Go-to-market (GTM) teams tend to operate in silos, and they are often not in perfect alignment with one another when it comes to understanding, prioritizing, and actioning data. The data is plentiful, but how it is organized, understood, and acted upon is a very different story.
SaaS customer intelligence is an elephant of a problem 🐘.
That is to say, its incredible large and complex. It is not uncommon for people and teams to analyze the same customer and come to dramatically different conclusions, which leads to a number of different problems 🤔.
Here's the thing, each division of a business looks at customer activity through the lens of how it impacts their teams objectives.
👉 For marketing, it might be generating demand.
👉 For product managers, we’re talking product usage and adoption.
👉 For sales, it’s signing new business.
👉 For customer success, it's retention and growth.
The truth, for every member of your organization, is relative to the particular problem or opportunity each team member is focused on at that given point in time. And because each team has unique KPIs, this unknowingly creates bias in how they interpret, prioritize, action, and value customer data.
Does every business face these challenges?
But, the nature of SaaS, and the rapid pace at which new customer tools are adopted has distributed customer data across any number of unique and siloed sources. Customer data now exists in your CRM, in your product, in your helpdesk, in your chat tool, in your community platform, in your sales call transcripts, in online forums, on social media platforms, on reviews websites … and there are new sources popping up all the time.
This truly is an elephant of a problem and businesses that become adept at connecting the dots and learning to leverage this data to better understand and effect the customer journey will truly have a competitive advantage in the years to come.
The Evolution of Business Analytics
1st Generation Business Intelligence
15 years ago, if you wanted real business intelligence, you needed an entire infrastructure dedicated to soliciting, interpreting and leveraging insights from customer data. This would require (at minimum), a data team, large time commitments from your data team and other company resources, and lengthy extract, transform, and load (ETL) processes from your data engineers in order to actually use the data downstream.
Not to mention the process of incorporating new tools into a usable solution takes months of time and requires highly-technical personnel.
These factors are part of why business analytics has always been an incredibly expensive and time-consuming process.
For these reasons, investing in business intelligence (BI) didn't really make sense for Software as a Service (SaaS) companies until they reached a certain size, and had enough revenue and expertise to justify it (Call it $20M+ ARR). Because SaaS businesses move incredibly fast, and need access to accurate and timely data, GTM teams were forced to operate in their siloed tools to optimize and grow their areas of the business.
2nd Generation Business Intelligence
The next generation of Business Intelligence tools came in the early 2010s with tools like Looker, Mode, Periscope, Tableau, Domo, and Segment. These tools make it a little easier, faster, and less expensive to bring BI into your business. By enabling non-technical people to explore data freely, but only after significant effort from data engineers and analysts. And, the cost of this software, combined with the cost of data engineers and analysts to run the tools, still priced many SaaS businesses out - opting to push data into SFDC or conduct ad hoc analysis in spreadsheets.
2nd Gen. Business Intelligence still requires:
👉 Technical knowledge to build and maintain the BI tech stack
👉 SQL expertise to run queries and conduct analysis
👉 Significant financial investment in both software and human expertise
👉 Extended periods of time to conduct ad hoc analysis
As you can see, while 2nd generation tools did significantly improve business intelligence capabilities, they still required significant data skills, large budgets, and deep technical expertise. All factors that continued to limit business intelligence investments for many organizations.
3rd Generation Business Intelligence
We’re now entering a third generation of Business Intelligence tools. These new tools have a strong emphasis on no-code (for non-technical audiences), actioning the data, and being available at lower cost.
This evolution has been happening for the past several years, and has been accelerated by a number of factors, including:
- Product-Led Growth (PLG) Movement – The product-led movement continues to build momentum and places a strong emphasis on product and customer experience, as well as a data-driven approach to prioritizing growth opportunities. PLG is also often community-driven, which creates a bulk of qualitative data that is very difficult to connect, analyze, understand, and action.
- Ongoing Data Privacy Legislation – A crackdown on the collection of personal data and privacy that came as a result of legislation like CCPA and GDPR has caused businesses to shift away from 3rd party data and turn their focus to 1st and zero party data. As businesses collect and own more and more customer data, they need tools that will enable them to leverage this data in real-time. This is a real opportunity that many businesses are still struggling with how to handle today.
- Economic Uncertainty – A downturn in the economy has caused venture capital (VC) firms to tighten funding requirements and force their existing portfolio companies to look within for growth opportunities and to focus on extending runway ahead of a potential recession. The days of throwing money at the BI problem have given way to searching within to identify and prioritize growth opportunities among your existing customer base.
- The Complexity of the SaaS Customer Journey - Lastly, customer experiences happen in a variety of different forms, across a multitude of channels. Some of those channels include businesses’ CRM, helpdesks, community, social media channels, chats, web traffic, and product usage, among others.
Amid this flood of customer touch points, it’s important to keep in mind that customer journeys are not sequential. They are more like a jungle gym of interactions and brand experiences that lead to an understanding and sentiment towards a company.
With that in mind, forward-thinking businesses are starting to focus on understanding both quantitative and qualitative data from all customer interactions. They are then using this data to gain a better understanding of their customers, build alignment internally, and deliver more amazing experiences.
And this brings us back to the problem ...
Customer data is messy, unstructured, and siloed.
Much of this data is free-form text, which complicates things further.
👉 CRM notes
👉 call transcripts
👉 in-app chat
👉 social media
It's these complex data streams that add the fine details to your customer journey and enable you to more accurately understand and deliver to your customers expectations 🎯.
Imagine how powerful your customer profiles become if on top of product engagement and CRM data, you layer on:
✨ sentiment data from social media
✨ contextual data from support tickets
✨ intent data from call transcripts
... and every other source of customer data you have 🤯.
The Promised Land
This web of customer touch points underscores the need to connect all of the disparate customer data sources in one place to make sense of it, without the complicated mess of data engineers, large investments and deep data analysis.
But the connection of this data is just the beginning.
For SaaS, data gets stale incredibly fast. The reality is we need to:
👉 join all of the data
👉 do it quickly ... in real time
And because the landscape is continuously evolving we need to be able to
👉 fold new tools in with ease
AND ... once the data is joined together, we need to provide a user experience that enables non-technical business people to:
👉 freely explore
👉 conduct ad hoc analysis
👉 share data, and most importantly ...
👉👉take data driven action based on real-time insights.
At LoudNClear, our mission is to provide you with the data, intelligence, and functionality required to help you WIN every encounter with your customers. Delivering amazing experience after amazing experience is the best way to ensure your customers stay with you and grow.
Today, teams are using LoudNClear to identify and act on growth opportunities, to identify and nurture advocates and referrals, to gain better visibility to the full customer journey, and to better understand and manage expansion and churn.
By effectively aggregating these disparate customer data sources, we hope to enable SaaS GTM Teams to up-level the orchestration of their go-to-market and deliver more amazing customer experiences across all customer touch points. But customer satisfaction is just the beginning.
Being aware of customer pain points and feedback also opens up growth opportunities with both existing customers as well as prospects. It helps teams across the organization get in sync, and creates a mutual understanding of the most important customer signals. This alignment enables GTM teams to more easily align to deliver consistent and timely experiences.
Downstream your customers reap the benefits of working with a business that deeply understands their perspective and is able to deliver communications of all kinds with empathy, focus, and purpose. They will love you for this.
Like the elephant, customer data is large and complex. If not considered in its entirety, it can lead you to inaccurate conclusions.
LoudNClear helps businesses look at the whole picture and deliver timely, relevant communications to more precise audiences.
The better your teams understand your customers,
the more quickly your business will grow.