Capability: Customer Insight

Challenging areas for the grey belt: London and the South West

Posted on: 28 October 2024
Challenging areas for the grey belt: London and the South West

In this final article in our grey belt series combining CACI’s housing demand data with VirginLand’s grey belt site identification, we take a deeper dive into two areas where the impact of the grey belt will be less obvious: London and the South West.

This is not to diminish the overall value of the initiative, but to point out that it is not the single answer to the UK’s housing challenges. To be most effective, grey belt reallocation should be considered alongside other mechanisms to accelerate housing delivery such as brownfield, infill, repurposing and urban regeneration.

How will the grey belt initiative affect London?

Home to over 7 million adults, London is by far the most densely populated region in the UK. As a result, demand for housing is particularly acute in the capital and the conversation is dominated by affordability. It’s easy to see why; house prices are 11 times the average household income, and private rent is 37% of income. Although households in London earn 17% more than the national average, these high prices mean that homes are 59% less affordable to buy and 32% less affordable to rent.

In this context, any initiative to increase the overall supply of housing in this region is welcome; particularly if it’s targeted at the more affordable end of the scale. So, what impact will the grey belt have in London?

Although home to 14% of the population, London can house just 0.4% of all grey belt homes – a total of 1,955 dwellings across 31 sites. This is not for want of green belt (22% of the London region, by area, is currently designated as green belt), but for want of suitable locations that could be re-designated. Analysis by VirginLand shows that just 0.2% of the available green belt land is likely to be reallocated grey belt, with much of the London’s green belt holding additional designations like Designated Open Space, Country Park, Woodland or Nature Reserve and Conservation Area and Grade 1-3b agricultural land grades.

The challenge in London is also compounded by the location of the sites relative to movers. Being on the outskirts of the urban sprawl, just 11% of all London home movers live within the catchment of the identified sites; roughly 192,000 individuals. Although more than enough to absorb any new homes delivered, the scale of movers puts into perspective how limited the impact would be on demand; if all sites were built out to their fullest, there would be 98 movers for each home.

While there is little doubt that the 31 identified locations would be additive to the overall housing stock, the question is over how much of an impact these limited sites can have on a particularly strained market. With the population set to grow by another 6.1% in the next 10 years, London will need other initiatives, alongside the grey belt, to accelerate housing delivery in more urban neighbourhoods.

How will the grey belt initiative affect the South West?

Just 4.5% of the South West is designated as green belt, well below the national average. It therefore follows that grey belt opportunities in the South West will be similarly limited, and just 228 potential sites have been identified with the combined potential to deliver 11,868 new homes.

While this is not an insignificant number of homes, it represents just 2.2% of the total grey belt opportunity spread across 9.4% of the population. The location of grey belt sites also limits the initiative’s regional impact, as just 18% of the 1.6 million potential South West movers live within the grey belt catchment, against a national average of 36%.

Although limited in scope for the region as a whole, there are some pockets where the grey belt will be more impactful, and the characteristics of the catchment movers in these locations point to the type of homes that should be prioritised.

With concentrations of sites close to urban populations in the likes of Bristol, house-to-earning ratios and rent-to-earning within the grey belt catchment are higher than those outside of it (7.1 times income and 27.4% of income respectively). High concentrations of Family Renters, Tenant Living and Cash-Strapped Families within this catchment, and relatively large sites averaging 1.7 hectares, suggest a particular opportunity to deliver larger mixed neighbourhoods with high levels of rental product.

As with London, the grey belt initiative has the potential to support some of the housing needs of the South West, but an overarching housing strategy for the region should also be mindful of the 82% of home movers that live outside of the grey belt catchment.

How CACI can help?

To learn more about how you can ensure that your developments are meeting the demands of local movers, contact CACI.

Missed the previous blogs? Find the links to the series so far below:

How grey belt sites will help tackle the UK housing crisis – CACI – Tolga Necar

Grey belt sites: what they are, locations & impact on housing – CACI –  Steve Norman and Sam Bedford,  Virgin Land

Assessing the impact of the grey belt initiative on a National scale – CACI – Tolga Necar

How will the grey belt initiative affect North West England & Scotland? – CACI – Tolga Necar

Why should businesses utilise the latest LLMs and latest NLP techniques?

Posted on: 30 August 2024
Why should businesses utilise the latest LLMs and latest NLP techniques?

In our rapidly evolving world, leveraging cutting-edge technologies is no longer a luxury, but a necessity, and Natural Language Processing (NLP) stands out as one of the most transformative tools available. NLP focuses on the interaction between computers and human language, this is commonly seen in systems such as Large Language Models (LLMs), Interactive Voice Response systems (IVRs), and voice assistants. These technologies have the power to revolutionise a company’s service by making interactions more efficient and effective, whilst reducing costs, so why haven’t more companies harnessed them? 

