AI can be used to answer the most important questions in retail - from the strategic to the operational. 

The efficient markdown of fresh produce is ripe for an AI solution.

In fresh food retailing, the balance between sales, availability and waste needs to be finely tuned. But no existing demand planning process is perfect, resulting in fresh products going out-of-code every day, in every store. 

The objective of the markdown process is to sell at the highest possible price and end the day with no wasted stock. If the process isn’t optimised, the result is excessive first markdowns, large late in the day markdowns to clear product, and/or unsold waste to dispose of – all “giving money away” unnecessarily.

Markdowns are often determined by a static algorithm which takes into account only the quantity of each SKU to clear - and possibly the time of day. The likelihood of customers buying markdown product is influenced by many more variables including price elasticity by product, availability of full-price alternatives, customer demographics, footfall, weather and location. 

Considering multiple internal and external data sets in the markdown calculation allows optimisation of sales and minimises markdown and waste costs.

The total cost of fresh markdowns and waste in the grocery sector is approximately £1.5bn a year, and can range from 2.5% to 3.5% of fresh sales for a typical large grocer. Improving fresh markdowns by as much as 5-10% is a multi-million pound opportunity for many grocery retailers. 

The high frequency of markdown decisions make them a perfect case to utilise AI and reinforcement learning methods to make markdown decisions based upon learned customer behaviours - decreasing missed sales and unnecessary costs. 

The Arca Blanca approach: Markdown powered by data science

We can utilise and optimise your full data landscape – ensuring the most accurate data possible is used efficiently and establishing data quality and availability for the key inputs by store. This, combined with data from external sources, is used to create the best markdown strategy throughout the day.

Inputs can include: inventory quantities of individual SKUs at full price, markdown quantities as % of total inventory, total markdown value, time of markdown, full price of markdown item, markdown value as % of full price, as well as external data such as footfall, local demographics, weather and more.

Our Data Science modelling goes far beyond the simplistic methods commonly used in the industry. Methods such as Reinforcement Learning and Deep Learning allow us to extract maximum value from the fresh food markdown process.

The size of the prize in getting markdown right is worth millions – and our data methodology gets closer to total accuracy than other systems.

Our models are built to your specifications and can integrate into your existing markdown systems and processes. 

The model will automate pricing decisions and apply them immediately throughout your stores, directly into existing POS technology. 

This means you can rely on the system to apply the right price at the right time to allow optimal inventory, price and minimal waste.

Fresh food markdown optimised by data science

Fresh thinking needed? AI-powered produce markdown

AI can be used to answer the most important questions in retail - from the strategic to the operational.

Price optimisation is primed for an AI solution.

UK retailers spend millions of pounds per week on pricing investments. These can be planned promotions partly funded by branded suppliers but are often reactions to competitive pressure from rivals.

It takes a lot of time and effort to (i) identify which SKUs need careful price management (ii) analyse the level of price change, cost, likely impact on sales, adjusted stock requirements and (iii) implement in stores.

Limited capacity to proactively manage prices combined with sales-based incentives can lead to knee-jerk pricing decisions by category managers. New prices may increase sales or basket size temporarily but often cause to longer term margin erosion. 

Advanced algorithms can recommend the right pricing trade-off across thousands of SKUs. An AI-based tool can also be linked to competitor’s price point data - usually outsourced to third party specialists who scrape websites, analysing 25-40k SKUs. SKU-level price band recommendation can allow you to perform drill-down analysis and simulate impact of a price change on sales. 

The solution: An AI-powered recommendation engine

Size optimisation powered by data science

Establish data quality and availability for the key parameters by store and channel, including full-price sales, discounted prices sales, stock availability, commitments, SKU hierarchy, and attributes.

Develop two data science models to (i) estimate SKU price elasticity and forecast sales at a given price, and (ii) estimate cost of a price change.

Establish link with 3rd party price comparison service providers to determine how a given SKU is performing against competitors.

Use data science to determine the optimum pricing bands based upon competitor pricing, price elasticity, and cost of price change 

Integrate the automated pricing system with store’s sticker labelling system in order to realise pricing value without impacting operational efficiency within stores.

Price optimisation powered by data science

The pricing puzzle: Price optimisation for retail

Size optimisation is a good fit for an AI solution.

The issue: Missed sales & disappointed customers

Retailers spend a lot of time and effort getting space, range and assortment right across channels. And yet sub-optimal size ratios still get purchased, leading to lost sales, higher markdown costs and customer complaints. 

