Ever felt the frustration of wrangling with mountains of data through web scraping? The landscape of marketing technology is constantly growing and #marketing teams are experimenting with new tools that are constantly generating a tons of data. As #MarketingAnalytics professionals, we constantly juggle data from such tools and sometimes, API connectors just aren’t available ready-made from tools like Fivetran or Airbyte. In such cases, we have to rely on building a custom Python scripts to either leverage the API of that data source (which is relatively an easier option) or scrape the authenticated web pages with the help of Python libraries like BeautifulSoup and Selenium.

Python based web scraping can be a real beast to tame, especially when dealing with massive datasets and dynamic web pages.

Join me on a journey where I harness the power of Python, Pandas, DuckDB, and web scraping to not just access data but to convert it into actionable data model that powers a very targeted marketing engagement strategy.

 

🕸️ The Challenge: Scraping a CMS Blog without an API

I recently faced this exact dilemma: scraping blog data from our CMS, which lacked an API. My trusty Python script, armed with BeautifulSoup (BS4) and Selenium, was ready for battle. Tackling the dynamic intricacies of web pages, especially in the context of pagination and the dynamic loading of client-side content (thanks to AJAX and JavaScript), posed a distinctive set of hurdles.

🛠️ Configuring UserAgent, Sessions, TLS, Referrer, and Chrome options

Setting up the basic script was a smooth sailing. The excitement kicked in when I started exploring the DOM structure of the web pages, pinpointing the specific HTML tag that housed the data I needed for this project.

The sight when any of my Python scripts first render web scraped data as list item is always such a joy and never gets old. It’s that satisfying feeling of problem solved!

driver.find_element(By.TAG_NAME, 'tbody').find_elements(By.CLASS_NAME, 'profile_groups')

load_more = driver.find_element(By.XPATH,  
                                '//*[@id="_profilegroup"]/div[2]/div/div[2]/div/div/button')  
load_more.click()

 

🕵️ The Webpage DOM and the “Load More” Conundrum

In this case though, the joy of seeing the first set of rows loaded was short-lived! Things got interesting as I hunted down the elusive “Load More” button, aiming to grab additional rows dynamically before I can read and store the data in a Pandas DataFrame.

Attempting to load all the data in one go turned out to be a problem, causing memory overload and web page crashes. With thousands of rows, my script started to buckle under the weight of the data.

Clearly this option of first loading all the data on the webpage and then scraping was not going to work. Traditional methods of storing everything in a Pandas DataFrame after full scraping is done or directly writing to a database like Snowflake were simply too resource-intensive.

🦆 DuckDB comes to rescue with Plan B

So, for my Plan B, I devised a strategy to leverage DuckDB. First, I would load the initial batch of rows, scrape the data, and then stash it away in storage. After that, I’d load the next batch of rows on the webpage and repeat these steps, employing 2 loops. The key differentiator in this approach was to use Pandas DataFrame with a companion, DuckDB for the iterative inserts and continuous updates.

To handle 2 different loop iterations in resonance, I had to come up with an interesting algorithm because one of the loops in my Python script was running on page load sequence but with every “Load More” click, the secondary loop was loading additional rows. These added rows were stitched onto the ongoing sequence from the very start of the webpage load, not kicking off a fresh sequence. While one loop had to kick off anew, the other needed to pick up right where the first loop had left off.

Wrapping my head around and implementing this puzzle piece turned out to be quite a delightful journey!

The mighty ‘f’ strings came to the rescue here and I could make the XPATH of the key elements dynamic – “tr[{counter + 1}]”.

if profiles:  
    if len(profiles[starting_point:ending_point]) > 0:  
        logging.info(f'beginning extraction of profile data for iteration no: {i}')  
        for item in profiles[starting_point:ending_point]:   
  
            # company  
            try:  
                item.find_elements(By.XPATH,  
                                   f'//*[@id="_profile"]/div[2]/div/div/div/table/tbody/tr[{counter + 1}]')  
            except NoSuchElementException as err:  
                logging.info(f'could not locate element for company on iteration no. {i}')  
                company = ''  
            else:  
                company = item.find_element(By.XPATH,  
                                            f'//*[@id="_profile"]/div[2]/div[2]/div/table/tbody/tr[{counter + 1}]').text  
  
            # domain  
            try:  
                item.find_element(By.XPATH,  
                                  f'//*[@id="_profile"]/div[2]/div[2]/div/div/table/tbody/tr[{counter + 1}]') 
            except NoSuchElementException as err:  
                logging.info(f'could not locate element for domain on iteration no. {i}')  
                domain = ''  
            else:  
                domain = item.find_element(By.XPATH,  
                                           f'//*[@id="_profile"]/div[2]/div[2]/div/table/tbody/tr[{counter + 1}]').text

 

This kind of algorithm and data volumes involve some serious input/output (I/O) action. Using just Pandas DataFrame to store this data incrementally in a CSV file or a database like Snowflake would burn a hole in my resources.

You see, dynamic and ongoing web scraping Python scripts have a tendency to get shaky, especially when faced with webpage refreshes and shaky internet connections. Given the hefty load of data and the repetitive looping through the loaded rows, I needed a solution to effortlessly fetch and store the data one iteration at a time, considering I had over 7500 iterations on my hands. And here’s where DuckDB stepped in!

🚀 Harnessing DuckDB for Stability in Python-Based Web Scraping

As part of my script, I initialized a DuckDB database and created a table with the relevant columns. Once a selenium iteration (as shown above) was finished successfully, I loaded the set of rows scraped into a DataFrame via a list of dictionaries. Once the DataFrame was built, I then inserted all the rows in the DataFrame into DuckDB table and clear out the DataFrame for next set of rows being scraped by Selenium Python script.

The primary objective of using this approach was to avoid the instability associated with Selenium scripts

when it comes to scraping a massive database from a dynamic (AJAX / JavaScript) based web page and avoid Snowflake credit usage associated with such I/O operations during the development cycle.

if company != '':  
  
                try:  
                    str(company).strip()  
                except:  
                    logging.info(f'company name clean-up not possible for iteration {i} and {counter}')  
                else:  
                    company = str(company).strip()  
                try:  
                    str(domain).strip()  
                except:  
                    logging.info(f'domain name clean-up not possible for iteration {i} and {counter}')  
                else:  
                    domain = str(domain).strip()  
                try:  
                    str(title).strip()  
                except:  
                    logging.info(f'title clean-up not possible for iteration {i} and {counter}')  
                else:  
                    title = str(title).strip()  
  
                extract.append(  
                    {      
                        'company': company,  
                        'title': title,  
                        'domain': domain  
                    }  
                )  
  
                logging.info(f'successfully extracted data for profile no: {counter + 1}')  
  
            counter += 1  
            logging.info(f'successfully finished extraction for all profiles data for iteration no: {i}')  
    else:  
        logging.info(f'no profiles found in the iteration no: {i}')  
  
    if len(contact_extract) > 0:  
        df = pandas.DataFrame(data=extract)  
  
        load_duckdb(df)  
  
        logging.info(f'successfully loaded set no: {i} into duckDB.')

