

Lavinia Pendleton - lavinia.pendleton@arcablanca.com

This month, Arca Blanca begins a year-long partnership with the British Neuroscience Association (BNA).

Arca Blanca begins new partnership with the British Neuroscience Association  

We are thrilled to announce that Arca Blanca has once again been shortlisted in two categories for the prestigious 2022 Management Consultancy Association (MCA) Awards. The MCA Awards recognise excellence in consulting, and these nominations are a testament to the exceptional results our teams have delivered with our clients over the past year. 

We have been shortlisted for the Change and Transformation Project in the Private Sector award for our work with Landsec plc and the Strategy award for our work with Avast. This builds on our strong performance at the 2021 and 2020 MCA Awards, where we were nominated and highly commended in multiple award categories. 

In the coming weeks, we will be releasing more information about each of these projects through a series of articles. The award winners will be announced during a ceremony on 14th November 2022.

The VP of Customer Success, Retention & Loyalty from Avast said...

About the Management Consultancies Association (MCA)

The MCA is the representative body for the UK’s leading management consulting firms. For over 60 years, the MCA has been the voice of the consulting industry, promoting the value of consulting to business, the public sector, media commentators and the general public. The MCA’s mission is to promote the value of management consultancy for the economy and society as a whole.

Arca Blanca nominated for MCA Awards 2022

Limited data is better than no data when it comes to business decision making. Arca Blanca data scientists have found useful relationships between the UK labour market and performance of workforce reskilling programmes from only 14 existing datapoints. The benefit is 53% better estimation of performance compared to alternatives, enabling accurate assessment of value for money of these programmes by business decision-makers.

The benefits of using data to support business decisions are now generally well understood across most industries. However, large amounts of high-quality data being available is a luxury not available to many decision-makers. Arca Blanca recently encountered one such example: a client in need of understanding the performance of workforce reskilling programmes.

The usual way to estimate the performance of this kind of programme is to look at the historical performance of comparable programmes. However, the dual shock of Brexit and Covid-19 has had significant ramifications on UK employment, with an unknown impact on the performance and therefore a programme’s value for money. Understanding the relationship between workforce supply, demand and programme performance would allow the business to plug in the most up-to-date forecasts of these metrics from government sources and then estimate performance. However, after realising that only 14 useful data points exist, (there are only 14 known comparable situations) the idea of modelling the relationship between workforce supply, demand and programme performance was quickly rejected.

Using the 14 data points available, Arca Blanca’s data scientists built a reliable model relating workforce demand and supply to reskilling programme performance. It was the careful consideration of all aspects of the dataset and utmost attention to detail that enabled success – all stages of the data science process were carefully crafted to suit the dataset and the objectives of the project. The full technical detail of the approach can be found here.

As a result, the small data approach to the problem enabled proactive, rather than reactive, guidance based on changes in the labour market. Small data is not the only undervalued approach that could bring benefits to businesses. Another example is creating a dataset by manual research or manual corrections of low-quality data. While “more is more” is a common mantra in the data industry and is definitely true, “imperfect data is better than no data” should be repeated more often.

Evan Hurwitz has researched applications of Artificial Intelligence for over a decade both in academia and industry, applying his insights to both the popular fields such as deep learning and also the lesser-known fields such as meta-heuristic problem-solving.

George Cevora has spent 10 years on the forefront of Artificial Intelligence research and has a deep understanding of the strengths, weaknesses and the potential of the technology.

The power of "small data"

The pandemic disrupted business operations in ways leaders could not have possibly imagined little more than a year ago. But forward-looking companies have used this as a catalyst for innovation, to test new ways of working and ensure they survive the crisis.

To avoid the risk of slipping back to business as usual, ill-prepared for the next crisis, companies should embed these changes to become more resilient than before and to be prepared for a continued environment of uncertainty.

Businesses big and small have had to innovate their offering due to Covid-19. From high-end restaurants offering takeaway services to hair salons offering video consultations and personalised ‘dye-at-home’ kits, few were able to operate as they did in 2019, without having to think outside the box for new revenue streams.

This ‘survival’ innovation has been essential to global businesses in 2020-2021, but how can a company go from one great idea, albeit hastily implemented, to an evolving approach to innovate their service? The answer lies in an embedded test-and-learn culture.