Merchandisers use various methods to get a profile of sales across sizes – using just historical sales, or historical sales adjusted to take account of availability issues. Either way, accuracy is poor. A ‘best guess’ is used to match new items to old items, and often orders are simply replicated from previous seasons, compounding errors.

These inaccurate size buys lead to customers visiting your store or site and finding products they like, with none of their size available. Beyond the lost sale, this impacts customer satisfaction. Overbuying in other sizes leads to markdowns, further impacting profitability.

Getting size optimisation right delivers significant benefits by improving full price sales and reducing markdown costs, adding profit to the bottom line. Additional benefits come from reduced inventory and, of course, happier and more loyal customers!

The Arca Blanca approach: Size optimisation powered by data science

We map and optimise your full data landscape – ensuring the most accurate data possible is used efficiently.

We establish data quality and availability for the key inputs by store and channel, including: full-price sales, availability, lost sales, stock, commitments, product hierarchy and item attributes.

Our model has the ability to ingest and process vast amounts of data - including multiple years worth of sales and availability

Our data science modelling approach is proven – by using historic sales and stockouts to predict future demand it consistently beats existing recommendations.

For continuity items, our recommendation is on average 60-70% more accurate than existing processes. For seasonal items, our recommendation typically has a 200-250% lower error rate.

3) Decision support tool bespoke to your needs

The model can be applied to a range of processes and built into a user interface that allows your team to make data-driven decisions.

Plan purchases for previously sold items using an adjusted size ratio from the historical data set

Plan purchases for new items by matching to a similar garment and using aggregated size ratios

Browse latest inventory, commitment, and aggregated sales for a product or group of products

Identify potential missed sales opportunities and where to increase buy quantities

Sizes matter: how to increase sales and reduce markdowns

Engines revving, smoke billowing, and they’re off… but wait, are they stalling? 

The automotive industry now faces some of the most radical disruption in its 126-year history – will it thrive or break down?

This is the first article in our ‘Smart Thinking’ series - from Co-Founder and Head of Technology Vignesh Chandran – which considers the far-reaching effects of disruption in the technology sector, and how MI, BI and AI is helping all kinds of industries to change in readiness for new business models.

Sales of electric vehicles (EVs) are steadily climbing as fossil fuel prices continue to rise, consumers become more environmentally aware and charging infrastructure (slowly) starts to catch up. Alongside this, post-pandemic car buyers are much more willing to shop online (62% are open to the idea (Statista)) and upcoming regulations from global governments could spell the end of non-EV and hybrid engines.

So where does this leave the many players with operations firmly embedded in the current business model of traditional ICE (Internal Combustion Engines) car sales? We’re talking everyone: physical retailers and dealerships such as Sytners and Lookers, newer digital businesses like Cazoo and Cinch (the car leasing and rental businesses which alone accounted for $119.28bn of the market in 2021 (Mordor Intelligence)), and the automotive manufacturers and OEMs (Original Equipment Manufacturers) themselves. 

Consumers may be ready, but are industry players?

As consumers go greener, expect to see a surplus of ICE vehicles on the forecourts with dealers (and buyers) unsure of their residual values. That’s compounded by improved digital platforms slowly but surely dis-intermediating these bricks-and-mortar retailers. In parallel, a steady increase in EV cars being bought on subscription is giving rise to shorter contracts and flipping the current seller-owner model on its head, mirroring the evolution of other technology markets in previous decades.

But as consumer behaviour advances at pace, little has changed structurally in the market – which could become a real problem for many players in the industry. Are manufacturers and retailers ready to make a radical shift, providing subscriptions and servicing? What will happen to the leasing companies who base their financing on car values which are no longer easy to predict? And with far fewer parts that degrade over time on an EV, what role will the OEMs play?   

Disruption breeds doubt. And for the automotive industry, that’s particularly wide-ranging:

Pricing: Auto-setting car prices using data has been on the rise for a while. But global disruptions have recently caused several companies to falter, with wildly fluctuating valuations exposing imperfections in the market and current approaches. When online platforms have a buying price higher than their own selling price, you know something’s not right.

Residual value: Another complication is Car Residual Value (CRV). The demand for EVs replacing ICE cars puts Guaranteed Future Values (GFVs) at risk. And widely-adopted, affordable Personal Contract Purchase (PCP) contracts simply won’t be viable – requiring a rethink.   

