Marketing Analytics Services - Data Analysis & Marketing Dashboards

The Foundation of Marketing Analytics: From Challenges to Process to Profit

20th June 202017th August 2020 Yasser Ahmad

Marketing analytics is the process of comprising data and technologies to establish trackable metrics and data-driven marketing activities.

The main function of marketing analytics is to import the business metrics, such as ROI, and marketing attribution into the core of the marketing game.

In other words, the analytics job is to gathers data from across all marketing channels and to combine it with the consumer databases then and consolidates it into an insightful marketing view. From this analytical view (360° Knowledge Graph), marketers can extract real-time analytics and actionable insights that can provide the steering wheel for effective targeting and personalized marketing efforts.

marketing analytics process

What can you do with marketing analytics?

From a general perspective, the analytics of marketing data plays an essential role in achieving business objectives. Eventually, the roles can be strategic or operational or financial.

Understanding your target consumer: analyze the target consumer, predict trends, monitor consumer behavior
Monitoring business goals: Connect online behavior with offline data, measure ROI and customer lifetime value CTV
Measuring the marketing performance: Monitor real-time performance, forecast future performance, campaign performance, return on Ad spend ROAS, bidding strategy, and budget allocation across channels and devices.
Analyzing the competition: Compare marketing activities against competitors’, analyze the market share, and monitor the brand visibility.
Enhancing the marketing team: Build a data-driven team, increase the credibility of results, improve the efficiency of marketing priorities, and ROI tracking.

Marketing analytics: The challenges of technology

Over the years, as businesses expanded into digital marketing techniques and the need for advanced targeting and tracking is becoming the main focus of marketing initiatives. With the higher demand for efficient solutions for analytics, the challenges started to rise.

Isolated environments: The new technologies were typically deployed in isolations and the result was a huge set of tools and platforms of disconnected data environments.
Marketers would have to implement several tools and platforms to monitor all the data sources (Google Analytics, Social Media tools, SEO tools, CRM platform, Automation platform, etc.).
This would require several resources including talents, API integration, IT capabilities, and multiple data aggregations.
Data Discrepancies: There will be always instability and mismatching results coming from different platforms.
For instance, comparing Google Analytics and Facebook conversions will reveal a big difference since they use different tracking.
At the end of the day, you will be facing the issue of which data source is the most reliable for making a decision?
Customization: Each business has its own technology stack and infrastructure. Connecting Sales data with online data is sometimes one of the biggest challenges for marketers.
Your company could be using SalesForce for offline data while you are using several tools for online data.
Combining these resources in one source for analysis could be expensive for SMEs who cannot afford the cost of enterprise clouds or solutions.

Marketing Analytics Process: Main steps to marketing analytics success

To get the highest value and greatest rewards from marketing analytics, follow these four steps:

1. Determine the objective function of marketing analytics

Marketing analytics relies on three pillars: econometrics, experimentation, and decision calculus.

CMOs can use econometrics when they need to make hypotheses about their marketing activities and test them by using experiments. Where the decision calculus comes down to individual digital marketing channels introducing their own intuition into the equation, marketing analytics as a whole allows the marketing department to identify best estimates for how to measure the effects of marketing activities.

Intuitively, the metrics and data analysis should provide the best relationship between marketing inputs and consumer response.

Set-up your objective function clearly. What are the business metrics the business wants to set as its goal for optimization? This may be one of any formulas for assessing business success, including market share, conversion rates, brand equity, customer lifetime value (CLV), retention rates, future growth potential, and business valuation.

2. Connect the data across departments

The second step is to connect the marketing data with other data sources within the firm. Bring the online insights of consumer interactions onto your customer database.

The value of connecting marketing data with sales and finance can help business managers in many complicated tasks. For example, if a company is examining gross profits, what are the attributes of the business that contribute to those profits? Another example, net profit is gross profit minus marketing costs. If both gross profit and marketing costs are known, net profit can be computed easily.

For decades, the relationship between marketing costs and unit sales is complex and driven by numerous unknowns. You cannot directly sum the investments in marketing (for example, organic, advertising) to obtain sales. Connecting data can bring more accurate analysis rather than a guess based on historical data, wherein several factors in addition to the price also affect sales.

Eventually, this is the main difference between an identity relationship and an empirical relationship when you are making a decision. Empirical implies is always a prediction while identities are certain.

Marketing analytics process

3. Set up the rules and analytics techniques

The third step is to identify your models, strategy, and analytical techniques. It is a critical step since a lot of businesses tend to fall behind in determining the right approach for analytics.

To avoid drowning into the huge ocean of your data, it is better to use a balanced assortment of effective analytic techniques which combines the following:

Find the data behind the data
Don’t only look for the obvious metrics like conversion, try to use analytics to reveal more about the customer journey which can help you to build a better experience and improve retention.
Analyze the historical data
Use marketing analytics to report on the past performance which will allow you to understand the trend.
Find answers for questions like which campaign generated higher revenue in the last year? How did your email campaigns perform over time?
Engage with real-time
Marketing analytics platforms would enable analyzing the life feedbacks and rates which would help you to answer questions like How the customers are engaging with your offering? Which channels your most profitable customers are engaging with? Who is talking about your brand on social media, and what is the feedback?
Influencing the future
The value of marketing analytics gets higher upon delivering data-driven predictions.
You can use the analytics to build an effective marketing strategy for the upcoming year by answering such questions as How you can design campaigns to turn short-term wins into loyalty and ongoing engagement? Which markets should you target next using the current portfolio? Which channels are more effective for conversion?
Get your model and reports fixed
It is important to know which marketing inputs of interest (season, promotional price, advertising, sales calls) should be considered as having an impact on the dependent variable? Once you set up the regression model, the CMO can predict the outcome metrics for different marketing input levels.
This is the mathematical model that describes the relationship between the independent variables (such as offers, advertising, sales calls) and the dependent variable (such as market share, profits, CLV)

4. Build the analytic strategy and actionable tasks

It’s important to know where you stand along the analytic spectrum, so you can identify where the gaps are and start developing the actionable tasks.

