Insights Archives - DVS - Data Vision Services

Understanding How Far You Are From Best Practice ARR Reporting Using Our Customer Cube Scorecard

Emily — Wed, 08 Oct 2025 14:35:16 +0000

INTRODUCTION

• In the rapidly evolving world of B2B SaaS businesses, the customer cube stands out as a strategically important reporting tool.

• A customer cube gives you a structured way to track your most important customer metrics, including Annual Recurring Revenue (ARR).

• A reliable customer cube is especially key for Saas companies backed by Private Equity, as investors will scrutinise these metrics rigorously during due diligence processes.

• After years of working with SaaS clients at various stages of growth, we’ve learned that pursuing “perfect” ARR reporting often becomes a barrier to meaningful progress. We believe that a better path forward is one that involves continuous evaluation and improvement.

• That’s why we’ve developed an ARR reporting scorecard that evaluates the end-to-end process of creating a reliable customer cube.

• It also helps firms understand how far they are from best practice and provides a framework for ongoing evaluation of their reporting capabilities.

THE 5-STAGE FRAMEWORK

Our scorecard is built to assess the 5 critical stages of developing a robust customer cube.

Stage 1: Input Data

All reporting solutions rely on reliable raw data with most customer cubes drawing their data from ERP and CRM systems. Quality and consistency are crucial when inputting and maintaining data, as this directly impacts all subsequent reporting stages.

Some key considerations for the input layer involve ensuring reliability of the source data, integrating systems and setting up automated validation processes.

Ask yourself: Does the input data cover all recurring revenue, customers and products with mappings between them? Are the ERP and CRM systems suitable for a business your size in your sector? Which teams are responsible for each input source?

Stage 2: Data Cleaning & Adjustments

Data from your input system rarely arrives in perfect condition. The cleaning and adjustment layer serves as the critical quality control checkpoint where errors, inconsistencies and anomalies are identified and handled. Businesses should also ensure that they have robust adjustment handling processes in place to handle known errors.

Ask yourself: Does the source data require minimal cleaning and adjustments? Are adjustments automated, and is there certainty that all necessary adjustments are made? Are cleaning and adjustment steps documented? Are adjustments made before a month’s numbers are finalised?

Stage 3: Transformation

The transformation layer is where disparate data sources are unified and restructured into a format that allows you to calculate the relevant metrics displayed on your final dashboard suite.

Ask yourself: Are there any manual processes used to engineer the data? Is data transformed using logical code and a clear order? Are all data transformations documented? Are transformations completed in a recommended tool which multiple people have skills in?

Stage 4: Calculations

This stage focuses on defining the calculations for key customer cube metrics. Beyond basic ARR calculations, this can include estimating churn and calculating metrics like GRR, NRR and CARR. The thought you put into your calculation layer directly impacts the depth and accuracy of the insights available in the final reporting solution.

Ask yourself: Are bespoke company definitions clearly defined and laid out? Are they documented in both code and text? Are key metrics validated once they are calculated and checked to ensure they make sense?

Stage 5: Visualisations

The final stage transforms calculated metrics into accessible reporting, often in the form of a dashboard. Effective visualisation goes beyond simple chart creation. It involves designing interfaces that facilitate quick decision-making while providing the flexibility to delve into detailed analysis.

Ask yourself: Are the right visuals used to answer your key questions on business performance? Can users drill down to row-by-row level data and extract it when they need to? Are self-serve dashboards connected to a single semantic model? Is the ordering of dashboards in line with common user journeys?

THE VALUE OF SYSTEMATIC ASSESSMENT

Our reporting scorecard comprises carefully crafted questions that assess each stage of the customer cube development process. These questions help businesses identify strengths, weaknesses and improvement opportunities within their existing reporting infrastructure.

The scorecard approach provides several key benefits:

Objectivity: By applying consistent evaluation criteria, businesses can objectively assess their reporting capabilities and track improvement over time. Regular assessments build confidence amongst internal teams and external stakeholders in the reliability of customer cube reporting.

Comprehensiveness: The 5-stage framework ensures no critical component of the customer cube development process is overlooked.

Actionability: Rather than simply identifying problems, the scorecard approach provides a roadmap for systematic improvement and indicates how far a business is from best practice.

EMBRACE CONTINUOUS IMPROVEMENT

The pursuit of “perfect” ARR reporting can become a barrier if it prevents businesses from implementing meaningful improvements. Our experience suggests that the most successful SaaS businesses adopt a continuous improvement mindset, regularly assessing and enhancing their customer cube, rather than waiting for perfect solutions.

This approach enables businesses to build more reliable, insightful and actionable customer cube reporting systems that evolve alongside their own growth.