In the realm of such Python web scraping scripts that stretch over hours,

custom Python logs emerged as unsung heroes, not only in keeping tabs on the script’s ongoing status but also proving handy when things go south,

and Python scripts decide to play hide and seek with errors. In a project-first, I opted to store these logs in a separate JSON file, just for the fun of it.

If only, I could do the same with SQL CTEs!

 

🧹 Seamless Data Transition and Cleanup in Pandas

After finishing up the scraping process, I effortlessly brought all the data back from DuckDB into a DataFrame with just one line of code.

From there, it was business as usual, employing the familiar tools of Pandas to tidy up and normalize the data. While I would like to handle all the data cleanup and normalization within DuckDB (or Polars for that matter) someday, I’m currently relishing the benefits of blending the best of both worlds.

💰 Optimizing Snowflake Costs with Python-DuckDB Alchemy

After the data was transformed, I turned to SQLAlchemy to seamlessly dispatch it to a Snowflake table. Once settled in Snowflake, the next step involved

transferring this dataset to Marketo, a move facilitated by a Reverse ETL process utilizing Census.

Throughout this entire process, especially in the development cycle, it marked the first instance where I had to tap into Snowflake credits.

🌐 The Power and Promise of DuckDB

Cost optimization is currently a key focus for every organization when it comes to Snowflake, and instances like these showcase how Python, coupled with the prowess of an OLAP database like DuckDB (harnessing the unlimited power of M2 Macs), can play a vital role. While I had been casually experimenting with DuckDB for some random testing use cases,

DuckDB’s robustness, simplicity, and particularly its Python API and integration with Pandas blew me away.

This implementation served as my inaugural semi-professional venture with DuckDB, as it’s not a script in constant motion via AirFlow or Prefect. Excited to delve deeper into leveraging DuckDB for more such use cases involving extensive development iterations and massive data I/O operations.

🤝 Join the Conversation

Have a similar use case or challenges to share? I’m eager to dive into new data frontiers. Let’s exchange insights and push the boundaries of what Python, Pandas, and DuckDB can achieve in the dynamic landscape of #MarketingAnalytics.

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The primary focus of the marketing analytics work that we have been doing with some of the most forward-looking B2B marketing analytics leaders across SaaS organizations is being augmented with the PLG analytics frameworks.

PLG analytics is now seen as the part of the core B2B GTM analytics frameworks

that include metrics like cohort-based funnel conversions, campaigns performance, tracking against funnel goals, marketing attribution, pipeline influence tracking, and database segmentation.

The emergence of PLG as the core GTM strategy has been a game changer not only for the B2B SaaS organizations from the GTM perspective but it has opened up exciting new possibilities for the marketing analytics teams.

PLG is built on the foundation of advanced data analytics capabilities. To drive a successful PLG strategy, marketing and growth teams rely on the analytics teams to understand product engagement data.

This event based product engagement data is made available in the data lakes and the data warehouses to be cross-hatched with the wider sales and marketing data to drive engaging product experience with the users – the essence of PLG strategy. The growing body of work that we have been doing in the past few months has been focused on PLG analytics and customer activation. Although we are very early in our journey towards building our core competency in PLG analytics, this post is an attempt to document our learnings and share the work that we have been doing with some of the most exciting B2B SaaS organizations that have adopted PLG.

PLG is a new paradigm.

In PLG model, the GTM motion relies on the product to drive growth. This is accomplished by letting the users rather than the decision-makers experience the value of the product. The intent is to provide limited friction within the product and let the users discover how the product will help them do their job more effectively on a day-to-day basis.

PLG GTM motion positions the product as the main driver of customer acquisition, activation, retention, and expansion. Most of the B2B SaaS organizations with data-driven marketing and GTM teams are shifting to PLG as their core go-to-market motion to accelerate growth. To date, about 25% of the organizations have some form of PLG motion in play. Given the success rate of the PLG motion at companies like Slack, Twilio, Dropbox, Airtable, Notion, there is a huge opportunity for the organizations to adopt the PLG model.

Strong GTM and marketing analytics capabilities are the core competencies that define the success of a PLG model. Capturing and analyzing a wide breadth of data points and metrics to understand how customers are engaging with the product and how they are deriving value for their businesses is critical to the success of PLG.

PLG takes marketing analytics to a whole new level. PLG presents a new set of metrics that define the success of the approach, and provide the needed intelligence for delivering an engaging customer experience and for continuous optimization of the strategy. Most modern B2B marketing organizations track the key metrics (including funnel performance and revenue metrics) that define the foundation of how marketing and sales teams operate on a day-to-day basis, measure and optimize performance.

Modern marketing technology and scalable data analytics platforms have made it easy to actively track the most detailed product engagements and behavior data within the product platforms. With the right analytics strategy and tools, marketing and growth teams can be empowered with the key insights from detailed product engagement data. This data allows teams to track, measure, and analyze user behavior.

These key data insights further enable growth teams to run go-to-market experiments that lead to incremental improvements to the user journey and funnel conversions.

Making data-driven decisions based on user behavior and product engagement is critical to PLG. Behavioral analytics can track every interaction that occurs within the product. Marketing analytics platforms allow analysts to mine this data to provide insights to help growth teams optimize the user journey, and product engagement behavior and further personalize the product experience through intuitive product design. We have been helping the growth teams define and track KPIs to track the performance of their PLG strategy. Key Metrics that are part of core PLG analytics frameworks include:

Acquisition/Sign ups: is the top of the funnel lead generation in PLG model. These are those leads who have signed up for the product rather than just downloaded a piece of content from a 3rd party website. Sign ups are the starting point in the PLG funnel. Tracking acquisitions from sources (inbound vs outbound) and their conversions down the funnel define the success of the PLG strategy.

PQLs: Product-qualified leads (PQLs) are activated users who have crossed a threshold of engagement by completing a set of milestones within the product.

PQLs are those users who have shown enough engagement within the product and have performed certain actions to derive the intended value from the product. These are the users that are then ready for a sales conversation and these have the highest probability of conversion.

TTV (Time To Value) is an important part of PQL as it helps provide visibility into the PLG funnel performance and goal attainment. The success of PLG is measured by reduced TTV to enable users to realize the value of the product sooner before they churn.

Defining the “activated” user from data perspective is a critical part of the PQL as the success of PLG strategy depends on tracking the activated users and the performance of all downstream funnel metrics in PLG is dependent on this definition.