The marketing organization would need to build a data-driven strategy that can bring the most profitable results. Of course, if you’re not quite sure where to start, well, that’s easy. Start where your needs are greatest, and fill in the tasks over time as new needs or potential arises.

Some of the most common tasks are:

Setting the Data Collection: APIs, Platforms, Tags, Cookies
Building Analytical Models: Descriptive, Diagnostic, Predictive, Prescriptive
Planning Campaigns: Combine analysis with creativity
Developing Measurement and KPIs: Go beyond CPC and likes
Visualization and Storytelling: Charts and dashboards that don’t suck
Optimizing Conversion: Testing UX, Personalization and Engagement
Set the Data-Driven Advertising: Budget allocation, ROAS, Bidding, and Targeting
Analyze Attribution: Top-Down and Bottom-Up Converge; Channels Optimization
Research on Tools: Develop the Marketing Tech Stack

5. Learn from insights and optimize

There is absolutely no real value in all the insights marketing analytics can give you – unless you act on it. In a constant process of testing and learning, marketing analytics enables you to improve your overall performance by adjusting strategies and tactics as needed.

Without the ability to test and evaluate the effect of each marketing campaign on your consumer and profit rate, you would have no idea what was working and what wasn’t, when or if things needed to change, or how.

6. Build a professional analytics team

marketing analytics team The last and most important step of the data analytics foundation is to build your marketing data team. Depends on the type of your business, amount of data and marketing technology, you can decide what is the best approach to build an effective data team. There is no straight formula for the structure, however, there are many common types:

Entrepreneurial: Usually the team consists of one marketing analyst, project manager, and data scientist. This team commonly placed under the management of “Performance Team” which is responsible for paid advertising as well.
Professional: The analytics teams might be larger than an entrepreneurial system and consists of several marketing analysts with different tasks as well as data scientists, developers, marketing technologists, and project manager. In this model, the analytic team is placed under a separate department and manager by the Chief Analytics Officer (CAO). This model is commonly used in mid to big size companies.
Superior: This is a very advanced team structure that is commonly used in tech giants and big players in e-commerce. The analytics team in this model is highly involved in BI as marketing.

Overall, most of the companies would hire only one marketing analyst or few and place them under the performance marketing, which is a common mistake since marketing now is all about measuring the performance. It is better to keep the analytical skills more centralized and connected with all marketing initiatives from strategy to branding to advertising and creatives.

For more information, read How To Shift Your Marketing Team to Data Science and Marketing Technology?

Marketing Analytics Outcomes: How to measure the Proft

For each marketing strategy, the company is looking to assess its return on investment (ROI).

But how we measure Marketing ROI? It is equal to profits related to marketing measures divided by the value of the marketing investment — which is actually money risked, not invested:

Marketing ROI = [Incremental Sales × Gross Margin – Marketing Investment] ÷ Marketing Investment

Determining ROI is simple arithmetic; however, estimating and defining the effects of ROI is difficult. Imagine that your department spends $2 million on Google Ads in 2019 and generates $10 million in incremental sales that year with marketing contribution margins of 50 percent. The company would determine its marketing ROI as follows:

ROI = ($10M × 0.5 – $2M) ÷ $2M = 1.5

A CMO would have therefore determined that his return is 150 percent on the marketing investment. But the CMO will likely still have questions. Will the investment in 2019 also pay dividends in 2018? Will increasing the investment will increase the returns in sales, or are there diminishing returns to marketing? What are the longer-term effects, and what is the CLV of the client acquired through this campaign?

These are the real questions and the goal of analytics is to accommodate these nuances of marketing’s influence on sales so that the estimate of incremental sales is an accurate reality.

What about future ROI?

I believe that marketing function is not only about generating ROI on the spot. Marketing departments should benefit from analytics to work on maximizing long-term profits or as I call it, future ROI.

In order to do that you cannot simply shift funds from low ROI to high ROI activities because of your CEO considerations about the marketing budget. In fact, you are harming the company in the long-term because there may well be strategic considerations not fully captured in the ROI measures themselves.

Examples are brand exposure versus short-term sales, balancing push and pull efforts to support distribution channels, and target segments that are strategically important in the long-term.

The role of analytics would jump in and help you to consistently make good decisions about which customers to select for targeting, the contribution of channels in the CLV, and nurturing the leads to increase future profitability.

Read more about this topic: The Future of Digital Marketing

Bottom line: CMOs must understand their marketing analytics foundation as precisely as possible to determine how to gain success in a data-first world. If sales calls are profitable only up to a point, the marketing manager must know at which point the calls start costing the company money instead of making it. The only way to measure this is through the insights and relationships revealed by marketing analytics. By using statistical analysis techniques, firms can use past customer behaviors to predict how customers will react to different marketing channels; managers can then optimize spending on each channel.

How a Marketing Analytics Consultant Can Solve Your Business Problems

27th April 201817th August 2020 Yasser Ahmad

In a data-first world, the marketing analytics is now becoming the process of connecting marketing function with the business intelligence. The demand for specialized marketing analytics consultant is becoming essential to bridge the gaps in marketing data and to extract business insights.

The main question in every business -from a small start-up to a well-established organization- will always be: Do we get the most accurate informed-decision/actions our of our data?

Reality scenarios:

The market research is telling you that the region you are targeting is highly profitable. All figures and surveys are providing very positive indications. You launched a campaign for few months but the campaign results are very poor. Where do you need to search for the problem? The common conclusions are: The market research is not accurate, the product offering is not competitive enough, the brand is new in the market and you need to allocate more budget for awareness. Any or all of this could be the reason but how you can get sure which one has the biggest impact?
You hired the best creative team or agency which provide you with great visuals and UX. Yet, the conversion is still low and you are you are losing a lot of money on advertising. How can you indicate the problem? Is it the creatives you need to change or the performance team that is not managing the budget allocation properly?
Your sales team are commonly reporting that the marketing team is not driving quality leads while your marketing team is telling that the sales department are not performing effectively. How can you find out which team of the two is underperforming?
You have so many analytic tools and each one of them has its reports. You are not sure how to combine and simplify all this amount of data and report the right insights to your management. All this data is still not getting you anywhere when your CEO asks about the actions required to increase ROI.

