Customer-centric organizations consistently outperform, win market share and grow revenue.
💡 Did you know that:
- While 80% of companies believe that they deliver “super experiences”, only 8% of their customers agree (Forbes)
- Organizations who deeply understand customer behavior drivers outperform with 85% higher sales growth? (Microsoft)
- Customer-centric companies are 60% more profitable than companies that don’t focus on customers
- and a moderate increase in customer experience generates an average revenue increase of $775 million over three years for a company with $1 billion in annual revenue (Microsoft).
How are they doing this? They’ve decided there’s already enough in-house customer data at their fingertips to deeply understand, design and execute the best customer experience strategy. Data collection isn’t the issue, it’s what comes next and leveraging that wisdom to help their organization achieve objectives.
What is a self service insight engine?
Definition: The self service insight engine is a process for deeply understanding customers (and their journeys) to drive revenue growth through continuous CX improvement.
Through a clever mix of superior people, processes and technologies, in-house customer insight teams efficiently deliver quality outputs. Business leaders are now free to make decisions with confidence and Insights Managers can execute on their insights strategy to achieve organizational objectives and deliver ROI.
Insights teams achieve these outcomes by strategically executing their own Self Service Insights Engine. This engine is fuelled by omnichannel data collection and new self service tools entering the market. In-house teams are drowning under the weight of customer data and this growth is not slowing down either. In addition, in-house teams now have access to self service technologies which they use to seriously reduce their time-to-insight by up to 90%!
The self service insight engine is a useful framework for understanding the challenge of discovering insights that inform business and customer experience strategies in today’s highly competitive landscape. In this guide we’ll cover how to build your own self service insights engine by focusing on five steps;
Step 1: Data centralization
Centralize your customer data to improve quality of inputs
Step 2: Self service technology
Adopt self service tools that enable your team to be more time and cost efficient
Step 3: Measurement
Secure buy-in from executives by measuring the dollar value of each CX initiative
Step 4: Monitoring
Proactively identify emerging CX issues before it’s too late to do anything about it
Step 5: Democratization of insights
Make insights accessible across your organization with visualizations and dashboards
Let’s get started!
Step 1: Data centralization
According to Gartner, 87% of business leaders believe they have low business intelligence and analytics maturity, partly driven by the problem of data silos. Data silos prevent your team from leveraging in-house customer data to its full potential. In their report, Gartner says that data silos “create a big obstacle for organizations wanting to increase the value of their data assets and exploit emerging analytics technologies such as machine learning.” If the customer insights team cannot easily access data (in a usable format), they won’t be able to uncover actionable insights that drive continuous CX improvement (and ultimately revenue growth).
Why are data silos detrimental to your insights engine?
As we’ve already established, business leaders find they are slowed down by inefficiencies with customer data collection.
This is how they can be detrimental to building your world class insights engine:
- They provide an incomplete picture of the customer journey and lead to the wrong assumptions being made about customer experience strategy.
- You miss insights that come from correlating one type of data or channel to another (for example NPS feedback, NPS score plus customer lifetime value).
- Cost inefficiency from storing the same data in multiple places.
- They reduce the quality of your inputs when a data point becomes inaccurate or obsolete. If data is obsolete in one channel, there’s no way to update this data in every other channel where that data resides without costly manual process and wasted time.
- They don’t facilitate democratization of insights (sharing insights widely across your organization and making those insights accessible to everyone). Data silos actually increase your time-to-insight and time-to-result.
Tips for centralizing your customer data
Here’s a few ways to centralize your customer data to improve the quality of your inputs:
- Review all the data you already have in-house and categorize by type and volume
- Collaborate with other departments to understand what they want to get out of customer data.
- Find technology to centralize customer data (such as a data warehouse) into a single source of truth.
- Onboard a Feedback Analytics platform that pulls in data from the warehouse and stores it for future use (for example to answer ad hoc business questions during a meeting).
- Get control over who can and cannot send surveys. This doesn’t mean all Qualtrics surveys must be sent by the customer insights team, but it does mean the team has a strong say in the data collection strategy. Survey fatigue is real and a business question might be answered with data already in your Feedback Analytics platform.
How to find the right data warehouse solution for your self service insights engine
This is what the high level architecture of a data warehouse looks like.
Architecture of a typical Data Warehouse setup
Data in its purest form is not easily understood by executives, so consider Feedback Analytics platforms for the analysis and reporting side. Dashboards and visualizations will provide insights that are accessible to non-technical users (such as executives or department leaders who want to get closer to the customer).
