What challenges do B2B marketers face when implementing multi-channel attribution models?

When I help companies with data, it always boils down to improving data quality initially. A few ways I've found useful to help improve have been the following: Running A/A tests can help showcase issues in reporting. One variant should never reach statistical significance, if it does, you can determine there is an issue with the implementation, and how it's possibly being reported down stream to other teams. Leveraging two analytics can help determine what issues are happening with the implementation when they both aren't in sync or have huge delta's. One source of truth (paid), one free version that isn't the source. And most importantly, making sure cross-device ID's are stitching for known users across your different toolsets.

One of the biggest mistakes made in B2B marketing has to do with attribution. Data quality and integrations shouldn't be trusted at face value. When people are tracking leads via CRM, they want to know what caused the source of those leads and often they are things that are not tracked properly despite all the attribution software we have today. Example, a lead might have come from someone listening to a podcast. Next they search for that brand in Google and click the link. Attribution would say they came from search but a lack of self-reported attribution means nobody will know the true value of that unique channel of podcasting or video.

In the fast-paced world of B2B marketing, data is the compass. Without a clear map, there is a risk getting lost in the digital wilderness. To conquer this challenge, start by defining leads, opportunities, and customers with precision.Then, blend data sources like CRM, marketing automation, and more into a single panoramic view. The secret to success is quality and integration. They ensure your multi-channel attribution models lead you down the right path.

You can have all the attribution models available, but they are worthless if you don't have the basics in place. Research, knowledge, and communication are key components but are often overlooked in favour of software or other quick fixes. How often do you engage with the sales team? Do you truly know your own business? Do you need to work more with your personas? Get these things right and you can start to improve the quality of your data. It's all about getting the foundations in place.

Solely relying on attribution in your MAP will result in poor data quality 100% of the time. Today, the majority of the attention and research is going to happen off your website. Someone may be consuming content the content before and after this post from individuals they trust. Then they'll type the company name into Google and convert. Your MAP will capture this as organic search. If you pull an attribution report, you could draw an insight that organic is a top channel that needs more resources -- pulling away from the content your team is creating on social (that actual channel that created the demand).

I'm sorry, but attribution models are fundamentally flawed. At best, they're more consistent than guessing. Data Limitations: the data pool for MTA is shrinking because of privacy concerns. Oversimplification: MTA oversimplifies the buyer's journey, especially in complex decisions with multiple stakeholders. Each lead cannot be assigned equal weight. Overemphasis on Digital: Biased towards digital channels, overlooking offline touchpoints that could be equally, if not more, influential. Time Decay Models: MTA doesn't know how to value touchpoint impact. Short-Term View: Typically focused on short-term impact of marketing actions. External Factors: doesn't acct for external factors like market trends, competitors, economic changes.

You cannot, in B2B markets with lengthy and complex sale cycles, attribute a single sale to a single act of marketing. You just can’t. You can know when someone entered your formal CMS, and you can know when someone purchased from you, but you cannot know which “marketing touch” tipped them into being a customer. What you can begin to understand is the typical length of a sales cycle, and good marketers will have a great instinct as to what marketing is working and what isn’t. But attribution of a single sale to a single act of marketing in this environment? Not a real thing.

It’s very important to consider multiple touch points especially in long and complex B2B buying cycles. One way to do this is map all the touch points from the first to the last touch point after you have closed a deal. This gives marketers clarity on which touch points are working, which ones are the most influential and identifying gaps if any. We have found this deal analysis to be far more useful than just limiting ourselves to attribution models which don’t really tell the whole story.

First party data and last click attribution are what most people need for B2B tracking. Of course the biggest thing missing in the B2B space is self-reported attribution which means asking your customers how you heard about us. When you use last click attribution, you might get Google or ads as the referral but in reality it might have been word of mouth that was the driving force. People need to stop relaying on tech that can miss the mark in tracking.

Unveiling customer touch points is more important than the model you choose. Imagine this: -> a user came from Organic Search --> then joined a LinkedIn Live event ---> then requested a demo from a retargeting LinkedIn Ads campaign. Do I care if the LinkedIn Live event gets 20% credit or 0% or 33%? No, what I care about is that (assuming more users had similar journeys) it was a significant touchpoint and we'll want to do more of those initiatives. So yea, having access to that data (via attribution software, CRM or analytics solution) is what matters the most!

Using another data collection platform or analytics tool won't help you optimize your marketing efforts or maximize ROI when validating your attribution model. It's start with a solid foundation! You must first ensure that your attribution model aligns with your business goals and objectives. Engage with key stakeholders, such as sales teams and customer support, to gather their input and insights. They will assist you in defining key metrics that reflect your customer journey stages and outline the touchpoints that contribute to conversion. This collaborative approach will assist you in validating your attribution model from different perspectives and ensure that it reflects the reality of your customer journey.

Validating an attribution model in B2B marketing is more than just setup; it's continuous refinement. One key piece of advice: Regularly cross-check the model against real sales outcomes. If there's a mismatch, it's a signal to adjust. Engaging with sales teams for their insights can also provide invaluable feedback, ensuring your model remains grounded in reality. Transparent communication about your methodology builds trust and ensures alignment across teams.

Most attribution models should be used directionally, not literally. It's unlikely your data is good enough, or comprehensive enough, to accurately asses an ROI to ever piece of your marketing mix. But you should be able to see which areas are more effective and least effective. It's not about finding the perfect way to spend every dollar. It's about recognizing the tactics to stop doing, and the ones to double down on.

Implementing multi-channel attribution in B2B poses challenges. Identifying accurate touchpoints across diverse channels is complex. Data integration from various platforms is challenging due to compatibility issues. Customer journeys are intricate, making it hard to assign credit accurately. Moreover, securing organizational buy-in and resources for the implementation proves difficult. Lastly, ensuring privacy compliance amidst data integration adds another layer of complexity. Successful implementation requires meticulous data management and collaboration among teams.

With the imminent demise of third-party cookies, B2B marketers must adapt to a cookieless tracking environment. Prioritize collecting and utilizing first-party data directly from your own platforms and touchpoints. This allows you to maintain control over your data and build accurate attribution models without relying heavily on third-party cookies. Investing in technologies like server-side tracking, identity resolution, and consent management platforms can help you navigate the cookieless future while respecting user privacy. These solutions enable you to gather accurate data, connect customer touchpoints, and attribute value more effectively.

One of the biggest challenges for small and midsize businesses in the B2B segment is keeping up with industry disruption. With so many things to chase and experiment with, it can be difficult to know what to focus on and structure your attribution models around it.

As someone that runs a SaaS company, I see first hand the power of LinkedIn and podcasting. At conferences, I'm known as the LinkedIn guy and people compliment me on my posts. When I ask how customers hear about us, LinkedIn is the number 1 answer. After that, the 2nd most popular answer is from our podcast or appearing as a guest on other people's podcasts. People want to buy from people and the more real and authentic you can be, the more you can win trust and have that lead to sales.

Self-reported attribution is a game-changer for B2B marketers. It involves directly asking buyers how they discovered your brand, often through a high-intent form on your website. This method provides insights into channels and touchpoints that traditional software might overlook. While it's a powerful tool, it's essential to be aware of potential inaccuracies, like recency bias. However, it offers a comprehensive view of the customer journey when combined with software attribution. The key takeaway? Don't rely solely on software; ask your customers directly. This dual approach can illuminate 'dark' touchpoints and refine your marketing strategies.