With all these issues a business can either go wrong and lose money or define the problem, raise flags and take an effective decision. Holding many meetings and listening to different opinions from the team is not what companies need to survive in such evolving situations. Numbers, technology, market trends and consumer habits are changing and you have to move faster.

How can a marketing analytics consultant shift the way you are looking at your data?

Bringing data to the core of your decision-making process doesn’t only require technology and advanced automation platforms. A tool can give you the real-time figures but creating a relation between data and extracting an action is the biggest problem for any organization.

One of the major challenges CMOs face: finding professionals who can not just analyze data but most importantly to extract the critical insights. Data analysts are qualified to analyze the data but the marketing teams are always in need of a consultant with experience in both analytics and marketing strategy. Here I would love to share some of the main functions of the marketing analytics consultants and how it can help the stakeholders to solve business problems.

Function #1: Building an effective marketing analytics strategy

Starting with the strategy, the marketing consultant can always organize the marketing analytics process by building an effective strategy based on their findings. Establishing a custom analytics plan would provide efficient outcomes and controllable workflow.

Research the data sources and define what the marketing needs to design a more effective process to collect, analyze, and extract insights and patterns.
Evaluate all structured and unstructured data sources and define what is the best Martech and analytic platforms required
Identify the gaps in the digital marketing strategy and areas for improvement
Develop a marketing analytics strategy based on the business objectives
Establish a best practice approach for reporting and insights from your analytics

Tip: Google Analytics reports and Facebook insights are not customize designed tools that are built to answer questions. Each business shouldn’t rely on extracting the built-in reports and develop his own customized insights. The process of reporting should start with a business question such as; how we are improving our ROI? The reports are meant to provide answers and indications and not just metrics.

For more information about the marketing analytics strategy, read the article: 5 Effective Tactics for Marketing Analytics Strategy.

Function #2: Marketing analytics consultant can fix the gaps in your data insights

Are you collecting the data you need at the right timeframe and at the right volume? Getting overwhelmed with the vast amount of data availability is a mistake.

It doesn’t matter if you have access to all data sources, while it matters the most to have an effective scope. A professional marketing analytics consultant can help you to build the right strategy where data are meant to answer the business questions. Finding the metrics which define the action is a process where a digital marketing consultant can help to connect the dots. The analytics consultant can cover the following functions:

Analyze all the reports and indicate the gaps in the data (metrics required, data correlations, dashboard structure, reporting automation process, and timeframes)
Set the business objectives for reports (consumer behavior metrics, brand awareness metrics, performance metrics, transactional and ROI metrics)
Apply the analytical models required for client lifetime value, retention and forecasting
Help the team to extract and create stories using the data visualization and deliver a high level of relevancy
Determine the most effective marketing strategies and defining the performance gaps which can help in managing the marketing budget more efficiently

Tip: Insights are more important than data. Choose a professional consultant who can help you to stop chasing all the metrics and organize your reports.

Function #3: Marketing analytics consultant is an outside eye

There are several issues in organizations that prevent management from solving problems. Among the top issues are the common ones; themselves. In many business cases, the way the senior management identifying the is the problem is not accurate and that’s why they need an outside eye (different perspective).

Consultants often work with many different companies and may have faced the same problem you are struggling with in the past and tested multiple solutions for it. They can provide a perspective based on what they’ve experienced and provided more insights into what is working and what is not.
An analytics consultant can bring in a more innovative solution and marketing technology ideas and advice which is suitable for your business which can save can cut the cost for budget and the resources required to implement a change in the department.
The consultants’ task is to define the gaps and highlight the performance issues which can resolve the conflicts between internal teams. This can solve several issues between different team members and defining the most relevant KPI. Understanding not just the performance of the channels but also the performance of the team members and roles.
A professional consultant can help you to understand the critical gaps in your marketing performance and raise the flags for urgent matters as well as drawing attention to milestone wins.

Tip: Each organization might be full of certain rules and policies that can prevent internal teams from implementing major changes. An outside expert can help you to push the change and implement new tactics in a shorter timeframe.

Function #4: Covering the knowledge gap for your team

One of the key roles for marketing analytics consultant is to cover your team’s knowledge. The consultant can save you time and budget required for an extensive amount of training or additional full-time talents by training your team and providing the right approach for data analytics.

A professional marketing analytics consultant can help to structure your marketing team and identifying the knowledge and resources required. In some cases, a consultant can help you to avoid new hiring by implementing automation tools or delivering a sufficient analysis on a regular basis.
The team can benefit from the consultant experience and enhance their skills as well as avoiding a longer time in technical troubleshooting and research.

In conclusion: CMOs say just 42% of marketing decisions are made using analytics. Eventually, CMOs still report that they are still struggling with analytics. While the marketing technology and cloud solutions are providing advanced capabilities in combining offline and online data, the lack of talents who are experienced in both data analytics and marketing is a challenge.

A specialized consultant who has the knowledge of data analytics tactics and marketing initiatives can drive the marketing department towards the business goals. Putting all the wide collection of data (media, paid advertising, organic, social media, digital marketing, mobile marketing, customer relationship management, and email marketing) into an insightful pattern can shift any business into a real customer-centric company.

5 Effective Tactics for Marketing Analytics Strategy

5th April 201817th August 2020 Yasser Ahmad

From a business manager’s perspective, successful results are achievable if a common objective and key performances are made clear across the organization. Marketing data analytics is one of the core competencies for data-influenced companies and play an effective role in connecting business objectives with consumer behavior’s data.

The main purpose of organizations to create a marketing analytics strategy is all about how to drive smarter questions, which will elicit thoughtful answers using the accessible data. Unfortunately, many stakeholders still tend to see marketing analytics as a way to measure the spending and marketing performance. That’s considered as a limit view of the data analytics capabilities.