Given the breadth and depth of behavioral and engagement data tracking that is available within SaaS products, defining “customer activation” and building data models around it is a monumental project for the analytics teams.

To begin with, all the product engagement data is piped from the product into a data lake where it is made available to be cross-hatched with the other data from CRM, marketing, and finance systems. Depending on the product and industry, the product data usually spans these categories:

• Sign ups: to track the number of users/orgs that are being created in a given time. This data is then combined with a data enrichment provider (like Clearbit, Zoominfo) for additional demographic details of the organizations.
• Product Engagement (Events): data tracks various actions that users take within the product. It is from this events data, we can identify the users who have taken certain actions like the frequency of logins, visits to certain pages with the app/product, certain buttons/links click events, etc. Working with the product managers, we identify those key actions that together would help tag the users/orgs who have completed these actions.

From analytics and data modeling perspective, we first spend time understanding the core database architecture of the product, the key actions/events that are being tracked within the product, and review all the data in the data lake to get a high-level overview.

Once all the raw data is assembled in a data lake, the next step towards building the definition of an activated user is to understand everything about the customers.

We identify the paying customers an organization has and start mining the product engagement data for users/orgs to identify patterns, heat-maps, and clusters of the actions that are most common across all the paying customers.

This is the critical part of the data modeling towards understanding the “activated users” and in turn defining the threshold for PQLs. Our goal here is to

identify all the key attributes of the users that define the tipping point when they are ready to convert and become paying customers.

The outcome of this exercise is a set of metrics with a set of ranges that are provided to the growth and the product teams to further contextualize the data. Summary of such analysis consists of data points, which vary depending on the product engagement data that is being captured and fed into the data lake. Here is one such example of customer product engagement analysis:

• 83% of the users had done actions A, B, and C at the time of conversion.
• 65% of the users had logged in 5 times per week in the past 3 weeks leading up to conversion.
• On average, there are about 4 users in the org at the time of the conversion.
• 70% of the orgs have connected the app/product with Slack for active notifications.

Once the core teams (especially growth and product) align on the definitions of what an ideal “activated” customer looks like the criteria are frozen and it becomes the guiding factor for defining and building the segments of engaged users.

These segments are built with the sign-ups (users/orgs) who have been showing the highest probability of meeting the criteria of the activated customers based on their engagements. This multi-dimensional data mining and analysis work results in building such growth segments of users showing a varying degree of alignment with the definition of the ideal activated customers. Generally, the number of such growth segments is related to the number of key metrics that are included in the definition (as described above) of the activated customer. For an organization with the definition of the activated customer as described above, these are the possible growth segments of the engaged users that can be built:

• Users/orgs who have done only 1 or 2 of the 3 critical actions within the product.
• Orgs where in only 50% of the signed-up users are logging in weekly.
• Orgs that are over 100 employees but only have 2 users signed up.
• Only 40% of the orgs that have more than 3 users have integrated the product with Slack.

The core theme in these segments is the product engagement. These segments provide actionable insights for the growth teams to leverage them in the campaigns and sales outreach to provide relevant and meaningful context for driving higher conversions. The primary goal for these campaigns is not to drive sales revenue but take actions (experiments, marketing outreach, targeted digital campaigns) to drive higher product engagement and bring each of these segments closer to the ideal activated customer segments. This increase in the alignment with the ideal customer engagement profile in turn drives the sales conversions.

Although PQL and customer activation are the foundations of the PLG analytics, there are 2 other key metrics in the PLG funnel that we have been tracking:

Trial conversion rate – tracks the percentage of trial users who eventually became paid customers and on average, how long it takes for the conversion (velocity). Tracking and optimizing this leading indicator is critical to have better visibility into pipeline and forecasting.

Expansion Revenue – is the core of the land and expand GTM motion that is driven by PLG. Tracking the additional revenue generated per account is effective to help with the cost-effective expansion strategy and is a key measure for the success of the PLG approach.

This framework has been our experience so far to further drive the success of PLG initiatives for our customers by taking their marketing analytics capabilities to the next level. It keeps getting more exciting every day!

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other growth marketing leaders and analytics practitioners around the approach to PLG strategy at their organizations. What metrics are you using to track the performance of PLG at your organizations? What does the definition of “activated” customer looks like at your organization?

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The most common goal and outcome of generally available attribution models/tools is the ability for the revenue (both marketing and sales) teams to track the impact of marketing efforts on generating new pipeline and helping accelerate the existing pipeline.

This is exactly where most of the marketing teams end up dropping the ball. The primary use of the attribution data is mostly limited to tracking how much revenue and pipeline marketing teams are impacting.

We live in a world wherein a multitude of marketing tactics and channels are used by organizations to engage with their customers. Given the connected world we live in and how we gather information, for every closed deal, numerous marketing touches are involved from web engagement (visits, clicks) to email engagement, to webinars, e-books, white papers, in-person events, and the list of possible touches goes on and on. While all these touches have a varying degree of influence on the wins but they all come at a cost and it is not possible for any marketing leader to allocate her budget to these different tactics without some insights into how these tactics perform when it comes to winning those deals. Gone are the days when a marketer could just go by what she “felt” was right.

Given the growing pressure on the marketing teams to continuously justify the spending and deliver higher ROI,

marketing attribution metrics are among the best available metrics to demonstrate marketing’s quantifiable impact on the pipeline.

Finance and sales organizations expect revenue marketing leaders to make data-driven decisions when it comes to budget allocation and also within marketing, the leaders increasingly want to feel confident about these decisions because it is these decisions that will help them meet their pipeline KPIs, the ones on which the success of marketers is increasingly being measured against.

While others see attribution as an end in itself, we see attribution differently.

For us, marketing attribution is more of a framework that opens up the gates to a wealth of information about touch points, marketing engagements, and customer behavior that is very unique to the GTM strategy of an organization.

This data, if synthesized appropriately with key business outcomes in the focus can be leveraged to provide insights for continuously optimizing marketing execution.

From a data analytics perspective, marketing attribution data at its core is nothing more than a framework for tracking every touchpoint across the customer journey.

All these touch points from the very first touch (by sales and marketing teams across various channels) to the last touch (till a deal is closed) are organized in a relational database along with the time stamps for when those engagements happened. These touch points are then further related to the different data sources about campaigns, channels, accounts, opportunities, etc. to provide an enriched data source with comprehensive information that can be further mined to derive key insights into customer engagement behavior and marketing campaign conversions.

