Data Science Archives | ASK BOSCO®

The Agency Hackers Agency Forecast Report: key Insights into Agency Effectiveness, Efficiency, and Confidence

Tom Pickard — Thu, 16 Jan 2025 10:57:24 +0000

The Agency Forecast Report, which was released October 2024 by Agency Hackers in collaboration with Wix Studio, provides an in-depth overview of the current agency landscape.

Collecting insights from over 80 agency leaders worldwide, the report highlights key trends, challenges, and opportunities shaping the industry.

For agencies, this report is a great resource, offering insights into how other agencies are changing their strategies to adapt and are improving client relationships. Here, we focus on the report’s implications for agencies and explore how they can adapt to stay competitive.

Agency Effectiveness and Efficiencies

Agencies are increasingly focusing on enhancing their operational effectiveness and efficiency to remain competitive. A significant 72% of agencies identified talent acquisition and retention, particularly in tech roles, as a major challenge. This shortage underscores the need for streamlined processes and effective resource management to maintain productivity.

Additionally, 55% of agencies reported a rise in client demand for shorter contracts or project-based work. This shift shows a need for greater flexibility and efficiency in project execution to meet client expectations within short deadlines.

Confidence in the Agency Landscape

Despite the challenges, agency leaders remain resilient and have an overall positive outlook. The report indicates an average confidence level of 6.6 out of 10 among surveyed agencies, reflecting a cautious optimism for 2025 and the evolving market dynamics to follow the new year.

Strategic Focus Areas

To address the challenges highlighted in the report and leverage emerging opportunities, agencies are adopting several strategic initiatives:

Exploring New Markets and Niches

62% of agencies are diversifying their services and targeting niche markets to expand their client base and revenue streams. By choosing a niche market agencies are specialising and focusing on individual areas meaning less competition against others.

Investing in Marketing Efforts

In response to intensified competition across the scope of services, 75% of agencies plan to increase investment in their own marketing to attract new clients, establish thought leadership, and reinforce brand identity. By investing in their own marketing, agencies are creating trust and building relationships with potential clients before working with them.

Strengthening Client Relationships

55% of agencies are prioritizing deeper engagement with existing clients, recognizing that strong relationships are key to securing repeat business and long-term success. Making their current clients feel valued is important to maintaining a strong client relationship, this includes regular check in’s, taking on board client feedback and continuing to produce high value work.

Embracing AI and Technological Integration

The report also highlights a growing interest in artificial intelligence (AI) within the agency sector. While 80% of agencies plan to increase their use of AI, integration challenges and team resistance are slowing progress. This indicates a need for effective change management strategies to fully harness AI’s potential in enhancing agency operations.

The ASK BOSCO® platform, looks to ease this growing team resistance within agencies, by making their work easier and quicker to complete. With its advanced AI capabilities, ASK BOSCO® enables agencies to connect all their data sources for seamless reporting, offering actionable insights at a glance, making it faster and simpler for agencies to complete complex client reports. The platform goes beyond reporting by predicting where to allocate media spend for maximum return and helping plan client budgets with precision. By integrating tools like ASK BOSCO®, agencies can overcome barriers to AI adoption and become more proactive with the use of data-driven marketing within everyday work.

The Agency Forecast Report underscores the dynamic nature of the agency landscape, marked by both challenges and opportunities. By focusing on operational efficiency, exploring new markets, investing in marketing, strengthening client relationships, and embracing technological advancements, agencies can navigate the complexities of the industry with confidence and resilience.

For a more detailed analysis and to read the full report, you can download the full Agency Forecast Report from Agency Hackers’ official website.

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Why Choose ASK BOSCO Over Supermetrics?

Matthew Fraser — Mon, 02 Dec 2024 15:16:26 +0000

When it comes to managing your marketing data, having the right tools can make all the difference. While Supermetrics is a popular choice for data integration, ASK BOSCO® offers a more robust solution that goes beyond being just a “data pipe.” With powerful analytics and forecasting capabilities, ASK BOSCO® empowers teams to make smarter, data-driven decisions. Here’s why ASK BOSCO® is the reliable alternative to Supermetrics.

What Is Supermetrics?

Supermetrics is a data integration tool designed to connect various data sources and transfer marketing metrics into platforms like Google Sheets, Excel, and BI tools. Acting as a “data pipe,” it focuses on consolidating raw data from multiple sources for further analysis. While useful for automating data transfers, Supermetrics lacks advanced analytics capabilities, offering limited insights and tools to act on the data it collects.

Why ASK BOSCO® Stands Out

ASK BOSCO® isn’t just a tool for collecting data—it’s a full-fledged marketing analytics platform. By streamlining reporting and delivering actionable insights, ASK BOSCO® enables businesses to forecast, plan, and optimize marketing strategies in one secure and user-friendly platform.

Here’s how ASK BOSCO® compares to Supermetrics:

ASK BOSCO® Benefits

Multi-Team Access

Unlimited users per account make collaboration seamless across teams without additional costs.

Ongoing Support

Our dedicated support team is always available to address concerns and provide recommendations, ensuring you get the most from the platform.

