3 Ways to Derive Actionable Sales Insights From Content Marketing Data
As we ring in 2020, talking about the importance of content marketing and why every brand should be doing it is a record that has been broken for quite some time.
Nearly all businesses these days are aiming to build content marketing strategies that enable them to “rise above the crowd” or “be heard above the noise.” Whether they’re succeeding or not is anyone’s guess. What’s for sure is that branded content campaigns are yielding copious amounts of big data about customers and their behaviors. Whether it’s web traffic, conversion rates, or engagement levels, the trick with content marketing data is to know how each dataset feeds into the bottom line.
With so much data being created and collected every day, it can be very difficult and overwhelming to translate this information into sales insights. In fact, one of the biggest challenges marketers face is associating content with revenue:
So how can you show ROI from content marketing without letting your head spin from data overload? Let’s find out.
1. Unify Data Streams
Data collection is only getting more complex as sources and systems continue to grow. Depending on how far-reaching your content strategy is, the data streams that relate to your sales regime won’t always yield black and white answers. Therefore, market research data, customer data, and pretty much all company data should be unified in a single ecosystem. This will let decision-makers spot key trends that tie directly into the bottom line.
For example, you need to know things like the content channels that are bringing in the strongest leads, the common threads among your most profitable customer profiles, the types of content that get the most engagement, where your referrals are coming from, and so on.
Marketers these days are growing increasingly dependent on the constantly-growing number of data sources. The major tasks at hand involve monitoring, analyzing, and finding benchmark performances for each campaign.
Until recently, it was a huge (and expensive) effort to develop tool integrations that aligned content marketing data sources in ways that boosted the sales process. Thankfully, AI-enabled business intelligence and CRM platforms allow businesses to efficiently analyze their data streams. One such tool is Salesforce’s Einstein, which can unify company data to identify new audiences, deliver sales projections, create in-depth customer profiles, and even automate storytelling.
AI-based content platforms are designed to score touchpoint information to discover patterns that help determine which leads are likely to convert. They can create associations between varied data sets, such as website engagement and publicly available demographic information, for example, and turn these into stories.
The way you set up these stories determines which datasets you will unify, and how your content or CRM platform will evaluate the information for predictive purposes. For instance, you might want to use a story to maximize potential earnings from a particular product. This could involve data sets related to engagement rates, lead nurturing, landing page conversion, and so on.
The more data you feed into such a system, the more precise the predictions you’ll be able to make. AI and machine learning are enabling data scientists to apply a combination of predictive analytics and meta data management to business. This lets marketers anticipate changes in consumer behavior and the impact of macroeconomic trends on business.
2. Identify Snags in the Buyer's Journey
Making a sale in B2B requires way more than flashy advertisements and bold promotions. The modern buyer’s journey is typically made up of three key stages: Awareness, Consideration, Decision.
Ideally, each stage should work as a vector to ultimately produce sales.
While it’s easy for marketers to design content marketing strategies to play to each stage, the parts that tend to get overlooked are the transitions. In other words, how well does your content bridge the gap between one stage of the buyer’s journey and the next? This is perhaps where data provides the most valuable insights related to sales.
Funnel visualizations can reveal patterns in regard to where people drop out or delay the progression through the buyer's journey. Using this data, businesses can refine their transitions and work to eliminate the major roadblocks. Some simple metrics to start out with are bounce rates, session duration, and conversion rates of your landing pages — all of which can be tracked via Google Analytics.
For example, let’s say you run a SaaS company and your Awareness stage content (blog posts, e-books, podcasts, etc.) is doing a fantastic job in getting traffic to your Consideration stage content on your website, which includes landing pages to sign up for a webinar or download a white paper.
However, you notice that the bounce rate for these pages is very high (around 95%) and the time on page is only a few seconds. This is a good indicator that there is interest, but the transitions from your Awareness content aren’t giving people enough information or motivation to convert. Therefore, it might be time to re-examine content at the transition point (email invitations to the webinar that you send to people who’ve read your blog posts or subscribed to your newsletters) or add more information to your landing pages.
Keep in mind, snags in the buyer’s journey can have much deeper-rooted issues than the example above — all of which can impact your sales numbers. Understanding how your content impacts the success or failure of your customer journey will likely require a great deal of critical thinking (and digging into funnel data).
3. Use Intent Data to Constantly Refine Your Sales Model
The term “intent data” is a buzzword that has been floating around the marketing world for all of a hot second. Intent data refers to behavioral information that gauges a person’s online activity and how likely they are to take a desired action. In terms of how this relates to your content marketing and sales efforts, these insights combine both topic and contextual data.
Topic data refers to the level of interest someone expresses about a subject when they search for something on the web. For example, if someone Googles "how to simplify customer service," and lands on your blog about how to program a chatbot, they are showing some degree of intent. There are generally four categories of topic data:
- Anonymous First-Party Behavioral — These are visitors to your website who haven’t taken any action that identifies themselves. It is possible to identify their company by their IP addresses.
- Known First-Party Behavioral — These are visitors to your website who have shared personal information by filling out a form.
- Anonymous Third-Party Behavioral — These are unknown visitors to other websites with similar content to yours. You can identify them via the topics they browse and track them via their IP addresses.
- Known Third-Party Behavioral — These are known visitors to other websites who’ve shared information and whose content preferences are recorded. You can then use tools to measure and capitalize on the purchase intent of a pre-segmented audience.
Now, topic data is more or less useless without the right context. Contextual data revolves around diving into the who of the person taking the action. For instance, if the visitor reading your article on chatbots is a business owner, there is a good chance the person is considering a solution for customer service needs. On the other hand, if the reader is a programmer, it’s very possible the professional is looking for information about how to build or improve a chatbot. In this way, intent data plays a key role in how you define your sales process.
Different types of web visitors will have slightly different views of the buyer’s journey in relation to your business. You need a system that gauges the intent of a visitor from how they interact with your content on various platforms; the insights you glean from this form the basis of how you craft your landing pages.
Intent data lets marketers put the right content in front of the right eyes. Start by personalizing your website to “anonymous” users. Solutions like Evergage can be synced with CRM data and use machine learning to better understand the intent of visitors. It can then draw on a wide range of behavioral insights to help you serve ultra-targeted content.
For example, the system can sort visitors by industry and automatically build segments based on key attributes. From here, you can deliver customized messaging that fits into the narrow views of each of these segments.
Next, you should base the processing of inbound leads on engagement. Ideally, this should work to quantify the visitor’s intent based on the manner in which they interact with your content. If someone is looking at your blog section, they would likely fall lower on your lead scoring model. If they are looking at pricing, they would obviously rank higher.
Intent data should always play a key role in how you nurture leads and go about making sales.
Over to You
In many ways, the data you get from your content marketing strategy is the lifeblood of your sales efforts. As big data continues to grow at exponential rates, both in size and application, the challenge will always be using these insights to boost your bottom line.
Refining your content strategy is a task that never truly ends. As long as you keep up with what your analytics are telling you, and identify and iron out the weak spots, spikes in sales are always around the corner. Good luck!
Rohan Ayyar is the regional marketing manager for India at SEMrush. His blog, The Marketing Mashup, covers digital marketing from the perspective of B2B, B2C, lead generation, mobile marketing, SEO, social media, content marketing, database marketing including predictive analytics, and conversion rate optimization. In addition, he'll look at emerging marketing technology and how marketers can use it. Reach Ayyar at email@example.com.