Let’s consider customer service – an area where the technology has already made significant strides. Many businesses still have systems that heavily rely on human operators, requiring them to tackle customer calls with highly specific and complex issues. Implementing new NLP systems can lessen the reliance on these human operators, leading to decreased wait-times, improved efficiency, and 24/7 availability. However, these systems often come with significant costs and require substantial infrastructure changes. If not executed properly, they can lead to unintended consequences and ruin the customer experience. Therefore, before adding new systems, you must understand and quantify why customers are contacting you and identify where systems can enhance the customer journey and reduce cost.  

What AI tools are there for text analysis

Various AI approaches are available to address a wide range of problems. We can categorise them as follows: 

  • Generative LLMs: Examples include GPT-4 (ChatGPT), Gemini, and Claude. These are the models that excel at generating content e.g. summarising a customer call.
  • Non-generative LLMs such as BERT, RoBERTa and their various forms: These models are used extensively in applications that require deep understanding of context or meaning e.g. accurately classifying known topics for a customer call.
  • Traditional NLP techniques: This category encompasses rule-based systems, Word2Vec, and more. They work well with simpler tasks. E.g. detecting if a particular service is mentioned in a customer webchat.

What’s the difference between generative and non-generative LLMs?

Fundamentally, LLMs like GPT-4 and BERT are built from the same building blocks called transformers, so what makes them differ?

Typically, a transformer is comprised of both Encoder and Decoder parts, but it’s been found that models can be specialised through stacking either encoder or decoder blocks. GPT-4, a generative LLM, is often referred to as a decoder-only architecture. This allows the model to receive an input, then generate text that is contextually relevant to the input. Not only does it mimic human-like text, but these responses can also be seemingly creative.

BERT, on the other hand, is built using encoder-only architecture, so think of it as a specialist in both reading and interpreting human language, rather than generating it. Non-generative LLMs, when utilised effectively, offer considerable power without a lot of the overheads associated with the generative LLMs. While some infrastructure is necessary for their implementation, the costs are not prohibitively high, especially when employing distilled models. For instance, users can avoid making expensive high frequency API calls to generative LLMs or using extensive computational resources. Additionally, users have greater control over model customisation, allowing them to achieve optimal performance for domain-specific tasks. These advantages make non-generative LLMs an excellent choice for handling highly sensitive data within a secure, isolated system e.g. a client’s secure inhouse database and system.

The following table offers a high-level comparison of the different NLP tools:

Are traditional NLP techniques still relevant? 

Although LLMs are highly adaptable and have great performance across a wide-range of tasks, traditional NLP techniques remain relevant due to their task-specific tunability. These methods have been in use for decades and continue to play a crucial role in various niche applications. Traditional approaches often benefit from cost-effective compute resources and specificity, but they require more manual tuning to achieve optimal results, and typically only work well on low-complexity tasks. In general, these techniques are better-suited for curated, lower-performance internal systems, where they can carry-out dedicated automated tasks inside a pipeline.

Intent classification in action 

Back to our customer service example – using a combination of NLP techniques, generative, and non-generative LLMs, we can identify the intent of customers when speaking to customer service operators. 

In the first instance, we can apply quick traditional NLP methods to identify if this alone is suitable for our task. However, due to the complexity of customer interactions, it is unlikely that this will produce robust results. The next step would be to employ a generative LLM on a subset of calls to identify intent topics. While this may provide sufficient insights to enhance the customer journey, for truly informed business decisions, it is essential to gain a holistic understanding. Therefore, quantifying the number of calls related to each topic might be of interest.  

To quantify the number of calls it is best to use a non-generative LLM like BERT, as they will outperform their generative counter parts, are much cheaper and far easier to implement at scale. Previously we have had great results using these types of techniques and methodologies in a range of different projects.

How CACI can help

If you’re looking to enhance your business with cutting-edge NLP solutions, our in-house data science teams are here to help. Contact us today to start transforming your use of data and stay ahead in the ever-evolving landscape of AI and data science.

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How Agilysis use Acorn to improve road safety interventions & outcomes

Posted on: 20 August 2024
How Agilysis use Acorn to improve road safety interventions & outcomes

Background

Agilysis is a transport safety consultancy specialising in road safety. There is a wealth of experience working with road safety and a company-wide mission of using data to inform road safety interventions and prevention strategies for casualty reduction and overall road awareness.

One of their key requirements is the ability to supply insights to road safety stakeholders about individuals involved in or exposed to different types of road risk in various local communities.