Here are some on-market data warehouse solutions to consider:
- Google Big Query
- Amazon Redshift
- Amazon S3 + Athena
- Azure Synapse Analytics
- Data Virtuality
- The PI System
But what should your buying criteria be? Intellectyx suggests evaluating based on 6 key criteria; performance reliability, usability, security, backup & recovery and company size.
The important thing to remember is to audit data already in-house vs. the data you’re missing. This will give you a more comprehensive picture of the customer journey. Then have a clear strategy for how you’ll use that data across the organization.
Step 2: Self service technology
New generation technology for customer insights is self service. Self service tools are putting power back into the hands of in-house teams, reducing their dependency on costly agencies and offshoring services. They’re intuitive and easy to use with focus on dashboards, visualization and storytelling. The self service revolution also supports democratization of insights, bringing insights into the DNA of everyday decisions. As such, it’s hard to skip past self service as part of your insight engine building. Without this crucial step component, insight generation is a confusing, costly and inefficient process.
As Forrester notes in their recent 2021 Forrester Wave report,
“Text analytics is table stakes, but not all text analytics capabilities are equal. Challenges with a vendor’s text analytics is a key reason why enterprises add other technologies to their stack.”
When considering your tech stack for customer insights, consider the utility of each tool for each job you want it to perform.
Benefits of self service technology
What are the expected benefits for insight teams when adopting self service technology?
- It’s easier to tell a consistent, visual story about customer experience (and their journey). Importantly, this storytelling is easy to access, consume and share with stakeholders outside the insights department.
- Self service supports in-house teams to produce the same level of outputs as external agencies, but with the added benefit of domain knowledge about how their unique organization works.
- Business leaders are always going to have questions about customer experience, journeys and impact on the metrics they care most about. Self service tools allow in-house teams to answer these questions in real time. This speeds up decision making, regardless of how much customer data you have to analyze.
- There’s no need to outsource or offshore tagging/categorization of customer issues because these self service tools will automatically do this for you. Unfortunately some vendors claim to be automated when in fact they’re offshoring the tagging without your knowledge. True insights are found without any human intervention or pre-training. Technology should reveal the problem areas and empower humans to find what’s important to their organization.
Step 3: Measurement
Measuring the ROI of business initiatives is hard, but not impossible. Start by evaluating your current level of insights maturity.
What do insights currently look like? Is the team just one customer insights specialist with limited funding tasked with manual coding NPS surveys every 3 months? Is it a team of four tasked with working closely with product, marketing and operations to support the activities of those departments? Are a suite of tools used to assist with collection and analysis or does the team get by with SPSS, excel and few other fan favourites? Where do insights go after they are uncovered? Once wisdom has been uncovered, are those insights actioned and delivering tangible results for the organization?
The higher your insights maturity level, the more value your organization gets out of measurement and reporting activity.
How to measure the value of your CX initiatives
“Return on investment (ROI) is a financial metric that is widely used to measure the probability of gaining a return from an investment. It is a ratio that compares the gain or loss from an investment relative to its cost. It is as useful in evaluating the potential return from a stand-alone investment as it is in comparing returns from several investments.” - Investopedia.
How do you measure the ROI of your self service customer insights engine?
The traditional approach to calculating ROI uses the following formula:
Another way to do it is by factoring initial value of invest:
For customer insights managers, the simplest method to understand ROI is by assigning a dollar value to your CX metric (such as NPS/CSAT) and then measuring the impact each CX initiative has on that metric. In this way, you’ll be able to prioritize initiatives by commercial outcomes rather than fixating on NPS movements. This is important because executives want to confidently make decisions about strategy and can’t do that until it’s clear what is and is not working with their CX delivery.
20 common business metrics for measuring customer insights
CX metrics aside, there are secondary metrics to consider when measuring the performance of a business unit using feedback analysis. Here’s 20 of the most common metrics high performance customer insight teams can use in their reports.
- Sales Revenue
- Net Profit Margin
- Gross Margin
- Sales Growth Year-to-date
- Cost of Customer Acquisition
- Qualified leads per month
- Lead-to-Client Conversion Rate
- Monthly website traffic
- Employee Happiness
- Cart Abandonment rate
- Average items per sale
- Average transaction value
- ROAS (return on advertising spend)
- Market share per region
- The viral coefficient (referrals)
- ROAS (return on advertising spend)
- App store traffic / website traffic
- Average deal size
- Average sales cycle time (taking longer to close a deal can add to the cost when calculating ROI)
- ROAS (return on advertising spend)
As you can see, there are multiple ways you can and should measure the success of business activity with customer insights. This wouldn’t be possible however without due consideration paid to data centralization and pulling together different types of data to run alongside your feedback data.
