8 GTM Tech Stack Mistakes to Avoid - Enterprise Edition
If you’ve been following our series on go-to-market technology stack do’s and don’t, you’ll notice there are some themes. The universal truths that carry across startup, growth, and enterprise organizations are:
1. Mismatched data definitions across departments lead to chaos
2. Unaligned goals across departments lead to chaos
3. Making a system difficult to use leads to lower adoption (which we could argue leads to chaos due to lack of data collection)
That being said, there are variations in the source of the problem. Startups are finding their way by inventing processes, system workflows, and frequently changing systems according to what fits their need (and what they can afford). Growth companies are working through growing pains by refining processes, fixing configuration that was often meant to be temporary, and building a more sustainable data strategy.
Enterprise companies have process maturity and larger budgets on their side. That said, they are prone to clinging onto inefficient processes because “that’s how it’s always been done” and overlooking smaller, more innovative vendors by adopting systems their experienced employees have already used.
Companies that have innovative RevOps technology professionals willing to think outside of the box and constantly challenge the status quo are likely to adapt quickly to market changes.
Don’t Forget: GIGO Is Real
There are some great marketing attribution, sales forecasting, and customer engagement tools out there. One of the biggest missteps we see in larger organizations is the assumption that a spendy tool will bolt onto existing technology and spit out better information than you were getting on your own.
This is only true if you’ve invested in a data management tool that deduplicates, merges, and automatically associates your data. You’ll also need to integrate all relevant tools, which means translating data that uses different identifiers into a universal language. This takes a lot of time and skill, but it’s the only way you’ll get insights rather than misleading metrics.
An expensive marketing analytics tool without paid advertising spend data, web interaction tracking, and a way to align person data with accounts (particularly if you’re a B2B organization) is wasted money.
The other major issue that will prevent you from getting meaningful insights is a lack of CRM user adoption. If your system is difficult to use (too many validation rules, field sprawl, clunky process flow, etc.), your managers don’t enforce usage, and your compensation plan doesn’t have an element requiring CRM usage, your CRM adoption will be low.
An expensive forecasting tool leveraging machine learning is useless if your sales team refuses to use your CRM.
Do Think Hard Before Making Your CRM the Single Source of Truth
You can do a lot with a CRM, and it will give you a lot of great information (provided adoption is good). However, it doesn’t make sense to integrate every field of every system with your CRM. Your sales team won’t care how much digital ad spend goes into a given campaign. Your customer success team won’t care whether a promotional item was delivered on time.
Smaller organizations may force all users to be in one system and keep everything in a single location. Even they won’t typically integrate LinkedIn data or Google analytics information.
As Jack Robbins, Sr. Business Intelligence Analyst at Slack, said:
“One of the biggest mistakes is trying to turn SFDC or your CRM into data warehouses. I've experienced it, and it is not fun! This is not news to many folks, but SFDC isn't an analytics tool. Yet many organizations attempt to make it into one and create a ton of tech debt and clutter. At the enterprise level, I think it makes more sense to take the sophisticated approach and combine everything at a data warehouse layer (we use Fivetran to export data into Snowflake then visualize the data with Looker). It’s more flexible, more robust, and tool-agnostic; plus you can always leverage a tool like Workato to push data back into SFDC or MA tools, or leveraged embedded analytics in SFDC as well (i.e., embedding a Looker dashboard on a page layout).”
Leveraging a data lake or data warehouse and having a data transformation layer can make things much easier on your system admins, data analysts, and end-users. Instead of gating your systems from creating duplicates, put processes in place to merge data passively. Instead of making people manually enter dates, automate as much as you can.
The easier you make your CRM to use, the better your data will be in the end.
Do Standardize Your Data Definitions!
As we mentioned above, inconsistent data definitions can cause major issues. This was my major motivation for embracing a move to Revenue Operations after years in sales, marketing, and customer success organizations.
Watching marketing and sales duke it out over who’s at fault for a bad quarter using the same metrics with different data drove me up a wall.
Creating a standardized definition and centralized reporting system frees analysts from the burden of proving their superiors right and frees them up to focus on adding strategic value. They can spot patterns and dig into why something may be happening, and even come to the table with potential solutions.
Don’t Outsource All of Your Reporting Capabilities
I’ve been in organizations that chose to centralize reporting in IT. There are benefits. I didn’t have to write a hundred lines of SQL code to normalize titles. There were also drawbacks. Every time we added a new system, we had to wait for weeks to integrate. A change in the CRM or marketing automation tool took diligent coordination between multiple teams.
The wait time was doable because I still had autonomy. We had socialized data definitions, and as long as we plugged into the same source, we could use a BI tool to summarize the information as we chose.
Business Intelligence teams are amazing, but it’s not possible for everyone to be an expert in everything. The BI team should either allocate a resource dedicated to your department to learn the business processes and context or allow an analyst on your team to dig into the data. Preferably the latter due to ad hoc needs with a quick turnaround time.
Don’t Forget: Automation Doesn’t Always Solve Problems
As Jack Robbins, Sr. Business Intelligence Analyst at Slack, said:
“Automatic task logging is very important if you want any sort of reliable analytics around sales productivity, lead/inbound SLA, or even funnel tracking. Just as important is actually enforcing the reps to work within those systems. Having Outreach is great, but if you still allow your reps to schedule meetings on Google calendar or other tools external to Outreach--or make calls without Outreach tracking--then you’re as lost as if you didn’t have Outreach to begin with. Strong process enforcement is critical to actually deriving the value from these tools. Your data, and thus your insights, are only as clean as your process hygiene.”
Usability, management support, and compensation are the three pillars of system adoption. Without adoption, your data has too many gaps to be useful. If pushing them into using a single system doesn’t work, leadership should either decide to find a tool that will be adopted or allow multiple points of integration (for example, Outreach integrated with Salesforce and an email plugin to catch interactions not logged in Outreach).
Don’t Let Operations Impede Customer Satisfaction
During launch meetings, our most intense debates were about making our internal processes easier versus streamlining the customer's experience.
It’s tempting to require a great deal of information to progress an order or to push implementation earlier in the sales cycle. Is it better for the person doing the information to gather the data? Will something be lost in translation? Will the implementation specialist just have to go back and ask more questions?
Always weigh customer satisfaction more heavily than internal efficiency. You may still opt to streamline your process, but the tradeoff has to be worth it.
Do Know When to Hold ‘Em and When to Fold ‘Em
A lot goes into making a system scale with the business. It can take a great deal of personal innovation and investment. Which can make it difficult to see when it’s time to throw out your legacy system and make an upgrade.
The pain of switching systems is pretty steep, but if adoption is dropping off and you’re hearing of other systems making traction in the market, it’s worth investigating your options.
When it comes to marketing automation platforms, there are a few that function pretty similarly. If the rates are climbing and your existing system is a bit of a mess (let’s say the existing platform doesn’t allow you to delete custom fields and you’ve had it several years), it may be worth making a change.
Do Remember: Bigger Isn’t Always Better
Being part of a large company makes it easier to see how large organizations get complacent. If they have a large deal of market share and what they’re doing is profitable, it’s easier to stay the course than make changes that may or may not go over well with the existing customer base.
The same can be said of large technology vendors. While some are known for continuously innovating, it’s not the general rule. Smaller vendors can be scrappy and are more willing to work with companies (let’s be honest, especially large companies) to meet their unique requirements.