Selling the Value of Analytics

Dominic Denison
May 30, 2024

Every B2B SaaS solution provider knows analytics is a cornerstone to differentiating their value in a marketplace where all solutions can look the same. Analytics is a way to differentiate, to capture attention and engage with customers resulting in higher conversions from discovery, through demonstrations, trials, sales and ultimately in market-share.


Nobody’s going to understand and position your SaaS solution better than you can, that’s not the intention of this post. It is, however, often the case, that customer-facing teams are more familiar with the operational aspects of process execution in your SaaS without, necessarily, clearly understanding, or being able to communicate the strategic value and context that analytics brings to those operational processes.The result might be: “let’s show you our cool dashboards”, and, while there is a colourful “eye-candy” aspect that analytics can bring to demonstrations; we need to lead customers, at least a little, “deeper into the mountain where the goldmine is hidden”.This post is designed to help with experience and ideas on how to do that.

B2B SaaS Customer Expectations

Use cases

Embedded analytics use cases are countless, each highlighting the specific context, intellectual property and value of the B2B SaaS solution provider from:

  • optimising operations:  remove/reduce inefficiencies, streamline operations, reduce costs, improve productivity;
  • improving customer experience:  satisfaction/retention, customer preferences, sentiment and purchasing patterns, drivers for NPS and LTV
  • Getting things done:  Rather than spending hours on analysis, users are empowered to cut-short analysis and quickly navigate to action at scale (not just one thing at a time).

Tip:  Ever tried asking GenAI for analytic use-cases in your market-segment?

…EHR & healthcare, energy & utilities, fintech, fraud & counterfeiting, HR, procurement, property management & real estate, retail & e-commerce, smart-IoT, supply chain & logistics and many more…but you can get much more granular than that…such as facilities management - lift maintenance in property management for instance…

Migration from legacy software

SaaS, has “arrived” and matured as independent software vendors have, long-ago, recognised the value of a build-once-use-many, centralised technology architecture and low-friction adoption for customers with amortised, on-demand licence models which drive lifetime value.

Often analytics can be the catalyst (carrot vs stick) for migrating those last legacy software users onto the SaaS platform.

What’s in the box?

End-customers expect relevant, out-of-the-box, analytics built into the SaaS which can drive value from day 1.  They want analytics which doesn’t need any specialised expertise or training yet, furthermore, want flexibility to innovate with their own unique analytic customisation too.  Customers, like the B2B SaaS solution provider also recognise the value analytics brings for them to differentiate with their customers.

Business (not technical or analyst) users

B2B SaaS customers, large and small, often lack resources to build in-house analytics capabilities and thus benefit from the B2B SaaS solution provider putting in the effort to include this in their solution.  Furthermore, the user is typically not IT, typically not an analyst, the skill-set is more aligned to the value, processes and actions driven by the SaaS solution rather than “show me the data”.  These users are not going to be satisfied waiting seconds for analytic feedback, so performance and scalability are key considerations.

Data Security & Compliance

Not perhaps the most exciting theme, but an important one none-the-less.  Customers want to know that their data is safe, not only from external cyber-gremlins (no, that probably isn’t a real term), but also ensuring that the right people inside their organisations only have access to the data and analytics that is pertinent and relevant to them.  Governance and compliance might have a role to play in this depending on the vertical niche of the SaaS solution, and furthermore business rules help to bring clarity to ambiguous terms such as good, bad, best…


Yes, yes, every sales guru will tell us to focus on the benefits rather than features…this, perhaps, is even more true with analytics, why?

  • The risk of trivialising something of highly differentiated value into “sales blah-blah”
  • The risk of missing the value-context based on the personas you’re engaging with
  • The risk of prescription-before-diagnosis
  • The risk of “snatching defeat from the jaws of victory” with a “near-miss”

So it’s as much an engagement process as a sales process.  It’s about eliminating the unimportant and focusing not only on what's important in general terms, but in the best scenario, with a clearly rationalised, enumerated and specific business case.

You already have at least some theories as to how analytics can bring value to customers.  What we’re looking for is some kind of reusable framework, so that we can learn from our customer interactions and get more “hits-than-misses” when pitching analytic use-cases to customers.

What is value?

What is value?  How is it formatted, what measures are used to define it, relative to what?  There needs to be clarity about what value is, and it has to be contextual to your customers as well as the personas inside your customers, who hopefully will become or are users of your SaaS solution.

Value is often measured in monetary terms, increasing sales, optimising inventory, reducing costs, but it could equally be something that requires other metrics such as improving patient outcomes or customer satisfaction.

Building a value proposition

Rather than coming up with a value proposition before talking to customers, we’re going to attempt to “reverse-engineer” this based on our customer engagement and build a reusable framework from that.

Obviously, the more we engage in this process with customers, the more commonalities will emerge, so that new value discoveries will plateau and it will become more a pick-&-match process from already uncovered value rather than “reinventing-the-wheel” every time.

Generally a value proposition will need:

  • Your (SaaS) Capabilities:  Some core features, functions and capabilities, ideally with at least some (individually or perhaps in combination), which are unique to your SaaS solution, which in turn drive
  • (new) Customer Capabilities:  Empower customers with capabilities of their own (relating to their specific business or operations), resulting in
  • Customer Value:  Benefits, ideally quantified using metrics that can be packaged into believable business cases, with
  • Metrics:  ROI and payback periods driven ideally by quantifiable (predicted or benchmarked) improvements

So let’s turn that around:  (i) ROI and payback driven; because of (ii) benefits brought about by; (iii) your customers able to do specific things better, faster or cheaper than they could before; because (iv) your SaaS solution makes that possible in ways that weren’t available to them before.

