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Driving team performance with metrics

In this lesson, we’ll discuss challenges that come with operationalizing funnel metrics to drive market strategies.

Designing metrics

Funnel overview.

For most B2B company’s like ours, structuring the go to market team around sales funnels and marketing is ideal. At the top of the funnel, your marketing team creates awareness for the company and works to generate leads through multiple paid campaigns, partner referrals, events and SEO optimization. Various marketing functions are rallied by the north star metric, the Marketing Qualified Lead MQL. These are the leads that quality to be followed up by the sales development team.

When a prospect reaches the MQL stage, we contact them through the sales development team and set up conversations to evaluate their expected budget and set up conversations. After the evaluation and negotiation stage opportunities go through other sales stages and are eventually moved into closed won deals.

MQL Metrics

This metric is an aggregated metric that often isn’t tied closely to specific business decisions because they are too high level so they are broken into two: acquisition entry points and referral sources. Acquisition entry points are specific to your company’s lead generation methods like livechats, lead via content download and referral sources like direct traffic, organic search and partner websites.

Opportunities metrics

The same way MQLs are broken down, opportunities are also measured by entry points and referral sources. Sales leadership plus regular insights on the performance of reps are provided throughout the month.

Conversion rates

Lead>Opp conversion rates through acquisition channels are monitored to be sure that the quality of leads stays consistent with each channel. If there is a dip in your metrics that is out of character, it could be an indication of a broken system, process or campaign which could lead to low quality leads.

Operationalizing metrics

Metrics are operationalized to drive business decisions like allocating SDR resources, improving funnel efficiencies and optimizing marketing spending.

Setting OKRs with metrics

We use a cross functional Top of Funnel initiative (TOFU) between the sales and marketing teams so they can collaborate closely and deliver results. We use driving Opp growth, improving conversion rate and driving MQL growth as indicators of key results and using these metrics as a guide of sorts, every team member is able to create actionable projects and OKRs that play a part in the overall OKR.

Optimize paid advertising spending

Business decision:

Among the critical decisions that the growth marketing team makes daily is deciding what paid campaigns should be doubled down on and which ones to end. Click through rates and traffic from platforms like Facebook and google may be useful but they are hardly down the funnel to use as a measurable and predictable impact on revenue. So, we pay attention to MQLs that each paid advertising generate and then place cost per MQL against them to estimate the efficiency of the campaign instead of using only cost per click.

Challenge:

After increasing our efforts at Google Paid Search campaigns and Google Display Network, we saw a massive increase in traffic to our website but we also saw that the volume of MQL associated with the campaign did not grow in proportion to our advertising budget. This was because we used a concept that was too broad and so it was difficult to attract prospects with high intents so we decided to barrow down to a specific product and show the value it had.

Takeaways

  • Cost per click should not be your only measure of your campaigns efficiency
  • Drive metrics that have a significant impact down the funnel.

Allocate SDR resources

Business decision:

We use a bottom up capacity planning model to plan for SDR growth by first forecasting the number of MQLs we are able to generate, we then break that down according to the workload an SDR can handle so each SDR has a marketing lead they can handle. However, we noticed that aggregate MQL numbers can be misleading as some high intent leads convert more than low intent leads so separating assumptions must be made for both.

Challenge:

Although we gave tried to set MQL criteria using firmographics data like company size and behavioral data like web activities there are MQLs do not behave the same. Some MQLs from high entry points tend to coverts to Opps 5 times more than others that come in from low intent channels. The differences in the conversion rates are important encase we evaluate whether marketing has enough volume to supply the SDR team with leads.  MQLs should be divided into low intent vs high intent leads and measured separately to confirm that supply meets SDR demand.

Takeaways:

  • Develop metrics that reflect business realities and best fit with workflows
  • Break down your metrics into smaller groups when in doubt so sift out those that are drivers. Aggregated metrics can cover up problems and opportunities

Improve funnel conversion

Business Decision:

We grow our MQL volume that feed the funnel to drive revenue to the bottom of the funnel. The other option is to improve the conversion rate. The SDR team can work on improving their outreach sequence, messaging and time allocation so the conversion rate can grow. In the past, we didn’t pay much attention to Workspace Sign –up leads as they were monitored by all teams and SDR reps focused high conversion channel leads however since we assigned dedicated reps to prioritize our sin up channel, there has been an incredible rise in lead >opportunity conversion and the sign up channels volume.

Challenge:

The sign up channel didn’t get a lot of priority because it had a history of low conversion rates which in turn made it have even lower rates. The solution to escaping this cycle was experimenting with assigning resources dedicated solely to working those leads.  di The lead> Opportunity is a great indicator of how to directly measure how well a channel converts and movements in this metric can be backed to taking a specialist approach.

Takeaways:

  • Experiment and create hypotheses to help you get out of monetization paralysis.
  • Create metrics that do not only measure end goals but progress metrics that impact the end goal as well.

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Developing a data driven company

Next lesson:

lesson 3

Motivating your team with data

After completing your data tracking and enforcing it, when your tools are connected, and each team is in the right place to effectively drive their own metrics forward, your product team can find the biggest drop offs for activation and develop tests to resolve them. Your marketing team can measure the effects of their advertising using behavior-based drip campaigns. There is always a chance to motivate your entire company and teams by using data. Data lets you know exactly what you’re doing right or wrong. Data can drive alignment and cohesion if it is treated transparently and everyone sees the results of what they are doing. We’ve outlined a few ways you can motivate your team and get them pumped up to serve customers better and achieve goals using data.

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