MOVE FORWARD WITH CONFIDENCE

Creating a trusted customer cube requires more than technical expertise; it demands a systematic approach that addresses each stage of the reporting process with equal rigour. Our scorecard provides the framework for this systematic approach, enabling SaaS businesses to build reporting infrastructure that supports both day-to-day operations and strategic initiatives.

For Private Equity-backed businesses and those preparing for investment, this systematic approach becomes even more critical. Investors expect to see not just accurate numbers, but also evidence of robust processes that ensure continued accuracy and reliability.

At Data Vision Services, we specialise in helping B2B SaaS businesses build trusted customer cube reporting systems that drive growth and investor confidence. Our team combines deep technical expertise with practical understanding of SaaS business models to deliver reporting solutions that evolve alongside your business.

If you’re ready to transform your customer cube reporting from a technical challenge into a strategic advantage, we’d love to discuss how our scorecard approach can benefit your organisation.

Get in touch with our team today to begin your journey towards truly trusted SaaS reporting.

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AI Agents are coming in fast, but what are they? And how are they applicable to your business?

Ciro García Agulló — Thu, 17 Jul 2025 12:45:54 +0000

introduction

AI Agents are the next wave of Artificial Intelligence. Unlike traditional AI tools that wait for user input, these systems take autonomous action

Key use cases for PE’s and PortCo’s include customer retention, customer journey attribution, business intelligence, and workflow automation

While the benefits are compelling, effective deployment demands rigorous oversight and thoughtful governance

What are AI Agents?

AI agents are intelligent systems built on large language models (LLMs) that can operate independently toward defined goals. Instead of simply responding to user prompts, agents:

Plan their own steps to reach a goal

Interact with tools and databases

Take actions like sending emails or pulling reports

Learn from past interactions to improve performance over time

This next step in AI evolution is being actively explored and implemented by leading companies across sectors including private equity, SaaS, and financial services.

What Makes Agents Different from Traditional GenAI?

Most of us are familiar with tools like ChatGPT, which generate text or other forms of content based on user prompts. But AI agents go further:

Autonomous execution: Agents don’t need step-by-step instructions. Set a goal (e.g., “detect churn risk in our CRM”) and the agent determines how to achieve it.
Tool integration: They connect to platforms like Outlook, Slack, HubSpot, and internal databases.
Decision-making: They evaluate options and choose the best course of action.
Continuous learning: They get better over time by processing outcomes and feedback.

Real-World Use Cases in Action

CRM Monitoring and Automated Outreach

Retool and similar platforms are already using agents to monitor CRM data for signs of churn. These systems:

Spot warning signals like inactivity or declined engagement

Flag accounts for review

Automatically send personalised emails or offers to re-engage users

In one case, an AI agent flagged a high-risk account three weeks earlier than a human rep would have.

Customer Journey Automation

AI agents are now playing a growing role in end-to-end customer experience management. Platforms like Beam, Voiceflow, and OneReach.ai are helping businesses automate routine but critical parts of the customer journey:

Instantly respond to common questions (“What’s my policy status?”, “When will my order arrive?”)

Trigger reminders and renewal messages via email or SMS

Collect documents or confirmations automatically

Execute service updates (e.g., changing details, rescheduling appointments)

Seamlessly escalate complex cases to human agents with full context

This type of automation is gaining traction across industries like financial services, e-commerce, and logistics — helping reduce support costs while improving response time and consistency.

Dashboard Copilots

Traditional dashboards are powerful, but require users to dig for insights. Beam AI is pioneering AI agents that:

Accept natural language questions (“Which region had the highest sales drop last quarter?”)

Query live data sources

Return answers with visual context in seconds

This not only speeds up analysis but also puts insights into the hands of more team members.

Competitor Intelligence

In private equity and strategy, staying on top of competitor moves is essential but time-consuming. AI agents like those developed by Dynamiq and Hebbia:

Scan press releases, financial filings, and job boards

Summarise market trends

Alert teams when key competitors make strategic changes

The result? Faster reaction times and more informed investment decisions.

Why This Matters for Private Equity and PortCos

Agentic AI presents a new frontier for operational improvement across the portfolio:

PortCos can cut cost-to-serve by streamlining support, reporting, and analytics

Deal teams can automate early-stage research and target screening

Boards gain faster visibility on key KPIs with less manual effort

The Risks: Not to Be Overlooked

1. Trust

Giving agents the power to act autonomously — especially in customer-facing roles — requires high levels of trust. A misfired email or incorrect decision could damage relationships.

2. Security

Agents often need access to sensitive data and internal tools. Without robust access controls and audit trails, they could introduce new vulnerabilities

3. Oversight

As Anthropic’s Claudius experiment showed, even advanced agents can make irrational or flawed choices. Human-in-the-loop governance remains critical.

