There are a lot of tools on the market that specialize in either engagement scoring or campaign attribution. While they serve two very different purposes, they rely on very similar data sets and demand a reasonable level of data hygiene.
Even with the structural similarities, marketers are often forced to choose one investment over the other because these platforms are costly.
This is a shame considering companies need both engagement scoring and campaign attribution to optimize their go-to-market motions in both marketing and sales.
What They Do
While marketers are very familiar with the concepts, indulge us for a moment while we present a brief (I promise!) recap.
There are many flavors of engagement scoring, but the purpose of each is to score the level of engagement the prospect is demonstrating with your company. The model could include web activity, knowledge base views, support tickets, and any number of internally logged activities depending on how you wish to use the model.
In general, marketing automation platforms offer lead engagement scoring focused on campaign engagement, and customer success platforms offer account engagement scoring that focus on internal activity logging and support tickets.
The purpose of multi-touch attribution models is to assign multiple campaign activities a percentage of pipeline or revenue dollars. These models acknowledge that marketing interacts with an account numerous times during a sales cycle and attempt to balance attribution across those interactions.
Attribution provides a better way to estimate return on ad spend for awareness campaigns such as competitive search campaigns pulling in visitors researching more established vendors. In this example, people who haven’t heard of your company before aren’t likely to fill out your landing page form, but they may revisit your site multiple times and eventually request a demo. Lead generation or source reporting alone for paid search would look abysmal and create an argument that search ads aren’t worth doing. Attribution reporting better demonstrates the value of awareness campaigns by highlighting opportunities that may not have happened without paid search driving the traffic to your website in the first place.
Attribution allows marketers to see what works at different points of the funnel on a macro level (e.g., is paid search or paid social more effective at generating awareness?) or at the individual account level (e.g., which pieces of content resonated with which audience members at different points of a key sale so I can optimize look-alike campaigns?).
Some vendors only incorporate campaign member data in Salesforce and limit customization, so choose wisely.
Who Cares About These Things?
From the viewpoint of a marketing ops professional, I would like to say, “Everyone.” More realistically, engagement has broader-reaching applications.
Engagement Scoring Impacts
Engagement scoring is a great way to improve handoffs between marketing and sales if your model is thoroughly tested. Oftentimes, organizations with best-in-class marketing tech stacks layer an ideal customer profile (ICP) score on top of their engagement score to help pre-qualify accounts even further.
Suppose your stack has a data normalization layer. In that case, you can aggregate the engagement score at the account level to flag sales when multiple people are engaging with your company (digitally and in person). In B2B, this is a huge benefit because of the impact buyer committees have on a sale.
While the number of engaged accounts and people can be interesting to the executive team and gauge brand awareness, the real value is in shortening the opportunity life cycle.
If we’re honest, attribution matters most to marketing. Marketers can use attribution to understand when their campaigns are resonating with audiences at different points in the buyer journey. It also helps them prove to the rest of the organization the effectiveness of their programs.
With attribution, marketing can prove that although a given campaign may not look impressive when using traditional lead generation metrics (names acquired, MQL, etc.), it may be pushing opportunities that are already in-flight over the finish line. For example, an ROI calculator may be a great asset for your champion toward the end of the deal, or a user group may give your prospective client the final reassurance they need that your company is the right business partner.
Flavors of Engagement Scoring
Which engagement scoring model or technology you choose should be evaluated based on what you’re trying to achieve. Ask yourself what your goal is. Some common goals include:
- Improving customer retention
- Accelerating new logo acquisition
- Increasing the number of expansion opportunities
If you’re a B2C company trying to retain your subscribers, a solution that measures a person’s engagement using stats pulled from your service portal, knowledge base, and logged support activity is probably the way to go.
On the other hand, if you’re a B2B company trying to scale up new logo acquisition–and these initial purchases are over six figures (indicating you’re dealing with a buyer committee)–you’ll want to measure Salesforce campaign data, web activity, inbound calls, and other early-indicator data and aggregate the score at the account level.
Flavors of Campaign Attribution
Campaign attribution models can be as elaborate or simple as you’d like, but selecting a model should be driven by several factors:
- Are you B2B or B2C?
- How long are your sales cycles?
- Do you have different sales cycle lengths across product types?
- Do you have regional variations in buying behavior?
We will say that single-point attribution (common models being first touch or last touch) is probably sufficient for B2C companies with a low price point and abbreviated sales cycle. You’ll want to know what drove them to your website immediately before they made their purchase.
B2B companies with a six-month sales cycle and expensive products should invest in solutions that incorporate data beyond traditional Salesforce campaigns (web activity, paid search visits without a form fill, etc.), ideally include activity logged by your channel and sales, and consider interactions across the account (including leads).
We also recommend using chain-based attribution to eliminate human bias, which you can read about more here.
Why We Need Them in the Same Place
Pushing scoring to your system of record–or at least the system sales is using–makes a great deal of sense. These should be dynamic connections. Engagement models should decay over time, so the further you get away from the last interaction, the lower the score drops.
While engagement scoring can be very useful to a sales team once they trust the data, they’re rarely motivated enough to log into yet another system. If you’ve already forced them to log into Salesforce, a chat application, and an email outreach platform, you’ve already pushed your adoption luck far enough.
Some platforms export your data, normalize it, score it, and push it back to your CRM. This works well if the platform performs both functions. Otherwise, you’ve got two companies cleaning the same data for two different purposes. Other platforms plug into your CRM and rely on you to have the proper configuration and clean data.
The final option is a platform that normalizes your data for you, runs it through your models (both attribution and lead scoring), and enables automation to pushes that information back into your CRM.
If it were me, I’d hit the easy button and invest in a platform that can do it all 🙂