Customer data: As online buying grows, so does the volume of consumer data – and our understanding of their needs and behaviour. This is a challenge for organisations who must shift from a traditional product- and asset-centric perspective to a customer-centric view on offers and propositions. Maintaining direct customer relationships by leveraging first-party data in a privacy-preserving way, especially when recommending the right cars to buyers will need to be a core capability. 

Vehicle subscription services: This market is still nascent, but financial institutions (such as Lloyds Banking Group), digital retailers, and some manufacturers (Volvo and Jaguar Land Rover) are already seeing early successes. Combined with any consumer concern around EV affordability, charging infrastructure and future values, these shorter and limited ownership contracts make for a very appealing sales proposition. As the trend for both new and used electric cars grows, leasing businesses, retailers and rental companies will need to consider how they retain and maximise their share of the revenue.

Data can play an unrivalled role in helping businesses effectively prepare for and adapt to market disruption. Data-backed decision making can sap the uncertainty out of the market – it’s less risky to adapt your business model, pricing or portfolio mix when you can predict the most likely outcome.

Data can play a big role in modernising the passenger and commercial mobility value chain including areas such as fleet decarbonisation, insurance, retail price optimisation and auto-financing, while also helping businesses shift from offering asset-centric to customer-centric propositions.

The question remains: who will adapt for success in the future of automotive and who will fall by the wayside? 

Arca Blanca has already supported several automotive clients on this road to transforming their business.    

Challenge: Arca Blanca partnered with the UK’s largest leasing company to use data to refine their customer proposition and optimise operations. 

Result: A rapid data maturity assessment highlighting high-priority interventions. Use cases were identified which could deliver £35m+ benefits per annum and a phased roadmap was drawn up to implement the changes.

Challenge: Arca Blanca was tasked with helping a leading automotive retail group to predict used car residual values to limit the risk arising from agreeing ‘Guaranteed Future Value’ or GFV contracts with customers and financial institutions.

Result: De-risked £100+m in GFV risks through a multi-country feasibility analysis and roadmap for building an in-house data-based GFV management tool. An minimum viable product that leverages data science techniques to predict residual values three, five or seven years into the future and spans 30+ countries is under development.

Driven by data: how AI is transforming the automotive industry

Global supply chain disruption due to macroeconomic shocks such as the pandemic and a significant squeeze on consumer income, combined with the rise of new technologies, business models and buying behaviours have created a volatile environment for business.

The post-pandemic world is still reeling from chip shortages, supply chain issues and geopolitical instability – UK production dropped to a 66 year low in 2022 (BBC).

2) Volatility of used car values impacting depreciation

Used car values and new car depreciation are continuously fluctuating  in a market filled with supply shocks and the rising popularity of electric cars. Used car prices rose ~40% in 2022 (JP Morgan) and a huge fall is expected in 23, breaking current assumptions on relationships between new and used car pricing.

3) Cost of finance & squeeze on consumer income

Consumer affordability and real incomes have never been more significant. Over 2022 the UK Consumer Price Index rose by 10.5% (ONS). The cost of borrowing increased across several global economies making financing new sales costly.

Managing customer choice of petrol/diesel cars versus plug-in hybrids and full electric cars is important as customers, the market and infrastructure play catch-up to transition to the future. Different speeds of adoption across global markets will present interesting arbitrage opportunities.

5) Shift from vehicle ownership to subscription models

The rise of all-in-one subscription models and short term leases is transforming customer attitudes from ownership to subscription. OEMs selling D2C, retailers becoming fleet owners and their convergence with vehicle rentals are interesting trends to watch out for.

6) Delivering seamless omni-channel customer experience

As new business models emerge and traditional roles in the value chain change, customer relationships will also transform. Their digital footprint will enable a personalised sales journey from informed attraction all the way through to conversion, retention, and developing advocacy.

As cars become smarter and connected, carmakers have an opportunity to extend their services on-road through charging, over-the-air through upgrades and deliver a different experience journey through new business models.

8) Emergence of software and digital as critical capabilities

Where powertrain engineers previously ruled the roost, the next decade belongs to software and data/ML engineers. Carmakers of the future will need to bridge this capability gap to tap into opportunities for innovative new products and services and compete with new & nimbler players.

Fleet owners are rapidly de-carbonising their footprints. Finding the right mix of ICE / Hybrid / EVs to optimise sales, operational effectiveness and total cost of ownership will be critical.

New paradigms require doing things differently to succeed. Data science and AI can turn these challenges into game-changing opportunities… 

Click here to find out how the industry is reacting.

Global supply chain disruption has created a volatile environment for business.