The marketing analytics strategy main objective is to maximize the use of data to create a form of data-influenced actions that are aligned with the business objectives and effective on the long-term strategic advantage.

If I could highlight the most important fact about marketing analytics, I would definitely say that the best strategy to follow starts with a sharp focus on the objectives. To get a better understand, let’s start with some common situations.

Reality scenarios…

Businesses would say that they know their marketing objectives clearly, But Ehm!.. Not true.
Once you investigate more with their situation, you will soon discover that those generic needs they have to be analyzed using the data.
Structured data and insights are not always ready and available. To build strategy you sometimes need to assist the company with their data aggregation and analytics.
Inside companies, the marketing departments are usually under the pressure of proving their performance results or struggling with 60 dense analytic charts and slides which is hard for senior management to understand a fraction of it.

When you are facing such realities, it is about time to start searching for a better understanding of the data role in your organization.

1. Understand the role of data analytics

First, we have to stat the fact that reports, slides, and charts generated by marketing analysts shouldn’t be for the sake of providing results. Results are not the key. When you are facing a gap in your performance, it is about time to start searching for a proper strategy which can help you to move from the phase of looking at results to the phase of understanding why these results are like this.

The toughest lesson I did learn about data that it is not just made of numbers and figures. If you don’t analyze the data properly, you will not be able to get an accurate action. In a data-driven world, a piece of metric can bring a major shift in your consumer journey if you are able to measure it in relation to another metric.

Describing the business objective as customer-centric doesn’t only require investing more money in product and customer relation while paying less attention to the real-time analytics. Marketing analytics is the playing an essential role in understanding the client behavior and interaction with the brand and the services provided.

Find out the objectives of your marketing analytics strategy and understand the role of it before you try to shift your business into data-influenced.

2. Set the effective KPIs and avoid the metrics delusion

The first questions, while you are navigating your metrics, should be: If I have a higher rate on this metric would it impact the business objectives? Does the higher engagement rate on social media increase the website conversion rate?

The metrics and dimensions should be selected based on the business objectives.

Marketing professionals should translate the business objectives into a set of effective KPIs. Each KPI should be based on a set of relevant metrics and data insights. You have to remember that chasing all metrics will always result in a huge waste of efforts and confusion in your daily or weekly tracking.

What’s the difference between a metric and KPI? KPIs are measurable values that are created by senior managers to achieve business objectives. Metrics are the moving status variables to track a specific business process.

All companies need to track profit is an essential KPI which might include website conversion, orders, monthly active users, and retention. Each KPI could have a strategic value for the organization such as market acquisition and CLV.

Eventually, the number of KPIs might increase from top to bottom when you set your marketing strategy. The CEO might have a set of KPIs which requires marketing to generate multiple additional KPIs. When those numbers grow bigger than your capacity and you find yourself experiencing data overload, then you will need to identify only the relevant metrics.

3. Define the target before you set the KPI

Targets are not easy to set, but once you do have a clear understanding of what are the indicators of success and failure, you will be able to identify your right targets.

Building an enhanced analysis for marketing is all about eliminating the irrelevant matters and focusing on the critical view. See the target clearly before you set a KPI.

By using the forecasting models, past performance, and competitive analysis the targets are easy to distinguish. Don’t set your KPIs without having preassigned targets. In any common situation, the stakeholders would always struggle with understanding the KPIs that doesn’t have a clear target. If you don’t have a target supported by figures, you will end up chasing the impossible.

Remember that not all professionals will be happy with assigned targets. It is important to always use the analytics effectively to prove the accuracy of your target figures. Make sure you always assist your targets with industry benchmarks and forecasting indicators.

4. Organize an easy-to-read marketing dashboard

Your marketing dashboard is your home. It should be familiar (uncomplicated), tight and present your main needs. Build a dashboard that takes no longer than minutes to read and include the main KPIs you need to monitor. Use data visualization to illustrate the data correlation effectively.

Marketing dashboard should always have the following features:

All-In-One-Monitoring: The marketing dashboard should collect all the required analytics from data sources (GA, SM, SEO, CRM, Email Automation, Paid channels, PM system, Finance, etc.)
Visual KPIs Tracking: KPI tracking should be visualized with average rates over a periodical timeline.
Outlined Objectives: Define your top factors for tracking marketing activities (campaign tracking, channel tracking, revenue tracking, engagement metrics, attribution modeling, etc.)
Set Data Deviations: Using 3 standard deviations to generate your rates. (According to researchers, 99.7 percent data distribution lie within three standard deviations).
Reliability and Accuracy: Your dashboard should have accurate clean data. Connect real-time data and reliable data connections to get an up-to-date view of your marketing activities.
Customization and flexibility: Adjust your dashboard according to your strategy and don’t use static settings for timelines and data validation.

Finally, you need always to be able to identify your performance top layer accurately and clearly. Marketing dashboard can help you to provide a transparent overview for the business stakeholders and clients and can decrease the amount used for reporting.

5. Get the managers involved with the context

If all the previous strategies fail, then I advise you to try this strategy. It is not an easy one because it’ll demand that you are truly expert in marketing analysis. You might think that senior management and client want data but that’s not accurate. They pretend that data is important for them but at the end, they are only looking at results. That’s how the business world works.

Therefore, I believe you should focus on telling the story not showing the tables and relations. There are useful ways to get this done by adding some context to your dashboard and extracted reports. List a set of clarifications to the metrics such as: What influenced this metric? why these rates changed? what is the set of actions taken?

Learn how to tell a story instead of reporting data. This will require you to have an effective understanding of the organization and the client motivations. Avoid going outside the track and showing irrelevant metrics or you will lose your case at the first meeting. Remember that you will need few rounds to be able to play this game effectively and expand their knowledge about the analytics. Once they will see the relation “the context” they ask for actions. That’s the time for you to show the actions slides and win the meeting.

It is totally normal that business managers will be always doubting and pushing on the marketing to show more growth. The best approach for marketers is to always have their analytics clean, accurate and telling a story. Showing reports for the sake of flooding the table with many charts will always lead the marketing to an uncertain path while inserting context will lead managers to shift their focus from the numbers to the insights.

