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• B2B Marketing Attribution Guide : How to Master It in 2024

  21 mai 2024, par Erin

  The last thing you want is to invest your advertising dollars in channels, campaigns and ads that don’t work. But B2B marketing attribution — figuring out which marketing efforts drive revenue — is far from easy.

  With longer sales funnels and multiple people from the same company involved in the same sales process, B2B (business-to-business) is a different ballgame from B2C (business-to-consumer) marketing.

  In this guide, we break down what B2B marketing attribution is, how it’s different, which tools you can use to set it up and the best practices.

  What is B2B marketing attribution ?

  Marketing attribution in B2B companies is about figuring out where your high-value leads come from — nailing down long customer journeys across many different touchpoints.

  

  The goal is to determine which campaigns and content contributed to various parts of the customer journey. It’s a complex process that needs a reliable, privacy-focused web analytics tool and a CRM that integrates with it.

  This process significantly differs from traditional marketing attribution, where you focus more on short sales cycles from individual customers. With multiple contributing decision makers, B2B attribution requires more robust systems.

  What makes marketing attribution different for B2B ?

  The key differences between B2B and B2C marketing attribution are a longer sales funnel and more people involved in the sales process.

  The B2B sales funnel is significantly longer and more complex

  The typical B2C sales funnel is often broken down into four simple stages :

  1. Awareness : when a prospect first finds out about your product or brand
  2. Interest : where a prospect starts to learn about the benefits of your product
  3. Desire : when a prospect understands that they need your product
  4. Action : the actual process of closing the sale

  Even the most simplified B2B sales funnel includes several key stages.

  

  Here’s a brief overview of each :

  1. Awareness : Buyers recognise they have a problem and start looking for solutions. Stand out with blog posts, social media updates, ebooks and whitepapers.
  2. Consideration : Buyers are aware of your company and are comparing options. Provide product demos, webinars and case studies to address their concerns and build trust.
  3. Conversion : Buyers have chosen your product or company. Offer live demos, customer service, case studies and testimonials to finalise the purchase.
  4. Loyalty : Buyers have made a purchase and are now customers. Nurture relationships with thank you emails, follow-ups, how-tos, reward programs and surveys to encourage repeat business.
  5. Advocacy : Loyal customers become advocates, promoting your brand to others. Encourage this with surveys, testimonial requests and a referral program.

  A longer sales cycle typically involves not only more touchpoints but also extended decision-making processes.

  More teams are involved in the marketing and sales process

  The last differentiation in B2B attribution is the number of people involved. Instead of clear-cut sales and marketing teams, revenue teams are becoming more common.

  They include all go-to-market teams like sales, marketing, customer success and customer support. In B2B sales, long-term customer relationships can be incredibly valuable. As such, the focus shifts away from new customer acquisition alone.

  For example, you can also track and optimise your onboarding process. Marketing gets involved in post-sale efforts to boost loyalty. Sales reps follow up with customer success to get new sales angles and insights. Customer support insights drive future product development.

  Everyone works together to meet high-level company goals.

  The next section will explore how to set up an attribution system.

  How to find the right mix of B2B marketing attribution tools

  For most B2B marketing teams, the main struggle with attribution is not with the strategy but with creating a reliable system that gives them the data points they need to implement that strategy.

  We’ll outline one approach you can take to achieve this without a million-dollar budget or internal data science team.

  Use website analytics to track touchpoints

  The first thing you want to do is install a reliable website analytics solution on your website. 

  Once you’ve got your analytics in place, use campaign tracking parameters to track touchpoints from external campaigns like email newsletters, social media ads, review sites (like Capterra) and third-party partner campaigns.

  This way, you get a clear picture of which sources are driving traffic and conversions, helping you improve your marketing strategies.

  With analytics installed, you can track the referring sources of visits, engagement and conversion events. A robust solution like Matomo tracks everything from traffic sources, marketing attribution and visitor counts to behavioural analytics, like clicks, scrolling patterns and form interactions on your site.

  Marketing attribution will give you a cohesive view of which traffic sources and campaigns drive conversions and revenue over long periods. With Matomo’s marketing attribution feature, you can even use different marketing attribution models to compare results :

  

  For example, in a single report, you can compare the last interaction, first interaction and linear (three common marketing attribution models). 

  In total, Matomo has 6 available attribution models to choose from :

  1. First interaction
  2. Last interaction
  3. Last non-direct 
  4. Linear
  5. Position based
  6. Time decay 

  These additional attribution models are crucial for B2B sites. While other web analytics solutions often limit to last-click attribution, this model isn’t optimal for B2B with extended sales cycles.