Nine trends shaping the mobility industry

Artefact, a global data transformation consultancy, has acquired UK-based Arca Blanca, a fast-growth and innovative data consulting company, to further expand its footprint in the region and support its goal of becoming one of the world’s leading independent data consulting groups.

The agreement will augment the Group’s offering, bringing new and expanded data and artificial intelligence (AI) solutions to Artefact and Arca Blanca clients and enabling enhanced partnerships with technology providers. The move comes as organisations in all industry sectors rapidly shift to data-driven business models to accelerate their business transformation and improve competitiveness through better prediction and enhanced decision-making.

Since being founded in 2018, Arca Blanca has achieved a compound annual growth rate of around 40%. The company combines data expertise with in-depth business knowledge to resolve complex growth and profitability challenges for clients, who consist of blue-chip FTSE 100 and FTSE 250 enterprises. It operates across a wide range of sectors, including healthcare, infrastructure and property, private equity and financial services, thereby complementing Artefact’s strengths, particularly in FMCG, retail, luxury and beauty, and eCommerce. 

Arca Blanca’s experienced team of 50 data scientists, technology specialists, business consultants and industry professionals will join Artefact’s data experts; they will be part of the Group’s wider operation that has over 1000 employees and locations in every continent. 

UK-based and international clients can further accelerate their business transformation, benefiting from the combined skills, services and technology expertise of the fully integrated collaboration between the two companies. The complementary nature of each company’s current remit will power a broadened range of data consulting services, with these including the definition of data strategy and governance, the implementation of data platforms and customer data platforms (CDP), the development of advanced AI use cases, change management services, and in-depth user training to ensure maximum ROI is derived from the new solutions. 

The acquisition of Arca Blanca is an integral element of Artefact’s objective to become the global independent leader in data consulting. Along with strong organic growth, Artefact has followed a fast-moving consolidation strategy. In 2021 it acquired Vivadata to facilitate the Artefact School of Data (which offers high-level education and training on data and AI to clients and professionals); in September 2022 it purchased Start-up Inside to deliver AI hackathons and data democratisation through large international conferences, and in November 2022 it acquired Moroccan consulting Adalyne to accelerate the deployment of Artefact Africa. With the strategic support of international private equity firms Ardian and Cathay Capital, Artefact’s stated aim is to triple both its revenue and workforce by 2025. 

Artefact is an international data services company specializing in data transformation and data & digital marketing consulting, whose mission is to transform data into business impact by delivering tangible results across the entire value chain.

Artefact's unique approach, which bridges the gap between data and business, enables our clients to achieve their business objectives in a dedicated and efficient manner. Our 1000 employees combine their multidisciplinary skills to help companies innovate their business. 

Our cutting-edge technologies in Artificial Intelligence and our agile methods guarantee the success of our clients' AI projects, from design to deployment, including training and change management. Covering Europe, Asia, North America, Latin America and Africa through our local presence in 16 offices, we work with leading international brands such as Orange, Samsung, L'Oréal, or Sanofi worldwide. 

Arca Blanca is an integrated AI and consulting company. We enable businesses to embrace uncertainty, evolve and prosper – unlocking their most valuable opportunities and resolving their most complex challenges. We do this by combining expertise in artificial intelligence with a deep knowledge of how businesses operate. Using leading thinking in data science, business model design and a creative approach to problem-solving, Arca Blanca enables clients to accelerate the delivery of results, speed up decision making and take control of the growth agenda.

Kate Alexander - +44 (0)7788 584413 - kate@alexander.co.uk

Artefact, a global data transformation consultancy, has acquired Arca Blanca.

Arca Blanca joins Artefact

Meet the latest of our Data Science interns - Antra Tripathi. Antra just finished her masters in Big Data Science at Queen Mary University. Prior to her studies, Antra worked as a junior Data Scientist for a consultancy firm in India. She discovered Arca Blanca's intern programme on her university career portal and was attracted to the fact that Arca Blanca, "isn’t a traditional consultancy, but a firm that is heavily data driven".

Antra was looking for an internship that would give her the opportunity to understand how the UK market works, understand business culture and gain experience in a more commercial environment where she would be able to put into practice what she learnt during her masters. 

Since joining Arca Blanca, Antra has been working on real-life business problems, exploring the data strategy of our clients to discover how machine learning can help them capitalise on commercial opportunities. Most recently, she has been analysing big sets of data to understand and explore potential solutions to our client’s problems. 

