Perform a diagnostic sales analysis, even if you capture almost no sales data
It’s not uncommon for a B2B business to have little sales data, even when they have been around for years. There are plenty of companies that sell a lot and are growing steadily, but still struggle with closing a data gap that is caused by the sales team barely using the CRM or not following a structured sales process that ensures reliable and consistent measuring points. The result is poor visibility on low-quality and incomplete data, which means that management cannot fully trust any of the sales reports either. But how do you do a sales analysis if you have almost no data?
Based on our experience with B2B companies, we have created a framework for diagnostic sales analysis that is based on the data we know many companies actually do have. It is useful both for growing companies that have won their first set of customers as well as for established companies that sell a lot but might lack a structured sales process.
If you don’t have a clear picture of where your company and product(s) are doing well, or where you need to focus your efforts, this article is for you! In this article we introduce 3 levels of easy-to-set-up sales analysis, starting with an analysis that can be done even if you have almost no sales data (yet). Next, a full diagnostic analysis that you perform manually and only uses free data tools, and therefore offers a lot for little. Finally, the third level makes use of a paid automated data synchronisation with your CRM and enables you to make an ongoing real-time diagnostic evaluation of your pipeline, sales process and ideal customer profile.
Read this article to find out what level of diagnostic sales analysis fits the amount of data you currently have at your disposal. However, before moving along we would like to note that data recording becomes more reliable and complete when the sales process is formalised and carried out with structure and consistency. We advise all B2B companies to always pay attention to this.
What is diagnostic sales analysis?
Diagnostic sales analysis is the process of breaking down your sales data to find patterns, trends, and opportunities. At its core, diagnostic sales analysis is the identification and explanation of the causes of past sales performance. Without this knowledge, your assumptions on what to do differently in the future will be just that: assumptions. In order to improve sales performance you have to know what caused past performance, both the good and the bad. However, we do not believe that diagnostic sales analysis is a way to improve sales efficiency by only highlighting errors or inefficiencies in the sales process – rather, we say it is a way to find out what you need to double down on.
So in diagnostic sales analysis, you look at what happened in the past: you examine your historical marketing and sales data to get a clear picture of all past events and trends. Based on that, you try to dig deeper and find explanations for why that happened: as long as you don’t know why things occurred in the past, you can’t make good assumptions about what behaviours in the present will have certain outcomes in the future.
If you have a small amount of data, don’t waste money on expensive reporting tools and suites. And especially don’t expect those to give you any AI-based recommendations: they will not have been fed with enough information to generate useful insights. When you have relatively little data, domain knowledge becomes important. You will have to dig into your own data and connect what you see there with your own real-life experiences. However, the problem is often that companies don’t know where to start with their analysis, or how to organise their data to get the right insights. Therefore, we have developed the following three levels of diagnostic sales analysis, which are based on the concept of ‘measuring at the finish line’ to facilitate the start of diagnostic sales analysis.
Start measuring at the finish line
Typically, diagnostic sales analyses focus on a large number of deep metrics that get to the bottom of the sales process and sales performance, and through this in-depth data analysis reveal deeper trends and underlying causes. However, this is precisely what often causes a problem in B2B: many companies do not have a rigid enough sales process that is executed with enough consistency that there is insufficient reliable data on all these deep metrics.
That is why we have developed a method to start with a basic diagnostic sales analysis that only requires data that we know companies do actually have – or can piece together retrospectively. We call this measuring at the finish line. The central idea behind ‘measuring at the finish line’ is that you only need to look at what exactly happens just before very specific milestones and only the accounts that manage to cross that finish line.
That is why we have developed a method to start with a basic diagnostic sales analysis that only requires data that we know companies do actually have – or can piece together retrospectively. We call this measuring at the finish line.
