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Introduction

If you’re reading this then the chances are you work in a marketing or media agency and you already know that, in today's fiercely competitive landscape, leveraging data effectively is no longer an option; it's imperative. The way in which agencies operate is evolving rapidly and the need to deliver greater value grows ever more acute.

But amidst the sea of numbers and metrics, there lies a crucial element often overlooked: data storytelling. Data storytelling isn't just about presenting facts and figures; it's about weaving a narrative that unveils hidden insights and drives actionable strategies.

It's the art of transforming complex datasets into compelling narratives that not only impress clients but also empower teams to optimize campaigns with precision.

While the necessity of data may be apparent, mastering data storytelling is an advanced skill—one that goes beyond basic client reporting. It demands a deep dive into the data, a comprehensive understanding of client needs, and a robust data architecture to ensure you have the right data at the right time.

In this guide, we'll delve into the essence of data storytelling, explore how to elevate your client reporting to a level that can deliver greater value for your clients, and what sort of data infrastructure you need to do this.

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Chapter 1

What do we mean by good data storytelling?

Good data storytelling isn't just about regurgitating numbers and metrics; it's about painting a vivid picture that focuses on actionable insights.

It's about transitioning your client reporting from mundane slide-by-slide recaps of spend and CTR to compelling narratives that drive action and deliver results.

And, it’s about being able to tell your clients not just how many clicks they got on Google Ads or Facebook campaigns, but being able to unravel the story behind those numbers to explain why this happened and what they can then do next.

From identifying the impact of weather conditions on sales conversions to predicting future trends based on data patterns, good data storytelling goes beyond surface-level reporting.

An example of good data storytelling

One fantastic example comes from Wes Nichols. During the From Data to Insights Roundtable, he describes how, by leveraging Google search term data alongside public transportation figures, they were able to advise their pharmaceutical client on when best to leverage their marketing spend.

"We saw bus ridership and metro ridership go down several days before the flu symptoms started to be Googled.

And all of a sudden that gave a very interesting combination of tools that we could use to predictively prescribe marketing money based on markets that we’re starting to see dips in public transportation ridership.

That gave this company incredibly cool tools to work with to completely crush the competition, who weren’t thinking of anything like this."

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Find out how the 76ers are using weather data to better understand their audience analytics.
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Chapter 2

Getting the right data for your agency analytics

So, the first ingredient for good data storytelling is, you’ve guessed it, good data. Now, we all know that data is a must-have for modern media or marketing agencies, but here's the real question: is it the right data? And by that we mean, can you draw the insights and conclusions from it that that is going to let you go beyond basic client reporting?

To make sure you're telling the best data story you can, let's dive into three crucial factors to keep in mind: connectivity, granularity, and frequency.

1. Connectivity

Naturally, you need to be able to collect data from all your clients’ data sources. For the standard sources like Google Ads, this is a breeze. But in today's landscape, businesses are utilizing more and more data sources, making it essential to connect to them all to construct a comprehensive data narrative.

Just look at the retail and CPMG sectors—the explosion of eCommerce has led to a proliferation of niche platforms that businesses sell on. For instance, if your client is a major CPMG retailer, you should be looking to gather data from all these platforms to merge sales data with things like Google Ads. This allows you to develop a better understanding of what factors are impacting sales, where, and when, ultimately delivering better value to a client.

Ideally, you’re going to want to automate the collection of this data to avoid not only issues with data quality (see below) but also the amount of time it takes to manually wrangle data from multiple sources.

But connectivity isn’t just about connecting to your clients' data sources. It’s also about collecting data from other relevant sources. The most commonly used example is weather data that, again especially for the CPMG sector, can provide a wealth of new insights into what is impacting sales and where budget can be best leveraged. So, when it comes to gathering data, think outside the box and look for new sources of information that can potentially help build that data story.

Need to connect to a data source?

With more than 600, Adverity has the world's largest library of pre-built API connectors.

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2. Granularity

Granularity is crucial— good data storytelling is not just about collecting data and reporting it back to clients; it's about diving deep into the intricacies to extract meaningful insights. For agencies leveraging data integration tools, API connectors automate the collection process from various sources. However, the granularity of data retrieved varies significantly among APIs, with some offering limited detail, which will impact your ability to paint a complete picture of what is going on in your clients’ campaigns.

But beware: it isn’t just about getting more data - you don’t want to be drowning in useless data points that obscure the story. It's about getting the right data at the right level of granularity. While basic metrics will lay the foundation, truly understanding a campaign's performance requires a nuanced approach. Metrics like conversion rates, audience demographics, and engagement metrics are essential for deciphering the effectiveness of marketing efforts.