Conclusion: If you find yourself stuck with showing your performance in the proper way, try these strategies. If this doesn’t work then take a deep breath and try to understand where is the gap. Marketing analytics strategy is not straightforward practices and it will always require time, testing and flexibility in implementation. Work on each data source and fix your reports one by one. Give it a shout and remember that finding the insights is more important than the data. Finally, if your company is not a data-driven and data-influenced then I believe that you will change it with your tactics.

Please share your ideas and theories via comments or contact me for marketing analytics consulting.

Yasser Ahmad

Marketing Analyst vs Data Scientist: What’s The Main Difference?

8th February 201817th August 2020 Yasser Ahmad

While data doesn’t come in neat little packages, ready to answer the questions marketers concerned about, the demand for sufficient marketing data is the main factor that is shaping the roles of marketing analytics field.

With is a huge potential for connecting the dots between data and marketing activities inside all types of organizations, the rise of data skillsets is booming in 2018. In the past few months, I’ve come across many managers and recruiters asking the same particular question all the time:

“What is the difference between marketing analyst and data scientist?”

Starting with the background: When data science brought many trackable capabilities to the marketing department, marketers had assign dedicated professionals who are accountable for using data to answer the marketing problems and furthermore to understand the performance metrics. Yet, the issue we did face in the last few years is the rapidly growing data resources (online, offline, internal and external). The data aggregation itself was a major challenge for every marketing departments.

In 2017, everyone in the field was talking about the need to grow their data analytics team. With the higher pressure on the efficiency and accountability of marketing KPIs, the marketing department functionality started to evolve and play more important roles within to the organization. We can see now how marketing analytics is not only limited to sales data but also connected with customer support, business automation, and financial department.

However, since diving into data team, in general, is becoming a complicated structure due to the rise of Big Data, Data Mining, AI, and ML, I will try to avoid the hierarchy of business intelligence filed and focus precisely on the marketing needs.

The main two roles in marketing data

Ideally, marketing requires two roles of data: Marketing Analyst and Data Scientist. Why? It’s quite straight-forward to the point of marketing needs, the two function is our way to define the data team role within the marketing department, how they are different?

The most common scenario in marketing departments is the need for an analyst to dominated the marketing analytics and a data scientist to operate the company data aggregation.

Why am I trying to simplify this into two main roles? In companies, we would always see a variety of business needs and marketing functions which requires a customized structure. Apparently, you might see a variety of positions and titles based on the common needs of the organization and nature of the business. An e-commerce business might have a large set of data team while a startup might be only hiring one person, so don’t get confused and let’s first focus on how to distinguish the main differences between the basic two roles every organization need.

The key difference between Marketing Analyst and Data Scientist

The main difference between marketing analyst and data scientist is that marketing analyst should be a native marketing-speaker with professional skills in driving insights to answer the marketer’s needs. On the other hand, a data scientist is a native data-speaker with skills in deriving BI and analytic insights from structured and unstructured data sources.

To understand the key differences between the functions of two roles, let’s summarize in 5 main points:

Marketing analyst should have a solid experience with marketing metrics while it is not required in data scientist role.
The data scientist is expected to formulate the critical questions that will help the business and then use the data to solve it, while a marketing analyst is given questions by the marketing team and pursues a solution with that guidance.
The marketing analyst not required to be advanced in programming side while the data scientist should be professional in writing queries. Yet, both roles should work with IT teams to source the right data.
The data scientist role requires a strong data visualization skills and the ability to convert data into a business story. A marketing analyst is more focused on analyzing the marketing metrics.
The data scientist usually work in a multidirectional and free form in order to extract better insights, while marketing analyst usually has a specific direction to work on.

Defining the Marketing Analyst Role

The marketing analyst is similar to any other analyst in terms of methodology but he is truly different when it comes to the functions. So why is that? I strongly believe that marketing analyst is a digital marketer which luckily become a master of analytic tools. You might disagree since you would meet a lot of marketing analysts who didn’t have any experience in marketing. I know this because I suffered from this for awhile and always had conflicts with data-savvy specialists who failed in understanding our marketing needs since they simply lack the marketing background.

However, marketing analysts should be very solid in understanding the function of marketing and its objectives. I am confident to say that marketing experience is crucial more than you might expect. The marketing analysts are located at the heart of marketing team and should speak their language and suffer with them from the same problems.

The main objective of marketing analyst

Measure the effectiveness of marketing activities and the online ROI, of various marketing channels used to position a product or service. Given the increasing variety and complexity of marketing channelsreaching this objective is a serious challenge.
Bring the data analytics into the heart of all marketing campaigns and tools while setting up the most effective metrics to measure and trends to manage.
Turn insights and data patterns into clear indicators and tactics for growth hacking, budget allocation, and performance management.
Maintain a reliable and effective connection between the marketing specialists needs and data scientist reports.

Who is the best Marketing Analyst?

A native marketer who knows how to play professionally with marketing technology tools and marketing metrics.
A scientifically minded person with an appreciation for design. He needs to know the effect of messaging and design on the consumer experience.
Analysts by the heart who dominate the dashboards and he have charts ready even for his grocery shopping habits and his girlfriend mood swing.
He knows that insights are more important than figures. He loves the data in front of him but he is more in love with knowing the consumer.
He is the honest guy who never takes any sides. Neither marketing performance team nor data team.

Technical skills for marketing analyst

Strong analytical, conceptual and reasoning skills
Professional skills in Web Analytics, Marketing clouds, AdTech, and Automation
Experience with Statistical Software, Business Intelligence Platforms, and Data Visualization
Intermediate experience with programming language and database querying
Experience with market research, segment analysis, consumer behavior and marketing channels

Defining the Data Scientist Role in Marketing Department

Business acumen is the main asset desired in a marketing data scientists, after technical skill. It’s so critical because a lot of quantitative candidates I’ve seen are getting so wrapped up in the elegance of the analytics that they forget that they’re hired to answer business problems.