Getting Started: Practical Tips

Start small: Pilot agents in internal or low-risk workflows

Monitor outcomes: Evaluate performance before scaling

Keep humans in the loop: Ensure oversight and review

Build safeguards: Use permissions, alerts, and version control

Conclusion: From Buzzword to Business Value

AI agents are already delivering tangible ROI. The key is knowing where they can add value — and deploying them with intention. Companies that move early, with the right controls, will gain an edge in decision-making speed, operational efficiency, and market responsiveness.

At DVS, we work with private equity firms and their PortCos to explore how agentic AI can unlock capacity, improve customer outcomes, and enhance reporting.

Let’s talk about where AI agents could make the biggest difference in your business.

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Why Integrated Channel Management and Revenue Management Is the Secret to Hotel Success

Ciro García Agulló — Thu, 10 Jul 2025 15:46:18 +0000

introduction

Hotel operators face a crucial decision every day: where should they sell their rooms to maximise profitability? This choice has grown from a simple booking question into one of the most important strategic decisions in modern hospitality.

Hotels entering new markets often face unfamiliar guest behaviours, low brand recognition, and significant pressure to hit occupancy targets. Simply put, what drives bookings at home might not resonate abroad.

At DVS, we’ve observed hotels that approach channel management strategically, integrating pricing strategies directly with distribution capabilities, to consistently outperform their peers.

But why exactly does this matter?

Distribution as a Commercial Lever

Many hotels still view channel management operationally, asking “Where should we list our rooms?” or “How do we manage online travel agent (OTA) commissions?” The strongest players recognise it as something more powerful: a critical commercial lever that directly shapes margins, occupancy, and business agility.

Think of it like a retailer choosing which shops should stock their products. They wouldn’t choose randomly. They would consider which locations reach target customers, which partnerships offer the best profit margins, and which provide flexibility when market conditions change. Hotel channel management works on these same principles.

And this extends beyond where to sell — to how to spend.

Is it better to drop price by £1, or spend £1 on marketing to generate new demand? The answer isn’t simple. It depends on your channel mix, price sensitivity, ROAS, and cost to serve. But the implications are profound: one choice erodes margin; the other can grow contribution.

The savviest operators don’t choose one or the other — they test both, leveraging integrated tech to make smarter decisions that maximise ProfPAR, not just RevPAR.

Hotels that effectively align their distribution strategies with revenue management are best positioned to:

Scale profitably to expand into new markets.
Defend and increase margins through smarter control of channel availability and costs.
Adapt quickly to respond to local market specifics and evolving guest booking behaviours

Conversely, poorly managed distribution strategies rapidly drain margins through excessive OTA fees or leave rooms empty due to channel mismanagement.

The Power of Integrated Systems

The real transformation happens when Revenue Management Systems (RMS), which forecast demand and set optimal rates, integrate seamlessly with Channel Management Systems (CMS), which manage distribution across all channels.

Here’s how it can make a difference:

Dynamic Pricing: When RMS and CMS are integrated, it enables the opportunity for dynamic pricing, where optimal rates reach the right channels at the right time – RMS identifies optimal pricing; CMS ensures it reaches every channel in real-time.
Inventory Yielding: Automatically restrict lower-margin OTA sales during peak demand periods to protect overall profitability.
Tactical Promotions: When demand softens, targeted promotions may be deployed through specific channels to fill quieter periods without sacrificing margins.
Channel Segmentation: Tailoring rates and promotions by channel to optimise profitability.
Data-Driven Feedback Loop: Real-time booking data from CMS continuously informs RMS, enhancing forecasting accuracy and pricing strategies.
Spend Efficiency: By connecting pricing and distribution, hotels can make smarter trade-offs — spending to attract high-converting direct traffic rather than discounting broadly and losing margin.

Results from the Industry

Across the industry, strategic channel management integration is already delivering significant, measurable results:

An independent UK hotel increased direct bookings by 14% year-on-year through strategic channel management, significantly reducing OTA fees and boosting overall margins.
A European boutique chain achieved a remarkable 39% increase in RevPAR by actively aligning their RMS and CMS strategies, driving higher direct bookings through carefully managed promotions.

These examples demonstrate how well-integrated channel strategy delivers far more than operational efficiencies. It creates genuine competitive advantage that translates directly to improved financial performance.

Where the Industry Is Headed: Integration and Flexibility

The hospitality landscape is shifting rapidly. Modern platforms like SiteMinder and Cloudbeds now allow real-time rate and inventory updates across all channels, while sophisticated analytics dashboards empower hotels to react instantly to market changes.

At the same time, regulatory developments – such as the EU’s ruling against Booking.com’s rate parity clauses – are giving hotels greater control to differentiate direct rates vs OTA rates and regain ownership of their distribution strategy.