The Challenge

Agilysis had been using another socio-demographic profiling tool to convey the necessary demographic profiling insights for over a decade. However, as time progressed, two critical issues arose:

  1. The tool’s provider wouldn’t allow Agilysis to view their socio-demographic profiling model at a low enough geographic level to make it as useful as needed.
  2. Agilysis was unable to expose all the available variables to their stakeholders due to license holder restrictions. This particularly affected their road safety stakeholders who generally work for local authorities and police forces.

The Solution

Agilysis began using CACI’s geodemographic segmentation, Acorn, to enhance the calibre of their road safety intervention design and deliver precise, robust results to stakeholders.

“What made Acorn an attractive product from our point of view is that those restrictions were reduced,” Bruce Walton, Technical Director at Agilysis, explained. “We were allowed to expose the kinds of information that are particularly relevant to our stakeholders. We’ve been able to make that available to our stakeholders and therefore sharpen the focus of the information that we are able to give them.”

The business dissected the available list of all the metrics, identifying those which felt most useful, easiest and relevant to understand and apply to the individual forces policing strategy.

By leveraging these insights, Agilysis can better understand the likely propensity of an individual Acorn type to partake in various acts of travel, walking and cycling, a key priority of many road safety stakeholders nowadays.

Read the case study

For more detail on how Agilysis are leveraging Acorn, and what the organisation plans to do next, read the full story here.

If you’d like to find out more about Acorn, visit our microsite, or get in touch to schedule a demo.

Connecting paid media to first-party data: a path to enhanced customer engagement

Posted on: 29 July 2024
Connecting paid media to first-party data: a path to enhanced customer engagement

In today’s competitive market, the cost of acquiring new customers is continuously increasing. Coupled with changing privacy regulations and consumers’ growing demands for personalised experiences, traditional acquisition strategies that are heavily reliant on third-party data are becoming less effective. Herein lies the crucial role of first-party data in acquiring the right customers and effectively retaining them.

Getting started: why alignment and collaboration matters

Realising the potential value of first-party data requires effective collaboration between Paid Media and CRM teams. When these teams operate in silos, valuable customer insights and behavioural data are not shared effectively, and brands risk a disjointed experience alongside increased ad spend.

Aligning these teams around cohesive goals and strategies ensures that the rich, actionable insights derived from first-party data are used to inform and optimise paid media campaigns. This cross-team collaboration can significantly enhance targeting accuracy, message relevance, and ultimately, customer acquisition and retention.

Unlocking the value of first-party data across CRM and social

When it comes to delivering impactful CRM campaigns, particularly on highly competitive social channels, first-party data is invaluable to delivering a relevant and cohesive customer experience. By implementing the following, brands can ensure the effective and impactful utilisation of their data:

  1. Comprehensive customer profiles: By integrating data from various touchpoints—such as website interactions, purchase history, and email engagement—brands can build rich and comprehensive customer profiles. These profiles enable precise segmentation and targeting, allowing for highly personalised ad content that resonates with specific audience segments to come to fruition.
  2. Behavioural targeting: First-party data can be used to understand customer behaviours and preferences. For instance, if a customer frequently browses certain product categories but hasn’t made a purchase, targeted ads featuring those products, along with special offers or discounts, can be highly effective in driving conversions.
  3. Dynamic and personalised content: Social media platforms offer advanced tools for dynamic ad content. Brands can use first-party data to create ads that dynamically change based on the viewer’s profile and past interactions. Therefore, creating personalised and distinctive comms at key customer moments not only increases engagement, but also adds a competitive advantage through an enhanced overall customer experience.
  4. Cross-channel consistency: Ensure that the customer experience is consistent across all channels. If a customer has already purchased a product, avoid retargeting them with the same product ads. Instead, use the opportunity to introduce complementary products or services, thereby adding value and enhancing the customer journey.
  5. Real-time optimisation: Leverage real-time data to continuously optimise campaigns. Monitor customer interactions and campaign performance closely, and use these insights to make timely adjustments to your targeting and messaging strategies.

How CACI can help

In the context of rising customer acquisition costs, the alignment and collaboration between paid media teams and CRM teams have never been more critical. This strategic integration not only enhances the customer experience, but also drives better business outcomes—improving acquisition efficiency, increasing customer loyalty, and ultimately, boosting the bottom line.

As the digital marketing landscape continues to evolve, brands that prioritise the seamless integration of their marketing efforts and harness the power of first-party data will be best positioned to succeed. The future of marketing lies in breaking down silos and fostering collaboration, ensuring that every customer interaction is informed, intentional, and impactful.