Working with marketing team is somehow challenging for data scientists. The marketing ever-changing periodical strategies can be a roller coaster for data team and they have to adapt and survive quickly. Unlike the majority of businesses where the top element of the data science job is the ability to use computing power to acquire the data, marketing needs could be problematic and tactically challenging over the time.

Who is the best Data Scientist?

Tech-savvy with different programming languages and statistics capabilities.
A scientist who applies statistical tools, economic tools, and different disciplines is another facet.
A coder who aggregate and clean data in the most efficient possible ways with ability to invent new algorithms to solve problems and build new tools to automate work or provide real-time reporting system
He is an expert in interpreting the visual display of complex data sets and tells a story.
He is sophisticated with analytics programs, machine learning, and statistical methods and quick with preparing data for use in predictive and prescriptive modeling
Without asking he is always busy with conducting undirected research, exploring and examining data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
He speaks the language of IT and able to communicate requirements and predictions to IT departments through effective data visualizations and reports

Technical Skills

Expert in Math (linear algebra, calculus, and probability), Statistics (hypothesis testing and summary statistics), Data visualization (Tableau, Power BI, SAP Analytic Cloud) and reporting techniques
Professional with Software engineering skills, Data mining, Data cleaning and munging
Professional skills in programming (R, SQL databases, Python or C/C++)
Professional with BigQuery, DynamoDB and cloud computing tools
Experience with ML tools and techniques (k-nearest neighbors, random forests, ensemble methods)

Collaboration between Marketing Analyst and Data Scientist

Your marketing analyst should deliver the clear results in marketing language while the data scientist should work on doing the math (statically and technically). Technically, a marketing analyst is solid at creating relations between data and marketing needs while data scientist is the true advocate in bringing the data and advanced statistics and bring the most reliable, clean, fastest results to the table.

You have known knowns, known unknowns, and unknown unknowns. Just be careful if both get a conflict. I have seen some violent fights at the office!

Finally, becareful with Data

There are many times where the underlying data that is the basis for what people have calculated is actually wrong. If you make a mistake with the underlying data, that could be a big problem while you analyze.

The premium on being able to understand what data you have, to understand what types of questions can be answered with it, and to make smart decisions is really, really high.

However, there are places where pure data science functions can fall short of what’s required to boost success in the marketplace. This is where marketers thrive.

Looking for your opinion on this and how do you see the difference between the two roles. Contact me if you are looking for marketing analytics consultant.

Yasser Ahmad

How To Shift Your Marketing Team Into Data-Savvy Marketers?

29th November 20173rd September 2020 Yasser Ahmad

To stay competitive in today’s data-first world, everyone in your marketing department—marketing data analyst or not—should know how to analyze and interpret marketing data, from customer insights and performance figures to overall ROI.

Marketing is at a crossroads, and now is the time for digital to stop monitoring and dive into data to extract more efficient tactics for targeting and spending. The marketing technology, cloud computing, and machine learning are expanding beyond the expectations and the face of marketing will change before you even know it.

According to a recent survey by Econsultancy, two-thirds of marketers said their organizations do not yet have data analyst and data-related goals. The study showed that leading marketers who outperformed their KPIs appeared to have found a solution for achieving these results: Enable everyone on the team on how to be data-savvy. Nearly 60% of leaders say that in their organizations today, marketers get specialized training on how to use their data and analytics resources.

It is not the tools, it is the human

Marketers are looking now at floods of real-time data that needs to be effective in consumer segmentation, content creation, and channel proliferation. Yet, the majority of the data are currently used for just monitoring and fixing nice looking monthly reports. The big issue is not about technology and tools under your hand, it is more about the human professionals who are creating paths for this data from demanding to analysis and decision making.

The human capital skills are the most important factor in the data-driven marketing department. They are the ones who can research and decided which data is more important for the business. The more sophisticated the data you have the more dynamic your team should be. Your team should be data-savvy and data-driven in every step of strategy and execution.

Start with the head

Over the years, I have been involved in the needs of CMOs and CEOs for reports that tell nothing, usually some traditional requests for traffic, organic and social media proofs which belong to the stone ages of digital marketing. This kind of directions can be misleading for the marketing department objectives since reports are more important for the marketers themselves.

Top management should change their mentality and start learning more about the importance of data through attribution models and funnels. They should be more aware of how the proper insights from their team can change the whole assumption of consumer persona and strategy tactics.

Shift your marketing mentality

The marketing team should have two main skills, creative and analytical thinking. The old times of launching seasonal creative campaigns are already gone. Marketing now is more about science mixed with creativity. Every approach should be supported by insights, every project should have data analytics in the early stages, every achievement in KPIs should be identified by analysis, every single marketer of your team should be fully responsible with measuring and analyzing the data.

Creativity will be always playing a key role in digital marketing, but with the right analysis in place, you are able to drive the creativity cart on the right track and adjust the budget allocation more sufficiently. For more information on this topic, check my previous article: How To Shift Your Marketing Team to Data Science and Marketing Technology?

Fix the skill gaps with your team

You will need to study your team structure and figure out their skills. Empower their skills with training modules on topics like data science, marketing technology, and automation trends. The team should master the use of CRM data, web analytics, sheets and charts. Don’t only rely on hiring data analyst who can do the magic. Your team should always have the skills of putting data insights into proper context and metrics.

75% of marketers agree that lack of training on data analytics is the biggest barrier to making more key business decisions based on data insights.

Maintain easy and reliable access to data

Before dealing with your data, make sure it’s presentable. As they say, Good data is usable data, and that means it should be always real-time, organized, secure, and understandable. Establish clear definitions and KPI metrics so everyone in digital marketing team can speak the same language.

Leaders are 33% more likely to say that their data analytics explains how the business defines and measures the consumer persona and touch points the online journey.

According to the Econsultancy survey, standing out as a point of difference between leaders and laggards is an understanding of the customer journey across channels; while an astonishing 90% of all marketers believe that understanding the cross-channel experience is “critical to marketing success,” only 43% of the mainstream report having a “clear understanding of customers’ journeys across channels and devices,” compared to 64% of leaders.