Some of the most common marketing use cases that I have implemented by leveraging this enriched data include:

1. Aggregating the attribution data over a time period across the deals/opportunities to create a cluster chart/heat map providing visibility into the distribution and conversion across various marketing offers and channels throughout the deal cycle.
2. Aggregating the attribution touch points data by different marketing tactics/channels/vendors and combining it with the spend data across different channels (especially digital channels) to track marketing ROI. Remember the questions you get asked from the board like: what was the impact of Facebook ads on pipeline generation last quarter? All those get answered now and on the fly!
3. Using the insights from these data clusters to create GTM campaign playbooks for different industries, persona, and especially for ABM campaigns. You are able to see generalizations and best conversion paths around what campaigns and tactics among the marketing mix drive the highest engagements from decision-makers through different channels and different stages of the funnel.
4. This attribution dataset provides the richest data with all the marketing engagements and sales touch points laid out in an organized time series format to track the journey of customers throughout the deal cycle along with the details around persona, frequency of engagement at different stages of the deal cycle. These insights are the foundation of customer journey analytics frameworks.

There are numerous companies dedicated to building attribution models for marketing organizations and many such 3rd party tools work some what well especially for very small organizations that are in very early stages of advanced marketing analytics and have a relatively simple GTM strategy. However, my experience across numerous engagements related to attribution modeling has shown that

most of the attribution tools lack one key thing – they are pre-built models with limited options for customization and most of the marketing organizations have to align with how the tools work and measure their performance per the pre-built models only.

I feel that this is a fundamental drawback with out-of-the-box marketing attribution tools with limited options to customize the tools to align them with the GTM strategy of the organizations that end up using them.

At marqeu, we live by the principle that marketing should drive how technology enables and empowers it to be much more impactful and not the other way round.

Across our marketing analytics focused customer engagements, I have seen customers using very rigid attribution models/algorithms that made them change the way they operate or measure their business. In some cases, these tools were customized to align with the business models but given how rigid most of the tools and methodologies are in the first place, they usually cannot withstand too many customizations and breakdown leading to never-ending “maintenance and support” cycle, that leads to nothing but wastage of time and money for the marketers. I am sure many of the enterprise marketing leaders reading have experienced this first hand.

At marqeu, I have been building multi-touch attribution models for the past 8+ years as part of our marketing analytics service offerings for some of the most innovative organizations and this priceless experience has equipped me with unparalleled expertise when it comes to multi-touch attribution. In collaboration with our diverse customer base and phenomenal marketing leaders, I have developed algorithms and frameworks that are easy to implement, understand and more importantly, they empower marketers when it comes to discovering insights about what tactics, channels are driving better returns across the marketing mix and the impact their working is making.

I have designed “a marketing-driven approach to multi-touch attribution” that has a set of guiding principles and parameters as part of this framework.

During our engagements, I review this approach with the marketing teams and further enrich it with the insights that I gather from the marketers around how they operate their business.

When I am fully armed with the knowledge of how a business operates and aligned with the marketing team on how they would like to attribute the pipeline across each of the tactics in the marketing mix, it is then I turn to technology to start building the attribution model

that is designed from the ground-up just to work for the business. Modern marketing analytics/BI (Tableau, Looker, PowerBI, DOMO)/Data Warehousing (BigQuery, Snowflake, Redshift) platforms have made it easy to make sure data from all the platforms across the marketing tech stack is easily accessible in a central database to allow for building highly customized attribution models in a very short amount of time. In this approach, there is no customization or fixing needed as the model is built from the ground-up just around how an organization runs its business.

Think of this approach as making your “own pizza” approach to multi-touch attribution. This approach of letting business drive the technology definition and implementation has been of great success for all our customers.

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other marketing leaders and analytics practitioners around the approach to attribution in their organizations? What attribution tools you are using and to what extent the attribution data is being used to further build models for advanced marketing analytics use cases?

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A well designed and intuitive marketing analytics scorecard provides the much needed at-a-glance view of the health of the marketing organization

across 2 broad areas:

• How well the team is pacing towards the quarterly/monthly goals
• How efficiently the team is generating and qualifying the leads through the funnel

Data-driven CMOs are increasingly relying on marketing analytics teams to lead the charge for demonstrating the quantifiable impact marketing organizations are making. Over the past 10+ years, I have had the privilege of working with numerous data driven marketing organization led by a revenue focused CMOs and marketing leaders. The one thing that has been

consistent across such organizations is their aptitude towards quantitative aspects of marketing (demand generation in particular) and relentless focus on making data driven decisions.

They leverage analytics to continuously optimize the execution and demonstrate measurable results with the help of extremely smart marketing analytics competencies that they have built over time. Marketing leaders across such organizations actively engage with the sales and finance teams towards driving top-line revenue.

All modern revenue marketing organization usually have a catalog of dashboards powered by numerous metrics that help the marketing teams track and understand the performance of the on-going campaigns. With the maturity of marketing analytics competencies at the organizations, there is usually

a dedicated CMO dashboard that provides an executive summary of the marketing influence on pipeline and the performance of each of the marketing teams

including demand generation, growth marketing, digital marketing, customer marketing, product marketing etc. All these dashboards and the insights they provide are an integral part of the modern marketing organization.

During my marketing analytics engagements and conversations with the CMOs, consistently I have been hearing about an ask to provide some sort of a high level summary view – the score card so as to speak that enables marketing leaders to understand the current state of the marketing performance.

These inputs helped me design the “marketing scorecard” (a sample shown below) that enables marketing leaders to understand the state of the marketing performance at a glance

and it acts as the gateway towards enabling further drill down into the “how’s and why’s” of campaigns performance that power this scorecard.

The approach towards designing this scorecard is inspired by the concepts of “the index in a document” or “the agenda slide (if you will)”. With a thorough understanding of the key metrics and dashboards that are already operational in an organization, the next step is to

map these metrics to a summary (aggregate) view of the KPIs that the marketing leaders leverage to confidently demonstrate the impact that their organizations are making.

This scorecard is interlinked with the key dashboards so that whenever questions comes up around these summary numbers, the answers are just a few clicks away and the users are able to get to the answers on the fly, in real-time.

Some of the common use cases that are addressed in these score cards include:

• We are past the middle of the quarter, how is the attainment looking against our quarterly goals across the funnel?
• How has been the funnel efficiency (cohort conversions) across different stages of the funnel?
• Are some stages of the funnel back logged due to higher volume or some stages are showing lower than expected conversions?
• How have been the cycle times (average time needed to move the leads from one stage of the funnel to the other) trending in a given time frame across different funnel stages, region?
• Leads from which campaigns are moving through the funnel faster and are converting better?
• Which MQLs are being worked upon by the sales teams faster as compared to other MQLs?
• Which campaigns are helping drive new pipeline generation better and which campaigns are helping accelerate the existing pipeline?