Step 4: Monitoring
Insights should always drive action. This is where the DIKW pyramid can be improved because it essentially stops at wisdom and fails to close the loop.
Criticism of the pyramid has usually focused on this aspect of the model. If the size of each stage is representative of time/effort then it really needs to be an inverted pyramid. The top of the pyramid (wisdom) is where you are widely sharing insights, identifying champions and creating a sense of urgency around the insight to drive action, results and long term success. This takes time and effort. The lower stages such as the Data and Information layers should be automated and as efficient as possible so your team actually has time to focus on those highest leverage activities.
This is where monitoring enters the picture. Monitoring means identifying emergent trends and actioning these issues before they become a problem across the organization. Monitoring also gives answers on the impact of decisions made over time. In the previous step we covered measuring the impact of CX initiatives on overall NPS and assigning a real world dollar value to every 1 NPS point. Your self service feedback analytics platform will be able to provide a level of predictive insight into the impact emerging issues are having on those CX metrics. Not all emergent issues from feedback analysis will be negative/complaints oriented. If you’re sampling widely with surveys and scaling up your voice of customer programs, you should be able to identify emergent growth opportunities as well.
Step 5: Democratization of insights
“Executive sponsorship is vital to this level of organizational change and the best champion sits in the corner office, [however]..most executives are not comfortable accessing or using data.” (Deloitte).
The fifth step in building your self service insights engine is to consider how accessible your insights are to the organization. For example, when an insight relating to the product department is discovered, is it shared with that department? Is the insight actioned? Is the insight made available across the organization so other departments can digest and make sense of it in the context of their projects/initiatives?
A recent Kantar Vermeer study found across high-performing companies, 79% of insights teams participated in strategic decision making at all levels of the organization (compared to just 47% with under performing companies). Creating these connections and emphasizing collaboration early on will help demonstrate the value of customer insights as a key function of the business.
Focus on impactful storytelling
Choosing the right technology to share insights can make or break your team’s success. Once the customer insights team has gathered the data, they need an intuitive way to share insights across the organization. Data democratization works with easy-to-use dashboards that decision-makers can dig deeper on, in order to understand the “why” behind your insights. These dashboards help keep everyone across the entire business informed (hence the ‘democratic’). Democratizing customer insights is essential for organizations adopting what Forrester call an insights 'center of excellence'.
A good way to share customer insights across the business is displaying a customer insights dashboard on TV monitors around the office or provide a link to a public dashboard in your preferred internal communication app ( Slack, Microsoft Teams etc).
Example of a dashboard visualization in Kapiche
Storytelling through customer insights supports the following goals:
- Improving credibility and trust with decision makers
- Empower staff outside of the insights department
- Retain and spread domain knowledge before it’s lost
- Build the brand of customer insights at your business
- Support a specific business level OKR
High level storyboards such as this one by Kapiche provide contextual understanding of the relationship between each of your themes
Close the loop: Customer data collection
At the beginning of this step-by-step guide we started with how to centralize your customer data. The problem faced by insights managers wanting to execute an insights engine strategy is there are ever growing volumes of customer data to wrangle. Enterprise level organizations are drowning under the weight of it.
Here’s a few examples to represent the scale of the problem:
- In 2020, people created 1.7 MB of data every second
- By 2022, 70% of the globe’s GDP will have undergone digitization
- In 2021, 68% of Instagram users viewed photos from brands
- By 2025, 200+ zettabytes of data will be in cloud storage around the globe
- In 2020, users sent around 500,000 Tweets per day
- By the end of 2020, 44 zettabytes will make up the entire digital universe
- Every day, 306.4 billion emails are sent, and 500 million Tweets are made
Ryan Stuart, CEO of Kapiche says, “The idea that you can collect all this customer data and if you do a good job at democratizing access to that data then you’ll get the insights you’re looking for and will suddenly become a data-driven organization, when it comes to interpreting experience data, I actually think this is incorrect.” In his view, more needs to be done to ensure insights are actioned and new data is collected to cover potential data black holes that arise when important sources and channels are ignored.