Consider website traffic—it's useful, but knowing which demographic segments are driving conversions matters just as much. Without this granular data, agencies risk providing clients with incomplete or even misleading insights.

So, it's vital for agencies to ensure they can access data at the necessary level of detail. This allows them to craft a comprehensive narrative and offer clients actionable recommendations based on a thorough understanding of campaign performance.

Many data storage and visualization platforms work off a pay-per-process model, meaning the more data you input, the more you pay — so data teams will want to optimize datasets for this. However, they need to find the balance between having granular data and flexibility, so you’re getting what you need without overpaying. 

3. Frequency

Lastly, let's talk about frequency. It's a no-brainer, really—getting up-to-date data when you need it is absolutely crucial. Gone are the days when you could skate by with just a monthly data review for your clients. To truly craft a compelling data narrative, as well as make real-time optimizations and deliver maximum value, you've got to collect data regularly to stay ahead of the game.

And here's the kicker: having this up-to-date data means that whenever you inevitably get hit with that client question—"So, how's XYZ campaign going?"—you can whip out an immediate, comprehensive answer with all the latest data, without spending a week scrambling to put together a client report.

Now bear in mind, not all businesses need data that’s actually real-time. For some, a daily fetch will be plenty, and for others, a solution that can run fetches every fifteen minutes will be more than enough. In actuality, there are very few use cases for streaming data in real-time — one example would be a sports betting website where live odds need to change based on the current scores. 

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Learn more about real-time data here!
What is data integration in real-time?
Chapter 3

Standardizing your data for cross-channel reports

In the bustling realm of marketing agencies, marketers are dealing with a diverse range of data points. Managing these multiple data sources can be a challenge. With more than half of CMOs now juggling an average of 14 data sources, the need for an automated solution to standardize this data into a unified, analyzable format has never been more pressing. Enter data mapping – the unsung hero behind streamlined client reporting and data analysis.

What is data mapping?

In simple terms, data mapping is the process of cleaning and combining data. It involves the reorganization of data based on a data dictionary, consisting of a predefined set of rules known as the data schema, which dictates aspects such as standardized table names, field names, and what values can be included in a field (i.e. is it a number, a date, text, etc.)

Data mapping is crucial because it creates consistency and accuracy within your data, while also saving analysts a lot of time by providing a unified language for field names across different sources, making data analysis easier and more efficient for your agency.

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Data mapping lets you easily combine data from different data sources

Why data mapping matters to marketing agencies

Let's take the metric "cost" as an example—a fundamental metric crucial for generating even the most basic reports. However, this pivotal metric may be referred to as “Spend” in Facebook Ads, “ga:adCost” in Google Analytics, and “Cost” in Google Ads. Each data source presents a different name, creating a labyrinth of confusion. This inconsistency poses a significant challenge during analysis. So, we need to align these field names before we can analyze the data.

By data mapping these different terms to one standard term, marketing agencies can eliminate this problem and standardize client reporting. For instance, in the above example, you could data map all those different field names to “cost”, allowing you to get on with analyzing your data across different channels and clients.

Data mapping: where does it fit in the pipeline?

Data mapping can occur either before or after data ingestion, depending on the agency's preference. Whether adopting an Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) approach, the ultimate aim remains consistent: to standardize and harmonize data for seamless analysis and client reporting.

Benefits of streamlining data mapping for agencies

Increase efficiency and save time

Standardized data mapping eliminates the need to rename fields each time they are used, enhancing overall efficiency and saving valuable time.

 

Create a single source of truth

Standardizing data format ensures everyone has access to the same data, promoting consistency and alignment across the agency.

 

Increase data accessibility across the organization

Simplified data comprehension facilitates data democratization, allowing all stakeholders to make data-driven decisions regardless of their level of data literacy.

 

Avoid confusion over ownership and management

Clearly defined ownership and management of the data mapping process prevents confusion and maintains data quality.

 

No missing or incoherent data

When you have the numbers, they need to be correct - or at least you should be notified if there was an error in the loading or transformation process. Making important decisions based on the wrong numbers can hurt, so you should also make sure to not only get but also pay close attention to the error reports.

Chapter 4

Client reporting and client reporting tools

Business intelligence platforms are a crucial part of data storytelling for many agencies — and there’s a lot more to Bl than simply generating reports on current performance. Bl provides an extremely useful outlet to closely examine big data to uncover trends and derive actionable insights.

Bl tools are a must-have for modern-day marketers: streamlining the effort required to perform searches, merge, and query data in order to obtain the data needed to sanction and support business decisions.

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Tableau

As a leading Bl and data visualization software provider, Tableau offers a powerful array of data analytics tools. Tableau is a highly interactive platform to explore your data and uncover trends by employing a range of statistical and methodological analyses.