In 2020, Arca Blanca worked with a hygiene product provider that lost many hospitality industry customers due to the pandemic, to create a new go-to-market model and rethink its target customers in a way that would create a more sustainable long-term business. Optimising their product range and adding these new customers in a thoughtful, embedded way meant they are now prepared for a new phase of accelerated growth and by using this low-risk test-and-learn approach, can continue to expand their offer into new sectors.

The lifting of restrictions is now on the horizon, but it would be unwise for firms to believe that an imminent return to pre-pandemic normal is on the cards, or that current behaviours and requirements are permanent.  Changes in behaviour might spring ‘back to normal’ post-pandemic, but behavioural reactions to the current change-heavy environment will be difficult to predict.

Arca Blanca worked with a property portfolio owner to create additional revenue streams by transforming their service offering. By using a well-defined and repeatable process to create an innovation incubator, the client was able to design, test and roll out new services in a way that was low-cost and low risk. These successful services are creating £40m annual additional revenue and the test-and-learn process for new ideas has been embedded into business operations.

Embedding innovation sounds simple, and in theory, it is; it is nothing more than process implementation – which does not sound like a revolutionary approach. However, there is a reason many organisations fail in their attempts to become more innovative; they overlook the process and cultural implications of an entrenched test-and-learn approach.

Many innovation processes are not designed to truly encourage test-and-learn. The most common issue is a failure to differentiate risk/ROI requirements for innovation work and business as usual, which can set any new idea up to fail. This kind of thinking further embeds a fear of failure and prevents the test-and-learn culture that enables good ideas to become great projects.

The importance of entrenching the test-and-learn process into the business is the key step that is often overlooked. People are creatures of habit and without creating (and incentivising) a cultural shift that supports adopting new practices and celebrating well-managed failures, it will be hard to make change last. The full benefits of innovation cannot truly be realised unless the process and tools are consistent, transparent, and embedded in the culture of the business.

Instituting simple, clearly articulated processes designed to support innovation and embed test-and-learn provides a strong starting point that goes beyond crises-led innovation. The first step must involve creating a focus on current and future key customers, and how any product or model will serve them. The last step of the process must involve the rollout and implementation of successful products.

Lizzie McKinnell is a manager at Arca Blanca. She is a business transformation expert specialising in innovation, disruption, transformation and new product development across multiple sectors; from global retail to utilities.

Arca Blanca is an integrated consulting and data company, resolving complex growth and profitability challenges. We help businesses of all types thrive in the face of unprecedented change and upheaval driven by new technologies.

Innovation Beyond Covid-19: Taking advantage of pandemic-enforced business changes

As the focus on sustainability grows, creating more sustainable products and operations has come to the fore. As part of this, businesses are recognising a complementary data strategy is the best way of facilitating this.

Almost every organisation is examining how they could become more sustainable. Whether driven by a quest for greater brand awareness, customer demand for action or the view that it can create value and competitive advantages, it is on every executive’s agenda.

Understanding how data can support a business achieve its strategic objectives has always been important. Sustainability is starting to play a more prominent role in any overall business strategy and having a well-defined data strategy is critical in achieving that.

Businesses have a variety of ways to move towards more sustainable operations. The concept of a Circular Economy introduces a focus on the business value of:

More efficient use of products and energy

Recycling products at end-of-life into high-grade base resources

Numerous examples range from Zipcar, which makes more efficient use of idle cars, to Ahrend, a modular office furniture designer, whose worn-out elements can be replaced cheaply to extend its overall lifespan. Similarly, Aquafil recycles used nylon into new nylon yarn without the loss of product quality.

Most of the new technologies and processes that support these sustainable ways of operating are highly dependent on gathering and processing large amounts of data.

For example, Bundles, a provider of IoT-enabled washing machines as part of a pay-per-wash subscription model, or Caterpillar monitoring modular component performance in engines that can easily be replaced. Kaer, meanwhile, manages office buildings’ air conditioning through air conditioning-as-a-service, keeping ownership of air conditioning installations in buildings and managing these efficiently through the Internet of Things (IoT) and analytics to constantly fine-tune the system.

All these applications rely heavily on turning data into usable insights that can inform, automate and make better decisions.

This requires any organisation starting or progressing its sustainability journey to think about an overarching data strategy.

The additional data gathered as part of becoming more sustainable creates a real risk of being inundated by the amount collected or not managing it correctly, potentially destroying more value than is created.

