Transforming Marketing with Big Data Analytics
Marketers are in the midst of a major technology-driven transformation. The upside is that we’re getting access to rich marketing data that’s showing us the effectiveness of our marketing programs. It’s also giving us visibility into customer behavior. The downside is that with each new marketing application that goes live, we’re creating a new silo of marketing data.
What’s the impact of this? Disconnected and poor quality marketing data is negatively impacting our lead-to-revenue processes. The marketing team at Informatica knows this dilemma better than anyone, not only because our customers seek us out to solve their data management challenges, but because we’ve been there ourselves.
Just a few years ago, we had islands of disconnected marketing data and sales data. We were held back because we couldn’t stitch together a single view of customer (SVOC) across functions and channels. We also lacked visibility into the aggregated customer view, so we had no visibility into the end-to-end customer journey.
Thankfully, now we strategically manage our data. We’ve automated the process of improving the quality of our marketing data, creating an SVOC, and unifying our data in one location. We have built a solid data foundation so we can fuel our marketing and sales processes and big data analytics with great data and connect the dots across the end-to-end customer journey.
During a recent webinar I hosted, Kim Salem-Jackson, senior vice president of worldwide field marketing and business development at Informatica, shared the story of how we built a solid data management foundation and a marketing data lake (MDL) to transform our lead-to-revenue pipeline.
“When people hear ‘data-driven’ they think of using the data, but not making data usable,” said Kim.
“Being a data-driven marketer means investing in your data, making sure you have the data you need when you need it, and using high quality data to fuel your marketing programs.”
How did we do it? We built a Customer Data Hub (CDH) and Marketing Data Lake (MDL).
- The Customer Data Hub automates our marketing data management. It improves the quality of our data, gives us an SVOC, and fuels our processes, applications, and analytics with great customer account and contact data.
- The Marketing Data Lake brings our online and offline marketing data together. It allows us to see the end-to-end customer journey, better understand customer behavior within and across accounts and channels, and get the insights we need to serve customers better.
“While Marketing teams are investing in automating their marketing processes, they often overlook the opportunity to automate their marketing data management processes,” said Kim.
Islands of marketing data resulted in a fragmented view of the customer journey
In 2012, we started investing in world-class sales and marketing applications to help us modernize and automate important processes.
By early 2016, we had 35 different marketing applications. None of these applications were connected. Our sales and marketing teams were struggling with a fragmented view of customer accounts and contacts. In fact, a lot of people were spending time manually reconciling data in spreadsheets, which was draining our productivity and our ability to deliver on revenue targets for sales.
“At the time, we had no data strategy,” explained Kim. “As a result, our marketing data and sales data were a mess. We couldn’t run an effective cross-sell campaign because we couldn’t see which customers purchased which products. We had little information about our customer preferences. We had no visibility into customer behavior to help us drive our marketing strategy and impact the bottom line.”
Here are the questions Kim and her team wanted to answer, but couldn’t:
- Which customers are engaging with our marketing programs?
- How can we track the customer journey across channels?
- Which channels and programs are delivering revenue and which aren’t?
- How can we get a customer account-based view rather than a lead-centric one?
- Who are all the members of a buying team that we need to influence?
- How can we better understand and predict customer behavior?
- How can we get visibility into the revenue that’s traveling through our pipeline?
Kim summed up the key issues in the pre-transformation marketing organization:
- Inaccurate and incomplete customer account and contact profiles
- Fragmented view of customers across systems
- Lack of insight to customers’ product interests or preferences
- No view of relationships within and across accounts
These problems created specific and painful areas of impact:
- No personalization in customer outreach
- Leaks in the lead-to-revenue process
- Difficultly showing the impact of account-based marketing (ABM) programs
- Lead-centric rather than customer account-centric measurement
“We had a wealth of data from our marketing, sales, and finance applications, but we could not get our arms around what truly worked and what would deliver pipeline that converts to revenue,” shared Kim.
Having her talented team spend their valuable time manually trying to bring together and reconcile this marketing data was not the answer. It would take too long and keep them away from proactively partnering with sales and delivering revenue generating marketing programs.