Modern marketing technology and analytics/BI/Data Warehousing platforms have made it easy to actively track key marketing metrics. With the right analytics strategy and tools, we work with marketing leaders to empower their teams with these critical data points so that each team member can actively contribute towards fostering a data-driven culture within the organization.

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other leaders and practitioners around growth the key metrics that leaders are using to demonstrate the impact marketing is making at your organization? What kind of questions do they get from the sales teams? What data points around marketing performance are shared with the c-suite?

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For us, nurturing is a data-driven, meticulously planned contextual, and hyper-personalized strategic marketing tactic to drive engagement in pursuit of influencing pipeline generation.

Lead Nurturing is an ongoing process, which prepares the engaged leads for sales conversations based on their engagements with the different phases of the nurture campaigns. For the marketing teams, it is critical to have an engaging and relevant nurture strategy customized for different persona who are in different phases of the buying cycle from awareness, consideration, preference to purchase. Nurtures themselves could have different goals ranging from spreading awareness about the product/services through thought leadership, driving engagement with the potential customers about the brand of an organization to accelerating the leads through the funnel.

Given that email messaging and contextual CTAs are the core part of the nurture campaigns, traditionally most marketing organizations have been focused on looking at vanity email metrics (email opens, email clicks, etc.) to track the results of nurture campaigns. As most revenue-focused marketing leaders would agree, these metrics do not cut it anymore when it comes to demonstrating the impact of nurture campaigns on the pipeline. This is where

our nurture performance analytics frameworks, data models, and dashboards have been extremely helpful at not only showing the impact of the nurture campaigns on the pipeline but at providing valuable insights to the marketing teams when it comes to optimizing the nurture content, offers, cadences, and targeting.

As part of our engagement and nurture analytics services, we work with the marketing organization to understand the sales and marketing operating model and the buyer’s journey. We then build and implement nurture analytics data models to provide visibility into understanding the performance of all nurture campaigns across 2 broad areas:

• The ability to drive engagement with the contacts added to the nurture campaigns. Opening and clicking a nurture email is a positive indicator of the relevancy and targeting of the nurture campaigns. Our approach has been to go beyond these metrics and further narrow down the criteria when it comes to tracking the true impact of the nurture campaigns on driving engagement with the contacts who influence pipeline generation. While the definition of the “engaged” contacts can vary across different companies but broadly it covers these data points:
  • The number of email engagements in a given time frame (usually past 90 days). Analyzing the frequency and recency of the engagements together is a strong indicator of qualification.
  • The number of responses to the CTAs in the nurture emails – the higher the number, the better it is.
  • The funnel status/stage of the engaged contacts.
  • The recency of sales conversation and sales activities.
• The ability of the nurture campaigns to influence pipeline generation. The importance of demonstrating how marketing activities are impacting the pipeline has grown manifolds in recent times. With the pipeline attribution frameworks, marketing teams can quantify the engagements driven by the nurture campaigns across the accounts and relate them to the pipeline generation. These pipeline correlations of the marketing activities (including nurture campaigns) provide valuable insights when it comes to understanding “when” the new opportunities were created in the marketing engaged accounts and for the accounts with existing opportunities, how marketing campaigns were able to influence deal expansion and accelerate deal closures. Pipeline influence data models also provide details into the metadata for nurture campaigns. These granular data points enable detailed comparison analysis among different nurture campaigns to better understand which nurtures are driving engagements across the accounts from different demographics. These insights further help with aligning the content and the CTAs of the nurture campaigns to drive higher engagement.

The set of these pipeline-focused KPIs provides insights to the growth marketing leaders so that they can objectively evaluate the performance of different nurture campaigns, leverage actionable data to continuously optimize the performance, and above all, confidently demonstrate the impact that their campaigns are making on the pipeline. As is demonstrated by this graph (with data till mid-July’21),

we leveraged our nurture marketing analytics frameworks to drive increased engagement and hence pipeline influence for one of our customers,

which like most of the B2B marketing organizations have lead nurturing as the core part of their marketing mix.

We are always on the lookout for inputs and examples from the marketing community to keep providing unparalleled value for our customers. We welcome the inputs from other marketing, revenue operations leaders, and practitioners around what metrics they are using to demonstrate the impact of nurture campaigns? What challenges do they face when it comes to accessing the data for nurture campaigns and how are they co-relating the nurture engagements with pipeline influence?

The post Marketing Analytics and Lead Nurturing – A Strategic Combination appeared first on marqeu.

marketing teams spend a lot of time and energy at continuously optimizing the performance of MQLs with the sole purposes of driving higher MQL to opportunity conversion

the leading indicator for driving pipeline growth. To progress a customer from MQL to SQL stage of the funnel, there is 1 aspect of the MQL performance optimization that most of the marketing and marketing analytics teams either overlook or do not spend nearly enough time at understanding and that

set of key metrics is the sales follow-up of the MQLs that are handed over to them.

Beyond the MQLs, B2B organizations are also relying more on sales accepted and qualified leads (SALs/SQLs) now. This increased focus shows more marketing organizations are digging deeper into revenue impact, and want insights into the qualified opportunities that are influenced by the MQLs.

As the advancement in the marketing tech stacks and marketing analytics keeps accelerating at an enormous pace, we can be sure that lead scoring algorithms will keep becoming more sophisticated and smart by leveraging numerous data points with the help of AI and advanced automation across the customer journey. We will continue to discover more sales conversation ready MQLs but

unless at the same time we continuously optimize the follow-up strategy around these MQLs, we will not be able to realize the full impact of all the effort that goes into discovering and qualifying the leads that marketing teams generate.

With the growing number of touches and the share of digital channels during the sales cycle, it is becoming increasingly important to have an efficient follow-up strategy across SDRs/ADRs/BDRs and sales teams. Over the years, the marketing mix has become increasingly diversified (from emails, contents, field events, social, chatbots, etc.) and the growing number of buyers in the buying committees have resulted in lengthened sales cycles across all the segments from SMB to Enterprise. These trends are putting tremendous pressure on sales efficiency. It is taking a lot longer and many more engagements to close a deal irrespective of the deal size, industry, etc.

All these new trends around MQL generation and the sales follow-up necessitate the need to better understand the sales feedback on MQLs. Sales/SDR follow-up of MQLs is where the rubber hits the road and the insights gathered from the sales touch points are critical for MQL optimization. Across all our marketing analytics engagements, understanding SDR follow-up and SAL/SQL qualification of MQLs is the critical aspect of the over-arching lead scoring/MQL and top-of-the-funnel optimization efforts. Key metrics include:

1) Time it takes for sales to make the first attempt at the MQLs: numerous research studies have established the fact that every 1 day of delay in making the contact with the qualified leads results in the conversion rates falling by up to 30%. Ability to see in real-time the average time it takes by the SDR team in general and an SDR, in particular, to make the first attempt at connecting with the MQLs has proven to be of immense value both for the marketing and SDR/Sales teams. These insights enable marketing teams to:

– understand how to time lead creation and routing in the marketing automation platform to make sure SDRs can connect with them promptly.
– have the conversations with the SDRs around the leads that have not been engaged with and are outside the agreed-upon SLAs between the marketing and SDR teams.
– understand the correlations between MQL conversions and SDR outreach to find patterns for matching certain kinds of MQLs with the SDRs to drive the highest conversions.