This shift towards integration and flexibility isn’t optional. It’s the new baseline. Operators who adapt fastest will shape the market rather than simply respond to it.

Why Partner with DVS?

At DVS, we see the integration of revenue management and channel management as far more than an operational necessity. We see it as a growth lever that transforms how hotels compete and succeed. The hotel groups that treat it strategically scale faster, adapt better to market shifts, and build stronger, more profitable channel mixes.

Our approach combines commercial strategy with hands-on execution, supporting multi-site operators facing seasonality, shifting guest behaviours, and rising cost pressures. We help them fill rooms while protecting margins, because sustainable growth requires both.

Whether you’re entering new markets, managing a varied portfolio, or refining your distribution mix, integrating channel and pricing decisions should be at the heart of your commercial engine.

Connect with us today and let’s explore how you can turn distribution into your next strategic advantage.

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AI Enrichment Tool: Enabling B2B SaaS Value Creation

Isaac Waby — Tue, 29 Apr 2025 11:05:58 +0000

introduction

Good enrichment data can significantly drive revenue growth for B2B SaaS companies, yet it is often either unavailable or comes at a high cost
At DVS, we have developed an AI-powered enrichment tool that helps you enhance your customer data with valuable attributes such as industry, sub-industry, employee count, and key decision-makers
Interested in seeing how it works? Watch the short demo below, and feel free to contact us if you want to explore value-creation opportunities for your B2B SaaS business!

watch our ai tool demo

why is this important?

Quality enrichment data enables B2B SaaS companies to enhance revenue through the expansion of both existing and new accounts
For instance, this can involve increasing the size of existing accounts with personalised offers that encourage cross-selling and upselling, or scoring and identifying leads based on factors like industry, size, and location
However, quality data is not guaranteed for B2B RevOps and sales teams. In fact, 92% of RevOps teams report that poor data quality hinders their ability to execute their go-to-market strategies
Additionally, current market tools can be expensive, with costs often exceeding $15,000 annually or only covering one-off data sets

how dvs can help you

We’ve built an AI-powered enrichment tool that enables B2B software businesses to create richer, more useful attributes for each customer or prospect —such as industry, sub-industry, headcount, and decision-maker contacts
Our tool is built from publicly available sources, delivered as bespoke datasets tailored to your business, and does not require engineering

This isn’t off-the-shelf aggregator data; it’s targeted enrichment that we also utilise in our strategic analytics work, including lead scoring, retention modelling, pricing, and go-to-market design

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Sweeping US Tariffs: What This Means for the UK

Isaac Waby — Fri, 04 Apr 2025 00:22:45 +0000

INTRODUCTION

On the 2nd of April 2025, President Trump announced sweeping tariffs on all goods imports into the United States.
Practically this means that all countries are facing at least 10% US import tariffs, with many facing far higher percentages.
In addition to these “reciprocal tariffs”, imports from all countries will face blanket 25% US import tariffs on steel, aluminium and automotive imports.
The UK is not exempt, though has the lowest reciprocal tariff percentage at 10%. Particularly impacted sectors are automotive and steel, which face the higher 25% tariffs. Key sectors without direct impacts are pharmaceuticals and services – which are both exempt from tariffs.

7% of overall uk exports are impacted

The tariffs only impact goods. Where US bound exports make up 7% (£60bn) of overall UK exports (£861bn) and 15% of goods exports (£394bn). 2023 Figures
Critically, these tariffs do not impact services exports (and thus the large financial and professional services industry in the UK) – where the US is an even more signficant market than it is for goods.

iNDUSTRIES ARE NOT EQUALLY IMPACTED

Within goods exports, not all industries are subject to the 10% tariff – with some facing 25% tariffs and some exempt.
Automotive, steel and aluminium exports to the US are subject to the higher 25% tariff.
There are 0% tariff exemptions for pharmaceuticals, semiconductors, lumber, copper, gold, energy resources and selected minerals not available in the US.

The worst impacted industries are those both with high exposure to the US market and high tariffs like automotive and steel, but also aerospace, chemicals, machinery and precision instruments.

Where the automotive sector is clearly exposed with one in eight UK made passenger cars (and 20% of value) being exported to the US.
Research by the University of Birmingham calculates that the impact to UK GDP will be £10bn between 2025 and 2030, with 137,000 jobs at risk. 85% of this impact is expected to be felt in the West Midlands and North West, impacting British brands like Jaguar Land Rover and Aston Martin.

Where are we going from here?