CACI’s team of experts have extensive experience in helping clients enhance customer engagement through a multitude of strategies and solutions. If you or your business are ready to explore how first-party data can lead to effective customer acquisition and retention, please get in touch to discuss how we can help you.

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How to estimate affluence with satellite imagery

Posted on: 19 July 2024
How to estimate affluence with satellite imagery

When looking to understand the geodemographics of a country, a segmentation can be an invaluable tool for describing the differences between neighbourhoods to drive decision making, guiding you towards the areas where your customers or lookalikes are. Indeed, CACI’s Acorn helps thousands of organisations better understand and target their audiences within the UK market. One of the key ways Acorn differentiates between top-level Categories is by affluence, which is a crucial factor for a segmentation in a business context. 

In the UK, there is a wealth of data we can draw upon to build geodemographic segmentations like Acorn, including a robust and detailed census, land registry, and most importantly, a well-defined, small-scale Postcode system. But in foreign markets, such detailed data often doesn’t exist, and where it does, it can be of poor quality, hard to verify and at a regional level. So, how can we build a reliable segmentation in these markets? 

Satellite imagery as a novel approach

In many countries, the nuances in affluence between neighbourhoods can be gleaned not from looking at tables of data, but by looking at them from above. Satellite imagery is incredibly useful when traditional data sources are lacking, but visual differences between affluence levels are clear. Take, for example, the below images of two areas in a desert country: 

In the image on the left, there are large buildings, geometrically defined roads, pools and greenery, which is expensive to maintain in a desert country. This area is likely to be of a generally higher affluence. In the image on the right, there are buildings of uneven height, densely packed together along uneven and jagged roads. This area is likely to be of a generally lower affluence compared to the image on the left. 

We can see by eye the differences between these areas, but we can’t feasibly label all the areas of a country manually. So, how to do we do this programmatically? 

Enter Convolutional Neural Networks (CNNs), a well-established deep learning technique that’s the bedrock of image analysis. Inspired by the visual cortex of an animal, they are trained to identify the patterns and shapes in an image and use this to predict the likely classification of objects or the image as a whole.  

For successful usage of a CNN, however, quality training data is vital. In classic examples of image recognition, such as the MNIST dataset of handwritten digits, most people would have no issue labelling the training images correctly to feed to the model. This is trickier for labelling the affluence of a small area, though, as you need deep local knowledge and the time to manually label thousands of images to achieve a model with usable accuracy. CACI has invested heavily in building a robust pipeline for this process, allowing us to achieve the scale required for accurate modelling. 

H3: The unifying geography

We now have a methodology for generating some information about affluence, but we still have another problem to tackle – what geography should a segmentation be built at? 

The natural response might be to consider administrative boundaries. This is the level at which most governmental social and economic data is released, so it makes sense to consider this as an option. However, the irregularity of the shape and size of administrative regions in many countries means that it can be hard to compare areas like-for-like, hampering effective decision making. 

H3 – a geospatial indexing system developed at Uber – splits the globe into a grid of tessellated hexagons at varying scales, from the largest scale at 110 hexagons to the smallest at ~570 trillion hexagons. It’s gained popularity thanks to its ease of use, speed and availability of algorithms and optimisations for working in its geography. 

H3 is a great alternative to Postcodes in areas where they can’t feasibly be used. It can be applied consistently across a country and at a low level of granularity, meaning that any segmentation applied at this level can clearly show the differences between areas in an accurate way. It’s also easy to aggregate up to other geographies, allowing you to integrate the data into other systems where data is not so granular. 

How CACI can help

Combining the power of H3 hexagon geography with the information gained from analysing satellite images, we can gain great insight into the relative wealth of areas in countries where existing data is simply not available. 

The ability to apply image analysis, however, means nothing without deep expertise in segmentation and location strategy. By combining your knowledge of your customers with our expertise in data science, insight and location, CACI can support you in your journey. 

Whether you’re an established international organisation or looking to move into a new market, contact us today to find out how we can help you take the next step in achieving your goals.

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Most substantial challenges for healthcare organisations to address in 2024

Posted on: 25 June 2024
Most substantial challenges for healthcare organisations to address in 2024

Tackling health inequalities is a tremendous challenge.  It requires healthcare organisations to understand the demographics, lifestyles, behaviours, needs, and external pressures that individuals across the country face daily with greater accuracy. Access to accurate and detailed data significantly impacts an organisation’s ability to develop a robust response to inequalities and determine which services will meet local needs.  

In our recent webinar for NHS England on “Tackling Health Inequalities with Effective Data & Insight”, we explored the impact of our datasets and insights on NHS England’s ability to tackle current health inequalities and devise strategies to improve future outcomes.  