These insights have proven super valuable for SDR and sales leadership.

They can see in real-time through their organization’s data how the delay in engaging with the qualified leads impacts the conversion rates. Seeing is believing, as they say! These insights also help the SDR leadership with the data-driven capacity planning for the efficient staffing of the SDR team to make sure there is enough capacity in the team to manage the in-flow of the MQLs. At the same time, SDR managers can set the goals around adhering to the follow-up SLAs and track the performance of individual SDRs against those goals.

2) Aging of the MQLs with the sales owners: while the first metric (from above) provides the insights into which MQLs have been followed up by the SDRs, there is always a good number of MQLs that are yet to be followed up. MQL aging provides insights around the MQLs that are waiting in the queue. Depending on how old the MQLs have become and their source, a decision can be made either to bump them up to the SDR queues or include them in a re-engagement campaign to increase the probability of their conversion to qualified opportunities.

3) Number of touches and time it takes to qualify the MQLs – these metrics are critical when it comes to goals planning and tracking their attainment. These insights provide valuable inputs towards building effective sequences (with appropriate offers, channels, and their timing) in the sales engagement platforms like Outreach, Salesloft. The insights from these metrics provide important inputs for building contextual customer journeys. The number of days it takes to qualify an MQL into SQL is the key metric that helps both marketing and sales teams alike as they all are charging towards attaining the common goal of pipeline generation.

Based on how many MQLs are already with the SDRs, their conversion ratio, and the time it takes for the ones that convert, teams can predict with a fair degree of confidence the attainment towards quarterly pipeline goals.

The most important part here is that instead of having to wait till the end of the month/quarter, with the goal attainment probabilities, teams have enough time to pull the relevant levers to accelerate execution and MQL generation that would help them get closer to the goals, if not beat them.

4) Cohort comparison of MQLs and SQLs in terms of campaign engagement and lead score: we all know that MQL to SQL conversion will never be 100% as there is some leakage; also stated in the law of thermodynamics (for all of us science nerds!)
From what we have been seeing, the distribution of campaign engagement (or the campaign engagement mix) and the threshold score of MQLs are many times different as compared to that of the corresponding SQLs. MQLs come from all kinds of campaigns and channels but only a part of them get qualified.

Understanding the campaign engagement mix of the qualified MQLs and their score (MQLs can keep engaging and getting higher engagement scores) provides valuable insights for the marketing teams

to not only tweak scoring but also use them for last targeted MQL engagement campaigns during the SDR follow-up (pre-SQL) phase for those MQLs who have not engaged with certain campaigns that are part of the SQL campaign engagement mix. This a very efficient way to increase the probability of conversions.

These SDR/Sales metrics focused on deriving the insights around MQL follow-up have become an integral part of our marketing analytics frameworks to help the marketing teams actively track the follow-up of MQLs and leverage these insights for building contextual campaigns and continuously optimize the lead scoring. We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other marketing leaders and marketing analytics practitioners around how they are tracking the MQLs/marketing leads follow-up by the SDR/Sales teams in their organizations. Apart from the 4 key metrics that we have been tracking, are there any other metrics that would help provide valuable insights into the marketing and SDR teams?

The post Sales Follow-up of the MQLs – Overlooked Part of Marketing Analytics appeared first on marqeu.

Revenue CMOs are growth marketers who are going beyond the traditional marketing execution role.

They are leveraging data and analytics to continuously optimize execution and demonstrate measurable results. They actively engage with the sales and finance teams to sign-up for driving top-line revenue for their organizations.

The Revenue CMOs are relentless when it comes to leveraging analytics to validate their plans and demonstrate with numbers the impact their work is making across the organization.

Unprecedented advances in marketing technology and marketing analytics have resulted in growing scrutiny and re-evaluation of the CMO’s role. The CEO needs to be able to turn to a marketing leader that can deliver tangible results in terms of what matters to the CEO most: revenue. These are the kind of marketers CEOs are beginning to rely on for driving growth across the organizations and run marketing as a revenue-generating function. This expectation from the c-suite has been the most important factor that has led to the emergence of the Revenue CMOs.

I have been working with the CMOs from some of the most innovative companies for over a decade now. I have experienced this transition happening first hand and it has been a fascinating ride. There were times when marketing leaders were all about shaping the brand of the company, driving the brand awareness to promote the value proposition but

now marketing leaders are laser-focused on marketing analytics

tracking MQLs, how are their organizations trending against the quarterly pipeline goals, marketing attribution methodologies to demand waterfall conversions. These data focused conversations were un-imaginable in traditional marketing just a few years ago.

The most common ask from the Revenue CMOs is to demonstrate the quantifiable impact that their organizations are making on the pipeline.

Given the growing scrutiny around marketing budgets and the asks for returns from the marketing spend, marketing performance insights and marketing analytics not only empower marketing leaders to have data-driven conversations with the c-suite, inspire/reward their teams based on performance but also optimize the execution of their strategy. Key questions that are top of mind for the Revenue CMOs:

a) Did we meet our goals both for volume and conversions?

We have been using demand waterfall performance to help answer this question. With our marketing analytics frameworks, we enable real-time visibility into the health of the funnel so that marketing teams can track inquiries, mqls, sqls, sals, and wins across different sales segments, product lines, by different channels, tactics, and campaigns. Cohort based funnel conversions are a key part of evaluating the health of the funnel to get a good read into the quality of the leads flowing into the funnel and discover what kinds of leads are moving faster through the funnel, what the velocity looks like.

b) How much did we invest and what is the pipeline impact?

Marketing ROI is the most important metric for the Revenue CMO. Finance and sales organizations expect marketing leaders to make smart decisions when it comes to budget allocation and more importantly within the marketing organizations, the leaders increasingly want to feel confident about these decisions because it is these decisions that will help them meet their pipeline goals. This is where multi-touch campaign attribution comes into play and it is the most valuable of all the KPIs as it powers the marketing investment decision making which in turn drives the results for the marketing organization.

c) What is working in terms of driving the pipeline?