There are early signs that the UK may eventually be able to negotiate favourable exemptions from some of these tariffs, coming from both White House and Downing Street insiders. Though there are no active or official announcements to this effect.
There are likely to be further second and third degree consquences to these actions, as they meaningfully shift global supply chains and international trading relationships, with follow on impact to the UK. The exact form of these is hard to forsee.
It is possible that the UK might gain competitive advantages in some sectors e.g. Scotch versus Irish whiskey in the US market (10% vs 20% tariffs) or UK versus US services in the EU market (especially if the EU follows through with reciprocal tariffs targeting US services.
Tariffs will directly impact profit margins and sales volumes in the US market for impacted products, and other than reprioritising to new markets or relocation production to the US, there is little that impacted firms can do to mitigate these impacts.

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From data to decisions: what sets leading RevOps teams apart

Isaac Waby — Fri, 28 Mar 2025 13:00:12 +0000

INTRODUCTION

RevOps, sitting at the intersection of sales, marketing, customer success, and finance, is evolving fast. As more organisations look to drive growth efficiently, the pressure is on to make better, faster decisions. That depends on clean, connected data and the ability to extract real insight from it.
At DVS, we work with high-growth and private equity-backed businesses to make that happen. We’re an end-to-end RevOps partner. We help define where data and analytics should be deployed to create strategic value, and we build the capabilities to support it—automating data flows, aligning teams, and delivering reporting that drives action.
Last week, we joined a session hosted by the RevOps Co-op: “The Good Enough Data Trap: Hard Truths from the 2025 State of RevOps Report.” It was packed with insight into where teams are getting stuck and what the best are doing differently.

AI IS ENTERING THE REVOPS MAINSTREAM

Around 80% of RevOps teams are experimenting with AI, from predictive analytics to content generation. Generative AI is especially useful for unlocking insights from messy, unstructured data. But adoption and impact remain uneven. The common blockers? A lack of clarity on use cases, weak data foundations, and internal capability gaps.

For CFOs and CEOs, the real question is: are our data and systems actually helping us make faster, sharper commercial decisions – or just adding more noise?

data quality erodes performance

More than 40% of respondents said their data was “good enough,” yet over 70% admitted it was undermining their ability to execute. Whether it’s segmentation, forecasting, or lead routing, small data issues quickly add up. They affect decision-making, slow down teams, and ultimately hold back growth.

This isn’t just a hygiene issue. It’s a performance issue.

the real gap is strategic alignment

One of the most telling stats from the report: 79% of companies with poor data quality don’t have a shared definition of what “good” looks like. Nearly half said leadership lacks visibility into the technical reality.

This isn’t about cleaning up Salesforce or buying another tool. It’s about aligning the business – across finance, sales, marketing, and ops—on what matters, how to measure it, and how to act on it.

Without that, systems become fragmented, reports become inconsistent, and valuable time is wasted reconciling numbers instead of using them.

what leading teams do differently

The high performers aren’t necessarily spending more – they’re getting clearer, faster. They:

Define what “quality” data means in the context of their commercial goals
Align cross-functional teams on shared metrics and systems
Automate low-value manual work to free up time for high-impact tasks
Give leaders access to timely, reliable insight they can act on

How DVS helps

DVS is an end-to-end RevOps partner. We help high-growth and PE-backed businesses turn information into a competitive advantage by combining strategy, systems and delivery.

Strategy consulting. We work with CFOs, CMOs, CROs and their teams to define where data and analytics can drive value. That includes selecting the right KPIs, aligning teams on metrics, and building the roadmap for data-enabled growth.
Building data capabilities. We prepare, clean and automate the data flows that power consistent, scalable reporting across the commercial organisation – from marketing performance through to pipeline, conversion, and retention.
Insight infrastructure. We design and implement tools that turn data into action. Dashboards, forecasts, and decision-support systems that leaders can rely on.
Driving alignment. We help teams build a shared source of truth, with platforms and processes that encourage real collaboration.
AI-powered data enrichment. We’ve developed a proprietary enrichment engine that scrapes publicly available sources – like company websites, LinkedIn, and Wikipedia – to enhance CRM and go-to-market data. That enriched layer gives you a stronger foundation for targeting, segmentation, and predictive modelling, and unlocks more value from both machine learning and generative AI.

If you’re trying to turn data into a real growth lever – not just a reporting layer – we’d love to help.

We’d also be curious to hear how others are thinking about this.

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Budget 2024: Bigger impacts in sectors with high wage bills and low average pay

Isaac Waby — Mon, 04 Nov 2024 22:17:29 +0000

The 2024 UK Budget, introduced by Chancellor Rachel Reeves, is projected to raise £40bn of incremental revenue for the Treasury. Whilst changes were long signalled, now that the details have been released, DVS have explored how the specific changes around National Insurance may affect businesses in different sectors.

Given the Government chose to increase Employer’s National Insurance rather than Corporation Tax, this impact is higher on those with high wage bills. The structure of the changes to the lower threshold means that those with more staff on lower salaries will be harder felt.