Reliable Solution

With a focus on security and reliability, ASK BOSCO® boasts a 99.99% uptime record, so you can trust your data is always available.

Extensive Data Sources

ASK BOSCO® integrates with over 500 platforms and offers custom-built connectors for unique data sources, ensuring your analytics needs are fully covered.

Supermetrics Limitations

Unstable Connectors

API connections between data platforms can be volatile, causing interruptions in data transfers.

Customer Service Delays

Users frequently report delays of several weeks for issue resolution.

Limited User Access

Supermetrics charges extra for additional users, leading to tiered pricing that can become costly for growing teams.

Additional Fees for Data Connectors

Accessing multiple data sources often incurs extra charges, adding to the platform’s cost.

A Trusted Solution for Smarter Marketing Decisions

As Ian Hyde, Head of Digital Performance, explains:
“By streamlining automated reporting and providing actionable insights, we are empowering our team and clients to make data-driven decisions that will propel their success.”

ASK BOSCO® provides a comprehensive platform designed to simplify your marketing analytics, improve collaboration, and enable smarter decision-making—all without the frustrations of unstable connections or unexpected fees.

Ready to experience the difference? Discover how ASK BOSCO® can transform your marketing analytics today.

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A guide to Marketing Mix Modelling (MMM)

Kylie Moody — Mon, 27 Nov 2023 11:53:39 +0000

Marketing Mix Modelling is a powerful analysis of sales and marketing data that helps estimate the impact of marketing activities on sales. It’s a popular tool with businesses, who use it to measure and predict the effectiveness of their marketing efforts.

Often referred to as MMM, Marketing Mix Modelling is an excellent tool for identifying which parts of your marketing contribute the most to overall performance.

In this blog post, we’ll delve into exactly what MMM is, how it’s used and how you utilise it to better understand marketing performance.

What is Marketing Mix Modelling (MMM)?

Marketing Mix Modelling (MMM) is a statistical analysis technique used by businesses to quantify and evaluate the impact of various marketing channels and elements on sales or other key performance indicators. The goal is to understand how different elements of the marketing mix contribute to the overall success of a product or service.

As businesses face tighter marketing budgets and use omnichannel strategies, they are searching for efficient ways to spend money, and so often turn to MMM to help them achieve this.

MMM is a statistical tool that helps figure out how each part of the marketing mix affects overall sales or profits. It helps identify which marketing activities work best so businesses can spend their resources wisely.

How does Marketing Mix Modelling (MMM) work?

Data Collection: Collect data on various marketing variables such as advertising spending, sales promotions, product features, pricing strategies, and distribution channels. Additionally, external factors like economic conditions may also be considered.
Analysis: Statistical models are then developed to analyse the relationships between these variables and the outcomes, typically sales or revenue. This involves using regression analysis or other statistical techniques to identify patterns and correlations.
Attribution: The models aim to attribute a quantitative value to the impact of each marketing element. This helps businesses understand which components of the marketing mix are most effective in driving sales or achieving the desired outcomes.
Optimisation: Once the impact of each element is understood, companies can use this information to optimize their marketing strategies. For example, they might reallocate budget to the most effective channels or adjust pricing strategies based on the findings.
Forecasting: Marketing Mix Modelling can also be used for forecasting future outcomes based on different marketing scenarios. This helps businesses make informed decisions about future marketing investments.

It’s worth noting that Marketing Mix Modelling is a complex process and requires access to comprehensive and accurate data.

How is marketing mix modelling used for digital marketing?

Marketing Mix Modelling (MMM) can evaluate and optimise digital marketing channels in the following ways:

Identify and understand marketing KPIs:
- Spend on channels like PPC, Paid Social and Display
- Assess the impact of SEO efforts on website traffic and conversions.
- Analyse the contribution of email campaigns to overall sales or conversions.
- Measure the effectiveness of social media platforms in driving brand awareness and sales.
Cross-Channel Analysis:

Assess how different digital channels work together. For example, evaluate how social media advertising and email marketing complement each other in driving conversions.

Attribution Modelling:

Help produce attribution models to understand how different touchpoints contribute to conversions. This is particularly important in digital marketing, where a customer’s journey may involve multiple online interactions before a conversion

Optimisation:

Once the impact of each digital marketing element is understood, adjust strategies accordingly. This could involve reallocating budget to the most effective channels, refining targeting strategies, or optimizing the timing and frequency of campaigns.

Forecasting:

Use the insights gained from MMM to make informed predictions about the potential impact of changes in digital marketing strategies. This helps in planning future campaigns and budget allocations.

Experimentation:
- Implement controlled experiments or A/B testing to isolate the impact of specific changes in digital marketing strategies. This helps in validating the findings from the modelling process.

If you’d like to find out more about how ASK BOSCO® fits into your marketing strategy, just drop us an email at team@Askbosco.com and we’ll be happy to help.

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What does it mean to be ‘insights driven’?

