Two weeks ago I shared my template for doing a quarterly retro of your business, which uses the Customer Journey Framework. If you haven’t already done so, go read that post first.

This week we start to look at how to use the data captured in your retro to diagnose issues in your go-to-market and figure out how to address them. To keep this post digestible and actionable, I’ve focused it on the first 3 stages of the Customer Journey (Awareness, Education and Selection) and covered the remaining stages in a separate post.

Let’s jump in:

Diagnosing problems in the Awareness stage

To recap, the goal of the Awareness stage is to attract qualified prospects and the key conversion metrics are Session to Lead, Session to MQL and Lead to MQL.

• If your Session to Lead conversion rate is low (<2%), you are messaging too cryptically. Cryptic messaging is usually rooted in being too seller-centric, with sections like “who we are”, “what we do”, “our technology” and jargon like “seamless”, “advanced”, “real time”. The solution is to rewrite your messaging from your customer’s perspective, focusing on their real-world problems and how you solve them. For more on this topic, see my previous post on making your messaging more effective.

• If your Session to MQL conversion rate is low (<2%), you are attracting prospective buyers too early in their buying journey. This often happens in the organic search channel when you aim to rank on high traffic, low-intent keywords because they are less competitive. While you can try to rank on the super-competitive, high-intent keywords, an easier solution is to direct this lower-intent traffic into your Lead flow, rather than trying to get them to MQL straight away.

• If your Lead to MQL conversion rate is low (<25%), prospects are experiencing a disconnect between the value props for sharing their contact info and evaluating your product. A common cause is creating gated content that is broadly relevant to your target buyer and therefore a lead magnet but not relevant to what you sell. No lead nurturing program can overcome this. You also want to measure how long it takes to convert a Lead to an MQL as longer periods mean waiting longer for your marketing investments to pay off, which is only going to test your CFO’s patience.

Diagnosing problems in the Education stage

To recap, the goal of the Education stage is to persuade qualified prospects to evaluate your product and the key conversion metrics are MQL to SQL (for inbound) and Target Account to SQL (for outbound).

• If your MQL to SQL conversion rate is low (<25%), your MQL criteria are too soft. The most likely cause is usually that your lead scoring has been set up incorrectly, often to make MQL goals more easily attainable. This has the knock on effect of encouraging over-investment in acquiring un-targeted leads, which if left unchecked can be disastrous. The best solution here is actually to go to the other extreme and make your MQL criteria as high as possible, for example booking a meeting with a salesperson or signing up for an online trial of the product. Doing this will force you to rethink your lead generation and lead nurture to focus on higher intent prospects.

• If your MQL to SQL conversion rate is average (50-75%), your lead follow up is too slow, or your AE’s calendars are too full. Remember that when prospects come inbound they are already quite far along in their buying journey and have likely inbounded to your competitors as well as to you. The

• If your Target Account to SQL conversion rate is low, your outbound messaging is not relevant. While you can tweak your scripts and email templates all day long, the root cause is more likely that you are targeting too broadly and trying to compete in a saturated channel (like email or cold calling), which is like swimming against the tide. The best solution is to shrink your target account list way, way down and leverage your network to get warm introductions with personalized messaging. I’ll detail this play in a future post.

Diagnosing problems in the Selection stage

To recap, the goal of the Selection stage is to persuade qualified prospects to become customers and the key conversion metric is SQL to Win, or Win Rate.

• If your Win Rate is super-low (<10%) you are talking to too many unqualified prospects. This happens a lot if you have an SDR team compensated on setting meetings, or if your MQL criteria that are too soft, or if your reps are judged based on how much pipeline they have, or if you simply don’t have the right product yet. Each of these problems can be solved by creating the right incentives; tying 50% of SDR comp to bookings, raising MQL criteria (as discussed above), focusing on bookings not pipeline and clearly defining your ideal customer profile.

• If your Win Rate is low (<25%), there are usually 3 reasons:

  1. Rushing to prescribe a solution before understanding the problem your buyer is looking to solve. The solution here is to do more thorough discovery, so that you can tie the solution to what your buyer cares about.

  2. Writing proposals before establishing your buyer’s critical event. This usually shows up in your data as “timing not right”, or “decision delayed”. If there’s no critical event, there’s no urgency and there’s no point wasting your time putting together a proposal that is just going to sit on the shelf.

  3. Sharing proposals before understanding the decision making process, in particular all the stakeholders and what they care about. This usually shows up in your data as “buyer non-responsive” and is highly correlated with single-threaded selling. The solution is to bring all the decision makers into your sales process, by identifying them, understanding their needs and reflecting their needs in your proposal. I’ve written in depth about how to bring decision makers into your sales process here.

• For a deeper dive on diagnosing the Selection stage, check out how to use closed lost reasons to find the gaps in your sales process.

Diagnosing the remaining 3 stages of the Customer Journey (Onboarding, Impact and Expansion) in is part 2 of this series.

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