Businesses are doing much of the heavy lifting in this budget, with £25bn on the £40bn raised coming from the 1.2% increase in employer NICs

The gross pay threshold at which employer national insurance is paid decreases from £9100 per year to £5000 per year
The percentage rate paid on gross pay above this amount increases from 13.8% to 15.0%
Increasing hourly minimum wage from £11.44 to £12.21 will also represent a significant cost for businesses, particularly for those employing a high percentage of their workforce at or near the minimum wage

In terms of impact on payroll costs, the highest percentage increases will be felt in sectors with higher proportion of staff on minimum wage, and lower average salaries

Figure 1. Increase in payroll costs by % of staff on minimum wage, and average salary of full-time staff overall

In practice, these changes will represent a 0.5-3.0% EBITDA (bottom line) headwind for most, with more labour-intensive businesses paying lower salaries hit hardest

Figure 2. Change in % EBITDA bottom line by payroll cost as % of revenue (Assuming 15% of staff on minimum wage and average salary of £30000.

The implication of this is that some industries will be relatively more impacted by the changes in terms of their bottom line

Figure 3. Estimated EBITDA impacted by industry (Assuming 15% of staff on minimum wage). N.B. a representative range of affected industries have been included here, with select industries broken out to highlight impact.

Low-wage, labour intensive industries like cleaning, care and customer support the most impacted by the changes in cost. Hospitality and retail payrolls are also increasing significantly by c.5%, but where the EBITDA impact is lower due to a lower labour cost burden.

There is a range of mitigations and strategies that should be considered by owners to prepare their businesses for these changes

Operational Efficiency:
1. Automation and Technology: Investing in automation can reduce reliance on labour, thereby minimising payroll expenses. We expect to see continued support for the use of AI in customer support and call centre roles
1. Shift to Less Labour-Intensive Business Models: In retail for example, this is likely to further incentivise movement to online & omni-channel
Financial Planning:
1. Budget Reassessment: Revising budgets to account for higher payroll taxes and business rates is essential for maintaining financial health
1. Tax Planning: Engaging in proactive tax planning can help identify opportunities to minimise tax burdens within legal frameworks
Employee Compensation Strategies:
1. Likely Impact to Wage Growth: Many employers may simply scale back planned pay increases to accommodate higher minimum wage and NIC contributions
1. Staffing Structure Review: The reduction in the employer NIC allowance threshold from £9,100 to £5,000 may reduce the tax efficiency of employing multiple part-time workers instead of a single full-time employee
1. Flexible Working Arrangements: This may incentivise companies to re-visit their hybrid or remote working policies to reduce overheads

Conclusion

The 2024 Budget introduces increased payroll costs and tax adjustments, which will undoubtedly present challenges for many businesses, especially those with higher labour costs and lower average compensation – such as Cleaning, Personal Care and Call Centres.

This likely provides a further catalyst for strategic and efficient transformation in impacted sectors including labour efficiency maximisation, strategy & process optimisation and deployment of artificial intelligence.

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The ‘Alpine Ridge’ promotion strategy

David Whitcombe — Wed, 05 Jun 2024 09:33:02 +0000

I was recently pondering on a problem experienced by one of our clients – and the apparel industry more broadly – how to maintain profitability with very high returns rates? As costs increasingly spiral, this is becoming even more important. Many ecommerce retailers have now started charging for returns, and the customer reaction has been rather negative. What about a mechanic to incentivise keeping goods rather than penalising for returns?

This blog posts explores ways to encourage higher basket sizes with intelligent discount mechanics. The application is much broader than returns, and so important in today’s challenged economic times: how can we protect profitability while giving value to customers?

We propose The Alpine Ridge Strategy¹ – an increasingly stepped discount rate to build baskets. It is well known that customers love free delivery and will basket build to just over the threshold, but this builds an incentive to continue basket building beyond that point: providing cash margin for the retailer and cheaper goods to the customer. An example Alpine Ridge Strategy would be:

Free delivery when you spend £40
£5 off when you spend over £60
£10 off when you spend over £80
£20 off when you spend over £100
You should carefully explore the interaction between promotions and returns rates, and how to nudge customers to items they won’t return

Alpine Ridge Strategy is named for the road in Colorado’s Rocky Mountain National Park we were driving through as I debated the finer points of the approach with a good friend of mine who also works in eCommerce pricing.