Set the standards and metrics

Set a baseline for knowledge requirements and analytics. Set proper timeframes that is effective for your business case and define the level of accuracy. Don’t just sail in the sea of tools and charts without a compass. With proper research, you can set the standards which the team should follow in their monitoring and reporting.

From my experience, too much of reports is a misleading thing. It is better to define what you really need to make a decision or otherwise you will be overwhelmed with the lovely interactive charts that data platforms provide. I also recommend to acknowledge and reward the team members who apply effective tactics based on data insights while they launch their campaigns.

Fuel the team with technology

Make sure you have the right technology needed to take action. To drive your marketing team towards a competitive edge in the market or among competitors, you need to select your technology and platforms wisely.

Based on your needs from insights and automation, define which platform you should invest in. Don’t follow the tones of advertisements online and the on-going offers from software companies. A powerful solution is not based on the brand of the software, it more based on your needs and how effectively you are going to use it on daily bases.

Your team should be always in the middle of every technology implemented. Arrange training for the whole team and get them always up-to-date with technology trends. Hire a marketing technologist in-house who can be always involved in researching an integration of technology.

With these few tips, I believe you can have an overview of this mission. If you have a question or looking for marketing analytics consultant drop me a line and let’s discuss.

How To Shift Your Marketing Team to Data Science and Marketing Technology?

27th November 201717th August 2020 Yasser Ahmad

Data-driven marketing has transformed from an innovative approach to a fundamental part of digital marketing, performance, automation and most importantly business strategy.

A few years ago, most of the digital marketers were more artists than scientists. Although creative thinking is one of the most required talents in the marketers but let me tell you, the future is so soon and we are getting more and more data about our customers every day.

Digital marketing is now based on the data more than before, and the need for data analytics and marketing technology is essential.

Over the course of my career in various entrepreneurial and marketing roles —in a wide variety of fields— I’ve discovered that any marketing team without advanced marketing analytics are helpless and certainly going off-track. Relying on marketing technology to extract and analyze all relevant data can help the marketing team to accomplish tasks they couldn’t before.

Collecting consumers data on every level internally and externally will allow the marketer to interact and target consumer intelligently and define the most efficient budget allocation that can bring ROI for the organization.

To accomplish the shift in your marketing department, you first need to change two main things:

Changing The Marketing Functions

Data science is fundamentally changing the way we view and interact with digital marketing functions inside companies. It’s had an enormous impact on the digital marketing department set of objectives and functionality within the organization. With professional data analytic team on board, you can accomplish several new tasks that you didn’t encounter before.

Let me give you a list of business tasks your data-driven marketing team can achieve:

Empower management and investors to make better decisions with online insights and analysis.
Connect business objectives more efficiently with marketing performance. You can track the marketing ROI and get the ability to invest more sufficiently in growing your customer base.
Identifying the customer’s persona by analyzing the online funnel and consumer journey. This can help businesses in developing their products, enhancing customer support and improving sales efforts.
Building data-driven approaches to reach prospects in strategic, scaled ways. Understand how we can grow is the most important topic for senior management.
Personalizing campaigns to generate more add value and better relation with the customers and to leverage your brand voice in the market.
Approaching clients effectively by highlighting products and content that solve specific customer pain points and interests which would lead to more sales.
Optimizing the marketing budget and advertising spend by investing efficiently and tracking intelligently (Which is a major headache for decision makers).
Challenging the digital marketing team to adopt best practices and focus on achieving ROI by following accurate KPIs.
Benefiting from AI and Machine Learning technology into the marketing department which keeps your team highly strategic and more accurate with future planning and forecasting results.

The new role of the CMO is to maximize the benefits of market and consumer data for the whole organization. Marketing is not limited anymore to attracting consumers or establishing new markets expansion, it is now a key player in building the business strategy and decision making.

Digital marketing case: The CEO is totally convinced that my digital department task is to drive qualified leads while I insisted that we can help with investing decisions. We analyzed the location-based data for many retail points and were able to come up with effective insights on where we should expand and how to customize our services for each location. The data collected from CRM, website analytics and social media trends were very effective in exploring the gaps and fixing it.

Building new schemes for data, sharing insights and providing real-time feedback to other departments is becoming one of the most important functions of the marketing department and CMOs. It is not anymore the game of promoting services and products, but it is expanding to calculate ROI effectively and enhancing the business structure.

Changing The Marketing Mindset

Machine learning, data science, and predictive analytics are the new, increasingly crucial complements to traditional marketing best practices. But they require new technical talents on your marketing team, specialists who can bring science into each step of your marketing process. That requires a mindset shift in how you manage in marketing a data-first world.

Experimental Mind: A critical part of our mindset shift with using new technologies and analysis tools includes fully embracing testing and experimentation. You should put this approach to work as part of your efforts to generate leads, create a user experience and distributing your advertising budget among paid channels.Given the huge volume of performance data and the need for real-time optimization, data analytics and machine learning will become the core of your campaign success. These tools enabled us to test hypotheses and make targeting decisions at the campaign level rather than the channel level—something marketer couldn’t have accomplished manually.
Statistical Mind: A critical part of our mindset shift with using analytic tools includes fully embracing statistics over prejudgements. The experimentation of extracting data about your qualified leads can totally shift the way you are tailoring your content strategy or marketing message.
Digital marketing case: Take the challenge of knowing who, of the people who sign for trial version, will convert to paid users. Typically you would need to wait weeks and different assumptions to understand which campaign was successful. Relying on likes and views might give you an indication of your most popular content but at the end ROI is ROI. With advanced analytics and machine learning, we’re able to know the likelihood of a channel bringing in the right people to lower funnel, significantly improving ROI.
Technical Mind: Marketing technology tools help you make better decisions by uncovering the information relations, context and flow at a level of granularity that wasn’t possible before. However, marketing technology is getting more and more sophisticated thanks to E-commerce major demands on technology and machine learning.Back in time, digital marketing team used to rely more on creativity and no wonder the field was full of advertisers moving from traditional to digital along with the shift in the budget allocation from traditional to digital.Today, digital marketing is becoming more technology-centric which require marketers to be more solid in data science, analysis, and strategy.Yes, digital marketing teams should hire a marketing technologists who are able to configure platforms and marketers who are able to deal with complicated tools such as marketing automation platforms and programmatic advertising. Your team of marketers should combine the skills of creative thinking and advanced analytics “the marketing and the science”. This is where I see the next wave of marketing innovation.

