When it comes to tracking the numbers of your SAAS product, Google Analytics is not sufficient anymore. This is the hard truth, believe it or not.
To go one step further, you need to properly implement a behavioural tracking analytics tool and think about your SAAS product differently.
Integrating behavioural tracking thoughts into the ones about your SAAS product can be tricky. Really tricky.
If in theory, the things are really simple, when you arrive at the point of concretely define what to track you may become really confused.
I’m going to explain to you exactly what changes in your thoughts about your SAAS application when you think at behavioural tracking and how to plan and implement it properly.
The differences between Google Analytics and behavioural analytics tools
Google Analytics is really a great product.
With it, you can measure a lot of things and get a lot of actionable data.
However, Google Analytics is not the best tool to track the numbers of your SAAS product (or of your e-commerce) as it has some limitations.
This doesn’t mean that you have to remove it completely.
This only means that you need to pair it with another tool, with different features.
Yes, I’m speaking about behavioural analytics apps.
Here are some of the main differences between Google Analytics and the behavioural analytics tools you can use.
When configuring Google Analytics, you need to already have in mind the numbers you need.
If at a certain point you need to measure something new or extract some other numbers, this can get really complex and the worst thing is that they may not be retroactive: this means that if you need to measure a goal you didn’t set early on, you simply have unusable data.
Also if concretely the data are in the Google Analytics servers, you cannot interpret them under a new light because Google Analytics doesn’t allow you to do it.
If you are running an e-commerce company, this may not be a big problem: after all, the metrics of an e-commerce are all really similar and well known in advance: in this case, you can properly configure Google Analytics beforehand and have all the data you need from the start.
But if you have to measure a SAAS application, then the things become really more heterogeneous and the metrics are not always predictable in advance.
Really linked to the previous point is this second one.
Google Analytics doesn’t make your life easy when you need to extract and link data in new ways at which you didn’t think the first time you configured the tracking.
In some cases, get additional data in an understandable format requires you to create new views or do tricky “hacks” to get the data you need or extract the evidence you need.
A behavioural tracking app removes this complexity making you able to elaborate and combine data the way you want, the way you need!
Point of view
This is maybe the best difference between Google Analytics and behavioural tracking tools.
Google Analytics is focused on page views while the behavioural tracking tools are focused on customers and the things they do WITH your application and IN your application.
Google Analytics measures pages first, and then, incidentally, users and their behaviours.
Behavioural analytics tools, instead, measure first customers, and eventually, if you like, also page views.
This is reflected also in the data you can get from them: while in Google Analytics, for example, you can get the keywords searched, this data is never present in a behavioural analytics tool (almost never as they anyway can track UTM params).
This kind of tools answers other, more relevant questions:
- How do my customers use my product?
- Why do they do one thing instead of another?
- What my best customers did before to become customers?
Google Analytics can answer to these questions, too, but only incidentally and anyway always partially, and not at the level that that kind of software provides.
This is an important switch in perspective: the main mantra in the startup world (and in the e-commerce world, too) is “Customers first”.
Is for that reason that methodologies like “customer development” become so popular (and useful!).
To put it at its full power, you need to measure properly the metrics that matter.
The issues of Google Analytics that harm you
You cannot collect PII.
PII stands for Personal Identifiable Information.
The main purpose of a behavioural analytics tool, instead, is exactly the opposite: identify the visitor as a person, so it is possible to link his/her journey as an anonymous visitor to his/her journey as a registered user.
This opens a new world of analytics possibilities as you are able to really understand what your users want and do.
Going deeper into the differences with Google Analytics: the solved challenges
So, to be more clear, let’s better understand which are the challenges that behavioural analytics tools solve.
Woopra explains them very well:
1) Non-linear Customer Journeys and Challenges in Data Democratization
The modern customer journey is highly complex and non-linear in nature. To understand the customer experience across channels, teams and tools, you must expand beyond the silos of strictly marketing or product related touchpoints.
Today’s businesses need to derive insights from across the organization, leveraging marketing, paid and unpaid advertising, customer success, product, support, sales and other groups within the company.
Most businesses find it challenging to democratize data across the organization and obtain a 360-degree view of their customers to drive end-to-end business objectives.
2) Limited Understanding of Product Engagement
It’s crucial to know how prospective users engage with your product or service in order to identify leads at the right time and on the right channel. This is especially true for SAAS companies that offer a ‘freemium’ model when acquiring new customers. Often, they spend an unreasonable amount of time and resources on lead acquisition by sending repeated communications to prospects on an action such as a free trial sign up form.
Unfortunately, given the vast array of options available out there, such actions are no more telling of lead’s propensity to purchase. What’s worse is that unwanted emails or calls can do more harm than good by deterring them from your product or service altogether.
For a company focused on the customer experience, actions such as repeated use of certain features, downloading how-to tutorials and viewing software setup videos are much more indicative of interested prospects. However, there are few tools that track behavior across a product or service while enabling teams to engage with their users in real-time.
3) Functional Data Siloes
With an estimated 66 different SaaS applications that are in use per enterprise – primarily for web and social traffic based analytics activity – companies are unable to properly leverage the data flowing in their organization to generate meaningful and timely insights.
Moreover, the market is crowded with solutions promising results such as higher conversion rates that are generating further confusion about what solution to implement, at what cost, and at what time. Such a conundrum gives rise to data siloes that often fail to render timely insights to business users. Most importantly, companies often fail to realize how difficult it is to bring all the customer-centric data together in order to find a single source of truth.
