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

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:

data-behind-data

  • 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.historical data
  • 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?
    real-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
  • 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?
    reports-charts
  • 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 teamThe 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 To Shift Your Marketing Team Into Data-Savvy Marketers?

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?

data-savvy marketers

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.