Driving a new pipeline has always been the most important KPI for any marketing organization. The importance of demonstrating how marketing is impacting the pipeline has grown manifolds in the charter of Revenue CMOs. The 2 commonly used metrics that demonstrate the impact of marketing are Marketing Pipeline Acceleration/Influence and New Pipeline Sourcing. With these 2 metrics, organizations can quantify marketing-driven engagement across the accounts and relate them to the impact on pipeline. These pipeline correlations of the marketing activities provide valuable insights when it comes to understanding “when” the new opportunities were created in marketing engaged accounts and for the accounts with existing opportunities, how marketing was able to influence deal expansions and accelerate deal closures.

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. Revenue CMO is still an evolving concept for most of the marketing organizations and especially for the CMOs who have to successfully manage the delicate balance between the traditional CMO role and at the same time lead the culture of data-driven marketing within their organizations.

We welcome the inputs from other marketing leaders and practitioners around how the concept of Revenue CMO is evolving within their organizations. What metrics are top of the mind for the CMOs? How are the Revenue CMOs defining the metrics for tracking marketing’s impact on pipeline and revenue?

The post The Revenue CMO – Leading the Data Driven Marketing appeared first on marqeu.

The core competency that differentiates the winning organizations is their ability to objectively evaluate the investments that drive the highest engagement and pipeline growth.

Whenever the term marketing analytics is mentioned, the first thought and rightly so is in relation to the “performance measurement” aspect of marketing – the means through which marketing teams are able to demonstrate the impact they are making on the pipeline and revenue growth. However, there is another critical aspect of marketing analytics and it is related to marketing database management.

Marketing Database Management is another core aspect of modern marketing that powers the marketing campaigns to create contextual customer experiences, which influences the buying decisions.

This customer experience is the key factor that differentiates the high performing and growth-focused marketing organizations. On a day to day basis, marketing teams rely on data base management for so called “list building” efforts for their campaigns. When it comes to segmentation, most of the marketing teams pretty much rely on the lists (or smart lists) that are built inside the marketing automation platforms.

As marketing analytics capabilities are maturing across the organizations, questions are being asked to go beyond just using the marketing engagement and demographics data for segmentations.

Modern marketing teams are leveraging the data across the MarTech platforms to provide a 360-degree view of the customer/prospects and enable contextual segmentation for campaigns. This is where the

concept of “Multi-Dimensional Contextual Segmentation Engine” comes into play where capabilities are built to continuously feed data from the core business operations platforms (including finance, sales and marketing) into one central data warehouse.

Having all the relevant data in one place enables marketing analysts to cross-hatch the data from different sources across different stages of the buyer’s journey and build contextual segmentations and micro-segmentations per the needs of the campaigns both for digital and offline campaigns. Leveraging our marketing analytics services, we provide marketing teams with an intuitive visual interface (built using modern BI tools like Tableau, Looker, PowerBI, DOMO) to build multiple micro-segments on the fly that are tailored to the strategy and call-to-action of their campaigns.

This ability to go beyond marketing automation platforms to build information rich and contextual segments on the fly was beyond imagination for our customers before we implemented the contextual segmentation engines at their organizations.

We are helping all our customers think beyond the usual demographics data (like role, job level, location, etc.) when it comes to building segments for their campaigns and leveraging historic campaign engagement, pipeline history, web engagement, and most importantly, the intent data that signals buying behavior. There are a lot of hidden insights that are captured by marketing tech, CRM, and sales engagement platforms that could be used in building micro-segments. With the contextual segmentation engine, we have enabled our customers with the capabilities to build these micro-segments on-the-fly and align messaging, offers, and channels accordingly to drive higher engagement for all their programs.

When it comes to managing the marketing database and getting the most out of them, there are 3 core competencies:

• The ability of the marketing organizations to have real-time insights into the health of the database via database health metrics reports and dashboards.
• Operational frameworks and procedures to continuously track and optimize the quality integrity of the database, which is constantly changing.
• Marketing Analytics capabilities to mine the database for segmentation to enable contextual experiences for all marketing campaigns, which in turn drive higher engagement and accelerate pipeline generation.

The real value adds for the database health insights that are powered by the database segmentation engine is to enable micro-segmentation for all marketing campaigns

especially for events, virtual events, webinars, and field marketing campaigns. When it comes to segmentation and list building for the campaigns, most of the marketing organizations still rely on explicit data (mainly demographics data). This does not seem to do justice to the massive investments that the organizations make in the marketing tech stacks. This concept of micro-segmentation is fascinating to see it in-action and this case study about webinar registrations paints a beautiful picture of the extraordinary results that can be achieved with this holistic data-driven approach.

One of our customers (a SaaS start-up) had been seeing good but relatively flat engagement and pipeline influence from their webinars as demonstrated by the campaign performance reports that we had built. They were planning on further investing in webinars to accelerate pipeline generation and wanted to make sure that they can increase conversions both in terms of engagement and pipeline from their webinars. During our initial discussion and data analysis for webinar campaign data, we realized that there were a lot of insights the webinar segments were missing out on leveraging as they were primarily relying on demographics data for driving webinar registrations. Leveraging our database insights frameworks and contextual segmentation engine, we helped them put together a comprehensive webinar strategy powered by micro-segmentations. Instead of 1 segment for the webinar, we helped them put together 4 different micro-segments. One of the micro-segment was of those leads who had been actively engaging with the webinars over a given time. This particular webinar was meant for practitioners (rather than for the decision-makers) so we helped them discover a few insights about the practitioner persona to appeal to the practitioners who were seen as using their products on a day to day basis. We even took this 1 step further and helped them further categorize the practitioner segment into 3 different micro-segments based on the past 90 days of the web behavior and engagement on their platform and their pipeline history with the organization.

With these insights, their team was able to craft unique contextual messaging for each of the micro-segments resulting in a 16% improvement in webinar conversions.

This case study reinforces the fact that the marketing database is a gold-mine for any marketing organization. Given that the marketing database is the foundation of any marketing and sales playbook, database insights are relevant for all marketing campaigns and sales conversations.

We did not have to go anywhere to get these additional insights for the webinar micro-segments. They were right there in the marketing automation and CRM platforms that our customer was already using. We just had to understand the business questions, leverage the existing marketing analytics capabilities to mine the data accordingly to surface the insights, and make them easily available for the marketers. We started with the key (and extremely relevant) business questions from the sales and marketing leaders. We then reviewed what we could answer with the capabilities we had put in place based on earlier asks, had follow-up discussions to understand further what was being asked for, how it would be used, and then we went about building the solution with the set of tools we already had in the organization.

Contrary to the prevailing wisdom, we did not jump to get the fanciest MarTech tools and then started figuring out how to use them and if they were of any relevance for what the business needs were. Déjà vu!

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs and feedback from other leaders and practitioners around how you are leveraging the insights rich data across the MarTech platforms? Have you built the capabilities in your organizations to enable marketing teams to build the micro-segmentation on-the-fly for their campaigns?