Given the fragmentation of investments in tools across all sizes of enterprises, a unifying customer journey analytics solution integrates with all the relevant applications to provide a singular view of each customer and better analyze their interaction and needs across all channels. Companies need to anticipate their customers’ behaviors and shift how they think about and engage throughout the decision journey.
4) Inability to Monetize from Data
Although every organization is now aware that data is their gold mine, few have managed to establish a sound data strategy that will allow them to consistently monetize from it. Most companies have trouble simplifying their data architectures to build a single layer that augments all the relevant customer related information in one central location. Connecting with leads and channelizing efforts to truly convert engagement to revenue is that piece of the puzzle that unfortunately many are still trying to solve.
Which are the alternatives to Google Analytics
There a lot of tools on the Internet that can help you better and more deeply analyze your SAAS application.
The most popular are three (in simple alphabetical order):
They differ for the price, but the features are quite the same: track events, track customers and combine data in useful reports.
Think at tracking when you think at your product
Tracking is not something to think about later, when your product is built and ready to be shipped (really early! Does the acronym MVP sound familiar?).
During the development of your product, you need to think at the permissions of your users: each set of permissions usually links with a journey (a funnel) and with an actor.
You have to merge the tracking part with the development part: still during development, when you design your internal flows, you have to highlight the KPIs: how many visitors became users? How many users became customers? You are understanding the song, don’t you?
In doing this, you need to also consider privacy.
This is called Privacy by Design and is a fundamental part of your product: the GDPR requires you to have precise documents that explain what, why, how and for how much time you treat personal data.
This job is useful also for your documentation: you need to explain to your customers how to solve their problems using your products.
All this without speaking about marketing (not only content marketing and blogging).
So, thinking at tracking already during the design phases of your SAAS product reverberates its beneficial effects on at least five areas of your startup:
- Flow design and usage paths;
This is not a win-win, this is a jackpot!?
Preparing for tracking
So, you are already doing the hard parts of the tracking instrumentation.
Below there is a step by step flow you can follow: it will turn useful also for developers and your product team: They will love you for this! ❤️
List your actors
Any person on your app can do a lot of things.
Now, some user can follow some journeys, some others can follow some other journeys.
When a user can follow a particular journey, we call him/her an Actor.
Practically, an actor is a User who is making a journey to do something.
For example, on TrustBack.Me the journeys that a User who is a Merchant can follow are different than the journeys that a User who is a Customer can follow. Incidentally, a User who is a Merchant can be also a Customer.
So, you should list your Actors.
Highlight the journeys (flows, paths, funnels)
Now that you have a clear picture of your Actors, you can go to the next step: identify the journeys they can make.
In your application, any user can do various things to accomplish a task.
This is commonly called a flow (the devs call it that), or a funnel (more salesy term) or path or a “journey”: yes, this last term is the one we prefer.
The term “flow” is aseptic, so logical.
The term “funnel” has implicitly a connotation of sell: you have sale funnel, the lead capturing funnel and so on.
The term “journey” perfectly highlights the experience your users experience using your product.
After all, the UX is the first thing you should take care of in building your SAAS application!
So, coming back to our main point, some journeys can be followed by some users, while some other journeys can be followed by some other users.
For example, in Facebook, a User can post a status, upload a photo, comment on a status or a photo, leave a Like, ecc.: all those things are a journey (or a flow from the development perspective).
So, the first thing you should do is to highlight the possible journeys your users can experience in your application.
2. Identify key events in journeys
Each journey fires some events: those are the events you are going to track and measure to see how they relate and which frictions exist in them.
Once you have your journeys highlighted, you need to understand which users can follow which journeys.
When a User follows a journey (s)he is not a user anymore: (s)he is an actor as (s)he acting like a particular kind of user.
For example, in Facebook, a Merchant who is promoting his/her page, act differently from a User who posts a status.
And also if the posting of a status can be done by a Merchant on his/her page or by a user on his/her own profile, those are two completely different journeys.
So, the next step is to give a name as an actor to each user who is following a journey.
For example, on TrustBack.Me, among the others, a User can be a Merchant or a Customer (someone who bought something on the store of a Merchant).
Highlight the relations between journeys
Journeys are not standalone sequences of events.
Practically, each journey can lead to another or to more than one another journey.
For example, on TrustBack.Me, the Reclaim Domain journey leads to the Getting Started journey which goal is to lead the Merchant to configure the connection between TrustBack.Me and his/her e-commerce store.
We also do our best efforts to make a buyer who releases a feedback (release a feedback journey) become a Consumer, following the Hard Email Reclaim journey: the two are linked. And once the Buyer becomes a Customer, we do our best to understand if we can make (s)he transition to the Merchant Actor figure following the Domain reclaim procedure (if applicable).
Highlight the transitions between actors
Continuing to navigate your journeys, any user takes the clothes of different actors.
So, you need to understand the possible transitions between those actors figures.
For example, with TrustBack.Me our interest is to make it happen that the actor Visitor transitions to the actor Merchant.
Or that a simple Buyer transitions to the actor Consumer.
When analyzing your data, you can track the progress toward your app’s main goals.
This was only a birds eye in your journey to implement a behavioural analytics tool to track your application.
There are many other things you need to consider, write and do before you are ready to fully implement and understand how to use a tracking tool like this.
Anyway, you now should have a better understanding of the differences between Google Analytics and behavioural analytics tools: Google Analytics focuses on Page Views; behavioural analytics tools focus on people and the actions they take in your app (events).
Remember to “Make. Ideas. Happen.”.
I wish you flocking users, see you soon!