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  Use a CRM to integrate customer data from multiple sources

  Use your CRM software to integrate customer data from multiple sources. This will give you the ability to get meaningful B2B marketing insights. For example, you can get company-level insights so you can view conversion information by company, not just by person.

  Done effectively, you can close the loop back to analytics data by integrating data from multiple teams and platforms. 

  Implement self-reported attribution

  To further enhance the data, add qualifying questions in the lead signup process to create a hybrid attribution model. This is also known as self-reported attribution.

  

  Your web analytics platform won’t always be able to track the source of certain visits — for instance, “dark social” or peer-to-peer sharing, where links are shared privately and are not easily traceable by analytics tools.

  Doing self-reported attribution is crucial for getting a holistic image of your customer journey. 

  However, self-reported attribution isn’t foolproof ; users may click randomly or inaccurately recall where they first heard about you. So it’s essential to blend this data with your analytics to gain a more accurate understanding.

  Best practices for handling B2B prospect data in a privacy-sensitive world 

  Lastly, it’s important to respect your prospects’ privacy and comply with privacy regulations when conducting B2B marketing attribution.

  Privacy regulations and their enforcement are rapidly gaining momentum around the globe. Meta recently received a record GDPR fine of €1.2 billion for insufficient privacy measures when handling user data by the Irish Data Protection Agency.

  If you don’t want to risk major fines (or customers feeling betrayed), you shouldn’t follow in the same footsteps.

  Switch to a privacy-friendly web analytics

  Instead of using a controversial solution like Google Analytics, use a privacy-friendly web analytics solution like Matomo, Fathom or Plausible. 

  These alternatives not only ensure compliance with regulations like GDPR but also provide peace of mind amid the uncertain relationship between Google and GDPR. Google Analytics has faced bans in recent years, raising concerns about the future of the solution.

  While organisations governed by GDPR can currently use Google Analytics, there’s no guarantee of its continued availability.

  Make the switch to privacy-friendly web analytics to avoid potential fines and disruptive rulings that could force you to change platforms urgently. Such disruptions can be catastrophic for marketing teams heavily reliant on web analytics for tracking campaigns, business goals and marketing efforts.

  Improve your B2B marketing attribution with Matomo

  Matomo’s privacy-by-design architecture makes it the perfect analytics platform for the modern B2B marketer. Matomo enables you to meet even the strictest privacy regulations.

  At the same time, through campaign tracking URLs, marketing attribution, integrations and our API, you can track the results of various marketing channels and campaigns effectively. We help you understand the impact of each dollar of your marketing budget. 

  If you want a competitive edge over other B2B companies, try Matomo for free for 21 days. No credit card required.

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• Death of A Micro Center

  21 septembre 2012, par Multimedia Mike — History

  The Micro Center computer store located in Santa Clara, CA, USA closed recently :

  
  
  

  I liked Micro Center. I have liked Micro Center ever since I first visited their Denver, CO location 10 years ago. I would sometimes drive an hour in each direction just to visit that shop. I was excited to see that they had a location in the Bay Area when I moved here a few years ago (despite the preponderance of Fry’s stores).

  Now this location is gone. I wonder how much of the “we couldn’t come to favorable terms on a lease” was true (vs. an excuse to close a retail store at a time when more business is moving online, particularly in the heart of Silicon Valley). But that’s not what I wanted to discuss. I came here to discuss…

  The Micro Center Window Logos

  The craziest part about shopping the Santa Clara Micro Center location was the logos they displayed on the window outside. Every time I saw it, it made me sentimental for a time when some of these logos were current, or when some of these companies were still in business. Some of the logos on their front window were for companies I’ve never heard of. It reminds me of the nearby 7-11 convenience stores when I was growing up– their walls were decorated with people sporting embarrassingly 1970s styles long after the 1970s had transpired.

  I thought I would record what those front window logos were and try to pinpoint when the store launched exactly (assuming the logos have been their since the initial opening and never changed).

  
  
  Click for larger image
  

  Here we have Lotus, Hewlett Packard/HP, Corel, Fuji, Power Macintosh, NEC, and Fujitsu. Lotus was purchased by IBM in 1995 and still seems to be maintained as a separate brand. The Power Macintosh was introduced as a brand in 1994. Corel’s logo has seen a few mutations over the years but I don’t know when this one fell out of favor.

  Fuji (vs. Fujitsu) appears to refer to Fujifilm, though this logo is also obsolete.

