The 2019 drop-in martech spending, as noted in Gartner’s annual CMO survey, is likely just the lull usually seen after a big year of investment. This is because CMOs and leaders still view tech as one of the best ways to drive growth.

Now is the time leadership spends to integrate, train, and utilize new tools to help scale efforts and adapt to the latest changes in content, SEO, ads, and more. It’s also the right time to talk about how well we’re using the martech stack and what will get in the way in the coming months. It’s time to learn and optimize.

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You can easily find guides and lists of the must-have tools for your stack, so instead, we’re going to look at the practices and procedures impacting your team’s ability to use what you have. These missteps and errors can get in the way (and addressing these concerns can give you a bump) regardless of tool or stack.

Data cleanup isn’t a priority

It takes expertise to ensure that software is capturing and understanding data correctly, as well as expertise to ensure teams are successfully using the insight delivered by the martech stack.

Inconsistent data quality, whether from tools or techniques, means your team can’t rely on the insights generated. As an industry, we’re laser-focused on acquiring data. This can put understanding data on the back burner, and lack of training on tools means a company might not know if it can trust data quality until it’s too late.

For example, data used to build audiences and create buyer personas and then execute campaigns can do significant harm when it isn’t correct. You’re targeting the wrong identities on channels and with the spend, minimizing returns, and creating the potential for very unhappy customers who want refunds, harming long-term viability and CLVs.

CMOs must look for ways to integrate target identities, validate data, and test models to prevent this, especially when they have multiple data sources. This aim is one reason many new additions to stacks tout integration capabilities across data, tools, and communication.

Integrations are a boon for every marketer, especially when using APIs is simple. Unfortunately, if your data isn’t valid in one platform, it expands the reach of that issue with each integration due to data contamination.

Experts are stretched thin across tools

It feels like daily a new tool gets added to the existing stack. Every new option also makes big claims, and it’s hard not to get sucked in by the promise of greater efficiency, better reach and leads, or fewer cart abandons for e-commerce companies.

At the same time, inward-facing tools are also multiplying. If you’re working with multiple stakeholders (at your company or across many clients), you’ve probably had to use Asana, Slack, Basecamp, and custom CRMs, along with with Dropbox, Google Drive, plus a host of online meeting tools.

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That list — and likely your list if you write down everything in your stack — duplicate a lot of work. They also come with their own logins, UIs to learn, reporting and analytics to manage, and notification tools. When a stack requires significant training across systems that can do the same thing, your team is wasting time, and you’re likely spending too much on licensing.

If you’re managing remote workers and teams, for example, you need video recording and editing solutions for sharing information and training. Look for opportunities where a single platform can support this as well as generate content used by sales and customer support teams. There’s no need for multiple screen recording or snipping tools across your martech stack or your company’s divisions.

To utilize your stack to its fullest potential, you first need to cut away the excess. Then, you’ll need to train your team and give them time to use the tech right.

Slow down, take a minute, and plan

Sometimes the thing that gets in the way the most is us. We’re getting too busy focused on using a tool and trying a tactic without first understanding it. When marketers don’t plan, they make simple but costly mistakes.

Mistakes can be complicated — such as using the wrong dataset to create a lookalike audience — with cascading impacts, or high-level confusion that wastes efforts. One common mix-up is confusing market and user research methodologies, where the stack helps automate but can quickly cause reliance on the wrong data or sample set.

When you use your martech stack, create a plan with clear goals and requirements. Establish how you’ll audit data and results — during and after the process — and give your team a minute to breathe. Taking time ensures you apply data and tools correctly, which can make even a small stackable to create a significant impact.

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