Systematic improvements are the best way for a marketing organization to make consistent gains in performance. To have an impact over the long-run, changes should be made at the fundamental level to how the budget gets allocated, how content is created, how performance is measured, etc. That’s why marketing performance management (MPM) has increased as a focus for so many B2B marketing departments.
What marketing performance management means to B2B organizations varies quite a bit. For those who are just developing marketing performance management practices, it can be as simple as tracking channel performance and trying to base budget allocation decisions on demand generation goals. At the other end of the spectrum, the most advanced marketing organizations are able to automate optimization and make smart decisions using predictive insights.
There clearly is some distance between what marketing performance management can mean, so we built this framework to help you think about where you are on the MPM spectrum, as well as how to develop and mature your practices.
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Below, we’ll look at each stage and break down what they mean across three dimensions: strategy, planning, and measurement.
Stage 1: Channel Performance
Strategy: Use as many channels as the team feels comfortable using and do your best. Optimization is reactionary. If emails have a low click-through rate, test a new sequence. If PPC ads aren’t generating enough clicks, try a new campaign.
Planning: At this stage, budget allocation is done mostly based on what-we-did-last-year thinking. Again, it’s mostly reactionary. While it’s done with intent to hit certain goals, leadership is not confident in the accuracy of their marketing performance forecasts.
Measurement: Marketing performance is measured with channel analytics -- impressions, clicks, open rates, and form fills. Visibility into how marketing is impacting the bottom-line is limited and largely a guess -- “According to industry benchmarks, 1,000 leads results in about 50 opportunities, which results in about 10 customers.”
At this stage, marketing leadership is typically aware that how things are run is not on the cutting edge, but they’re stuck using legacy technology due to inertia, red tape, or budget limitations.
Stage 2: Revenue Performance
Strategy: Place marketing bets and optimize based on down-funnel performance metrics.
It’s still largely reactionary, but the depth of data that you’re using to inform your decisions is much richer.
Planning: Budgeting is still based on historical data, but it now includes marketing data that’s connected to revenue. With the knowledge of how much marketing dollars and resources went into making $X revenue last year (by channel and campaign), marketing leadership can create forecasts with more confidence.
Measurement: In addition to top-of-the-funnel metrics like leads, the marketing department can now measure their performance with down-funnel metrics like opportunities, closed deals, and revenue.
Using opportunity and revenue data to inform your marketing decisions is a big step forward. Additionally, it aligns the mindset and performance metrics of the marketing team with the sales team and finance team, which adds credibility for marketers.
At this stage, marketers either do not yet have the capability for predictive analytics or are not using them to inform marketing decisions.
Stage 3: Predictive Performance
Strategy: Use predictive insights to make smarter decisions while prospects are still in the funnel.
With predictive insights, marketers can say things like, “If I engage Prospect A right now on X marketing channel, they are more likely to take a demo because other prospects who look like Prospect A did the same.” The ability to make decisions based on predictive insights allows marketers to place smarter bets, as well as modify bets in real time.
Planning: Allocating the budget looks pretty similar here to what it looked like in the previous stage. The only difference is that with predictive insights, you have a better idea of which accounts will close in the next period. It makes your forecasts a little more accurate.
Measurement: Marketing performance is still measured using full-funnel metrics; however, the metrics are then used to create forward-looking insights.
This stage takes marketers beyond using historical data by leveraging it for predictive analysis. Organizations at this stage take the step from using data to assess past performance to the next step of using that data to improve current performance.
Here’s the difference. Using historical data allows you to say, “When we ran campaign A to these 100 prospects, it converted 20 of them, which is 2x the average. Therefore, we should run campaign A to all of our prospects.” On the other hand, predictive data allows you to say, “If we run campaign A to these prospects, X of them will convert. If we run campaign A to this other set of prospects, Y of them will convert. Therefore, we should run campaign A to this set of prospects and campaign B to the other set.” Predictive allows you to use both historical and current data to impact current prospects.
Stage 4: Proactive Performance
Strategy: Place marketing bets based on down-funnel metrics and allow the predictive engine to deliver insights and proactively optimize budget allocation.
Rather than just getting the data and then having to manually analyze and apply the learnings, a predictive insight solution automatically runs the analysis and outputs the action that will optimize performance.
Planning: Using a predictive engine that incorporates historical data and machine learning, marketing teams can enter the desired output and see exactly where the budget needs to go in order to achieve their goals. This results in more confident forecasting and smarter budgeting.
Measurement: Measurement is the same as in the previous stage. Marketers use full-funnel measurement and a machine learning algorithm to produce predictive insights.
The most sophisticated organizations use marketing performance management not only as a way to measure performance, but to proactively improve performance through reallocation to higher performing programs. It leverages the richest historical data with powerful machine learning to both deliver and act on marketing insights.
While marketers at Stage 3 can answer the question of “Will we hit our goals?,” marketing organizations at Stage 4 can answer “How do we hit our goals?” using their predictive insights.