CMOs: How Well Do You Know Your MarTech Stack?

CMO-MarTech-Stack

By Van Diamandakis 

There’s a rumor out there that there are at least 6,000 tools available for your MarTech stack. In an industry prone to shiny new object syndrome, it may be tempting to try more than your fair share. Each new product promises to magically auto-solve every problem a CMO has.  

Over time, these stacks come to resemble Frankenstein rather than a supercharged marketing machine. Dig through the stacks, and you’ll likely find duplication and tools that the marketing team rarely uses. As a CMO, you know how tight budgets can be. Not only that, but an inefficient stack doesn’t deliver the end to end insights that CMOs and CROs demand to manage their business. 

Related Content: If Your MarTech Stack Doesn't Have Time-to-Value, It Doesn't Have Much Value

The time has come to KonMari your MarTech stack.

After committing to a Martech audit and clean-up, it’s essential to align with your sales team to decide on key KPIs, such as engagement, conversions, attribution and so on. Envision your forecasting model and the type of journey you’d like your customers to take with your brand. The combination of sales and marketing is a huge component here — if each department is using different benchmarks, it will only stymie your company’s ability to create a one-of-a-kind customer experience, tailored to their needs.      

Once marketing and sales are in full alignment on goals and KPIs, it’s time to do an audit. Poor lead and data management are often a big source of contention for marketing staffs. MQLs aren’t turning into SAOs, and sales representatives aren’t bothering to follow up on a marketing lead or convert it to an opportunity from the campaign source. It’s also often the reason MarTech stacks continue to proliferate unnecessarily. We as CMOs feel like almost every month there is a request to buy a new tool that promises to fix everything. But in actual fact, less is more. No amount of technology is going to help if lead management isn’t done with efficacy. It’s really volume vs. value or volume vs. quality. Smaller companies with less budget to throw around and lower web traffic are better off loosening their lead scoring and getting sales to agree to it. “Look,” a CMO might say. “We don’t don’t have thousands of people coming to our site each day, so let’s say that if someone fills out a demo, it’s an MQL. Otherwise, you’ll have two leads a day.”

After goals and lead scoring are in effect, you need to make sure you have clean, accurate data and closed-loop measurement. Many teams make the mistake of uploading purchased lists, and junk inquires into CRM, and they all get converted to contacts. BAD IDEA. Keep your lists and unqualified inquiries in your marketing automation tool or as separate record types in CRM. It all sounds logical enough, but I have seen many marketing-sales-MarTech stack issues come down to data management issues, such as messing up parent and child relationships with enterprise accounts. Fixing these issues can take six months or even longer, but it has to be done. Understanding the customer journey with clean and accurate data is what underpins it all. If you put garbage in you will get garbage out, hindering your ability to make high-quality, data-supported decisions to improve customer experience and revenue.

Clearly clean, accurate analytics and insights into lead generation and campaigns can be a struggle. Typical CRM and marketing automation software will simply not do the trick. The reporting out of these systems is not robust enough to get to a proper 360 CMO dashboard. What’s needed on top is a business intelligence platform such as Qlik or Tableau. And with a flexible BI tool on top of CRM, you can really start to do interesting things like full attribution modeling.

The ultimate goal is to enable a single source 360-degree dashboard for marketing and sales, which helps all levels of the organization to understand the levers of performance. A dashboard that allows drill down from high-level KPIs into the underlying data and associated data points. This provides far richer insight into key attributes across campaigns, leads, contacts, accounts and opportunities. If you get this right you will build a model that delivers predictive capability and supports understanding and interventions to increase customer lifetime value, reduce customer acquisition cost and churn rate.