  
  
  Click for larger image
  

  Hayes– I specifically remember reading the Slashdot post accouncing that Hayes is dead (followed by many comments reminiscing about the Hayes command set). Here is the post, from early 1999.

  From Googling, it doesn’t appear IBM still has a presence in the consumer computing space (though they do have something pertaining to software for consumer products). Then there’s the good old rainbow Apple logo, something that went away in 1997. I suspect 1997 was also the last hurrah of the name ‘Macintosh’ (though I remember mistakenly referring to Apple computer products as Macintoshes well into the mid-2000s and inadvertently angering some Apple enthusiasts).

  
  
  Click for larger image
  

  As for the next segment, obviously, both Sony and Toshiba are still very much alive. Iomega was acquired by EMC in 2008 but is still maintained as a separate brand. USRobotics is still around and making — what else ? — 56K modems (and their current logo is slightly different than the one seen here).

  Targus seems to be a case maker (“Leading Provider of Cases, Bags and Accessories for Laptops and Tablets”). I wonder if that’s just their current business or if they had more areas long ago ? It seems strange that they would get brand billing like this.

  Finally, searching for information about Practical Peripherals only produces sites about how they’re long dead (like this history lesson). It’s unclear when they died.

  The interior of this store was also decorated with more technology company logos near the ceiling (I didn’t really register that fact until I had visited many times). Regrettably, I now won’t be able to see how up to date those logos were.

  Based on the data points above, it’s safe to conclude that the store opened between 1995 or 1996 (again, assuming the logos were placed at opening and never changed).

  Epilogue

  Here’s one more curious item still visible from the outside :

  
  
  

  “See the world’s fastest PC !” Featuring an Intel Core 2 Extreme ? That CPU dates back to 2007 and was succeeded by Nehalem in late 2008. So even that sign, which is presumably easier and cleaner to replace than the window logos, was absurdly out of date.

• Greed is Good ; Greed Works

  25 novembre 2010, par Multimedia Mike — VP8

  Greed, for lack of a better word, is good ; Greed works. Well, most of the time. Maybe.

  Picking Prediction Modes
  VP8 uses one of 4 prediction modes to predict a 16x16 luma block or 8x8 chroma block before processing it (for luma, a block can also be broken into 16 4x4 blocks for individual prediction using even more modes).

  So, how to pick the best predictor mode ? I had no idea when I started writing my VP8 encoder. I did not read any literature on the matter ; I just sat down and thought of a brute-force approach. According to the comments in my code :

  // naive, greedy algorithm :
  //   residual = source - predictor
  //   mean = mean(residual)
  //   residual -= mean
  //   find the max diff between the mean and the residual
  // the thinking is that, post-prediction, the best block will
  // be comprised of similar samples
  

  After removing the predictor from the macroblock, individual 4x4 subblocks are put through a forward DCT and quantized. Optimal compression in this scenario results when all samples are the same since only the DC coefficient will be non-zero. Failing that, when the input samples are at least similar to each other, few of the AC coefficients will be non-zero, which helps compression. When the samples are all over the scale, there aren’t a whole lot of non-zero coefficients unless you crank up the quantizer, which results in poor quality in the reconstructed subblocks.

  Thus, my goal was to pick a prediction mode that, when applied to the input block, resulted in a residual in which each element would feature the least deviation from the mean of the residual (relative to other prediction choices).

  Greedy Approach
  I realized that this algorithm falls into the broad general category of "greedy" algorithms— one that makes locally optimal decisions at each stage. There are most likely smarter algorithms. But this one was good enough for making an encoder that just barely works.

  Compression Results
  I checked the total file compression size on my usual 640x360 Big Buck Bunny logo image while forcing prediction modes vs. using my greedy prediction picking algorithm. In this very simple test, DC-only actually resulted in slightly better compression than the greedy algorithm (which says nothing about overall quality).

  prediction mode	quantizer index = 0 (minimum)	quantizer index = 10
  greedy	286260	98028
  DC	280593	95378
  vertical	297206	105316
  horizontal	295357	104185
  TrueMotion	311660	113480

  As another data point, in both quantizer cases, my greedy algorithm selected a healthy mix of prediction modes :

  • quantizer index 0 : DC = 521, VERT = 151, HORIZ = 183, TM = 65
  • quantizer index 10 : DC = 486, VERT = 167, HORIZ = 190, TM = 77

  Size vs. Quality
  Again, note that this ad-hoc test only measures one property (a highly objective one)— compression size. It did not account for quality which is a far more controversial topic that I have yet to wade into.