As a cloud-based platform, sharing data, visualizations, and reports is effortlessly simple - allowing for easy collaboration between teams and departments.

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Looker Studio

Looker Studio, nestled within the Google ecosystem, emerges as a cloud-based business intelligence platform championing accessibility and collaboration. Designed to make data insights accessible and easy to understand, Looker Studio empowers marketing agencies to uncover actionable insights and foster seamless collaboration for client success.

Looker is a versatile dashboarding and visualization tool that enables teams to craft impactful reports and share them across their organization. As a complimentary addition to your Google Cloud stack, Looker Studio offers a free, accessible solution for data-driven insights.

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Microsoft Power BI

Equipped with a range of self-service business intelligence capabilities, Microsoft Power Bl users can create interactive visualizations, reports, and dashboards without any specialist IT staff or database administrators.

With 25 types of visualizations available, users have plenty of options for representing real-time and historical data. With a sleek and highly intuitive interface, Power BI's position as one of the market-leading Bl tools is well-founded. There's even a free version available too!

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Adverity

Adverity was built specifically for marketers and agencies to help them stay ahead of their business. Users can build highly customized dashboards that provide a real-time overview of their marketing performance. Datasets and dashboards can be easily shared with stakeholders, according to the level of access they have been granted.

After integrating and harmonizing their data, users can click - not code - their way to generating compelling marketing insights within seconds. Adverity is able to support an unlimited number of metrics, KPls, and dimensions.

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Chapter 5

How to structure your agency data stack

So, you’ve got the right data, it’s all being standardized, and you’ve got the right client reporting tool for your agency, but it’s also important to plan how to manage multiple clients across your data stack.

While we know that every agency is different and the ideal agency data stack will look different depending on a whole host of variables, there are a few common setups that you can use as a jumping-off point when considering what architecture is best for your agency.

For all of these setups, the general process is the same - you’re pulling all the relevant data, standardizing it, and then sending it off to your preferred destination, whether that’s a BI tool, an analytics platform, a data warehouse, or an in-house analytics solution.

However, the way that these data pipelines are structured will dictate how data is grouped before it gets sent to its end destination. More importantly, it is also going to define how you will scale your agency as it grows.

Before you get into what data architecture you need, ask yourself the following questions:
  1. Security and privacy - How important is it to keep client data siloed?
  2. Agency size - Does your agency operate in multiple markets?
  3. Data maturity - Does your agency have the capacity to analyze marketing data at a holistic level?
  4. Existing data stack - Does your agency already have a data warehouse and an in-house solution that you’re working with?
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The 4 most popular data architectures for agencies

Agency Architecture 1 - by agency

1. By client

  • Data is organized by client
  • User permissions or authorizations are governed at the client level
  • Ideal for simplifying the separation of client data
  • Most common for single-market agencies

 

Grouping data pipelines by client is by far the most popular setup that we see - especially for single-market agencies. There are a few reasons for this but crucially it means that client data remains separated, which simplifies security and privacy concerns.

On the client side, there’s the security of minimizing data sharing through client data silos. On the agency side, you still get the benefit of a high-level cross-client overview of all your markets and data and the trends and benchmarks that can help guide budget decisions.

For example, you can see overall trends by filtering Google’s data to see if CPC has gone up over the last few years, and determine either that Google is increasing prices, or you’re going for more expensive keywords.

For agencies, it makes sense to have as many global enrichments as possible so that data can be compared like for like across clients, markets, and channels. So, if you can collectively agree on a template for client reporting, then this will make the model much more scalable — however, for agencies with a more bespoke approach to each client, it’s also a flexible infrastructure to customize.

Agency Architecture 2 - by market

2. By market

  • Data is organized by country or region with client teams grouped within each market
  • Cross-market reporting for each client is done at the overall agency level
  • Ideal if you have separate contracts for individual markets across the same clients
  • Most common for global agencies

For agencies that work in multiple regions, separating data pipelines by market is a great way to group client data. This is a similar setup to the first one, except client workspaces all fall under the umbrella of whichever market they’re attributed to whether that is a country or a region — so it’s an excellent choice for global agencies.

If you’re setting up an architecture like this, the best way to go about it is to choose a few model markets that best represent the data and processes you want to replicate and use these as a template for other markets.

Of course, there will always be some kinks to iron out when you’re implementing a large-scale model like this one. Automated data mapping to transform formats for different currencies and languages can help with these, but it’s important to reach a consensus on the most efficient template while considering the differing perspectives of each market.

The goal is to be able to compare markets like for like. While it may take some discussion to settle on your preferences for processes and definitions for things like metrics and KPIs, putting in this groundwork is well worthwhile if you want an accurate comparison across all your markets.