Emily Hakner — Thu, 18 Nov 2021 16:10:46 +0000

With the demand for technology and insight-driven solutions on the rise, the pressure on organisations using data to remain competitive increases. Businesses can use a combination of current and historical data, combined with technology to improve their business functions and reduce time and cost. This guide explains what a data and insights-driven approach is, how to become insights driven and its advantages.

Being insights-driven as an organisation is considering data, analysis and logical reasoning into the decision-making process on a daily basis.

Becoming insights-driven can be a hugely important step for companies of all sizes, however they must understand the fundamentals of how to get there and ultimately stay there.

If you were to look at becoming insights driven from a real-life example, you would look at how plastic is made. Polymers are taken to a factory and get transformed into plastic which can be used for a magnitude of different things. This is the same as analytical data. Companies take in data which can be analysed, from this analysis insight can be formed to make better decisions, this is insights-driven.

If your company has decided to make the transition to become more insights-driven, there are many variables which will need to be overcome first. Firstly, although technology is a necessary requirement to crunch the data, successful insights-driven organisations require many further components.

It is important that organisations have a clear strategy on what the vision is. Without direction, the data won’t work well together and could end up being a waste of time and money. A good strategy will include stakeholder management, innovation and value drivers.

Although technology is a key factor, technical people are required to help setup the process to gain the vital insights. This could be business analysis, accountants or even data scientists.

An organisation will need talented employees with leadership qualities to take the insights to form a clear action plan and implement it into the organisation.

Quick thinking, bright employees will be able to make important decisions, which could lead to a successful transition.

Organisations must be entirely transparent when using data and provide data sources for any external data. The data being used must have quality checks and be from a trusted source to avoid fake data which would implement success or worse, become a legal case.

All data must be ethical and shared and comply with all regulations. Organisations can use third party data to aid in becoming insights assisted, but they must not abuse the use of third-party data.

The process needs to be scalable and agile to be able to adapt to change. It is also important within the process to be able to identify which parts need to be prioritised. Governance of the process is key to enable the technology algorithms to learn from previous data and predict future data more accurately. Finally, process re-engineering will allow companies to map their current strategy, identify holes and look how to improve them with cutting-edge mapping.

The technology needed to be insights driven is powered by a complex algorithm with the ability to learn over time. Organisations can use technology from providers such as BOSCO, which are secure, reliable and continuous.

Build upon existing competitive advantage from historical data.
Produce insights that predict future trends.
Convert the growing amount of data into quantifiable business value.
The decision-making process speed and cost will be significantly reduced.
The organisation culture will be adapted to becoming more data and insights driven.
Insight-driven decisions will be brought to an employee level as a collective rather than being stuck at executive level.

The organisations agility will be improved to navigate the rapidly changing digital industry.
Organisations can build up their overall analytics capabilities, from integrating all levels such as the workflows and systems, to the core business systems.

BOSCO™’s unparalleled forecasting capabilities can help you make better, data-driven decisions with your media budgets. Our easy-to-use interface provides a single source of truth, enabling you to track and implement for optimal performance across all your marketing channels. Book a demo to find out more.

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Did data science predict the US election outcome?

Matthew Fraser — Wed, 11 Nov 2020 16:50:34 +0000

As the drama of the US presidential race winds down for another four years, electoral forecasts have been ongoing, as the results have trickled in from the last of the states. Our Principal Data Scientist Dr James McKeone shares his knowledge and thoughts on the subject.

There have been two notable forecast models involved in the 2020 election race; the fivethirtyeight 2020 election forecast model lead by Nate Silver, which had Biden at 89% probability to win and the modelling team lead by Andrew Gelman and Elliot Morris building the US potus model for The Economist, which had Biden to win at 97% probability.

For me having never followed a US election before and not realising all that comes into play with the electoral college votes, the state-by-state differences in practice and the media circus – it didn’t feel like such a certain outcome for Joe Biden as the votes came in.

As there are only two possible outcomes to the US election race, it’s much easier to apply a pass/fail mark to any forecast that predicts the correct or incorrect result on the balance of probabilities. This is shown in such a way that isn’t so obvious for other predictions which we widely rely on, such as the weather or some economy forecasts. For anyone seeking a lesson in interpreting probability and understanding uncertainty in forecasts, Andrew Gelman’s blog is a masterclass in forecast calibration and statistical reasoning from the Bayesian perspective.

It’s interesting to see in both the fivethirtyeight and The Economist models, the post-election analysis by each team now that the majority of the results have come in. For instance, it seems that, as was the case in 2016, the pre-election polling data is a potentially biased, but also can be seen on the ‘messed up polls’, Biden’s predicted win and a post-election update from Gelman’s team together with comments on their final election update, Biden’s victory and on exit polls from Silver’s team.

This questioning of results, being made even before the dust has settled on vote counts for all states, is a critical part of true forecasting models that are so often glossed over in industry applications of data science. Where the model is not only tested out-of-sample but assessed from the most fundamental elements:

Model specification
Data input
Interpretability by the end-user.

In my experience, the careful review of these three areas are what separates an out-of-the-box, elementary model-to-get-an-answer build and a model built to be built upon, a living forecast model that is built and re-built, torn down and re-fit, with results presented in ways that the user cares about and understands.

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