Dive Into CRM Analytics (+ Free Template)
In SaaS, there’s a hunger for more data, more metrics, and more dashboards — but there’s been little maturation in the general understanding of data.
This venerated resource is misunderstood, poorly used, and often maligned to make a narrative ‘true’ rather than crafted into a story that changes a company’s destiny.
Enter CRM analytics.
I'll show you how to set up your data for analytics, my favorite ways to use CRM analytics in everyday workflows, and three free templates for field mapping, metric mapping, and forecasting.
What is CRM Analytics?
Not to be confused with the Salesforce CRM feature of the same name, CRM analytics is the practice of taking data from both the past and the present and combining it with system knowledge and a variety of computational methods to understand and predict trends in your business.
CRM Analytics vs. CRM Reporting
Where CRM reporting concerns itself with a pulse check on your business and each report should answer a simple question, CRM analytics seeks to construct a picture of an overall scenario and predict it forwards using a blend of reports, analysis, and charts to thread together multiple answers to questions into a story.
Why CRM Analytics?
The CRM software market, currently worth about $70 billion, is predicted to grow to over $200 billion in the next 10 years. Industry leaders like Salesforce will continue to roll out new ways of incorporating more customer data into their 360° models.
It’s never been easier to track a business's performance — but, it’s also never been more complicated to understand the connections, the relationships, and the actionable insights within this growing morass.
Unpredictable success is nearly as unnerving to a board as unpredictable failure. Modern companies should see both coming and have a plan for every scenario. CRM analytics acts like a crystal ball for your business.
Getting Started with CRM Analytics
Whether you have access to an advanced analytics platform or nothing but a spreadsheet, finding meaning in the data lake (or swamp) is up to you.
Actually — I highly recommend starting with a spreadsheet to help you understand your data beyond the fancy algorithms. Look at your data and start thinking like an analyst.
Questions to ask yourself
- How does it work?
- How are your columns related?
- Which rows stand out?
- Is it good or bad that they stand out?
Dig a little deeper to unlock more actionable insights:
- How does this clarified understanding of the data inform our strategy?
- What does it mean for our value proposition?
- Where does this get us compared to where we think we will get?
Curiosity may have killed the cat but it forged the RevOps professional into an analytics expert. And this expertise is what will elevate you as a respected leader in your company.
Build a Data Map
In the words of Covey, begin with the end in mind.
What are your North Star metrics? What do you NEED to measure? And what makes these up? What are your numerators, denominators, multipliers, and risk factors? Where do these come from? What systems are involved?
That’s a lot of questions to answer, so I’ll break it down even further. Let’s make a field map!
How to Make a Field Map
- Open your spreadsheet tool of choice or download the free field map template above
- Place the important fields you need in Column A on different rows (one tab for one object [Opportunity, Account, Lead, Contact, etc.] — use different tabs if you have multiple objects you need to integrate or map)
- In Column B, place all the data types (number, string, choice set, etc.)
- Title Column C as another system of record with Column D as the data type again
- Complete the rows in C & D with the names of matching fields and data types
- Add conditional formatting to check if D = B (or is equivalent)
- Repeat steps 4-6 for every extra system, comparing each data type column back to Column B
By resolving your data type mismatches, you’ll take significant steps toward improving data integrity.
You’ll probably have a lot of blanks, where you can confirm whether there should be mappings or not. You can extend this by adding columns to list all the options of your choice sets and comparing them, too. And from this sheet, you’ll have enhanced your knowledge of your overall ecosystem.
While there are tools out there to complete this for you, they all come with a cost. Depending on business size and the control you have over your systems, this may be a one-time exercise and you may not need to pay the ongoing fee.
Map to North Star Metrics
Once you’ve mapped your fields, you can start to map the components of your North Star metrics (check the second tab of the field map template).
- In a new tab of your spreadsheet, make a list of each of the metrics you need to track in Column A
- Use Column B to write down the formula or process to work out each one
- In Column C, make a list of the cell references from your field map for the fields needed to calculate the metric in Column A using the method in Column B
You now have a documented record of how your metrics relate to your fields and a map of where that data is. Your data maturity is now comfortable for Series C+. You have achieved the foundations of data integrity and data traceability.
6 Everyday CRM Analytics Examples
Once your data map is built, you can move on to using CRM analytics in your daily workflows. These are a few of my favorite ways to put my analytics skills to use:
1. Streamline processes
By taking an analytical look at your Opportunity data, you can start to understand where there may be bottlenecks in your sales process(es) or where you may have process breakdown/failure.
The best metric to leverage here is time in stage and look at it weekly or monthly to understand if it’s rising or falling. Generally speaking, time in stage increasing asymmetrically in one stage is a sign there’s something you need to dive more deeply into.
Case Study
An enterprise client’s services pipeline had a significantly higher time in stage for Solutioning, which got greater every month.