Agency Architecture 3 - by channel

3. By channel

  • Data is organized according to specific channels and the teams that work on them
  • Cross-channel reporting for each client is done at the overall agency level
  • Ideal for agencies with specialized teams

This setup is less used than the previous two options however it can work extremely effectively in agencies that feature specialized teams working on specific channels or platforms. For example, an agency with this kind of setup might have a team for social, a team for search, a team for programmatic, etc.

If the directors from these only want to see their data and budget information when working in their workspace, then separating workspaces by channel makes the most sense. The downside to this is that it can make visibility across departments more complicated, and obstruct a more holistic view of marketing activities.

This setup also lends itself to a more streamlined architecture where you have one datastream per channel for all your clients as opposed to one datastream per channel per client. While it is more common for agencies to maintain separate datastreams for each client, having one say, Google Ads, datastream, that brings in all your clients’ Google Ads data and then segmenting it afterward, can be hugely beneficial.

For instance, one major issue that this setup helps tackle is authorizations — if you’re using an integration tool and the person who created the account leaves your agency, the account gets shut down, and everything has to be reauthorized and reconnected.

Having one global authorization rather than one per client allows you to control permissions, control which customers are in your system, and easily add new customers without having to go through each data stream. However, depending on your clients, getting a global authorization can be a challenge.

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4. Combination

  • Data is organized according to a combination of structures
  • Depending on the structure, overall client reporting most likely to be done at the agency level
  • Ideal for agencies that want a customizable and flexible approach

As you’ve probably guessed, this option is a combination of the above three structures and, if we are honest, what many agency setups tend toward as all agencies are different and have specific needs and requirements.

Templating and blending elements from structuring data by client, market, and channel forms a versatile data infrastructure for agencies that have a more bespoke approach. Often agencies will need a combination tailored to diverse client portfolios and team specializations to optimize efficiency, security, and insights.

By considering factors like security concerns and existing infrastructure, agencies can design a flexible data architecture that fosters growth and innovation, ensuring competitiveness and excellence in a data-driven landscape.

Quick case study: Mindshare

Let’s take a look at Mindshare which, by creating a single location to collect and integrate their data, provided a way for teams to deliver compelling, facts-driven stories from campaign results.

Chapter 6

Building an effective data storytelling strategy

If you want to deliver impactful client reporting, then here are a few additional ways you can implement an effective data storytelling strategy.

By harnessing the power of data storytelling, agencies can not only present insights but also inspire action and drive client success. Here, we outline three essential steps tailored to marketing agencies aiming to elevate their data storytelling game and deliver unparalleled value to their clients.

1. Understand Your Audience

 The first step in crafting an effective data storytelling strategy for client reporting is to deeply understand your audience. Identify key stakeholders within your agency and your clients' organizations, considering factors such as their roles, level of data literacy, and specific interests in marketing insights.

Tailor your storytelling approach and ensure that your insights resonate with each stakeholder group. By understanding your audience's needs and preferences, you can deliver client reports that drive meaningful action and engagement.

2. Select the Right Methods

Once you have a clear understanding of your audience, it's essential to select the most appropriate data storytelling methods for client reporting. Consider a range of techniques, including standard reporting, interactive dashboards, infographics, and use cases, among others.

Evaluate the advantages and disadvantages of each method in the context of your agency's objectives and your clients' preferences. Aim to incorporate a variety of storytelling approaches to cater to different audience preferences and communication styles. By selecting the right methods, you can effectively convey complex marketing insights in a compelling and accessible manner.

3. Establish a Consistent Schedule

Building an effective data storytelling strategy requires consistency and regularity in client reporting. Establish a consistent schedule for sharing insights and updates with your clients, whether it's through weekly reports, monthly newsletters, or quarterly presentations. Align your reporting schedule with your clients' business cycles and marketing campaigns to ensure timely and relevant insights.

Additionally, monitor the impact of your storytelling efforts and solicit feedback from clients to continually refine your approach. By establishing a consistent reporting schedule, you can foster trust and transparency with your clients and demonstrate the value of your data-driven marketing initiatives.

By following these three steps—understanding your audience, selecting the right methods, and establishing a consistent schedule—marketing agencies can build an effective data storytelling strategy for client reporting that drives actionable insights and strengthens client relationships.

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Chapter 7

What next?

Want more? OK, here's a few next steps you can take:

Download the Strategic Playbook for Data-driven Agencies for more advice and tips to improve your client reporting.

 

 

Learn more about how Adverity can help elevate your data storytelling and manage your clients data.

 

 

Check out some more case studies from Adverity clients.

 

 

Book a demo with one of our specialists to learn more about the Adverity platform.