Every CRM has its pros and cons. Whether you use Salesforce, HubSpot, or Microsoft Dynamics, you’ll find that each tool is better at some types of reporting than others. If your company is in its early stages, like Series A or early Series B, you can get away with relying on CRM reports with some occasional help from Excel or Google Sheets.
But as you grow, your analytics needs will change. Your leadership team will start asking more sophisticated questions about what your data has to say about product market fit, the success of your sales motions, whether you’re targeting the right people, and where you see the most churn or retention.
Salesforce offers a traditional data structure that’s pretty close to relational databases. While objects are linked in a straightforward way through lookup fields, you’re ultimately limited in regards to how many objects you can join together and report on at once – even with the built-in analytics snapshot tool. Reporting on trends, lifecycle stage durations, and pipeline velocity is pretty difficult without exporting into a spreadsheet – even with custom fields and flows.
There’s a major drawback with marketing campaign reporting on opportunities. Salesforce campaign influence is the tool’s answer to marketing attribution but it’s designed to allocate 100% credit to any campaign touchpoint. The result? Marketing’s influence is exponentially inflated. Plus, opportunity contact roles must be added manually (or automated from call summary data) in order for campaigns to be visible at all.
HubSpot, on the other hand, offers useful automations for capturing date stamps for deal stages but, overall, reporting is very limited. If you’re not already exporting your CRM data into a database, you’re stuck dealing with a clunky UI, limited visibility, and little flexibility to move beyond report templates in general.
While HubSpot is good at cataloging digital touchpoints, it’s a struggle to report on them in a meaningful way and offline touchpoints are awful to report on (raise your hand if you hate the “Offline” category ✋). The tool's multi-touch attribution capabilities definitely function better than Salesforce, but count on spending a lot of time in spreadsheets to answer fairly basic questions from the leadership team.
The first sign that you’ve hit your CRM’s limit is that teams are asking to see trends around your customer lifecycle. They want to understand your opportunity stages, how long it takes from first touch to opportunity creation, and how long it takes for a lead to convert into an opportunity. They’ll also want to see cross-object historical trend information.
Another sign is that the marketing team needs more advanced reporting capabilities. They can’t determine what works and doesn’t work because their efforts aren’t tied to pipeline and bookings (or ARR or whatever your business uses to indicate money in). They also need data for marketing budget planning.
The third sign is that your team wants to uplevel customer engagement calculations. This could be intent or engagement scoring for prospects and engagement or health scoring for customers. Many times this involves integrating additional tools with your CRM and summarizing large amounts of data.
If you’re seeing these signs, it’s time to start looking outside of your CRM or marketing automation platform.
Whether you build your solution or purchase a vendor tool depends on your organization's size and data governance rules.
Smaller companies tend to be more relaxed about who can see different types of data, making it easier to get the access necessary to build your own solution and manage it within the RevOps org. We’ve seen teams use Microsoft SQL server in the past and Snowflake, RedShift, AWS, or BigQuery. There are also freemium tools like PostgreSQL, MySQL, and others. All of these tools will also require a data visualization layer like Tableau, Looker, Sisense, or PowerBI and some kind of data integration tool (ETL or streaming).
Cloud-based databases and data lakes, like Google Cloud BigQuery, can be cost-effective solutions depending on how much historical data and the type of data you’re working with. Keep in mind that if you want all your website data and product usage data in your cloud-based data warehouse or data lake, you’ll blow up your costs. Analyze the volume of data you’ll need to pull meaningful reports and spot trends so you advocate for the correct budget.
Clearly these tools require a lot of specialized skills. Small, scrappy organizations need the right talent on their team to properly manage the data pipeline that goes back and forth between systems. You also need people who understand the problems your go-to-market leaders are trying to solve and can visualize data. The ability to communicate patterns and anomalies to the rest of the business is vital. You’ll also need deep technical chops to set up and maintain your data mapping and quality assurance to make sure your calculations are correct.
At larger organizations with established business intelligence teams, your analytics systems will usually roll up to your CTO or your IT team. In this situation, RevOps typically struggles to get read/write access to the databases directly and acts as the mediator for go-to-market teams, requesting specific updates or new changes. When there’s a tool change, RevOps is dependent on IT to handle an ETL or streaming change, data mapping, updates to data models or calculations, and update to existing reports or dashboards.
The change management involved in a DIY decentralized reporting solution is extremely painful. Don’t be surprised if it takes weeks to months to see progress. Prepare to spend a lot of back-and-forth time with IT, who are prioritizing requests across the business and may not understand the requirements given to them on the first pass.
A DIY analytics solution is like building and maintaining a product. To operate well, the reports need to be intuitive, the data needs to be fresh, and the information needs to adapt as the business evolves. Some of the cons of a DIY solution include:
If you have the right skill sets, an extremely customized CRM instance, and sophisticated reporting requirements, building your own solution may be the right choice. Some of the pros of a DIY solution include:
One of the most overlooked expenses of an analytics solution is the cost of maintaining it. System updates, integration users, and tool software updates can all break integrations. It can take time to recognize a break and fix it. Go-to-market teams are also notorious for changing technology frequently. Every time tools are swapped, data must be mapped and incorporated into data models.
The cost of talent and tools necessary to maintain the tools are steep. Breakages and major reporting overhauls will also pull your attention away from what the business sees as valuable – deriving insights and making business decisions.
If you want specific numbers, there are plenty of analytics vendors out there who have attempted to calculate the cost of building and maintaining your own analytics platform for a range of estimates.
There are a lot of analytic tools available today. Some have more broad applications across the go-to-market while others are hyper-focused on things like marketing attribution or forecasting accuracy.
A benefit of purchasing a tool is that the commonly used vendors in your vertical will already have pre-built integrations and data mapping. Most point solutions, like digital advertising platforms or sales engagement platforms, have standardized schemas that are fairly easy to plug and play into data models. But – and this is a big one – integrating marketing automation systems and CRMs can take a very long time. Companies extensively customize these tools, making it impossible to assume that standard fields and objects will be used consistently across companies.
If you have a lot of custom objects and fields, especially calculated fields, make sure to ask vendors how they will handle data mapping. Plus, lots of things that happen in CRMs can’t be exported out. How do they handle historical data limits? How far back can they show a trend? How do they calculate metrics your organization relies upon?
Try to find a vendor that has the flexibility to adapt to your many customizations and try not to assume that anything you have customized is “normal” across similar businesses. The alternative is to change your configuration and metric calculations to match vendor standards.
Pros of purchasing a solution:
Cons of purchasing a solution:
Revenue operators are naturally skeptical about vendor promises. With an analytics tool, it’s hyper critical that your vendors understand what you’re trying to achieve and how what you do in your systems is different from similar companies.
When your company outgrows your CRM or marketing automation tool, you’ll have some big decisions to make. Is it better to DIY an analytics solution or purchase a tool? Do you have the skill sets to manage data pipelines and visualizations? What is the true cost of your options?
Hint: don’t just look at the cost of technology, you need to factor in the costs associated with resourcing, time to implementation, and change management.
Wondering where to start? Check out our article on how to build a business case.
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