Through CRM analytics, I found that every solution engineer in their business was significantly over capacity, and sales hiring had outpaced its supporting divisions, worsening overall customer experiences.
We implemented a stricter qualification methodology to reduce strain and they backfilled a few roles.
It took 4 months, but they saw their metrics return to a more even distribution and used this reporting to stay on top of whether they had the right mix of sales:sales support hires.
2. Define your ICP
Every sales team I’ve ever spoken to believes they understand their ICP, but how many are actually selling to it?
Take your total (including closed won & lost) Sales pipeline and pivot it. Segment the data to align to your ICP and then look at whether the distribution of latter/won stages trends towards the clients most aligned with your ICP fields.
If it does, you’re aligned with your strategy. But if it doesn’t, and your won data is outside your ICP, it may be the wrong target or a pivot for you. And if your won data is within your ICP but your late stage deals aren’t, your team has been prospecting wrong or the market is shifting before your eyes.
3. Predict trends and customer behavior
I see a lot of companies using free text boxes to get input on certain things. This is predominantly unhelpful for analysis unless it’s categorized.
To get started in cleaning up text data, run a basic frequency analysis on all the text in your field and find the most common trends. Using formulae or workflows, you can then set up booleans or picklists that will update based on the free text.
4. Build a forecast and track performance
If you’re in a business with a reasonable sales velocity, you can use geometry to your advantage in forecasting. You can set this up in a spreadsheet — or download the free forecasting template (tab 3 in the field map template).
- Enter Days in Column A and To Go in Column B
- Now put your target against day 0 on the first row of your table.
- For each sale that comes in, record the day and then subtract the value from your target, entering the amount ‘To Go’ in the aligned row.
- Once you have one entry, add a new row as day 40. Insert a chart and add a line of best fit to your series.
As you add more deals into this, you’ll see the line of best fit intercepting your horizontal axis — this is when you’re likely to have made your target.
This forecasting chart is effective because it shows how many days ahead or behind you’ll be rather than just what you’ll miss and by how much.
5. Identify new opportunities
A relatively simple one for anyone to do if they have multiple product lines or a strong upsell motion.
Take your customer base and average customer spend based on size of business (headcount or valuation) and, if you’re in a larger business, vertical/industry.
You should then have a rough idea of whitespace in your account and see who’s paying less than they can presumably afford. Simple density analysis like this can lead to a lot of insights depending on the variable you use.
You could look at how many times customers (by size and industry) are repeat buying, how long they’re your customer on average, and how many support requests they submit and then see how it compares to your norms.
This broadly indicates where you can pull back and where you should focus and is a great analytical activity to accompany coverage planning.
6. Flag at-risk customers
This one is trickier, as you should probably stand up a machine learning model to really break down retention, however, you can start by building a strike framework.
Take each facet of your customer experience you can accurately track and average participation/engagement/usage for your happiest and most likely to renew customers for each part of your offering.
Then work backward and use these as your strikes:
- For each other customer, are they participating/engaging/using your product in the same ways?
- For those who are markedly underutilizing it, is it localized to a specific area?
- Could not tapping into that value be a risk factor?
From there, you can build a series of checkbox formulas on your customers to indicate which ‘flags’ are raised for them.
The number of flags raised can then be the ‘status’ where 6 (for example) makes them go red.
This model is simpler to implement than full health scoring while being more actionable to your team as they can see which flags are raised. Looking at this scoring across your entire customer base, what percentage is green? Is it broadly aligned to your retention numbers?
Do You Need A CRM Analytics Platform?
If you’re using a data analytics platform that tries to go a few steps further and work out your metrics for you, stop and question whether you will need to drill into that metric in a board meeting or when reporting up.
Stop using the platform as a major metric and use it operationally and directionally instead. If you don’t know how something is calculated, where the data comes from, its error tolerance, or its volatility, the platform is functional but not insightful.
Platforms like Salesforce CRM Analytics (formerly Einstein Analytics) or Tableau CRM to give you a deep understanding of your data but still necessitate a broad and deep understanding of the data itself.
Other platforms can feel more like black boxes and have their own trademarked deal scores which you cannot see the inner workings of. While these are big selling points to small companies, the lack of integrity and robustness in these metrics makes them unsuitable for real reporting or analytics.
Leverage CRM Data for Greater Business Impact
Where common wisdom would tell you to take cautious and tentative steps into the deep, my advice is rather bold: Jump in.
Whether you choose to jump into the shallow end or straight into the deep, I will leave it up to you. But there is a world of meaning out there.
Analytics is the intersection of the science of data with the art of divination. You must be immersed in it to know it.
You’re constructing your own Shashibo cube with the data you find. Coloring the faces and sculpting the shapes as you find fit. There’s structure to the chaos and chaos within the structure. And that is the joy of understanding CRM Analytics.
Open a spreadsheet. Deploy an analytics platform. Play with your data.
I dare you.