In a disproportionately male industry, Antra has enjoyed the equal opportunities on offer at Arca Blanca. Her advice for young female data scientists is to, "put your names forward, be consistent and keep learning". 

Our Data Science Internship is open to anyone with with experience or education in a data science or adjacent field. Get in touch at felipe.parra-avila@arcablanca.com if you think it might be right for you.

Antra reflects on her time at Arca Blanca

Meet our data science intern: Antra Tripathi

The Management Consultancies Association celebrated 25 years of their annual awards recognising the best of the sector with a ceremony hosted by Sir Trevor McDonald at the Park Plaza in Westminster.  

Arca Blanca were finalists in two categories and delighted to be Highly Commended in both.

Our work with Landsec was highly commended in the “Change and Transformation” category for the implementation of their new retail strategy.

Read more here: Predicting the future of retail rents

Our work with Avast was highly commended in the ‘Strategy’ category. We worked with Avast to unlock the value of their data.

Read more here: Discovering new revenue streams

Celebrating our work with clients Landsec and Avast

Arca Blanca highly commended at MCA Awards 2022

Arca Blanca recently partnered with TechVets, a leading not-for-profit within the Forces Employment Charity that delivers training to veterans to accelerate their transition into civilian life. Over the course of six intense weeks, our data science experts taught participants the mechanics underpinning AI and machine learning. This provided them with the ability to build, test and run their own machine learning models, effectively taking them from ‘zero to hero’ in the discipline.

It’s no secret that veterans represent one of the most capable target demographics for private sector recruitment – but arming transitioning service members with the right tools can be a challenge. Veterans at Arca Blanca highlight this issue, saying that, "while many companies have unique and helpful initiatives of their own, most don’t transfer any hard skills”. 

Given Arca Blanca’s unique value proposition and blend of team member backgrounds, our ESG Committee and data science team was able to quickly design and deliver a course to meet this need. Participants not only received taught lessons but were also tested and challenged on the practical application of concepts designed to highlight the commercial 'so what?’. 

Working with an organisation like TechVets is a way for businesses to influence the bottom line and create value. Arca Blanca strongly believes that a key ingredient of maintaining an equitable society is to ensure well-laid paths of progression for those that serve. By playing our small part in serving our veterans, we strengthened our values as an organisation. 

Find out more about TechVets and the work they do:

Arca Blanca recently partnered with TechVets, a leading not-for-profit delivering training to veterans

TechVets & Arca Blanca: our mission upskilling veterans

Artificial intelligence dominates the headlines, part promise and part spectre, as society grapples with how technology is changing the way we live – and work.

It is becoming almost certain that every senior business leader will sooner or later need to leverage data-led technologies to advance their organisation – and their career.

The Data Masterclass aims to equip executives with enough knowledge to unlock the opportunity and avoid the pitfalls of harnessing AI.

Misalignment between stakeholder expectations and what data science can realistically deliver.


A bewildering array of products, services and providers mostly speaking in a language that is not well understood.


A long list of competing demands on limited investment capital.

The masterclass is a 2-hour face-to-face session for the executive team and covers:

Introducing the size of the opportunity, AI mythbusting and the role of data leadership from the top.

The basics of data science and the different methods that can be used to create value.

How retailers can use AI to generate sales, improve pricing, reduce mark-down, identify high lifetime-value customers and create sophisticated customer insights.

What techniques and technologies does your business need to successfully execute being data-led?

Discussion of bias in data modelling and how you can safely use data to drive insight without compromising GDPR

What questions should you ask of yourself, of your teams and of your data experts? Speaking the same language can make a big difference.

The aim of the masterclass is to equip you with the confidence to use AI to quickly and accurately answer the most important questions in retail:

AI can be used to answer the most important questions in retail.

Executive Data Masterclass: the essential guide to practical AI

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.

We helped a leading software company reset an ambitious programme to reap $30m in incremental benefits delivering hyper-personalisation through a Customer Data Platform that combined experience management capabilities. 

Read more: https://www.arcablanca.com/case_study/a-customer-data-and-experience-platform/ 

We helped an organisation with millions of customers globally deliver individual, bespoke engagement strategies for each one of them, tailoring when to engage and through which channel. 

The AI tool also determines the optimal content and incentives to drive up new conversions, upsell, cross-sell and boost renewals across the customer base. This model represented a novel application of reinforcement learning into this space.

Read more: https://www.arcablanca.com/case_study/saasproviderchurnreduction/

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.

Our client had a familiar problem, but wanted and needed an atypical solution.

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.

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