A well-thought-out data strategy is essential in embedding and managing these new, more sustainable, operations into existing ways of working. Clearly defining a data strategy will help a sustainability journey by:

Understanding which data and insights are needed to make effective and timely decisions at a strategic and operational level.

Defining uses of the different data available and how to derive business value from it.

Being clear on where data and the insights it provides delivers the most value.

Providing clarity on where data forms a core part of operations is crucial to the way those processes function. For example, automated systems stop working if they receive no data to base their decisions on.

A true focus on a data strategy, how it is connected to and helps deliver an overall business strategy is even more crucial when moving to sustainable business models. It will help provide a clear thread across all various applications, IoT-enabled products and automated processes that will unquestionably be a key part of moving closer to being more sustainable.

The first step is to define a data strategy tailored to supporting the business in understanding and achieving its core strategic objectives. Failure to do this could derail a sustainability journey before it has even begun.

Eelco Spinhof is a senior manager at Arca Blanca. He has a strong track record of leading teams in delivering key business projects for clients, employing a varied and robust skillset from business & service design (including BPO), change & project management and contract & C-suite stakeholder management.

Creating a data strategy to spur sustainability

The pace, volume and value of business decision-making are accelerating. Data, analytics and artificial intelligence are increasingly powerful and important capabilities to automate tasks and support those responsible for making decisions.

Machine learning algorithms can be used in bespoke decision support tools to analyse large volumes of data better than the human mind and can drive improved insights that will lead to enhanced decision making, operational efficiency and financial outcomes. 

The business case for investing in decision-support tools is a simple one. By focussing on high-value business needs Arca Blanca delivers measurable improvements in decision quality and cycle time.

Arca Blanca worked with a retail service provider to predict sustainable rent levels for tenants across 300 different sub-sectors and geographies, allowing users to quickly understand the effect of economic scenarios on their industry. These predictions have been proven to have a mean absolute percentage error of <10% which shows how close data-led predictions have been to the actual results.

The brain’s computational constraints mean that a well-performing tool will always be capable of doing more in a shorter time scale. This will improve existing business processes and allow users to do fewer repetitive tasks and focus on work that requires more judgement.

Arca Blanca worked with a logistics company to gauge demand for warehouses based on size needs. The bespoke tool we designed reduced the time analysts took to iterate a site mix from weeks to just minutes by replacing manually adjusted excel spreadsheets with the tool that could recalculate results upon changing parameters and combine this with the insights from large data sets.

Faster decision making will lead to improved financial outcomes. Creating price predictions for a housebuilder based on local transactional data gave the company an unprecedented amount of local knowledge on demographics, past and future lease events, while customer segmentation data allowed for more targeted marketing. It resulted in multi-million-pound profit improvements. Another decision support tool for a logistics company reduced costs by £8m while generating the same income, increasing yields by 8%.

For those concerned about giving up too much control, decision support tools do not eliminate intuition entirely but support faster, more accurate decision making while also delivering improved results. By addressing human bias concerns and our inability to process data the way machines can, these tools allow for the elimination of intuition’s flaws, while employing its strongest facets.

A user can then influence final decision-making by overlaying scenarios and modifying macroeconomic, sociodemographic, consumer and business indicators. This can better help business leaders plan for the unknown and adapt quickly to shifting economic conditions and see how resilient their planning is. In the context of COVID-19, our decision support tools have enabled our clients to see how retail and commercial space rents would change based on different UK recovery scenarios.

There is a competitive edge to be captured by employing decision support tools to improve the accuracy of data and speed of decision making. Through these tools, data-driven businesses can get more value from both internal and external data, highlighting previously unknown improvements.

Maria Paris is a senior consultant at Arca Blanca. She is a chartered accountant specialising in business model and customer experience design and has a strong track record in delivering decision support tools in the property sector.

Building the case for AI-driven decision making

Arca Blanca – the integrated consultancy and data company – has been named as a finalist at the MCA Awards 2021. In this series of short articles, we reflect on our shortlisted nominations.

Few companies can deliver the highly specialised technological solutions that Tharsus deals with on a regular basis. But when the pandemic struck, the robotics specialist was determined to find a way to return UK PLC to work.

In a matter of days, it identified and conceptualised a social distancing device, Bump, to help businesses return their staff safely to the workplace.

At the onset of the biggest health crisis in generations, Tharsus knew Bump had huge potential and could play a vital role. Yet, despite its wealth of experience in robotics, it was largely a solution-oriented firm, with limited experience in producing and commercialising consumer devices.