So, what have the findings from our various datasets and our Voice of the Nation (VOTN) Q1 2024 survey shown regarding the behaviours and health concerns of different demographic and affluence groups across the UK? How can healthcare organisations apply these findings to improve outcomes for their local communities? 

Half of the survey respondents are concerned about their personal wellbeing and mental health

Personal wellbeing and mental health are incredibly important considerations for the NHS. According to our survey results, these have been hugely concerning for people of various ages across the UK, with 50% of our VOTN Q1 2024 survey respondents claiming to be concerned about both. This is the highest number of respondents for these sentiments that CACI has ever seen in the four years of this survey being conducted, demonstrating the need for healthcare organisations to review their current offering of personal wellbeing and mental health services avoiding a ‘one size fits all’ approach that targets all ages.  

Millennials are the most concerned of all age groups about their health

While the traditional assumption may be that younger generations are more carefree and less preoccupied with the concerns of the world, our survey results have shown the opposite. Millennials were the most concerned of all age groups (from Boomers to Gen Z) for their personal wellbeing and mental health, with more than two-thirds feeling this way. This further reiterates the necessity of ensuring that all age groups—particularly Millennials—are offered relevant personal wellbeing and mental health services. 

Affluence does not shield from health concerns

Our survey results indicated that personal wellbeing and mental health concerns have been affecting individuals across all affluence levels. While one might assume that higher-affluence individuals experience fewer wellbeing and mental health concerns, our findings revealed that as many as half of the respondents from the higher-affluence Acorn categories of Luxury Lifestyles and Established Affluence expressed concern about these aspects of their lives. Respondents from the Low Income Living Acorn category expressed the highest level of concern for both areas.  

These insights provide concrete evidence for healthcare organisations to tailor their services based on the specific needs of different affluence groups, rather than relying on open data or assumptions. These results demonstrate the right healthcare services must be accessible across all affluence levels.  

How can CACI help?

CACI can help healthcare organisations tackle health inequalities, supporting a range of clinical areas of health inequalities from severe mental illness (SMI) to maternity and chronic respiratory disease (CPD) to early cancer diagnosis, hypertension case-finding and more. Our partnership with NHS England provides all 42 integrated care boards (ICBs) with free access to a variety of datasets that are being used to tackle health inequalities.

Contact us today to learn more about our partnership with NHS England or to find out how our datasets can improve outcomes for your healthcare organisation. 

Get ahead with CACI: Unlock the power of AI and ML in your CRM

Posted on: 19 June 2024
Get ahead with CACI: Unlock the power of AI and ML in your CRM

Setting the stage:  

The field of Machine learning and AI has evolved rapidly in the last few years, especially in fields where large quantities of data and quick response times to queries are crucial. But given lots of these techniques and methods have been around for a much longer period, why has it taken so long for other industries outside of small start-ups and ambitious tech giants to leverage these methods in similar ways? 

CRM is an essential component of any company’s strategy. The ability to communicate with and understand customers is more important than ever due to the low barriers to entry in highly competitive global markets. Companies have only brief moments to convince customers that they are the right choice for shopping, spending time, or engaging. Optimising these initial and subsequent contacts is paramount to success. 

Beyond just expanding your customer base and attracting new clients, CRM is vital for any company’s retention strategy. The most advanced cutting-edge models in the world are utterly useless if we don’t know how to activate and capitalize on the value they represent. 

ML Foundation:  

In the CRM space our main goals are increasing consumer retention or spend, and we do this via figuring out the most effective ways to communicate with people. This can be broken down into when to speak to them, how to speak to them and why to speak to them.  

Recommendation engines lie at the core of many of these architectures, models that are designed to figure out what you want before you even know you want it. Broadly they work by looking at the kind of customer you are, then at customers like you, then finding things that they’ve bought recently that you haven’t.  

You can even simplify this down into just looking for customers who have an identical purchase history to you. Maybe a laptop you can buy on Amazon doesn’t come with a charger, so commonly when people buy this laptop their next purchase is a charger!! (You can often see this simple logic in the “People also bought” section of Amazon). But even these simple implementations are incredibly powerful in some ways, an educated guess is always going to be better than a random one. 

So how do these methods relate to CRM? Well, the general structure can be pulled away and applied to any subject. When we think about how to engage with a customer, we’re going to look for ways we engaged with similar customers and how these performed. The customer who likes Sabrina Carpenter will probably need to be spoken to in a different way to the Motorhead fan. 

This is simple stuff, right? Well exactly, but it’s a method to show that the underlying AI processes in these platforms aren’t really all that complicated – there’s a lot of room for improvement especially when implementing bespoke solutions with larger data sets.  