How would we uncover and express this in a way which is customised and contextualised to SaaS solution provider customers using analytics?

The diagnostic process

Like any sales process, this starts with discovery or diagnosis.  With analytics, this is about understanding which data points are relevant to which people and processes.

For example, if we asked one person to walk us through a day in the life of their job…pretty quickly, key data points will emerge as well as satisfaction or dissatisfaction on the extent to which these data points help them in their job.

This table is a simple example for shaping that discovery conversation and could be used to build templates for this purpose.

Persona Review Process Review Analytic Value IMPACT
Starting with a Job Title, but also visible in this person’s (LinkedIn?) background…What is this person’s job description?

How are they measured, what constitutes doing their job well?

Might include: purchasing, selling, marketing, operational planning and/or execution, workflow processing, workforce or asset assignment…and many more…

What processes are they participating or engaged in?
Contextual to the persona and the process(es) they engage with, typically expressed in some combination of better, faster, cheaper.

Explore data points and metrics which help define these.

Not always monetary.
Metrics should hopefully now be agreed to measure impact

Bonus for those able to also agree (conservative) improvement anticipated, typically expressed initially as a %age, but usually translatable to something more tangible.

Of course the same questions from different people will likely result in different, sometimes conflicting answers.  That’s not at all a bad thing, it’s a good thing that allows clarification, validation and ultimately consensus.  That can often be customer value in itself.

Pain -> IMPACT -> Prioritisation

You can create your own unique, contextual information-gathering templates relevant and specific to your SaaS solution.  You could even turn this potentially into questionnaires or online surveys to gather information in a consistent way which is well formatted for comparison and evaluation.

We should now be in a better place to understand the daily challenges of personas at our customers, what the important data points are and which could be enhanced to bring value.  If we’ve taken the extra step to identify the quantified impact of these new analytic capabilities we also now have business cases validated with the customers.

We’re no longer theorising possible value with customers we’re simply summarising, back to customers, value that they have themselves described in a format facilitated through our robust, discipline in information-gathering.

What’s left?...Prioritisation.  Depending on your ideal customer profile, some SaaS-selection decisions might have input from tens of people, smaller decisions are normally touched by at least 3 kinds of people (an operational/technical evaluator, a commercial evaluator and an executive sponsor or approver).

Each of those will have different analytic perspectives and priorities, so we want to be sensitive enough to cater for those in our conversations and presentations…”now Frank, I know you’ll be interested in this”...”Janet, this should interest you”.

Analytics can drive navigation

So we’ve gone through a diagnostic process to prescribe which analytics will be important to which personas and, by implication, which processes.  In order to understand impact, we also know which actions would be taken to rectify or optimise issues to bring value or improvement.

So is it analytics showing up sometime or when the user feels like doing some analysis or looking at a dashboard?  It can be, but the ideal objective is to bring analytics as a pervasive and contextual part of your user experience, continually coaching, guiding, advising, informing to bring users comfort, confidence and convenience throughout their daily work in your SaaS.

That can start with a dashboard, a dashboard makes a great start to a navigational journey, because it uses business rules and contextual/conditional formatting to highlight anomalies which typically guide the user to start a root-cause navigation.  It’s a beginning, not an end.

That navigation might end with a single record - “Ah…there’s the issue - I know what to do now!”, but might equally result in many records where each needs custom, perhaps unique parameters to rectify an issue.

The beauty of using analytics in getting things done, is not typically one-at-a-time, but actually 10s, 100s, or 1,000s of things being done perhaps at the click of a button.  By tightly integrating or embedding analytics into your SaaS, this parameterisation becomes viable.

Analytics ≠ analysis

It’s normal, best practice even, rather than demonstrating features or modules, instead to tell a story from the perspective of a person, ideally a named person, even better if the named person is in the meeting!

What does a story look like?

Here is a highly stylised, synthetic example, which hopefully helps:

“Angela, come into the office grabbing a coffee and a muffin before sitting down to work.  She’s stressed because she knows the first thing she needs is…

Once Angela has got this done, she needs to get onto…”

Which important pieces should the story include?

  • It asks us to imagine what it’s like to be “Angela”
  • We empathise with her, because we have felt the same feelings she has
  • The story is contextual and relevant to what we do in our jobs or perhaps the people who work for us
  • There’s a “before-and-after” where the impact of the SaaS solution becomes clear even if we haven’t explicitly called the individual features/functions out.

Perhaps the most important take-away from the story-telling process, is that analytics is an enabler to getting things done.  Its purpose is not to have Angela spending half her day analysing.

Value comes from getting things done, not from analysing or “getting the data”.  Analytics brings context to the data, that’s a job facilitated by the SaaS solution provider.  The value is more than that, it’s the SaaS solution provider connecting analytic result sets to actions in the solution and at scale…schedule these, optimise those, reassign these…”click”.


It’s difficult to cover so many unique and exciting opportunities analytics brings for so many SaaS solutions with one generic blog designed to help us move forward on the analytic value-establishment and selling thinking process.  SaaS companies are innovating everyday on new value they can bring customers with analytics.

Analytics is constantly evolving as end-customers are continually looking for improvement in their organisations.  It helps, then, to think about analytics delivery/fulfilment iteratively, perhaps something every quarter or every other quarter, rather than some kind of a big-bang approach.  Don’t worry though, it’s absolutely something that can be templated and standardised, it’s not all ad hoc and we can use powerful filters, navigational, clicks, drills and other tricks to facilitate a frictionless user experience.

The best possible, though not the only, source of information in defining that value is customers.  Does it need saying then, that we’d better make sure our customer-facing people are really well equipped to conduct, format, shape those conversations to maximise and sell value both for the SaaS solution provider AND its customers?