“As a data company we had to practice what we preached and treat our own marketing data as a strategic asset,” said Kim.
To get a unified view of prospects and customers at the account-level, get visibility into the end-to-end customer journey, and to show a return on marketing investment (ROMI) we couldn’t use tactical approaches to managing our marketing data any longer.
“We needed to automate the one thing that is so often overlooked by marketers – the marketing data management process,” said Kim.
Tackling the basic marketing data management challenge
We started with the basics of marketing data management to get our data in a usable state before moving on to more sophisticated technologies. Together, our Marketing Operations, Sales Operations, and IT teams laid out a solid data management foundation by automating how we:
- Connect our data: We can quickly identify all the valuable customer data that exists in the company and easily add new data sources if we need to.
- Clean our data: We have high quality marketing data that we trust.
- Master our data: We have a Single View of Customer (SVOC) for each customer account and contact.
- Verify our data: We have fewer bounce backs and less returned mail.
- Enrich our data: We have information about industries, company revenues, and employee counts for a more complete understanding of our customers.
- Relate our data: We know which accounts are parent companies, which subsidiaries belong to them, and which parts of our customer organizations own which products so our global sales teams can identify whitespace opportunities and collaborate more effectively.
- Share our data: We have access to this data through the technologies we’re used to: applications like Salesforce Sales Cloud and Marketo, the data warehouse, and new this year, our MDL.
- Govern our data: We have the people, process and technology to keep our data usable and trustworthy.
“Data never stands still,” said Kim. “In less than a year, 30% of any marketing database is out of date. If you use a tactical approach to marketing data management and clean your marketing data once year—and neglect governance—your strategic asset will depreciate quickly.”
Building a marketing data lake (MDL) to see the end-to-end customer journey
Once the basics were in place, the really exciting work started. Using our own big data management (BDM) technology our Marketing Operations & Analytics team built a centralized MDL in 60 days! The MDL links our trusted customer profiles with other types of data that are important to us in one central location. Where is the data coming from?
Below are the types of data in our data lake today:
- Marketo: form submits, and asset download data from the website, event attendance data
- Salesforce Sales Cloud: sales data, product and services purchase data
- Adobe Analytics: web clickstream data
- Lattice Engines: predictive lead scores
- Demandbase: demographics for user’s IP addresses
- RioSEo: social sharing data
- LinkedIn: social data
- Our Own Cloud Products: usage data
We use Tableau to access and visualize the integrated data in our MDL.
Getting new insights into the customer journey improving the lead-to-revenue process
Thanks to our CDH and MDL we have visibility into the end-to-end customer journey. The MDL is a one-stop-shop where we ask important questions that we couldn’t answer in the past. It’s a single lens to view all of our marketing activity across channels. It allows us to do marketing attribution and cohort analysis. Because we can see what’s working and what’s not, we can make improvements to the end-to-end customer journey and the lead-to-revenue process.
“Prior to 2013 I didn’t know anything about my prospects and customers,” said Kim. “By using this marketing data lake I now see what my customers own and don’t own. I can anticipate their behavior and determine what they are likely to purchase next.”
Website visitors, once completely anonymous, are now identified as they browse our web pages. Sales reps can see who they are, what they are viewing and sharing, and what they’ve purchased in the past. For example, recently a sales rep was able to see the connection between an existing opportunity in Salesforce and a website visitor from the same account. Based on data from the new web browsing session, the sales rep was able to significantly expand the scope of the deal and double its value!
“This type of data-driven insight is a game-changing innovation for the marketing organization,” shared Kim. “By shrinking the window from pipeline to revenue, each prospect increases in value.”
- Listen to Kim’s story on the webinar playback
- Read “The High Cost of Hidden Customer Data Issues,” on cmswire.com for insight into how fragmented marketing data holds companies back—and what you can do about it.
- To find out more about Informatica’s own Marketing Data Lake, including the process, technology and people involved, read this blog series: Naked Marketing: Putting Big Data to Work for Marketing