The next (generative) step:  

So, we already have ML methods that can tell us when and why to talk to people, great! But what’s the next step? 

All that’s left of our final stage is how to talk to them and what to say, stages which can and are currently being revolutionised by the advent of enterprise grade Generative AI. 

A current pipeline for devising CRM processes may involve creating template communications that are then populated with more specific information, for example customers in a certain segment defined by age and tenure are assigned one template and differing segments are shown another. 

This approach can be time consuming if it needs to be completed for each campaign, and may miss a level of personalisation that people will respond to, feeling as though each message is tailored to them rather than being an email blast they just happen to be caught up in. 

Skilled AI engineers armed with LLM’s can create a unique voice for each consumer, ensuring that quite literally all communication they will ever receive are exactly personalised to them and their engagement habits with your brand. 

Imagine attempting this even a few years ago, assigning a team of people to trawl through millions if not billions of rows of data to ensure that each customer got the perfect messaging for them would have been completely impossible. 

In practice this level of granularity in communications is probably unnecessary but it speaks to the potential these models have in this space – the sky truly is the limit. 

Even starting off small with these steps, giving a small part of a communication a generative component, allowing for large scale A/B testing and continuous model training, the effectiveness of these comms will improve over time. 

Freeing this time up from your CRM team will give them more time to tackle more involved problems that can’t be automated. 

How can we help you on this journey?

Don’t get left behind. Partner with CACI and our experienced in-house data science teams to integrate cutting-edge ML and AI into your CRM processes and experience unparalleled growth and customer satisfaction. Contact us today to learn how we can help you stay ahead of the curve.

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Most impactful food-to-go transaction trends into 2024

Posted on: 12 June 2024
Most impactful food-to-go transaction trends into 2024

With the continuing trend of hybrid work within worker hubs, consumers’ food-to-go spending in quick service restaurants (QSRs) remains concentrated on some days and displaced on others. Consumers’ wallets also continue to face an ongoing squeeze, resulting in pressures on day-to-day convenience spend.  

So, what transactional trends are being observed across different demographic groups, geographies and price-points as these trends continue? What impact do these trends have on operators’ future openings strategies and overall performance? 

Food & beverage have become increasingly prominent on High Streets 

Over the course of 2019 to 2023, most retail centres in all asset classes have grown their share of food and beverage (F&B) outlets, noting an increase in over 90% of centres in the top four classes— City Centres, Regional Malls, Major Town Centres and Satellite Centres. Despite F&B having become increasingly prominent in shopping and retail parks, there has been a mixture of increases and decreases observed in towns, transport hubs and leisure parks, raising the question of whether oversaturation has had a role to play in some locations.

Centres are polarising

Over the same time period, city centres, regional malls, major towns centres and satellite centres have dropped in their overall level of consumer attractiveness in line with consumers’ changing behaviours. So much so, that the four largest asset classes have seen declines in over 90% of their centres. The picture is a bit more mixed as the retail hierarchy descends into towns, transport hubs and leisure parks, however, with an average of 40% of centres in these asset classes seeing a decline. The ever-increasing proportion of consumer spend moving online has undoubtedly prompted these downward trends.

Given the vast differences in changes at an asset class level, and with many exceptions at a centre level, having access to detailed data on the changing attractiveness and demographics at centre level is vital. 

Customer behaviours towards QSRs continue to change

Many may think that post-Covid QSR demand is just about Tuesday to Thursday, driven by changes in working behaviour, but this is an over-simplification. CACI’s local centre mobile app data analysis within our Location Dynamics suite shows that while areas like Fleet Street/St. Paul’s in the City of London now do have a pronounced Tuesday to Thursday peak, it’s far from the universal norm. As shown by the dark-shaded time segments in the graphs below, places like Barkers Pool in central Sheffield have a very pronounced Friday and Saturday night economy. This further contrasts with central Eastbourne, which has maintained a more traditional Monday to Sunday 9 a.m. to 4 p.m. custom and a strong weekend daytime custom.  

Ultimately, locations are different, and successful operators must understand the different ‘missions’ their customers will be on to ensure they meet their customers’ needs and ensure that they staff their outlets to provide the right level of services at times demanded by their customers.

For food-to-go retailers to engage with consumers at the right time and in the right place, it will be critical for them to consider:  

  • The F&B offers in local areas 
  • Changing consumer behaviours as a reflection of new and embedded worker patterns, 
  • Centre attractiveness 
  • Overarching market shifts that impact footfall on specific days and times.  

How CACI can help?