Of course, there is nothing new here – stepped discount mechanics have been used intermittently by many retailers for years. What is important now is:

We are operating in a highly challenging environment for both customers and retailers. Distribution costs – and the cost of living – are growing rapidly. Retailers who can give value to their customers and protect margin will win share
The exact nature of the mechanic for your business requires great precision based on: distribution of basket sizes; gross margin by category; returns rate; share of wallet; customer frequency; expected customer elasticity and many others

Some examples of when the Alpine Ridge Strategy is best applied

Times to apply Alpine Ridge	Times to be careful
High gross margin	High customer frequency and risk of ‘pull forward’
High returns rates	Categories with lower than average gross margin (e.g. high priced electricals)
Low customer frequency or share of wallet	High spread of basket sizes – risk of ‘cannibalisation’

DVS are expert in understanding each of these and working with you to refine, launch and test your Alpine Ridge Strategy. We combine data analytics expertise with the strategic mindset to ‘think bigger’ and understand the wider implications for your business. We can support you to build a launch mechanic and also to measure the effectiveness and impact on your customers and profitability.

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Introducing CLV-Adjusted RoAS: The Metric Every Marketer Should Be Using

Thanusan Balakrishnan — Sun, 05 May 2024 09:24:56 +0000

As a business, it can be hard to identify the most effective way to measure campaign performance. At DVS, we understand that modern marketing is an ever-changing landscape and there are valid alternatives to the commonly-used Return on Ad Spend (RoAS). Today, we introduce a more refined metric: CLV-Adjusted RoAS (Customer Lifetime Value Adjusted RoAS). This offers a much more holistic view of our clients’ marketing efforts, helping them make more informed decisions.

Why RoAS Doesn’t Cut It Anymore

The traditional RoAS metric focuses on the immediate revenue generated from a specific advertising campaign. While it’s easy to calculate and understand, it’s also a short-term view. By concentrating solely on immediate revenue, RoAS overlooks essential aspects of customer relationships, such as their lifetime value and the profitability of the products they purchase.

The Three Pillars of CLV-Adjusted RoAS

1: Long-Term Customer Relationships Over Immediate Return

CLV-Adjusted RoAS extends the attribution window beyond immediate outcomes to include the long-term revenue a customer generates. Instead of just asking ‘How much revenue did this campaign bring in today?’, we ask ‘What will this customer contribute to our business over the next year or more?’

At DVS, our unique approach involves creating detailed customer profiles to predict long-term behaviours more accurately. This foresight ensures our clients invest in strategies that will continue to pay off in the future.

2: Profit Over Revenue

While revenue is essential, profitability is the actual key to sustainable growth. By focusing on profit, our clients have greater confidence that their marketing strategies are paying dividends in the short-to-mid term. The CLV-Adjusted RoAS metric goes one step further than traditional RoAS by focusing on the margin at a product level. This granularity allows our clients to discern which customer segments and channels are most profitable for their business.

Our tailored data analytics solutions at DVS dig deep into our clients’ sales and marketing metrics. We don’t just look at the surface; we analyse product-level margins to optimize advertising campaigns for sustainable profitability.

Recently, we helped a client set their annual marketing budget – they were struggling to identify the channels that brought the most profitable customers over the long-term. By creating the CLV-Adjusted RoAS metric, it became clear which channels were better at attracting the customers that generated ongoing profit for their business. Furthermore, we could make recommendations on who to target (customer segments), how to target them (categories, product type, campaign design) and when to target them (nursery journeys that reliably creates profitable, repeat customers).

3: Multi-Touch Attribution for Fair Assessment

Different marketing channels contribute to the customer journey in various ways. CLV-Adjusted RoAS incorporates multi-touch attribution to provide a more nuanced view of each channel’s contribution. This is critical for recognising the importance of channels often overlooked in last-touch attribution models, such as display ads for brand awareness.

Our team at DVS is skilled in leveraging multi-touch attribution to offer a more balanced overview of our clients’ campaign performance. We’re able to connect directly with attribution data, whether it’s Google Analytics or another platform. This ensures that budget is not misallocated and that each channel is getting its fair share of the credit, increasing overall efficiency.

Future-Proof Your Marketing Strategy with DVS

Understanding the impact of your marketing initiatives shouldn’t be a short-term game. CLV-Adjusted RoAS brings a broader, more profitable perspective to campaign performance assessment. The ongoing benefit of this approach is a more strategic allocation of marketing budget, aimed at long-term profitability and customer relationship building.

Our methods are not just beneficial for one project or client; they are universally applicable. The skills we have applied in crafting a more holistic view of marketing metrics can be tailored to suit the unique needs and challenges of each and every business.

If you’re interested in working together to make smarter, more profitable marketing decisions, reach out to us at contact@datavisionservices.co.uk

The post Introducing CLV-Adjusted RoAS: The Metric Every Marketer Should Be Using appeared first on DVS - Data Vision Services.