The first level of diagnostic sales analysis we present today is perfect for companies that do not actively capture sales data or have no structured process. These companies often have too little data to apply to any of the usual sales analysis methods. By focusing solely on a thorough analysis of who crosses the most important finish line (i.e. who your customers and ex-customers are), you can already uncover enormously valuable information if you know how to extract it properly.
The second level requires a bit more data, but is still based on very concrete and logical metrics that companies can easily extract from their sales process. Here we make use of several finish lines to allow for better visibility on the sales process: we analyse prospects, first touches, leads, opportunities, customers, and ex-customers. On these finish lines, we also measure not only who reaches them, but also how long they take and in what percentages they cross that finish line. By combining this information with very straightforward firmographic data that can be retrieved from free and public sources, a thorough diagnostic analysis of the pipeline, customer base, sales process and ideal customer profile can be made.
For the third and final level, we set up an automated synchronisation of data from the CRM so that you do not have to manipulate the data manually but instead have continuous and real-time insight. This level is based on the same data and analysis as the second level, but supercharges your ability to make decisions directly as data becomes available. This does require a structured process of performing sales and registering data, so that you can be sure that your CRM is always filled with complete and reliable data.
Customer value scoring
An important section in these sales diagnoses is focused on analysing what your ideal customer profile looks like. This section uses a score that you have to assign to your customers yourself: the Customer Value Score, which you can read a short description of below and an extensive description in this article I wrote about it previously.
The Customer Value Score describes how well a company fits yours as a customer: how much effort does it cost to service and retain them, and how much potential does the account have (for further growth)? You can see this as an importance score for this account: how hard will you fight for this customer or otherwise how ok are you with this client churning. You score it on a scale from 1 to 10 based on the following components, and be honest about the answers:
- Does this company fit the general profile you are looking for?
- Is their current need (pain point) a good fit for your product/solution?
- Is it a low-effort customer to maintain and service?
- Are they bringing in high (enough) revenue (-potential)?
Because the Customer Value Score is an aggregation of various attributes, it adds a thick layer of knowledge to your analysis that you can cross-reference with other firmographic attributes of your customers. In combination with revenue generated, we use the Customer Value Score as an indicator to define the ideal customer profile. In the diagnostic analyses explained below, the ideal customer profile can be explored in quadrants that show us which combinations of properties have above-average Customer Value Score and MRR. Furthermore, we can cross-reference this with the other data points in this analysis: you can refine this further by using the filters on the dashboard to zoom in on the different segments you discovered in the previous section. This means that you can gain deep insight into which characteristics of companies determine the types of customers that are successful for you.
A quick overview of the three levels of diagnostic sales analysis
The easiest sales breakdown
You can use this report even if you don’t have a CRM or aren’t really using it. It only requires information that you definitely have about (former) customers or can be pieced together retrospectively.
Even if you are not actively capturing sales data, you can use this reporting template to bring up useful insights with very little data. We only use the data that you already have about your (former) customers or that you can easily obtain.
Gain insight into:
- Basic overview of customers and ex-customers, revenue and churn
- Who you sell to
- Who you should sell to: your ideal customer profile
If you really capture almost no data you have to focus on the pure essence of diagnostic sales analysis, and that is what we did with this first level. This easiest sales breakdown can be performed with completely free tools (being Google Sheets and Data Studio) and requires very little data; nothing that you do not have at hand or can’t obtain from public sources.
We apply the concept of ‘measuring at the finish line‘ in all its essence: we only look at what customer typologies are actually won, and we don’t worry about measuring everything in the entire sales cycle. Even if you are not able to reliably measure all touchpoints, this diagnostic analysis can provide valuable insights by examining who crosses exactly that last finish line.
By reducing the analysis to only this amount of data, we make it:
- Easier to capture and gather all the data needed;
- A much more reliable analysis;
- More straightforward to get valuable insights.
This analysis is perfect for you if you don’t already collect data on the sales process itself, for example data on when a company becomes a lead or opportunity.