Without this background in taking products to market, Tharsus needed to create a new organisation structure, sales channels and market Bump. Because of the evolving nature of the pandemic, it had an extremely limited timeframe to execute testing and production to meet high, but concentrated demand.

Arca Blanca is a consultancy that prides itself on its teamwork and collaborative approach to work. The conditions imposed by the first Covid-19 lockdown required a quick adjustment.

Embracing remote working meant adopting technology to operate at full speed. We integrated our consultants and Tharsus’s staff into a joint team to replicate what it would be like to work on a project before lockdown.

Production and commercialisation considerations were front and centre. Tharsus needed to deliver Bump within months, an uncommonly tight timeframe for a technological device. Moving too slowly would jeopardise the company’s chances of capturing global market share, especially if it was unable to expand production or fulfilment in time.

Arca Blanca’s extensive experience in new business and operating model definition and implementation proved to be the perfect match for Tharsus in producing Bump. We helped deliver the first product just three months after it was conceptualised.

A new organisation was created to support the sale of Bump to a new market segment in weeks. In addition, a go-to-market strategy was devised, including a rapid partner evaluation process.

The project served as a very good exercise in handling uncertainty and ensuring progression when several unknowns exist. Working at warp speed meant there were a lot of uncertainties throughout, with unforeseen questions always around the corner. The technological challenges of Bump meant we had very little clarity of future supply chain constituents. In addition, the reduction of international travel, logistical concerns and supply chain risks that had not been envisaged had to be addressed in short order.

Both sides of the partnership adopted a very pragmatic approach to working. This meant that within an agile construct we were always progressing, despite endless hurdles.

Getting a product from concept to market in only three months is difficult at the best of times, even without the technological complexity of Bump and pandemic-related restrictions. It is a testament to the collaborative approach applied that we were able to ensure success that continues to this day.

As a result, Bump and Tharsus received both large orders and wide praise in the market, with the device being used in workplaces and events such as the London Marathon. It has also opened wider conversations for Tharsus’s business, including creating a launchpad for developing new products in new markets.

AB Spotlight: Tharsus

For too long, the UK property sector has been reluctant to embrace the power of data. Returns were predictable, leases lengthy and the location was what mattered most. There was little incentive to change but significant costs and potential downsides to rocking the boat. Why fix what’s not broken?

As lockdowns took hold across the UK, commercial property landlords realised significant and long-lasting behavioural changes were here to stay. Previously simmering trends are becoming anchored. Online shopping, working from home and the convenience of food deliveries have changed how consumers interact with the built environment. The power of location has diminished and landlords must increasingly compete with non-property-based offerings.

They must, often for the first time, truly leverage data to remain relevant. No longer are businesses limited to multi-year, multi-million-pound, high-risk digital transformation projects but can instead deploy micro-interventions that leverage data science tools to generate actionable and instant insights. These can be obtained at a fraction of cost and effort but deliver significant immediate value.

“Location, location, location” has always been the driving motto in the property industry and its key generator of value. Location is the industry’s most effective barrier to entry. As long as commercial landlords owned the right locations (and had access to the capital to acquire and hold them) then their returns were predictable and near guaranteed. This without having to contribute much effort. Additional services and amenities were always classed as nice-to-have; tenants would fight for the right address. Leveraging and engaging with data was too often a priority for tomorrow.

It has taken a massive business problem to provoke action. Commercial tenants have felt the brunt of the pandemic, exposing the inherent inequality in risk-sharing that exists with landlords. Covid-19 has become an opportunity to re-appraise the intrinsic purpose of retail stores and re-examine working patterns.

Over 16,000 stores and 30 nationwide chains have either gone into administration or rationalisation in 2020. The commercial landscape is rapidly changing, and tenants are rethinking their property needs. HSBC is reducing its office portfolio by 40%, Aviva, Clifford Chance and others are following suit.

Those that retain a property footprint want to change their dependence on fixed property costs and the leases that bind them to it. Tenants require more value, more flexibility and a more individually tailored solution. They also now need to balance their property needs with their online shopping or remote working initiatives. This past year has been a wakeup call to most landlords.

Now that competitive advantage is not simply achieved through the attributes of physical locations but rather within a complex, omnichannel ecosystem. The value curve has dramatically shifted. To remain attractive, landlords need access to instant, actionable insights that are adaptable to volatile market conditions. Traditional market reports provide ‘static’ aggregated predictions which are too general, lack specificity and are rarely tailored to the needs of multiple users.  It is time executives learn to harness data and use it to better align their products to the needs of their customers, or risk following in the footsteps of Intu.