What should we learn the most?

Here are three lessons I’ve learned about blending science and marketing:

- Insights are more important than data: Focus on extracting the right insights that matter the most. The analytic team should be connected tightly to the business objective. Break your business needs into smaller, discrete questions. Rather than trying to drive more overall traffic to your website, for instance, focus on driving repeat visits within a particularly valuable segment.
- Optimize the data process and availability: Capturing and managing data in a consistent manner throughout the organization. But remember that it can quickly become overwhelming, especially when you’re working across different countries and regions.
- Focus on establishing reliable and accurate data sources: One team may input data that are incompatible with another team’s data, making it difficult to analyze both sets in consistent ways.
- Maintain data sources and access: Focus on data integrity in how you collect, manage, and store information, you’ll avoid longer process for decision making and spending time figuring out how to compare apples to oranges.

This is my contribution, feel free to get a free ask questions or request for marketing analytics consultancy.

The Difference Between Artificial Intelligence And Machine Learning

15th November 201717th August 2020 Yasser Ahmad

The hottest topics that everyone is talking about right now are Artificial Intelligence (AI) and Machine Learning (ML), and often seem to be mysterious terms for many people.

AI and Machine learning are not quite the same, but since the majority of new trends in technology are using these terms can sometimes lead to some confusion. In this article, I thought it would be worth writing topic to explain the difference.

Since Big Data, analytics, IoT and many other waves of technology are hugely investing in enhancing the Machine Learning, I had so many questions about what it means? and why it is getting bigger and bigger every day?

The Definitions of AI and ML

The short answer for that is: “Machine Learning is an advanced application of AI based on the concept that we shouldn’t only automate the data but to let the machines learn for themselves”.

Now, what is AI? “Artificial Intelligence AI is considered as automation, it is the broader concept of machines being able to access data and carry out tasks in a way that we would consider smart”.

With understanding the basic concepts of the two methods, let’s dig further…

AI from complex calculations to decision making process

Artificial Intelligence has been around for a long time already, the idea started since the Greek myths contain stories of mechanical men designed to mimic our own actions. In modern times and after the rise of computers technology, software engineers started to move from complex calculations towards creating mechanical brains. The field of AI concentrated on mimicking human decision making processes and accomplishing tasks intelligently and more efficient.

Devices designed to use AI are often classified into one of two groups which are applied or general. Applied AI is currently more common in our daily life such as manufactories, stocks trading, autopilot systems and more. While general AIs systems can, in theory, handle different tasks or let’s say it is less common tasks. It is also the area that has led to the development of Machine Learning.

AI and Marketing Technology

AI is already in use in a myriad of digital marketing use cases. From analytic tools, to search engine optimization, to Google AdWords to content curation, to email marketing, to marketing automation platforms, different tools are already being used as fundamentally in digital marketing sphere. Those tools are not only to make human marketers’ lives easier but to get do the tasks in more responsive and cost-effective ways. When processes are optimized and made faster by technology, not only can businesses achieve better conversions, but marketers also have more time freed up for strategic thinking, campaign design, and data analysis when they aren’t bogged down with more basic tasks.

The Rise of Machine Learning

The Machine Learning has been shaped by two important factors: Arthur Samuel in 1959 released the concept of “Instead of teaching computers all what they need to know to carry out tasks, why we don’t teach it to learn for themselves”.

Later on, the internet took control with a huge increase in the amount data being generated and stored, providing the second breakthrough for machine learning. The urge for more advanced solutions to handle the data floods was increasing rapidly.

Apparently, engineers realized that it is far more efficient to code machines to think like human beings, and then plug them into the internet to give them access to all of the information in the world.

Neural Networks

The development of neural networks was the key to start teaching machines to think while retaining the innate advantages they hold over us such as efficiency, processing speed, and lack of bias.

So what are neural networks? It is a computer system designed to classify the information in the same way as our brain does. These networks can be programmed to recognize complex things such as images or voices and classify them accordingly. This coding works on a system of probability based on data fed to it. The neural networks are able to generate statements, decisions or even predictions with a degree of certainty. The magical step for those systems is the addition of a feedback loop which enables “learning” or customization of the experience.

Tech giants such as Facebook, Google, Amazon, and many more, started to take advantage of machine learning to not just enhance their products but get it more advanced. Machine learning now is controlling your Facebook feeds, search engine results, Netflix recommendations, display advertising and Email spam filters.

Furthermore, another field of AI called, Natural Language Processing (NLP) has become a source of hugely exciting innovation in the last few years, and one which is heavily reliant on ML. NLP applications attempt to understand natural human communication and able to respond. This technology currently has one of the biggest competitions in products such as Google Home and Amazon Alexa.

The Game of Marketing and Advertising

The term Machine Learning certainly gives marketing teams a shiny and attractive language to impress. The big players in digital advertising are pushing aggressively on increasing advertisers budget by developing a programmatic bidding with ML.

I believe the game is not that sufficient yet for many reasons, programmatic learning for platforms such as Facebook advertising and DoubleClick is still full of gaps and not doesn’t serve will with smaller budget advertisers. The machine learning is still on the producer side and heavily serving it is objectives. Although the Facebook algorithm is so advanced and presents a great value for digital marketers but yet the efficiency is not perfect in many cases.

Marketing automation and analytic platforms are still far behind when it comes to machine learning. There is still a long road to go with machine learning capabilities that can hugely shift the digital marketing once and forever. In my next article, I will investigate more in the case of machine learning for marketing and another trending buzzword – deep learning.

I hope this piece has helped a few people understand the distinction between AI and ML.

Yasser Ahmad
Digital Marketing Consultant