Marketing Mix Models in Account-Based Marketing

Seeking a precise attribution model is a sort of holy grail quest for most emerging startups. Marketers and executives surely want to know what channels are worth investing in. Thus, many consider marketing mix modeling (MMM) as an evaluation method that reveals true channel ROI.

However, the MMM demands multipronged data analysis and tons of resources, which early-stage startups usually cannot afford. Yet, you can already adopt tried-and-true practices to build a starter mix model and continuously enhance it as your startup matures. Let’s get to them.

What is the Minimum Viable Mix Marketing Model?

The truth is that despite its 360-degree vision of ROI by channel, marketing mix modeling is demandful. MMM adopters generally rely on ample historical data, advanced statistical analysis, and a huge sales stack. Early-stage companies need a more balanced, minimum-viable approach to meet their budgets and short-term goals. In particular, they might need MMM to assess the impact of their B2B demand generation efforts to track how many prospects turn into qualified leads and then convert.

In a nutshell, minimum-viable MMM should rest on four pillars:

• Prioritizing channels, campaign types, and KPIs. Choose what marketing efforts fuel your growth now and model viable programs for your top-performing channels.
• Identifying the common multi-channel pathways. Investigate the successful customer journeys to understand what channels do best as entry points, where you land leads, and what areas demand conversion rate optimization.
• Labeling site traffic with UTM tags. You need to track traffic from each source easily and match it with sales records. It will inform you what campaigns pay off with the most significant returns.
• Laying the robust tracking foundation. Build a coherent and scalable system for customer journey analysis that aligns with your strategic goals. Thus, your attribution will stay reliable and precise over time.

Core Nutrition Groups of B2B Marketing Mix Models

The mix model engine relies on up-to-date and precise data inputs. Since we’re talking about starter-level modeling, early-stage startups don’t need to dig that deep into complex data structures and advanced analytics.

The pragmatic and easy-to-implement approach requires only four essential nutrition groups to feed the MMM engine with sufficient data:

1. Organized data schema for campaigns. UTM tagging is the key to meaningful attribution. You can map the channel activity with it and enhance tracking by adding enhanced dimensions in the future.
2. Core web analytics. Utilize analytics and advanced martech systems to determine who interacts with your ads, what pages they visit, and when it happens. For instance, Primer ABM targeting allows you to build hyper-precise target audiences and reach them across multiple channels. Thus, you can focus on high-intent, high-value audience segments and examine their buying behavior.
3. CRM conversion tracking. Analyze CRM conversion signals to detect possible conversion blockers and sales funnel bottlenecks.
4. Efficiency of your pool of media. Start comparing the outcomes (engagement rates, conversions) to historical spending. It will clarify what tangible winnings you get from investing in particular media and what growth areas are most promising.

Tradeoffs and Workarounds to Succeed with Early Stage Mix Modelling

Although many early-stage companies chase perfectly calculated revenue contribution insights, there are a lot of roadblocks on their way. Even the UTM tagging demands a lot of manual effort and time. That’s not to mention the limited historical data when you’re insufficient in sales records or when small teams get overwhelmed with responsibilities. The latter leads to slow campaign tracking structuring and superfluous data analysis.

That said, you can progress with your early-stage mix modeling gradually. Follow these rules to succeed:

• Stay outcome-focused. Remember that your campaign results evaluation models must strategically guide your marketing and sales moves by providing actionable insights. Precision is not an end in itself for you.
• Improve data enrichment approaches constantly. Implement new cross-channel analytical data step by step as your martech stack evolves and grows bigger. Steadily, you’ll reach a more sophisticated level with increased visibility into traffic sources and their contribution to conversions.
• Revise model performance regularly. Consistent assessment and re-evaluation of your mix model is the key to perfection. Make a hypothesis based on your observations, run tests, and see what needs to be fine-tuned in your current model.
• Share metrics across teams. Lastly, you can immensely benefit from developing a company-wide culture of sales and marketing data analysis. Get a cross-team buy-in on key metrics to track and report on first so everybody knows where to double down.

The Go-To Practises to Make Your Mix Model Drive Growth

To inform your B2B marketing strategies today rather than the next funding round away, you can already start with mix modeling by taking the following steps:

• Set UTM tags. Increase visibility on mediums, campaigns, and traffic sources by labeling inbound links with UTM tags from the start. It’s the foundation for developing analytics and scaling up mix modeling.
• Run short tests. Designate the promising channels and test them for short periods. Such controlled experimentations will show what KPI values should be set as reference points.
• Model funnel stages one by one. Start with mix modeling for funnel stages, in which historical data is ample. Then, you can gradually include the other stages.
• Don’t rush on adding new channels. You can grow the complexity of MMM as you fill in the informational gaps and expand your analytical capabilities.

Your attribution modeling success will build up over time. Just remember to move incrementally and keep that pragmatic mindset.