The competition for your prospects’ mindshare has become more and more intense over the past several years.
I’m not only talking about the number of communications and materials that prospects are receiving through each channel. I’m also talking about the increasing amount of relevant content available when a prospect is looking for a solution to a challenge they face.
When I first started with DemandGen many years ago, we saw a notable lift in results just by implementing the foundational elements of the Demand Factory™, such as a lead management framework and a lead nurturing program. However, as MarTech systems have become table stakes for B2B marketers in the past couple of years, just getting out campaigns with marketing automation is no longer enough to drive significant results.
As my colleagues have noted in their recent articles about digital transformation and the customer journey, tools enable the strategy rather than define it.
And at its most fundamental level, that strategy requires you to make human connections.
Being able to add a famous new logo to your website is only possible because of the human connections your marketing and sales teams make when demonstrating how your solution can solve your prospects’ unique challenges. And data is what makes establishing and strengthening those human connections possible.
Three key types of data
After you’ve formulated your strategy and implemented it in your systems, the most vital element for driving success will be your data.
And let’s face it: we all have more data than we know what to do with. Or better put, our digital systems generate more data than we can often efficiently use.
Three core types of data drive your sales and marketing systems:
- Profile data includes firmographic, demographic, and contact information — personal and company data that makes it clear who you are talking to. It is usually gathered through forms or other ways of explicitly asking the prospect for the information. Company data might be enhanced or appended by third-party data.
- Activity data comprises not only prospect activity, but also activity by sales, marketing, and any other company touchpoint, such as customer service, app usage, etc. It also includes process activity, such as movement between funnel stages. Often, this type of data is timestamped in various systems (such as web or marketing automation analytics), but you usually need to setup additional data structures in order to snapshot vital activities such as funnel stage movement.
- Reporting data is the metadata that accompanies profile and activity data to allow for granular and roll-up reporting. It includes information like lead source, touch channel, touch offer, and more. For example, activity data would identify an email click with the email name, but you would have to map that name to other information, such as campaign type or offer, to get to aggregate, actionable reporting. It is often more reliable and easier to setup and maintain key reporting data structures and values instead.
These data types work together to help you make the jump from strategy to execution. And the finesse and expertise with which you setup your data structures to capture the right data — and how you then use that data — will determine the success of both your strategy and your tactics.
It is very fashionable these days to capture as much data as possible and then use data science and machine learning to identify new patterns and insights. However, the bread and butter of efficiently operating any Demand Factory is optimizing and standardizing the capture and use of the above data types.
And since most marketing teams are expected to operate faster with less these days, the processes for turning the above data into human conversation are more important than ever.
Turning your data into a human connection
We like to say that the essence of the Demand Factory is “delivering the right message to the right person at the right time.”
And when you get right down to it, it is that simple.
The complexity always comes when interpreting the data to identify what message to which person at what time.
- Formulate your strategy
First off is the strategy. If you don’t have a voice for your message, and if you don’t have an excellent view of your target prospects, there is no way to develop a message that will resonate with them and get them to engage further.
In his post on digital transformation, DemandGen COO Greg Carver quotes Sun Tzu: “Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat.”
Without a clear view of your strategy for addressing your prospect, marketing tactics can just become more noise to that prospect. And, while it could’ve been said in the past that anything was better than nothing, noise now hurts more than it helps. Just look at how many marketing emails you ignore on a daily basis.
On the other side, I’m sure you’ve received emails that have spoken to a problem you’ve faced that you actually read, and which led to more engagement with that company.
- Map your data
After you’ve developed your strategy, it is time to map your data to that strategy.
It would be easy to say that profile data maps to the who, activity data maps to the when, and reporting data maps to the results, but it usually takes a blend of these together.
For example, when looking at a single prospect:
- Profile data may identify a broad level of messaging by company size
- Activity data of what they engage with may provide better insight into their role
- Reporting data around the lead source may identify specific interests
When you have all three types of data, this is the point where you can really craft a meaningful human connection or conversation. Too many times, I’ve seen great prospect segmentation wasted with lackluster messaging and offers. Or, excellent content sent to such a broad field that there is no way to efficiently continue the conversation.
Rarely does this work perfectly on the first go. Like any process or tactic, you must optimize your messaging and data mapping through the ongoing feedback loop of reviewing results and then making changes. Data hygiene, completeness, and normalization also play a huge role in effectiveness.
- Find the balance between what you have and what you want
If we had unlimited resources to craft messaging and develop processes, and the unlimited attention and cooperation of our prospects for data capture, life would be much simpler.
Inevitably, though, we have to make tradeoffs between which data we can capture, what content we can create, and more. That’s why establishing reporting data structures is so important when it comes to capturing the insights that matter as easily as possible.
Other times, strategy and messaging have not been clearly laid out, forcing the marketer to map their prospects to incomplete personas.
An iterative and agile approach to optimization is essential to make sure you identify, address, and incrementally improve these limitations. If you don’t, your prospects may switch their attention to something else (like a competitor’s email).
Data doesn’t exist for its own sake
It is easier today than ever to collect huge volumes of data and use any number of ever-increasing MarTech tools to drive a conversation with your prospects. Be sure to take the steps to turn those 1s and 0s into meaningful human connections.
Interested in learning more? Check out DemandGen’s data, consulting and best practices, and reporting and analytics services to get started.
Ryan Johnson develops and implements marketing automation strategies for DemandGen clients. As a DemandGen Consultant, he has helped clients across a wide range of industries to streamline and optimize their marketing and sales processes to drive measurable success and ROI.
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