The challenges in deploying data science tools to property were often condensed to beliefs that 1) existing systems are poor at talking to each other, 2) data collection is complex and expensive 3) associated implementation costs and complexities are substantial.

These concerns remain somewhat valid. The technological landscape is still shifting and improving but is far from perfect. Most property companies are poor at installing sensors and creating processes to automatically collect data, and finally, the market is awash with stories of digital transformation projects that have cost millions, taken years to build but yielded debatable benefits. There is an understandable reluctance to engage in large-scale digital transformation projects.

Not all problems should be solved by all-singing, all-dancing digital platforms. Rather executives should consider what is their most pressing issue? What insights do they lack to make the right decisions? Which scenarios do they want to test? What is the critical challenge which human intelligence alone cannot solve?

Part of the solution lies in delivering short, hard-hitting, single-issue, rapid-delivery data science projects. Building user-friendly decision support tools that address a clear and specific need. These tools can be built within a short timeframe and are comparatively affordable, often tangibly proving their worth within six months if not in a single quarter.

While data tools on their own cannot answer every question, they will inform strategic discussions, frame negotiations and provide specific and valuable insights. These can be tools to better predict future rent levels and sources of demand, optimise sustainable levels of turnover rents or use local site attributes to suggest the highest and best site use for developers.

Brick and mortar properties will remain at a significant disadvantage due to business rates, service charges and significant costs of occupation. Despite this, our stores and offices are not going anywhere, we still have significant utility and affinity for them. A return to normal will see us embrace the high streets and celebrate our returns to offices as if it were the ‘Roaring Twenties’ all over again.

The coming market shifts will demonstrate which landlords have used the pandemic to re-think their strategic approach to a resilient and digital future, and which have remained complacent. The property industry is littered with low-hanging data fruit, the time has come to start picking; carefully and deliberately.

Chris de Gruben is an Arca Blanca property expert, a chartered valuation surveyor and urban planner with more than 15 years of experience consulting and carrying out real estate market research for the private and public sector. He specialises in urban development, property and construction in the UK and several emerging markets.

Data is transforming the property sector – at last!

First world health and care systems went into the pandemic with big structural issues. These issues have grown significantly because of Covid, but Big Pharma has an opportunity to move before being compelled.

Fatigued health workers now expect improved pay and conditions, indebted states need to radically reduce spending yet drug costs keep increasing and the backlog in non-pandemic activity must be addressed even though countless patients will now die before being treated. That is merely the tip of the iceberg.

This is uncomfortable for big pharma. The historic business model was quite simple and highly profitable. High-value drugs sold for good prices with lengthy patent protection.

The new model will need to combine increasing delivery complexity with local data on patients and populations to demonstrate public value – outcome-based payments. This transfers a host of new risks to the pharmaceutical company and demands new capabilities to price and manage them, while also increasing the number and range of commercial agreements.

We have already seen the start of outcome-based pricing, such as Novartis’ Zolgensma at ~$2m per patient to treat spinal muscular atrophy. And these moves are welcome in the context of targeting life-changing and life-ending diseases. But so far this remains very ‘narrow’ in terms of patient data and use cases and does nothing to deal with the politically-charged issues of population health outcomes, especially the widely reported discrepancies across socio-economic divides.

Orienting into this new landscape requires data: the ability to generate robust medical and commercial insight by combining many different individual and population-level sources.

Everyone wins through better analytic insight, forecasting and the experience of working together, building a bridge towards the outcome-based commercial mechanisms of the near-term future. Yet pharma companies have shied away from holding patient data, outside of narrow technical research fields.

A first step would be the use of existing machine learning and data science to improve expensive drug targeting. Federated learning allows AI-training and pattern recognition across different data sets without sharing the data, and ‘deep fake’ methods offer entirely fabricated information that closely mirror characteristics of the original.

These approaches allow different organisations (and divisions within the same global company) to come together and collaborate, whilst controlling overall data risk exposure. Such steps can allow piecemeal collaboration over time, benefitting from new information as it becomes available. This includes enabling and seeking patient sharing of information from ‘smart’ wearables.

