Mike Moran is the co-author of the new book on Content Marketing and Big Data, Outside-In Marketing. You can see more from Mike Moran at Biznology.com.
One of the initial reasons you started Bizology is that, traditionally, B2B companies tend to have little marketing experience of any kind. What are some different marketing methods that have become available to B2B companies in the last 10 years?
In the last 20 years, everything digital has opened up B2B marketing–before then, there were no CMOs for B2B companies, because there was nothing C-level about printing brochures and picking which trade shows to exhibit at.
In the last 10 years, every B2B business has discovered content marketing, so now they need to understand their analytics just to break through with their message. The kind of audience analysis that’s possible today for fueling content marketing was unheard of even five years ago. Techniques exist now that help B2B companies identify visitors to their websites and personalize their content on-the-fly to the person visiting. These are great days to be a B2B marketer.
According to searchdatamanagement.techtarget.com, data management is defined as “the development and execution of architectures, policies, practices and procedures in order the manage the information lifecycle needs of an enterprise in an effective manner.” With so much data coming from so many sources, why is it important to have this architecture in place, before beginning any data-driven marketing in earnest?
Well, I agree that it is helpful to have a strong data architecture in place, but I honestly don’t think it is as imperative as it once was. In the old days, you had to know what data you needed because your database had to be designed to store the data. Nowadays, schemeless databases allow you to be much more flexible about how you collect and use data. In my book, Do It Wrong Quickly, I explain how it’s more important to test which data is the most important for your decision-making than it is to keep your database stable. And the advent of Big Data means that the form of data is more volatile than ever before–what marketers collect constantly changes. So, if it is possible to have a strong data architecture in place, that’s a plus, but it isn’t as critical as it used to be.
Biznology recently published an article about New York’s happiest B2B database marketer, where the author advocates for adopting an “ideal state” data model and then filling in the blanks to make it actionable. Can you describe “ideal state” data, and why it’s important for long-term marketing goals. Secondly, how can the data be improved upon to make it more actionable?
We’ve got some great authors at my Biznology site–that article was written by Ruth Stevens, one of our best. As I said above, I think it’s hard to get to the ideal state. If you read that article closely, you’ll see that few people feel they have the ideal state. I worry that people believe that they need to have an ideal state for their marketing to work, which isn’t so.
Marketers are letting the ideal drive out the workable, in my opinion. It’s important that all marketers take the data they have, ideal or not, and improve it, as you suggest. The best way to improve data (and make it more actionable) is to constantly take actions based on your data and see how effective they are. A/B and multivariate testing force you to take a stand on your content based on data with specific market segments and personas, driving constant efforts to improve not only the data but the actions that you drive from them.
At the beginning of that same article, the author talks about some of the common problems with B2B marketing data, including duplicates, out-of-date or incomplete data. What are some ways that data management can be used to help solve some of these common problems?
Data-based marketing requires constant cleaning and updating. People think of marketing as being very creative, but this is a case where strong, detail-oriented processes make all the difference. Using multiple sources of data cross-checked against each other and constantly updated as changes occur is your best bet to keeping data fresh. A constant focus on cleaning data means that the processes, employee training and internal reward structure must focus on always being sure your data gives you the edge you need against your competitors. And the best way to keep data fresh is get customers to update the data themselves–personalizing your site entices clients to keep their profile data current.
In the study, The State Of B2B Marketing Data Management, published by OpenPrise, 72% of marketers surveyed reported that “Improving ROI Measurability” is the most important goal of a data management strategy. What are some different methods that data management can be used to make ROI more measurable, and why is that important?
“Improving ROI Measurability” through data management begs two questions. The first is to understand how ROI can be improved. The second is to understand how ROI can be better measured. Improving ROI for B2B marketers typically involves improving what marketers know about their clients combined with correctly choosing the right messages to show them–this is the essence of personalization. But being able to measure that improvement has historically been a challenge for B2B marketers–often sales happen offline, closed with a different employee than visited the website, months after the initial contact. Improving measurability is about connecting the dots. You must entice prospects to identify themselves early in the process, by providing benefits, such as inside information, to help them with the buying process. Once identified, you must track them through the steps of the buyer journey, tie them together with other employees involved in the same sale, and tie online activity to offline sales.
In that same study, 26% of marketers claim that making data richer is the most important aspect of data management. Can you talk a bit about what rich data is, and how it can be used to improve ROI?
Sure. In our book, Outside-In Marketing, James Mathewson and I explain numerous ways to enrich data. Creating a master database for customer information that becomes the repository for everything you know about each person is the start. Casting a wide net for data sources encompassing traditional demographics, firmographics, email addresses, and other widely available information is the start, but adding topic preferences from ad clicks, social media discussions, web activity data, and conversions can enrich your traditional data to allow better message targeting for improved ROI.
A lot of companies are using multi-channel marketing efforts, across a wide array of different networks, from social media to their website. What are some different techniques for managing multichannel marketing data, and how can this be used to make data richer?
The hardest part is stitching together the activities across channels so that they all point back to the same actual person. There are a variety of methods for going about this – using anonymous identity detection, such as cookies; social handles; and email identities. Beyond that, text analytics and machine learning can identify themes in content that your visitors engage with, so that you learn more about what they are interested in and can target them with your own similar content.
A lot of online marketers rely solely on page views and shares for their data, which neglects how potential clients are actually perceiving the usefulness of content. Can you share a technique or two for measuring things like how long visitors are spending on the website, whether they’re reading or skimming the content, bounce rates, etc.? Can you give any advice on how to get that data to work with other data sources, to reveal a 360-degree view of your customers needs and interests?
As mentioned in the previous answer, tying together identifying information across venues is the key to that 360-degree view. More important than some of the measures you cited are understanding what topics the prospect is interested in. That’s where text categorization and clustering techniques allow you to develop insights as to which topics people care about. Metrics such as time viewing a page, views that do not bounce, likes and more can help you to know that the content is interesting to the particular person.
Content marketing, or search marketing, are both vitally important for B2B companies, as customers are likely to be searching for you, rather than the other way around. Biznology offers a course in content marketing, where you ask the question, “Are you getting the most out of your content marketing efforts?” How can data management be used in conjunction with content marketing for next-level B2B marketing?
Marketing is persuasion. The more you know about your clients and the more you know about your content, the more likely you can select the content that will be most persuasive. “Knowing more” is about the data, customer data and metadata about your content. The more timely, comprehensive, and accurate your data, the faster you can reach that “next level” of B2B marketing.
In your Content Marketing course, you also state “Content marketing is more believable than ads,” as well as being less expensive. Can you give an example or two of data that backs this up? Can you also touch on why content marketing is so essential these days, for everybody, but particularly for B2B companies?
It’s clear that content marketing is more effective than ads, because content shared by people that you know is more persuasive than ads.
Additionally, content found by Google is more persuasive than content shown as ads. It’s also clear that content marketing is cheaper than ads. Even if the development costs are the same, content marketing has virtually no media costs.
B2B companies are keenly aware of this. 20 years ago, B2B companies did far less marketing than B2C companies, mostly because B2C companies were effectively using advertising for decades, while B2B companies could not afford to do any really effective marketing at scale until digital, and especially content, marketing came along.
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