With these trends in mind, it is critical for food-to-go retailers to have a detailed understanding of who their customers are, where they are located and what times of the week they are most likely to interact with your chain or restaurant. It is equally important to understand your place in terms of its attractiveness to customers and the effect of its location on driving footfall.  

Data is key to maintaining a competitive edge amidst evolving trends, an area where CACI excels in providing support. Find out how we can keep you and your team ahead of the curve by reaching out to us today.

Most impactful holiday and air travel trends for 2024

Posted on: 30 May 2024
Most impactful holiday and air travel trends for 2024

If the last few years of pandemic uncertainty and budget constraints amidst the ongoing cost of living crisis have shown us anything, it’s that travellers have become increasingly conscious of the cost of travel. As a result, they’ve placed increased value on having an optimal travel experience to justify its cost.  

We examined the current driving factors behind optimised travel experiences in our Voice of the Nation Q1 2024 survey, where we asked 2,000 respondents how they felt about an array of travel changes and how the cost of living, airline loyalty and more have impacted their travel choices into 2024. 

So, what shared values and needs do travellers of all ages and affluence levels seem to have in common this year? How have these forthcoming trends been affecting the wider travel industry?

Travel spend will increase in 2024 despite decreases in most other sectors

When asked whether their anticipated spending will decrease, increase or stay the same this year compared to last, holidays actually rank third among areas people expect to increase spend in 2024– with groceries and commuting costs coming in first and second– despite an overall expected decrease in spend in other areas this year.  

Plans to holiday abroad skew significantly on affluence lines 

From Boomers to Gen Z, more than half of respondents from every age group plan to holiday in some capacity– both in the UK or abroad– in 2024.  

When it comes to taking holidays abroad, 38% of respondents are making plans and budget room to do so this year. Of these respondents, as much as 50% come from the higher affluence Acorn categories of Established Affluence and Thriving Neighbourhoods. Approximately one in three of the lower affluence categories of Steadfast Communities, Stretched Society and Low Income Living share the same sentiment.  

A quarter of all respondents have no intention of travelling this year, and 22% plan to visit another part of the UK, which would appear to be in an effort to save on travel spending. In reality, no matter where you go for your next holiday, the same proportion of respondents agree that cost will be the biggest determinant behind their destination. 36% of those staying in the UK say that they will go on holiday within the UK because they prefer it to going abroad, showing that while cutting travel costs is a major driver, it is not necessarily the only one.  

Half of respondents claim no loyalty to an airline

When asked what the contributing factors towards airline loyalty are, half responded that they have no loyalty to any airlines.  

Roughly one-third (31%) of those who are loyal towards an airline felt that their loyalty is driven by more than one factor, such as convenience, discounts and luggage/check-in benefits. In comparison, 18% felt there was only a singular driving factor behind their airline loyalty, showing that where loyalty is in play, it is usually multi-factorial. 

Convenience is the most significant driver behind airline choices

Apart from price, respondents’ most significant contributing factors towards airline choices when booking trips came down to flight times and route, both of which are also the only factors heavily skewed by affluence. Nearly 60% of the Established Affluence and Thriving Neighbourhoods category respondents reported this to be significant, compared to just 35% among Low Income Living. Gen Z, however, scored this even lower, with just 32% finding this to be significant and instead placing more emphasis on the ease of booking at 37%. 

Families are much more affected by cost this year

In terms of holiday planning this year, one-third of respondents said that they wanted to keep their holiday costs as low as possible to maximise value for money and felt that costs would be the greatest determinant of where they holiday in 2024. Among those with children, 40% said that cost is the biggest determinant of where they go on holiday. 

Sustainable transport options appeal much more to Gen Z

Of all demographics, Gen Z appear to be the most motivated by sustainability when planning their holidays, both in terms of those taking immediate action but also those who would like to travel but feel unable to presently. In fact, 18% of Gen Z respondents said that they will be cutting down on air travel in 2024 due to their growing environmental concerns, compared to just 8% among the rest of the population. 

How CACI can help?  

As the travel industry evolves with travellers’ changing sentiments, holiday and air travel operators must be equipped with the necessary understanding of who their customers are, what their motivations for travel are, what they seek from their travel experiences and how to deliver optimal experiences that will drive loyalty. Data is integral to this, which is where CACI excels in providing support.  

To find out how we can keep you and your team amidst turbulent times, get in touch with us today.

Impact of turnover vs. footfall for shopping destinations in 2024

Posted on: 23 May 2024
Impact of turnover vs. footfall for shopping destinations in 2024

Footfall has historically been the go-to method for measuring a shopping destination’s performance, conducted through pressure sensor mats, light sensors tracking shoppers’ entry and exit movements, advanced camera systems and more. Although ubiquitous across the retail industry, only measuring the number of people entering and exiting a store misses important aspects of true store performance.  The current pace of change in consumer behaviors demands that commercial landlords and occupiers know more about their performance drivers if they are going to thrive.