These approaches offer big pharma companies a stepping-stone: building alliances, generating trust, and even building competitive positions and patient-accessible data-sharing platforms. But deeper disruption is coming from data-rich tech giants. They are already working towards their first £-trillion in annual health revenues.

Alasdair Ramage is a director at Arca Blanca. He specialises in modernising the way organisations are run: using data to drive performance, shifting operating models, transactions and digitisation.

Pharma’s big data opportunity

We are delighted to announce that Arca Blanca has been shortlisted in two categories for the prestigious 2021 Management Consultancy Association (MCA) Awards. The MCA Awards recognise excellence in consulting, and these nominations are a testament to the extraordinary results our teams have delivered with our clients over the past year.

We have been shortlisted for the Change and Transformation Project in the Private Sector award for our work with robotics specialist Tharsus, a project that responded to social distancing requirements brought about by the pandemic.

This is in addition to being shortlisted in the Strategy category with a project for Landsec plc, which forecasts retail trends using the application of data science and artificial intelligence.

It builds on a strong performance at the 2020 MCA Awards, where we were highly commended for Best New Consultancy and finalists for Change and Transformation in the Private Sector with both Ocado and McCarthy Stone and the International category with Vision-Box.

Change and Transformation in the Private Sector – Tharsus

‍Over the coming days, we will be releasing more information about each of these projects through a series of articles. The award winners will be announced during a ceremony on 1st November 2021.

‍Arca Blanca is an integrated consulting and data company, specialising in business design and implementation. We bring together accomplished individuals from industry and consulting with experienced data scientists and technologists to design and evolve business models that utilise data to deliver better outcomes.

About the Management Consultancies Association (MCA):

Arca Blanca nominated for MCA Awards 2021

Ask any CEO what keeps them awake at night and most will point to their growth, profitability or staying ahead of the competition.

Exploiting data and technology appropriately can create that competitive advantage, especially with advancements in storage and computational power. But the reality is that organisations and leaders at the cusp of a data journey still rarely know where to begin and how to lead successfully.

A strategy is about choices – knowing what to do and crucially what not to and why. Understanding the ‘why’ behind data requires tough introspection, in addition to an understanding of the strategic context and business environment.

Firms embarking on this process must assess their ambitions for growth and the role data will play in it – lead or enable. Traditional businesses will often choose paths where different sources of data can enable their value propositions. Whether it is customer data to understand detailed segmentation and offer personalisation, or in-software telemetry used by product development teams to design and improve customer experience journeys.

However, the true opportunity lies in the ability to unlock new propositions and business models that enable the monetisation of data assets. Forex companies offering APIs so accounting software providers can access real-time data for travel and expenses submissions, for example.

Organisations looking further into the future to get ahead ought to consider how data will become core to their proposition, as opposed to simply enabling it.

Use Cases: A vehicle to ground the strategy in reality

Too often, strategies risk becoming disconnected from the harsh realities of execution. Stories of ill-conceived investments that did not pay off, along with boil-the-ocean data projects that are not value-focussed are a common occurrence.

One of our clients was led to believe a data strategy is an organisation design effort to build a business intelligence capability. However, they were unaware of how data would add value to their proposition, opening the door to significant investments with unsatisfactory results.

This highlights why a good data strategy needs proof-points in the business to translate into successful execution. Though big picture considerations by decision-makers are important, the best way to start a data journey is with use cases that bring the strategy to life.

Use cases are a proving ground for both technology and data practices to be tested and rapidly learnt from to establish a high-performance operational capability. They help to put the data strategy into effect with best-in-class data science techniques used to address real problems and deliver outcomes, while also testing the strategy in-flight.

Being data-led is a means to unlock the potential in data. We cannot ignore that a well-executed strategy will drive a fundamental shift in an organisation’s people, processes and practices.

However, implementation needs to cater to the differences in learning and adoption curves presented when deploying use cases across different user groups. Some skilled practitioners will have their years of experience, methods and approaches challenged by data and decision-support tools.

Without careful consideration, entire sections of a business may reject the ideas, risking the successful creation of a data-driven mindset.

There are emerging schools of thought on valuing data as an intangible asset, owing to the enormous promise it holds. However, as will be the case in several businesses, untapped, underperforming, or nonperforming assets do not have much value.

Also, a business must resist the urge to follow suit and copy a competitor’s approach. No two organisations are the same, especially when it comes to how data can drive value.

A well-thought-through data strategy can prevent missteps that sap confidence in data initiatives. In combination with the execution of use cases to build a data-driven mindset, it will unlock the true potential of data.