Its clear volumes are increasing year-on-year and good advice is always to work with the data you already have in-house.
Customer data is the fuel that powers your insights engine. The insights engine is however a virtuous cycle rather than pyramid. Closing the loop means figuring out where you have data block holes and opening up new sources and types of customer data.
As has already been established, self service technologies have made it possible to wrangle large volumes of customer data in 90% less time than manual coding. With this in mind, don’t be afraid to scale up your data collection efforts. More fuel for the engine drives deeper insight into where to improve customer experience strategy to deliver commercial outcomes.
Building a self service insights engine takes time but the effort does pay dividends for organizations that want to constantly align their business strategy with customer experience.
Customer data is the fuel that drives this engine. An operational insight engine is capable of handling any volume of customer data you throw at it. Collecting more customer data across a diverse range of channels gives more context to each feedback analysis project. Combining what customers say about you in a survey, their NPS and LTV for example allows your team to identify the highest leverage points of friction in the customer journey. While you can do this easily enough at the focus group level, scaling up across all survey responses gives you more confidence to make business decisions.
This scale is achieved through self service technologies that allow in-house teams to do more, for less. Precisely measuring the impact themes are having on overall NPS helps connect the dots between your CX initiatives and revenue growth. Your organization is then free to allocate resources more effectively and also grows the credibility of customer insights as a business function. A key benefit here is you’ll be in a stronger position to capture market share from competitors while also defending your current position in the market. Precise insights measurement is therefore both an offensive and defensive tool in your arsenal.
As more customer data is collected for your insight engine, there are more opportunities to spot emergent trends in that data. These trends might be serious issues with customer experience or their journey but could also be revenue growth opportunities that your organization won’t want to miss out on. Monitoring and then widely sharing these emergent trends will further strengthen your customer-centric culture.
Lastly, your insight engine must close the loop by incorporating internal demand for insights into your data collection strategy. How could data collection be improved? What data is missing? Which channels/sources/types are required to further enhance the engine? Ultimately the benefit of the self service insight engine is it becomes your engine for revenue growth through the power of customer insights at-scale.
In this section are links to additional resources which you might find helpful.
- Kapiche Demo Video [Insights Analysis Product]
Merlin Stone, Liz Machtynger, Jon Machtynger, ‘Managing customer insight creatively through storytelling’
[Video] Simon Sinek - Start With Why - TED Talk
Sam Ernest-Jones, ‘Data Storytelling: Using Consumer Insight to Strike a Chord’
Think with Google, ‘Creative storytelling built on insights to win over time-poor audience’
CMO, ‘Bringing customer insights and brand storytelling together at ASB Bank’
Resonate, ‘10 Things We Learned About Insights-Driven Storytelling’
Forrester, ‘Improve Your Insights Storytelling With Speedthrough Stories’
Import.io, ‘8 fantastic examples of data storytelling’
BuzzSumo, ‘How To Create Engaging Data-Driven Stories: 5 Tips & Ideas’
⚙️ Insights Engine
HBR, ‘Building an insights engine’
Boston Consulting Group, ‘Rewiring Customer Insight to Generate Growth’
Forrester [Paid Report] ‘Predictions 2020: Customer Insights’
Christine Barton, Lara Koslow, Ravi Dhar, Simon Chadwick, and Martin Reeves, 'Building a better customer insight capability'
Unilever, ‘How to Build an Insights Engine’
Frank van den Driest, ‘How to Build a Successful "Insights Engine"’
CMS Wire, ‘Here's Why Insight Engines Are the Next Big Thing’
💹 Insights ROI
How to measure the ROI of your Customer Insights program
Assigning a dollar value to your NPS results
Boston Consulting Group, 'Measuring the ROI of Customer Insight'
The myth of NPS as a lagging indicator
Why you should still focus on NPS even if your score is good
Common NPS mistakes and how to avoid them
4 ways to improve your Net Promoter Score (NPS)
NPS Approaches: Operational vs Strategic
Deanna Lazzaroni, 'The Top Skills Companies Need Most in 2020—And How to Learn Them'
Tom Davenport, Tim Smith, Jim Guszcza, Ben Stiller, ‘The insight-driven organization’
🎓 Wisdom Hierarchy
DIKW Hierarchy – Understanding the Concept of Wisdom
Journal of Information Science, ‘The wisdom hierarchy: representations of the DIKW hierarchy’
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