So, why is this the case? What do commercial landlords need to know about turnover and footfall to stay afloat?

How consumers’ changing behaviours towards shopping locations affect footfall

Since 2019, vendors across the UK have experienced an overall 11.5% drop in footfall. While this may sound like catastrophic news for retail destinations, the truth behind the headline footfall figures is perhaps surprising– an overall rise in consumer spending. Although a shift in consumers’ shopping behaviours is undeniably present, its impact may not be as profound as it seems.

Frequency has been a major driver of this, dropping by 31% over the last five years, meaning that consumers have been visiting shopping places much less often. However, the amount being spent by consumers when visiting shopping locations has climbed 29% over the last five years, counteracting declining footfall. 

This increase in trip spending is not just an inflationary rise – the fundamental reason to visit and our behaviours on visits have changed as a result. Successful locations are those that are adapting to the new shopper landscape.  

How consumers’ changing spending habits, values & “missions” affect footfall

What consumers are spending any disposable income on has also been changing. While retail conversion has remained relatively unchanged, there have been evident increases in Catering and Leisure conversions on the same trips, meaning consumers are increasingly combining a shopping trip with food/drink or a leisure activity. It is this combination of shopping, browsing, eating/drinking and leisure that has led to the overall increase in spending per trip.  

These comparisons can be illustrated through what we at CACI call “missions” from our Shopper Dimensions dataset, which illustrate the trip someone is on at a given time, and attribute “missions” to the tangible actions someone takes once at the shopping destination, such as browsing, spending, time spent, etc., to assign a “mission” to each trip.  

According to our findings, consumers are relinquishing their less engaged “missions” but concentrating trips around the “Big Day Out” trip. This is illustrated in the shifting profile of the top three missions in Shopping Destinations, which explains why a decline in footfall does not necessarily equate to declining spend. At a glance:

  • “Big day out” missions are our more engaged trips. They may be less frequent, but they are ones where multiple retail stores are often combined with Catering and Leisure, resulting in a trip spend 2.4x the average mission. Since 2019, these missions have grown to 23% of all shopping missions. 
  • 37% of “spending time” missions have no purchasing associated with them. While they may contribute to footfall figures, they do not directly contribute to sales-through-tills. Having dropped off post-Covid-19, these trips are now holding flat at a lower shelf. 
  • “Routine top-up” trips are quick, functional and emotionally disengaged trips that a spend of just 47% of an average trip. These trips are dropping out of our repertoire and can be substituted online.

We can therefore see that looking in greater detail at the changing nature of the trips made provides a clearer understanding of commercial asset performance than simply tracking the overall volume of trips.

Key levers to conclude turnover & application methods to target growth outcomes

To make a meaningful impact in asset performance, commercial landlords must move beyond measuring just the number of visits and start reporting the different levers of shopping location spend.  
 
While there are nuances behind the headlines that apply individually to each location, all spend at a shopping location can ultimately be boiled down to three key levers:

  1. The volume (number) of unique shoppers they have 
  2. The frequency of consumers’ visits to a shopping destination 
  3. The value that each shopper spends per trip.

Commercial landlords should consider applying the following methods to each lever to effectively target growth outcomes:

  1. Volume: Convert footfall (visits) into ‘spenders’ and target engagement strategies at driving scheme trial; measured by the percentage of the catchment population currently shopping with you (penetration). 
  2. Frequency: Embrace the different role that your asset plays for different cohorts, diversifying the occupier offering to give shoppers more reasons to return on different missions. 
  3. Value: Determine the highest spending shopper groups to target, segment customers and tailor offers to them to increase cross-shopping opportunities and drive value.

What does good look like?

Now is the time for commercial landlords to leave pre-pandemic comparisons behind. Footfall may be down overall, but the evolution of consumers’ shopping destination behaviour serves as a reminder that relying on the past as an indication of how assets should behave will not lead to longer-term success. If anything, these behaviours have demonstrated that the types of trips people continue to use shopping locations for are more engaged and valuable than ever before.  

Our unique view into how and where consumers are spending has been made possible with the help of datasets like Shopper Dimensions, which enable KPI benchmarking of assets against similar locations across the UK and leverage transactional and data spend insights to enhance decision-making. We can help you calculate the impact of each shopper metric and the headroom compared to peers and catchment.  

To find out more about what Shopper Dimensions can do for you and your business, speak to one of our experts today.