Marketing metrics are the state-of-the-art trend in today’s analytics industry. Marketers appreciate seeing their campaigns and strategies converted into fluctuating figures and numbers. And while this rising tendency paved the way for powerful brands and mind-blowing campaigns, sometimes misguided metrics can drive misguided decisions and investments.
If a marketer’s performance is being evaluated by the number of responses on a targeted message campaign on social media, then that metric would skyrocket, even if the conversion rate remains unaffected.
The upside of the story is that marketing executives are increasingly being involved in the full sales cycle. According to Forrester, 82% of CMOs align their goals directly with their company’s profit targets. Nevertheless, a fully transparent and upfront method to measure progress against conventional methods across the company remains absent.
For a relatively short-run, multi-touch attribution held the mantle as the go-to solution. With the multi-touch approach, marketers are able to map every interaction on every account to then be able to pinpoint which touchpoints are most influential in driving deals. As good and straight to the point as this methodology may sound in theory, when it’s put into practice marketers are faced with the following challenges:
a) Marketing skepticism:
the way a marketing strategy is being modeled has a great impact on channel’s performance metrics. switching from a first-touch to a last-touch model would change nothing at all to what happens to an account during the course of its sales cycle, but the change in models attributes revenue to a different channel. For marketing analysts, the switch may be totally justified; but for channel owners, it could seem as if efforts are being hindered and misplaced.
b) Sales skepticism:
Sales already have that conflict with marketing regarding whether or not the latter have any impact on the success of the deals. So any sort of consistent models that gives credit whenever a prospect happens to bump into a certain marketing campaign does not do much for the sales credibility and efforts. In the event that sales teams agree with that data they would still disagree that it mattered when it comes to advancing the deal.
c) Channel rivalries:
Attribution operates around a given monetary amount for any deal and splits that sump across channels that happen to influence that deal. Channel owners would try to prove that they brought the biggest share of the revenue generated. And this in turn neglects how much more revenue came in due to their individual strategies and efforts.
a) Conflicts with an account-based strategy:
While the primary objective the multi-touch attribution is to identify the most profitable accounts for a sales deal, rivalries created through quantified strategies leave marketers tempted to credit all accounts in an effort to increase their own performance metrics and prove the effectiveness of their sales funnels. And this goes against the whole point of the attribution which is supposed to prove efficiency.
Marketers aspire to know what drives revenue for the company. Unfortunately, mastering the whole process isn’t simply about tracking existing revenue and allocating given revenue amounts to certain channels. What Attribution models actually overlook is “what happens if investments are cut from a given account, and whether or not that would affect the funnel process”. Overlaying an attribution model on top of an existing funnel can always run into the risk of misinforming and misguiding decisions among the team.
So if multi-touch attribution is not as effective as it is thought to be. How would marketers be able to measure the impact of their marketing strategies? In order to better answer the question, let’s better reformulate it: What incremental impact does an investment have on the targeted accounts?
In order to provide an answer to the question, these best-practices should contribute towards solving the conflict:
b) Start at the audience level:
Everything marketing-related starts with a group of accounts. Running campaigns, testing new tactics or tracking leads. At the basic level, marketing efforts are aimed at driving leads through the sales funnel and analytics should show where these accounts stand: How many visits the website gets, whether or not the content is engaging and whether conversion goals are being met.
c) Compare to a control group:
This may be the most critical practice of all. Without a control group, there is no way to know whether a set of leads were impacted at all by the investment. This allows to develop and execute a plan against a set of leads, and to report on the leads that turned into closed/won deals. Setting aside a group of leads, ideally ones with profiles similar to the target leads enables to understand not only how the audience performed, but also how much better it performed compared to a list of leads that didn’t get the same level of investment.
d) Consider opportunity rate:
Opportunity rate is the percentage of target leads that are converted into sales opportunities. This critical rate bridges the gap between Sales and Marketing and serves as a meaningful metric that helps with forecasting. While revenue is the main metric tracked, it is challenging to wait 3–10 months for those results. It is better to start with the percentage of your target accounts that are going through all the stages of the pipeline.
There is no denying that data is incredibly powerful but can be harmful if interpreted in a way that does not acknowledge the realities of complex B2B sales cycles. Multi-touch Attribution works well in a B2C model, and that’s because individual tactics can be linked to specific deals since the entire buying cycle might boil down to one click on an online ad.This does not apply to B2B where there are always multiple channels, multiple buyers and mutiple influencers involved in a deal.
An arbitrary split of revenue confuses the company, arises counterproductive rivalries and ruins an account-based marketing strategy. Instead, cross-channel collaboration should be embraced by analyzing the inceremental impact of investment on a specific set of leads.
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