The manual sales diagnosis
Perfect for you if you have data you can export from your CRM, but don’t want to spend on expensive analytics suites or reporting packages. A solid sales diagnosis with free tools.
You don’t even need to collect a lot of data to generate a lot of insights. In this case, it is about getting the right data points from the CRM and structuring them well so that the data starts working for you.
Gain insight into:
- Current status of leads, opportunities, and clients
- Basic revenue and churn overview
- How well your sales process is working: conversion journeys from first touch to customer
- A detailed overview of conversion rates and average time per phase of the sales cycle
- Sales insights about who you sell to
- Extensive insight into who you should sell to: your ideal customer profile
What makes this dashboard great, in comparison to the first level: ‘the easiest sales breakdown’, is that we now measure a number of fixed reference points in the sales cycle. Without getting completely lost in all individual touchpoints, we look at just the most important milestones: first touch, lead, opportunity, customer, and ex-customer. By only measuring these finish lines, a very clear picture of the sales process emerges. While many companies tend to start by collecting far too much information on all the individual touchpoints, they actually find it hard to make that information actionable. This structure of multiple finish lines makes the data much more actionable, allowing useful insights to emerge sooner. By combining this knowledge with other data points, such as lead source channel and use case, we can then compare the different initiatives in your go-to-market: for example, which acquisition channel has the shortest sales cycle, and which subscription level generates the most successful or profitable customers?
This analysis is perfect for you if you are already collecting some information during your sales cycle, but do not yet have a very comprehensive suite of reporting and analysis tools.
The automated sales diagnosis
When you want to take the next step and get all your data out of your CRM into a more complete and versatile dashboard. Get synced and automated insights.
If you already have a structured sales process that is being executed consistently, it is worth investing a little bit in a data connector that pulls your data straight from your CRM. This offers much more flexible ways of analysing the data and in many cases cheaper than the reporting suites offered by the average CRM.
Gain insight into:
- You get the same insights in this sales diagnosis as in the manual version, with the difference that this dashboard is directly integrated with your CRM through Supermetrics. This way, data from your CRM is directly synchronised with the dashboard and you will always have real-time insights without having to manually process the data.
- Current status of leads, opportunities, and clients
- Basic revenue and churn overview
- How well your sales process is working: conversion journeys from first touch to customer
- A detailed overview of conversion rates and average time per phase of the sales cycle
- Sales insights about who you sell to
- Extensive insight into who you should sell to: your ideal customer profile
This report is similar in content to the second level: ‘The manual sales diagnosis’, but includes a strong improvement in the automated synchronisation of data from the CRM to the dashboard. This means you don’t have to handle your data manually to make it ready for Data Studio and you also always have direct and real-time insight into the current state of affairs in your sales organisation.
In contrast to the other two levels, this variant does not only make use of free tools, but we also use Supermetrics to synchronise data from the CRM to the dashboard. By using Supermetrics you can link your CRM directly to Data Studio (or any other data visualisation tool), or automatically export to Google Sheets so that you also have visibility of the data being extracted from your CRM. With automatically scheduled queries your data is continuously synchronised and your dashboard is always automatically up to date.
This analysis is perfect for you if your sales process is performed with enough consistency and data is recorded with enough consistency that you can be confident that the data you synchronise from your CRM directly to the dashboard does not need manual intervention to be cleaned or completed.
In conclusion
Whether you are a startup analysing your first customers or a mature business looking to refine your existing sales process, the first step toward evaluating your sales system is understanding what kind of data you have.
The good news is that sales analysis can be done even without a lot of data. All data can help you make better informed decisions, whether you are looking to make a major change in your sales efforts or just want to improve little by little every quarter. The main thing is that you structure your data well so that it allows you to make sense of the insights they contain.
Diagnostic sales analyses are great at helping you understand where to focus your efforts and tracking improvements over time. Check the three different levels and see which one suits your current data situation best. If you want to structure and streamline your sales process first, we’d love to hear about it too. We are happy to help.