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With the onset of this pandemic, all marketing organizations had to adjust their annual plans and more importantly, double down on the “digital-first” strategies to account for the cancellation of the in-person events, which have been biggest driver of marketing pipeline and revenue.

The core competency that differentiates the winning organizations is their ability to objectively evaluate the investments that drive the highest engagement leading to pipeline growth. This is where

marketing teams are relying more extensively than ever before on marketing analytics to help guide the decision making around investments and execution.

Marketing performance insights and marketing analytics not only empower marketing leaders to have data-driven conversations with the c-suite, inspire/reward their teams based on performance but also optimize the execution of their strategy. Such modern marketers with the growth mindset incorporate analytics rigor as well as revenue mindset for every decision they make. Data-driven CMOs are increasingly relying on marketing analytics teams to lead the charge for demonstrating the quantifiable impact marketing organizations are making.

Latest (July 2020) DemandGen benchmark research around marketing measurement has once again highlighted that marketing analytics continues to be the top priority for B2B marketing organizations.

The research report has listed the top challenges that are preventing marketing teams from realizing the vision of data-driven marketing and how critical it is to build these competencies. This transformation to digital-first marketing has only accelerated the need for rigor around marketing analytics. According to this research,

82% of respondents believe enhancing the marketing analytics capabilities is a growing priority.

Even though many forward-looking marketing organizations have some kind of marketing analytics capabilities in place but 40% still believe their ability to measure and analyze marketing performance needs improvement.

With the experience of working with over 30 B2B marketing organizations for various marketing analytics projects, I have been working with the marketing teams when it comes to deploying these marketing analytics capabilities. Here is my approach when it comes to handling 2 of the highest priorities as are listed in this report.

The top priority (for 75% of the marketing teams, and not 100% – interestingly) is to show the impact on pipeline and revenue.

The importance of demonstrating how marketing is impacting the pipeline has grown manifolds in the recent times. There are numerous frameworks to track marketing’s impact but the 2 metrics that are my go-to when it comes to demonstrating the impact of marketing on pipeline and revenue are: Marketing Pipeline Acceleration/Influence and New Pipeline Sourcing. With these 2 metrics, organizations can quantify marketing-driven engagements across the accounts and relate them to the on pipeline growth. These pipeline correlations of the marketing activities provide valuable insights when it comes to understanding “when” the new opportunities were created in marketing engaged accounts and for the accounts with existing opportunities, how marketing was able to influence deal expansion and accelerate deal closures. These 2 KPIs are very influential when it comes to evaluating the market mix across marketing tactics and channels in different phases of the buying cycle.

The approach to designating an opportunity as marketing sourced vs marketing influenced is purely data-driven.

It is based on the timelines of the first marketing engagement in comparison with the sales engagements within an account. Given that the volumes of data are enormous now, the most efficient and automated approach to this analysis is by loading all the relevant data from marketing automation and CRM platforms in a SQL server (Redshift, Snowflake, MySQL, MS-SQL, BigQuery). Once the data is available, the logic described below can be easily implemented with the help of a SQL script and automated.

• Identify all the engaged leads and contacts from each of the accounts with open or closed/won opportunities in a given time frame.
• Find all the marketing engagements for each of the accounts through for the leads and contacts on those accounts using lead/contact IDs or/and domains.
• Find all the activities (sales calls and outreach emails) for each of the accounts through all the leads and contacts.
• Designate and combine all sales and marketing engagements data along with their respective dates and account level information.
• Compare each marketing engagement and sales activity with the opportunity created date to find if the engagement happened before (how much before) or after the opportunity creation
• Based on this data set, if the marketing engagement happened before the sales engagement, that marketing activity (or combination of the marketing activities via attribution) usually gets the credit for sourcing the opportunity.

Once completed, this analysis can be rolled-up to see the overall marketing sourced and pipeline number. This analysis can further be expanded across campaigns and account dimensions to track the performance of different campaigns, channels across different account segments and regions.

At a general level, this is the fundamental approach towards quantifying the impact of marketing on pipeline and revenue. Modern marketing analytics tools make it easy to deploy these frameworks. Depending on the business model, go-to-market strategy, and the marketing mix, I have been working with different organizations to deploy a version of this approach towards calculating the marketing sourced and influenced pipeline.

58% of the marketing teams consider tracking ROI from all marketing investments as the 2nd most important priority.

Marketing sourced and influenced data (as discussed above) enables tracking of Marketing ROI through marketing attribution. For every deal (especially in the enterprise space), countless marketing touches are involved from web engagement (visits, clicks) to email engagement, webinars, e-books, white papers, in-person events, and the list of all the touches goes on and on. While all such touches have a varying degree of influence on the deals, they all come at a cost from the limited marketing budget. It is not fair on part of marketing leaders to allocate their budgets to different tactics without having access to the insights around how these tactics perform. This is where multi-touch campaign attribution comes into play and it is the most valuable of all the KPIs as it powers the marketing investment decision making, which in turn drives the results for the marketing organization. Whether it is the First Touch, Last Touch, Multi-Touch, Equal Weight Multi-Touch, or Weighted approach to marketing attribution, when implemented correctly and validated, the insights provided are of immense value for the marketing organization.

To get to Marketing ROI, tracking marketing spend data is critical. Spreadsheets are the go-to tools for most of the marketing organizations when it comes to tracking and managing the program spend. It is cumbersome to track the spend on these numerous spreadsheets and more importantly relate them to the actual campaign data in CRM (usually Salesforce.com – SFDC) where all the pipeline data lives. Since all marketing and sales data is connected via campaigns (especially when it comes to tracking ROI), it makes sense to have the ability to track marketing spend at the campaign level within SFDC. To make this happen, I built a simple MRM (Marketing Resource Management) app within the campaign object in SFDC. It allows for the tracking of marketing spend over time for each of the campaigns and once rolled-up (at each campaign level), it shows the total marketing spend, itemized by date of spend at once place as shown here. Combining the attributed pipeline data with the marketing spend data is what brings Marketing ROI data to life. Here also, this data can be combined with other campaigns and accounts data to help the marketing teams understand what campaigns and tactics are bringing in higher ROI across different account regions and segments.

The set of these KPIs provides strategic insights to the marketing leaders and CMOs so that they can confidently demonstrate the impact that their organizations are making and help foster a strong working relationship for marketing within the c-suite, especially with the CFOs.

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other leaders and practitioners around what challenges they face when it comes to deploying marketing analytics capabilities? How are they defining the metrics for tracking marketing’s impact on pipeline and revenue?

The post Top 2 Marketing Analytics Priorities for B2B Marketing Teams appeared first on marqeu.