Optimizing Portfolio Performance: The Power of NLIDB Systems in Private Equity

Thyrsos Maklokas — Fri, 05 Apr 2024 09:23:13 +0000

As a PE business, monitoring owned businesses with highly complex datasets can become a highly complex task. Answering new critical questions with data-backed answers is still a time-consuming activity that requires weeks of data analyst and engineer time. LLM models such as ChatGPT 4 integrated into various business systems will be the next advancement in business, especially in the data analytics space. The latest showcase of where the market is headed with AI-led analytics is the introduction of Power BI Copilot.

Natural Language Interface to Databases (NLIDB) or Natural Language Querying (NLQ) system. These systems use Natural Language Processing (NLP) techniques to understand and interpret the user’s queries and then translate them into SQL or other query languages that the database can understand. Then this data can be summarized in graphs, tables, and written reports by interacting with the system in natural language.

There are 3 ways this will affect PE Businesses:

Centralized Analytics for Owned Businesses:

NLIDB systems can serve as a powerful tool for centralizing and streamlining analytics across a portfolio of owned businesses. By connecting to multiple databases and data sources, these systems enable PE professionals to access critical financial and operational data from various portfolio companies in a unified and coherent manner.

This centralized analytics approach offers several benefits:

Consolidated Insights: PE firms can effortlessly gather data from different businesses and create consolidated reports and dashboards. This provides a comprehensive view of the portfolio’s overall performance and financial health.
Benchmarking: NLIDB systems allow for easy benchmarking of key performance indicators (KPIs) across portfolio companies. This aids in identifying underperforming or overperforming businesses and implementing strategies accordingly.
Risk Assessment: PE professionals can quickly assess the financial risks associated with each portfolio company by comparing financial data and market trends. Early detection of potential issues can lead to proactive risk mitigation.
Efficiency: Centralized analytics reduce the time and effort required to compile and analyse data from multiple sources, enabling faster decision-making and more efficient allocation of resources.

Democratization of Analytics Across the Business:

NLIDB systems empower not only data analysts but also non-technical professionals within the PE firm to access and interpret data. This democratization of analytics has several advantages:

Self-Service Analytics: PE professionals, including investment managers, can formulate complex data queries in plain language without relying on data experts or IT support. This promotes self-service analytics.
Data-Driven Decision-Making: With easy access to data and insights, decision-makers throughout the organization can make more informed and data-driven investment decisions.
Reduced Bottlenecks: By reducing the dependence on a limited number of data experts, NLIDB systems eliminate bottlenecks in data access and analysis, leading to faster response times.
Cross-Functional Collaboration: Democratized analytics encourages collaboration across different departments, as professionals from legal, finance, and operations can all engage with data to contribute to investment strategies.

Portfolio Monitoring:

NLIDB systems significantly enhance portfolio monitoring capabilities for PE firms:

Real-Time Insights: These systems can provide real-time updates on portfolio company performance, financial metrics, and key events. PE professionals can quickly react to changes and make timely adjustments to their investment strategies.
Alerts and Notifications: NLIDB systems can be configured to send alerts and notifications based on predefined triggers. For example, deviations from financial targets or significant market developments can trigger automatic alerts, ensuring timely attention to critical issues.
Predictive Analytics: By leveraging historical data and predictive analytics models, NLIDB systems can help forecast future performance and potential challenges within the portfolio. This foresight enables proactive decision-making.
Customized Reporting: PE firms can create customized reports and dashboards for each portfolio company, tailoring analytics to specific business needs and objectives.

In summary, NLIDB systems offer private equity businesses centralized analytics capabilities, democratized access to data and insights, and advanced portfolio monitoring features. These benefits collectively contribute to more effective decision-making, risk management, and overall portfolio performance.

When are NLIDB systems joining the workplace and what are the challenges?

There are challenges in the implementation of AI systems in the workplace. Data focused AI systems are going to be mostly constrained by long standing problems in data quality, modelling, resource scalability and cataloguing that plague businesses.

At DVS, our capabilities lie both in the identification of business applications or NLIDBs and the development stages of the infrastructure pieces needed to deliver them. Natural Language Annotations, Metadata and Semantic Enrichment will be key to the integration of NLIDB systems to existing databases, enabling AI to find and understand how the underlying data can be used to answer our questions.

References:

Alshammari, A., & Alshammari, M. (2021). A Review of NLIDB with Deep Learning: Findings, Challenges and Open Issues. In S. K. Singh & S. K. Singh (Eds.), Advances in Data Science and Management (pp. 217–228). Springer Singapore ¹
Alshammari, M., & Alshammari, A. (2021). A Survey of Natural Language Interfaces to Databases: Challenges and Opportunities. In 2021 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 1–8). IEEE ²
Alshammari, M., & Alshammari, A. (2021). Natural Language Interfaces to Databases: A Review of the State-of-the-Art Techniques and Open Challenges. IEEE Access, 9, 101156–101178 ³

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