What is the core reason vacations are so great? The absence of serious decision-making.
Luxuries like room service relieves the need to decide what to wear, when to arrive and where to go. Room service is a reward, telling you, “relax, time doesn’t matter, the place is right where you’re sitting, the attire is whatever you’re already wearing (and probably woke up in).”
While luxury vacations have their time and place, this doesn’t mean marketers should have to make every decision themselves. Decisions around how much to spend, where to spend it and who to spend it on don’t all need to be highly difficult decisions for B2B marketers.
Wouldn’t it be nice to have a well informed source of help to make these decisions easy?
Assistance from a knowledgeable source is always beneficial for questions like, how do I hit a net new customers goal by Q4?
In this post we’ll show how predictive marketing technology is changing how B2B marketers do their jobs. We’ll use two examples of how predictive marketing assists decision-making related to demand generation and account-based marketing.
Example 1: Using Predictive Marketing To Decide Who To Target
Before creating lists and campaigns the core question is whether these companies are the right ones to go after. Deep within every marketing database is a pattern of firmographic traits that can tell marketers which companies are the best fit and which types of companies close the fastest. With this knowledge marketers can quickly put together target company lists where product-market fit exists. In this case, product-company fit.
List expansion is done by first analyzing the patterns in customer data to understand how firm size, revenue, and other firmographics contribute to a likelihood estimate of a company purchasing your product.
At Bizible we can see a strong trend for certain technology usage among our customers. But that is one signal and as we grow it will be important for us to understand and separate out additional signals in order to build even better lists of target companies.
Using just one signal casts a wide net and it’s always nice not to have to guess which of those companies in that net should be targeted by marketing.
A simple sports analogy illustrates this point nicely. Consider the NFL combine where prospective athletes complete a variety of physical tests. There’s the speed of which they can run 40 yards, their maximum jumping distance, timed agility drills and many more. Imagine you are in charge of drafting these athletes for your team. Now imagine trying to do this job with access to only one signal, say, the 40 yard dash time.
This is not an ideal situation because there are many signals that can tell you whether this athlete is a good fit for your team. With predictive marketing technologies like Radius, teams will use more signals and get help in answering questions like: who do we need to target in order to meet a revenue goal by the end of next quarter?
Example 2: Using Predictive Marketing To Decide When To Target
The phrase “timing is everything” rings true in B2B marketing.
Marketers must have predictability in the pipeline in order to set a goal that is timely. The “T” in the “SMART goal” refers to timeliness.
If marketers have no clue when accounts are expected to close, how can they confidently do the planning required to hit a time-sensitive performance goal?
What marketers need is a living scoreboard telling them which accounts are highly predicted to close and which ones will require more time/effort. This aids in deciding when to target based on the marketer’s goal.
For example, if you would like to focus on closing what’s in the pipeline, focus on accounts that are closest to becoming customers.
If you are focused on long term goals then identify accounts that require more nurturing and begin planning how you will move them through the funnel.
The big question here is how to do you know which accounts are warmer and which ones are colder?
This is where predictive marketing can help. We call this predictive account-engagement score and it helps marketers decide when to engage an account given the marketer’s current performance goals.
At its root account-engagement score is an indicator of how close an account is to closing. The score is based on the quantity and quality of touchpoints. Let’s break that down this explanation to better understand how it works.
Predictive Marketing Case Study, Account Engagement Score
Account-engagement scoring is an algorithm built with touchpoints data.
A touchpoint is simply a marketing interaction like a web visit, form fill, phone conversation, or email click. Touchpoints in aggregation carry patterns and signals, one of which is whether an account is likely to close or not.
With an analysis of marketer’s databases, a data scientist can study the buying journey of two groups of accounts: the group that became closed won and the group that become closed lost. Knowing the difference can help marketers understand what stage an account is at, which ones are predicted to close and which ones aren’t based on buyer journey data.
Deciding when to engage an audience becomes simpler because marketers understand where accounts are in the buying cycle. If the goal is to close pipeline quickly then immediately focusing on accounts predicted to close soon will help reach that goal.
Marketing Touchpoints Are The Building Blocks For Predictive Marketing
The quantity of touchpoints can signal to you that an account is engaged, i.e. your leads and contacts are interacting with your sales and marketing collateral or personnel.
But is quantity a signal you should use to determine whether an account should get attention in the form of marketing engagement or contact from the sales team? It depends on whether the touchpoints are high quality or not.
“Touchpoint quality” sounds subjective. For instance, it’s easy to say that a web visit to a product page is worth less than a conversation with a sales person. But there numerous touchpoints in between whose value can be hard to distinguish.
Consider this example. There are two leads from the Acme account who attend a webinar, this is two touchpoints. And then consider two leads from XZY Company, one lead downloads a buyer's guide to your product and the other lead interacted with a sales rep via online chat. Again, this is two touchpoints.
Now consider your goal is to close a deal by month’s end. Which account should you focus your efforts on given resource and time constraints? Clearly, the account that is most likely to close.
But which one?
This is where knowing the quantity and quality of touchpoints helps. Each account has two touchpoints in this example.
Now how do we discern which touchpoints are higher quality and more influential in moving the account closer to closing?
We need a multiplier to each touchpoint that scores its quality. For example, adding that quality multiplier below we can see that after each respective pair of touchpoints per account, one account has a higher engagement score, meaning they are predicted to be closer to becoming a customer.
Let’s look at a conceptual example of scoring. Here, the total score refers to how influential the sum of touchpoints are. The quantity may be the same (two) but the total influence is difference given that touchpoint quality varies.
Let’s address the elephant in the room. What determines the touchpoint quality weight?
Predictive Marketing: How Touchpoint Quality Is Determined
Let’s continue our example of Acme and XYZ Company described above.
Account-engagement scoring relies on an analysis of the marketer’s past closed-won deals to assign a weight to each touchpoint. Higher quality touchpoints get more weight in the scoring algorithm.
Each account gets scored based on the quantity, recency, and weight of touchpoints, telling marketers who is further along in the buying cycle.
It is difficult for marketers to analyze large swathes of customer and prospect data to surface the value of every touchpoint. Account engagement scoring does this with the creation of an algorithm. The algorithm is created based on an analysis of closed-won customer data.
Once created the algorithm is fed an open account’s touchpoints data can predict which open account is most likely to close.
Based on this information marketers can gain better control of their destinies. Marketers can hit a time or resource constrained goal, by maximize their efforts by targeting the right accounts at the right time.
Before marketers begin a new campaign they can use predictive marketing to learn in great detail which kinds companies are a great fit for their products and expand their existing lists.
With predictive marketing it is now easy to discern which accounts are closer to becoming closed-won, putting control in the hands of marketers.
If a marketer wants to go after low-hanging fruit and focus on revenue, they’ll know who to target. If the goal is to nurture stagnant opportunities they can easily identify those accounts and begin a nurturing campaign.
Knowing which accounts to spend marketing dollars on doesn’t have to be an exercise in gambling away marketing budget, or spending wildly on a complex analysis of closed-won deals.
This was a description of just two predictive marketing technologies shaping the way B2B marketers work. There’s much more ahead as science and data continue to improve the functioning of B2B companies.