The secret to success lies in starting with the ‘why’ (strategy), before making choices on the ‘what’ and the ‘how’ (data capabilities, standards, operations, governance).

Designing a data strategy to leapfrog the competition

The onset of the Covid-19 pandemic had massive ramifications for businesses worldwide. Few were harder hit than the hospitality industry and its suppliers.

Arca Blanca’s client, a provider of hygiene products to the hospitality sector, was left highly exposed by the initial lockdown and needed help to move quickly and seek new demand sources.

When the pandemic began, our client was on the receiving end of several order cancellations. With fewer people staying in hotels and visiting restaurants, demand from existing customers slumped, even though there was global desperation for cleaning products.

It was clear that their products would be invaluable in responding to the pandemic, but a reprioritisation of resources was needed in order to optimise their product range and work with new customers. They also had a great desire to become a partner in pandemic mitigation and prevention efforts.

Scaling up and repositioning the business while under lockdown, with work-from-home orders in place added to the challenge.

Arca Blanca used the disruption as an opportunity to reshape the client’s operations, prepare for a new phase of accelerated growth and expand into new sectors.

Our experience in delivering award-winning transformation programmes meant we were well-equipped to support the client both in the short- and long-term.

We created a new operating model to support their commercial proposition, applying our experience in designing supply chains to ensure resilience.

We created a joint team, which fostered engagement and created an alignment of objectives, to develop a product strategy that would best position the company for success during and beyond Covid-19. New ways of working were implemented to embed these changes long-term, leading to better interoperability between business functions that is often not implemented effectively during change initiatives.

A proliferation of their product range had created a lot of confusion among customers. We consolidated their portfolio, streamlining their offering to provide products customers needed to use.

Furthermore, using data analysis, we discovered significant improvements that could be made to their pricing mechanism, boosting profitability.

The results were a resounding success, Arca Blanca created a framework for change, achieving the client’s three main goals of business improvement, Covid-19 management and creating a foundation for post-pandemic growth.

AB Spotlight: Pivoting to a new client base during Covid-19

This is part of a series of articles detailing Arca Blanca’s experience and considerations in the Life Sciences.

Much of the past year has been spent with the life sciences in the news. But the focus on Covid-19 treatment, testing and vaccine development conceals the unrelenting charge of new players into the market.

The entrance of big tech has created an increasingly crowded and more competitive arena, yet incumbents need not feel like sitting ducks. Moving thoughtfully and with the future of the industry in mind has the potential to revitalise long-standing brands.

A mindset shift towards real and deep customer service offers a path forward. This is not about just meeting rising customer expectations. It means thinking through unmet needs and reimagining services and products together to deliver a richer and more valued experience. This will eventually lead to consolidation, partnering and vertical integration, resulting in a dramatic change in the mix of organisations and their market share.

Data will be a major differentiator, prompting inwards reflection on operating models, genuine points of customer differentiation and user journeys. The winners will likely pivot and become something different, outcompeting traditional competitors.

The immovable object vs the unstoppable force

Big tech and startups have made inroads to the life sciences in several countries. Amazon Pharmacy and Apple Health are emerging as household names for healthcare in the U.S. and Google’s recent acquisition of Fitbit builds on its existing ‘Home’ offer.

Their entrance to UK and European markets is certain and will accelerate because of governments promoting digitalisation and easing regulation. The clock is ticking for traditional European health and care organisations, particularly for those exposed to fast-changing high streets.

Delivering a customer-oriented proposition

News, commentary and industry expertise

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Hyper-personalisation: what, how and why?

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Arca Blanca begins new partnership with the British Neuroscience Association

Arca Blanca nominated for MCA Awards 2022

The power of "small data"

Innovation Beyond Covid-19: Taking advantage of pandemic-enforced business changes

Creating a data strategy to spur sustainability

Building the case for AI-driven decision making

AB Spotlight: Tharsus

Data is transforming the property sector – at last!

Pharma’s big data opportunity

Arca Blanca nominated for MCA Awards 2021

Designing a data strategy to leapfrog the competition

AB Spotlight: Pivoting to a new client base during Covid-19

Bridging the data gap in the life sciences

Supporting our team’s well-being under lockdown

AB Spotlight: Creating the insurance platform of the future

Data science-backed rowers win race

Leveraging the power of data to thrive post-pandemic

From stranger tides to data science insights