There are many ways to segment your marketing data to measure program success. To truly understand business impact, you should use ratios to bring context to the data. It will also help your company's leadership understand your work!
A data point of one is a data point of none.
It's very easy for inexperienced entrepreneurs to be mislead by vanity metrics. These are the data points that don't tell you very specifically how well your business is performing, but look great and make you feel even better in the process. Data dopamine for those focused on the wrong things.
There is however a much bigger threat we rarely discuss. It's looking at the correct data, but not understanding its context. Which is why you should try to use ratios when analyzing and reporting on your marketing data.
Let's consider Marketing Qualified Leads (MQLs). It's one of the most common KPIs we're measured against. We're all aiming for hockey stick growth, right? I argue that without context it's directionally interesting only. Would it be a failure if MQL growth flatlined, but you reduced ad spend 90%? What if you grew MQLs 200% YoY, but increased ad spend 1000% to achieve it? Clearly, in this latter case, the cost of acquiring a lead matters. In both cases, the data is sensitive to the business requirement at the time.
Then there is the impact of other events on our data. Let's say your site experiences a dramatic decline in pageviews. Bad, right? Not if you improved technical docs search, reducing the number of pages a visitor needed to view before finding the answer to their query. It would be hard to argue this is not a much better user experience. After all, the prospect achieved success more quickly. It's a big win.
What about placing a free trial button in the top navigation of every page on your site? It might lead to a short-term decline in pricing page views, but you'd see an increase in marketing leads. This is where you need to get granular. Pricing page views compared to signups. Free trial button clicks to signups.
Consider events that impact your data, and wherever possible measure using ratios. A quick search on "marketing analytics ratios" and you'll find a lot of information. Just make sure it's relevant to your present circumstances.
Some of the ratios you can/could/should regularly measure
If you're new to the idea of measuring and reporting on your data in ratios, here are some of the ratios I regularly monitor. I hope you find them directionally helpful.
- CAC:LTV. The so-called "magic number" for SaaS businesses. If you're not familiar, this SaaStr podcast with Dave Kellogg is a must. The tl;dr, ensure the cost of acquiring a customer doesn't exceed the dollar value they bring to the business over the entirety of their lifetime. If you're super early stage and don't yet know LTV, aside from guessing you could use CAC:TCV (total contract value).
- Lead:MQL conversion. I use conversion ratios through the entire funnel, starting with the number of "unqualified" leads who enter the pipeline and then become MQLs.* The results show very clearly the effectiveness of the marketing activities that work.
- MQL:Closed/Won conversion. As marketing becomes more involved in all stages of the funnel, I like to know how many MQLs become revenue generating business. As a revenue-focused marketer, I care about this most.
Other important metrics
- Lead Velocity Rate (LVR). Depending on the business model, how quickly I qualify a lead is important. As a general rule, I would rather qualify a lesser number leads more quickly and more frequently, than a larger number of leads over a proportionally longer period of time. Speed almost always wins.
- Revenue/Visitor. A reason that marketing could/should carry revenue in comp plans, is that it guarantees tight alignment with sales. Revenue generated per site visitor is interesting in that it directionally tells me whether a wide range of on-site activities are working.
- Revenue/Lead. Similar to the MQL:Closed/Won ratio, the actual revenue generated per lead is helpful in ensuring the marketing org is focus on the metric that matters most to business. Revenue.**
If you're ready to go ninja, add these to your dashboard
- Lead Velocity Rate (LVR)
- Raw Lead to MQL conversion ratio
- MQL to Customer (closed/won business) conversion ratio
- Revenue per lead
- Revenue per visitor
- Sign ups (esp. sign-up page source)
- Real time, number of visitors on site (trend)
- Downloads, by quantity, source (trend)
- Audience overview: users, sessions, new users (trend)
- Behavior overview: avg session duration, bounce rate, pageviews, pages per session, sessions per user (trend)
- Ecommerce overview: revenue, transactions, revenue per user, cart abandonment, etc
- Google Analytics Goal daily performance
- Cohort analysis (of various metrics in this list), this is key to lifecycle marketing success
- Users by time of day (useful if you want to optimize live chat, or scheduling email sends)
- User by geography (if most of your signups come from non-core business geos, they're probably relatively worthless)
- Google Analytics "events" by event category, action and label (helps understand which actions work)
* I actually start measuring the number of site visitors and ratios to bounced, to engaged, etc. In other words, as soon as a visitor hits the site, I measure.
** A note on revenue and whether it's more important than brand ... Brand is built over a lengthy period of time by delivering a consistent message to market. By keeping our promises. By not hyping (lying about) our solutions. By being honest, engaged and caring for our customers. If as a marketer I deliver revenue generating activities with this in mind, brand takes care of itself.
There are obviously activities you can do to build brand: deliver thought leadership, engage with your community, have a clearly defined and unique voice, ensure all customer engagement is a seamless, fun (?) experience. I consider these fundamental to modern marketing, but just in case they needed to be stated.
Ping me on Twitter @robertcollings if you think there's an important ratio I missed.