Optimizing Your Organization for Data-Driven Growth Marketing
The pace of change in business today is unprecedented. Consider this stark statistic tweeted by Vala Afshar, the Chief Digital Evangelist at Salesforce: Twitter only took 2 years to gain their first 50 million users1. This is only a fraction of the time compared with everyday products like the automobile and airplane, which took 62 and 68 years respectively to reach the same milestones.
To keep up with the rapid pace of change, marketing organizations are evolving as well. Rather than focusing on the top-of-funnel and customer acquisition, marketing organizations are valuing the entire customer journey, measuring the cost of acquisition, customer retention, and total lifetime value. As such, marketing is seen much more as a revenue driver. Additionally, to ensure growth in a constantly changing environment, tried-and-true methods and long-term projects are becoming a thing of the past. Speed, constant experimentation and continual improvement are becoming vital to marketing success. All of this is captured by the growing trend towards “Growth Marketing”.
One of the core tenants to the success of any growth marketing effort is data analytics. In an article published by McKinsey & Company on marketing analytics, they said that “the benefits can be enormous: our review of more than 400 diverse client engagements from the past eight years, across industries and regions, found that an integrated analytics approach can free up some 15 to 20 percent of marketing spend.”2 As growth marketing relies on constant testing, you will only know the success of any test with proper benchmarking and statistical analysis of key metrics to understand if a test performed well or not.
However, setting up the data workflow in an organization to support flexible and iterative analysis can be challenging. Additionally, while performing tests on a specific step in the customer journey, the effect on the rest of the funnel needs to be measured to understand the ultimate impact on the business.
We’ll elaborate on the role that data plays in successful marketing organizations, discuss challenges in organizing your data to support constant experimentation, and show you how to optimize your data workflow for successful growth marketing.
What’s the Role of Data?
In many ways, the movement towards growth marketing would not be possible without the ever-growing amount of data. Today, the preferences, behavior, and even specific actions of customers can be traced through different channels, from social platforms and search advertising to web and mobile apps. With access to this amount of data and the pinpoint specificity of the data, marketing organizations can understand how each prospective customer proceeds through the marketing funnel.
Career marketers will tell you that marketing is part art and part science. Data has tilted the balance more towards science. In a blog post by Optimizely, a marketing experimentation platform, Amanda Swan wrote that growth marketers “should be fearlessly creative”, but also be “willing to measure everything and be able to admit failure”. Successful marketers are obsessive about objectively analyzing the results of each campaign and can trace it back to the impact or ROI it delivers for the organization.
What should I be measuring?
Depending on your product and how you reach your customer, the key metrics that you should be measuring to understand the health of your funnel could differ. It is always important to consider metrics that measure performance throughout the entire funnel. Here are some key sample metrics to consider:
- Customer Acquisition Cost
- Lifetime Total Value
- Customer Retention Rate
- Unsubscribe Rate
- Performance by Channel
- Conversion Rate
- ROI for Marketing Campaigns
- Marketing Content Performance
Data Challenges in Growth Marketing?
Although the importance of data is well acknowledged in marketing circles, actually implementing an effective data workflow isn’t easy. According to BrightTalk, “42% of B2B marketing professionals state that lack of quality data is their biggest barrier to lead generation.” There are a number of challenges that organizations face in organizing, analyzing, and deriving value from their data.
The volume of data
Data is growing at an unbelievable rate. 90% of the world’s data has been generated in just the last two years3, and the pace is only growing. For an organization, collecting, storing, and managing this data is already a challenge. Being able to analyze the deluge of data is even more daunting.
Data in multiple places
In recent years, marketing organizations have increasingly adopted best-in-class tools for different needs. You now have specific tools for email marketing, social marketing, SEO, SEM, website analytics, marketing automation, and that’s just scratching the surface. As a result, marketing data lives in all of these disparate tools. In a recent survey of marketing decisions makers, 61% of respondents indicated that they struggled to access or integrate the data they needed4. This creates a challenge for marketing organizations to understand the whole picture of the marketing funnel.
Siloed data teams
In most organizations, data analysts and engineers are a scarce and valuable resource. As a result, data analysts and engineers are frequently placed in specialized teams that are siloed from the rest of the organization. Others have looked to “democratize data” by putting more ability for marketing decision-makers to perform self-service analytics without the on-going assistance of data analysts and engineers. While this shortens the timelines for certain analyses, it can frequently lead to inaccuracies and flawed decisions.
Not being able to answer the questions you want
Traditionally, analytics or business intelligence solutions were designed for static, departmental dashboards that served as a reporting function for key company metrics. Since the key metrics were well defined, predefined data models with associated charts and graphs were provided to help simplify implementation processes. However, this approach limits the ability for decision-makers to answer questions outside of the ones that were initially considered when creating the data model.
Set it and forget it
Many organizations still think about data analytics as a project with a defined start and end date. The problem with that approach, especially for growth marketing, is that the data model is constantly evolving. In a situation where data is not constantly maintained, organizations can quickly fall into what Brian Balfour, the CEO of Reforce and previous VP Growth at Hubspot describes as the “Data Wheel of Death”5.
If data isn’t constantly maintained, data quickly becomes irrelevant. This leads to stakeholders losing trust in the insights gained from data, and they use it less. This then causes even less attention to be put on data maintenance. Organizations caught in this cycle can quickly fall out of using data to drive their decision making.
