Challenge: How do we set targets for next FY?
As we cross into a new fiscal year, many thoughts lie in planning for the coming year. Evaluating demand gen programs require some form of an attribution model and process which can cause significant confusion and concern over accuracy. This post will address a number of common questions in the process and some various solutions with their tradeoffs. Which one(s) should you use? Of course, the answer is ‘it depends.’ Please note that this post builds upon a past blog note here that spoke to relative maturity levels of attribution.
My assumption here is that your company has a reasonably mature attribution infrastructure where engagement/response is tracked using both channels and campaigns. What I mean here is that the systems track both ‘what’ the prospect or customer did (e.g. registered for a webinar, attended a field event, stopped by the tradeshow booth, downloaded content, requested a demo) and ‘how’ they came to that action (digital ad, organic search, sales email, marketing social post, partner promotion). So the company can report both on ‘campaigns’ as well as ‘channel’ sources). Let’s get onto the questions.
One thing you will not see here is any mention of MQL. While MQLs can be very helpful to understand the larger funnel operation, the purpose here is to understand either opportunity creation or closed-won deals. So MQLs can be a nice ‘early detection’ for future opportunities to be created (assume you have a consistent conversion rate) but are not really an objective themselves and therefore not part of FY planning. They certainly will be a derivative of the decisions made for FY planning that should be pretty straightforward.
Side note on data quality. If your GTM team is not adding all contacts involved in a deal to the opportunity, then the data you are using is correlation and not necessarily causation. What I mean is that if you are looking at contacts associated with an account that interacted with your programs before the opp was created but not associated with the opportunity, then you are assuming that there was some interaction (correlation). If you are associating all contacts with an opportunity and using only the engagements with those contacts, you can be more certain that the campaign participation was related to the opportunity creation (causation). BTW – there are some great tools that help automate the process of adding contacts to opportunities – saving your GTM team lots of time and making the process less dependent upon updates to your CRM.
What timeframe should I use for program evaluation?
First, let’s say that this depends if you want to look at opportunity creation or opportunity closed-won. For this article and for many marketing organizations performing planning, I will look only at opportunity creation. Running a review on the average time from first prospect engagement to opportunity creation is an important input here. Let’s say it averages 45 days from first engagement to opportunity creation. I might suggest doubling this to be 90 days. However this will bias your results to look at only those engagements that happened close to opportunity creation. There will be many times when prospects engage with your content a year or longer before they become an opportunity and those campaigns/sources will get no attribution. So why not go longer to include more touchpoints? Well the issue is really one of relevancy. An interaction that occurred a year ago may have introduced your brand to that prospect but was not directly relevant to the creation of the opportunity. Our search here is to understand what were the most seminal causes for the creation of this opportunity.
This just reinforces that marketing will have to make investments now that are not expected to pay off in the near term. Think about the impact of user conferences, press coverage, analyst coverage and more. These all impact general brand awareness and affinity – but will likely not impact short term pipeline creation. So marketing pros need to take this understanding into account so they do spend on longer term value programs that will pay off in future quarters.
Which model should I use for program evaluation?
Without considering every option, there are two main options: First Touch or Multi Touch. First touch attribution is great for understanding how you met the prospects who influenced your opportunity. There are a few good things going on here. First, it fully attributes the success of the contact with the opportunity to ONE source and campaign. This is super clear and it aligns with the important decisions on which programs we should run to create new prospects that will help us meet our quarterly and yearly goals. Here you will typically see a few sources dominate: organic/direct, paid search/social, and third party events. However, what this effectively ignores are field events, internal webinars, and tech demos that typically get prospect participation through offers to your internal database.
Here is where multi touch attribution provides great strength. This gives each campaign/source interaction some weight (equal or not proportionally allocated) so those campaigns that typically occur as a second or later touch point get their fair share of attribution. Since this treats all campaigns more equally, why not only use multi touch? Well, let’s say you need to allocate how all of your programs will contribute toward a targeted number of opportunities to be created in a given quarter, first touch does a better job of not ‘double counting’. To counteract this, multi touch models can ‘divide’ the relative value of each touchpoint to an opportunity. So if there were four touchpoints, each one would receive .25 ‘points’ or credit. If your model accommodates this, then this can greatly simplify your budget planning process.
Which model should I use for FY planning?
Having the review of FT and MT above, it becomes clearer that we need a model that does not double count opportunities. We need a solution that will tell us for a given spend, how many opps can we expect. FT is a simple way to get there but it completely ignores the value of secondary touchpoints required to create an opportunity. So the better option is a MT attribution model that breaks down the relative impact of the touchpoint. So if we know that a webinar attendee will generally drive .25 of an opportunity, we can then associate the expected opps for that webinar spend.
In the end, we are trying to create a budget and plan that shows we can spend $X to drive Y opportunities for $Z program spend to meet the required pipeline creation. Looking back at past program performance, we can estimate future expectations. The best news of planning this way is that you are handing the people on your team a specific budget to run specific programs with an expectation of specific performance. This enables your team to figure out the best way to manage their programs to achieve this level or more. Or, they can come back to you and discuss if these expectations are really realistic. Better to know this now – in advance of the quarter so you have time to make adjustments.
How do I review or assign sales versus marketing sourced pipeline/deals?
Oftentimes, opportunity creation goals will be distributed across sales, partner and marketing teams. But how do we segment across these teams? If there are five touchpoints on an opportunity across sales, partner and marketing teams, who gets the credit for that opp?
Again, if we have a sophisticated MT infrastructure set up so that each touchpoint is credited to one of these three organizations, then this dramatically simplifies the process. We can use past tracking to estimate future results. If you don’t have this in place, using a FT model can help you get to an estimate but you need to then manually allocate more towards those programs which you know are secondary touchpoints and have evidence of their success. While this is not an ideal or perfect process, it can get you pretty close. And, after all, the planning process is designed to be directionally accurate with the real impact being on how effective your team is in executing the programs.
In any event, you must track your plan to actual results in a retro analysis so your planning process can more closely predict future results. Understanding why your plan over or under estimated returns is critical to getting more accurate each quarter. In this way, you are building a reliable revenue generating machine. And this is what the public markets value most.
Lesson: Planning demand gen is not a perfect science but with accurate data, open communications and team reviews, your planning will get better quarter over quarter
Practical Strategies for B2B Technology Companies 