Optimizing Your Data for Growth Marketing
In the face of these challenges, you may be thinking that implementing data-driven growth marketing will be an insurmountable initiative. The good news is, there are organizational structure and process best practices that will set you up for success.
Treat data as a product, not as a project
A successful growth marketing organization evolves based on the results of continual experimentation.6 Rather than treating data as a one-time project that you set and forget after you’ve created your initial set of dashboards, your data needs to evolve as your product and marketing needs change. As you learn more about your market, users, product, and channels there will be new questions with additional data sources that drive different ways of looking at data and performing analyses. Treating data as a product which has evolving requirements allows you to continually improve your processes.
When building out your data workflow, you should also account for a constantly evolving data model. Just because you have a certain set of data sources and definitions today does not mean that it will remain the same six months or a year from now. Ensure that you have the ability to quickly and easily update your data model. By implementing a flat data structure where you analysts can work directly with the data sources.
All your data in one place
In order to see the full effect of each experiment on the entire customer journey, you must collect and combine your data. Rather than analyzing data directly from the sources, you must centralize the data in a single place by using a data warehouse. Here, you can establish the relationships and connections from the data in your different marketing systems and tools. By allowing data to be combined and blended, you will also uncover insights that you may not have been able to see before.
Crunchbase utilizes data to get a holistic view of their marketing performance. Alex Mack, Head of Marketing at Crunchbase says that, “We wouldn’t be able to create the campaigns that we want to create. Being able to target more effectively, being able to understand our users better, helps us to communicate with them that expands their experience across the entire Crunchbase platform.”
Additionally, Crunchbase uses certain events to trigger specific actions to optimize their marketing efforts. Emma Lloyd, Marketing Operations Specialist, says that “We’re really trying to think about how we can learn more, how we can do more segmented marketing campaigns, and data is giving us [additional events] that we haven’t been able to trigger off of.”
Design for flexible analysis
While your data model is evolving over time, the questions that you want to analyze should also be changing with each marketing campaign and experiment. A flat data model will allow you to perform specific analysis of questions you hadn’t considered before. Additionally, you must ensure that your organization has the tools and personnel with necessary skill sets to perform deep analysis of specific questions - to not only understand what has changed but why a certain change occurred.
Organize for agility through collaboration
The faster analyses can be completed, the faster additional experiments can be performed. Whether your business operates with a centralized data organization or have analysts embedded directly within the marketing organization, adopting an agile approach to data supports the rapid iteration necessary for growth marketing.
While it is valuable to enable marketing decision makers to directly interact with the data, rather than introducing siloes by relying heavily on self-service analytics, data should be democratized by promoting close collaboration between data engineers, analysts, and decision makers. To achieve this, you must ensure that there is a flexible and agile model -taking an iterative, modeling-as-you-go approach.
Crunchbase has done this successfully by connecting their entire marketing organization on a single, flexible unified data platform. By doing so, they are able to run many of their marketing programs off of data triggers. Alex Mack at Crunchbase says that, “if people are triggered at the wrong moment or being triggered two weeks after, or 10 minutes after sometimes, the communication ceases to make sense.” Thus, having data that is up to date and analyses that are performed quickly is vital for them.
The opportunity ahead
Marketing has an opportunity to be the most strategic organization within companies today. Andy Johns, VP Growth at Wealthfront, says that “Growth [marketing] owns the flow of customers in and out of a product.”7 There is nothing more important to a company than having a growing amount of happy, returning customers. Growth marketing moves the marketing organization past the top-of-the-funnel to support the entire customer journey. However, the level of competition is also higher than ever before.
Data drives the success of marketing organizations. Being able to generate accurate and timely insights from data enables you to make the right decisions. To achieve this, you must ensure that your marketing organization has the right level of attention, consolidation, flexibility, agility, and collaboration in organizing and analyzing data. By doing so, you can overcome the challenges that has prevented most organizations from deriving value out of their data and stay ahead of the competition.Watch Demo Start a Trial
1 “Vala Afshar on Twitter: “Years to reach 50 million users: 68 62 50 ....” 26 May. 2017, https://twitter.com/valaafshar/status/868276113314902016. Accessed 26 Dec. 2017.
2 “Using marketing analytics to drive superior growth | McKinsey ....” https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/using-marketing-analytics-to-drive-superior-growth. Accessed 3 Jan. 2018.
3 “Big Data, for better or worse: 90% of the world’s data ... - ScienceDaily.” 22 May. 2013, https://www.sciencedaily.com/releases/2013/05/130522085217.htm. Accessed 26 Dec. 2017
4 “Dealing With Data: Today’s Marketing Analytics ... - Think with Google.” https://www.thinkwithgoogle.com/marketing-resources/data-measurement/marketing-analytics-data-challenges-opportunities/. Accessed 26 Dec. 2017.
5 “How You Battle the “Data Wheel of Death” in Growth — Brian Balfour.” 4 Apr. 2017, https://brianbalfour.com/essays/growth-data-mistakes. Accessed 26 Dec. 2017
6 “Datapine.” https://www.datapine.com/. Accessed 27 Dec. 2017.
7 “What Is Growth Marketing?.” 17 Dec. 2015, https://blog.drift.com/what-is-growth-marketing/. Accessed 26 Dec. 2017.