Only the smart survive. Organizations that deliver exceptional experiences grow fast and hit their objectives. As more organizations realize the value of customer experience they will need a dedicated customer insights function to align customer experience strategy and initiatives to the experiences customers actually want. This guide will help you realize the full potential of the next phase of CX maturity - the customer insights team.
💡 Did you know?
- While 80% of companies believe that they deliver “super experiences”, only 8% of their customers agree
- It can cost up to 25x more to acquire a new customer than to retain an existing one
- Leaders in customer experience, on average, experience 5x the amount of revenue growth compared to companies who deliver a poor customer experience
Boston Consulting Group surveyed CEOs, Presidents and COOs and found more than 75% believed the customer insight team was essential for accelerating growth. BCG also identified building out the customer insights function as a top spending priority, far ahead of other investment areas such as talent development, pricing analytics, brand development and big data/customer analytics (see chart below).
Unfortunately, only 19-32% of customer insights teams felt they had the full support of their CEO and executive team. This is surprising because Forbes says companies which embrace customer centricity are 60% more profitable, so why is it recognised as important by decision makers, yet those same decision makers lack confidence in the abilities of their own internal insights function? The answer has to do with quality of insights being produced. If you manage a customer insights team, how actionable are your insights? Does the insights team report the ‘what’ or do they take it a step further and also cast light over the ‘why’?
Simon Sinek argues, businesses who know ‘why’ they exist, are always more competitive because they tap into a part of the brain responsible for ‘gut feel’. Customer insights don’t always achieve the same level of cut through with c-suite executives because they similarly focus on facts (NPS declined, sales were down in this region and after hand coding qualitative data we found a lot of people were complaining about the checkout process). But why did this happen and what can be done to improve? Understanding the ‘why’ underpins most decision making whether we like to admit it or not. If customer insights are to be taken seriously, your team needs to build that customer insights engine decision makers want to continue to invest in. In this guide, we’ll run through, step-by-step, the process of building an respected insight engine.
These are the steps involved:
Step 1: Get the right people in place
Step 2: Optimise your technology stream
Step 3: Know your current capability so you can plan a destination
Step 4: Build credibility and put the customer insights team at the centre of all decision making
Let's get started!
What does a successful customer insight engine look like?
Before we get stuck in, it’s important to get our definitions clear around customer insights, consumer insights, customer experience and insights engines because many of these terms will be used throughout this step by step guide.
Customer insights: A customer insight is a deep and meaningful understanding of customers. It’s actionable and ultimately leads to improvements in customer experience delivery.
Consumer insights: A consumer insight is a deep understanding of how customers think and feel that drives better messaging and improvements to product design and marketing strategy.
Customer experience: A customer experience is the overall experience and perception of your brand each customer has at every touch point on their journey (for some types of businesses this includes each support interaction once they become a customer).
Insights engine: A system that draws upon multiple internal data sources (both quantitative and qualitative) and which is augmented by artificial intelligence to drive the discovery and storytelling of customer insights.
Proving 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 customer insights engine? It’s actually quite hard to effectively connect the dots between what customers say and those key business metrics decision makers care about (such as revenue). When this is done however, the credibility of your insights engines increases in the eyes of decision makers.
There are two ways to calculate ROI:
and the other approach is:
For customer insights teams, the simplest method to understand ROI is by assigning a dollar value to your CX metric. Other metrics such as CSAT, CES etc will follow a similar methodology.
Practical Example: A home flooring supplier was analysing their customer feedback and identified a theme around Timber Floors. They find everyone with something to say about timber flooring is dragging NPS down by -11.4 (which translates, according to their own ROI calculations, into $4.1 million in lost annual revenue). The team could deep dive into what is going on with timber flooring, discover if this is unique to timber flooring or flooring more broadly and also look deeper to see if this affects a specific customer demographic. Armed with these insights, actionable conclusions could be drawn and linked back to a dollar figure.
This is an example of how a sceptical c-suite can be persuaded to see the value of customer insights. It’s not about, spreadsheets, slides and going through numbers, but understanding what the story is behind every number. This is where the human element very much comes into play. From the Wisdom Hierarchy, we know Knowledge becomes Wisdom when given greater insight. Elite customer insights teams should therefore invest more time into uncovering those light bulb moments and crafting better stories, rather than needlessly hand coding data and manually going through each line in an excel sheet.
Case Study #1: Schindler
Schindler’s main goal was to achieve complete customer centricity for each of their 400,000 customers. With more than 140 years in business, Schindler recognized their success was based on earning the confidence of customers. They have achieved this by delivering exceptional customer experience and producing quality products that listens to the needs of those 400,000 customers. Net promoter score (NPS) is the primary metric used to measure customer experience. Led by Head of Global Customer Excellence - Sergio Rossini, Schindler has built an insights engine that outputs consistent, reliable customer insights from their NPS survey responses. Prior to this, they struggled to connect the dots between CX initiatives and key drivers of customer satisfaction.
They wanted three things to emerge from an insight engine. Firstly, since data was being collected from different countries and regions, the team felt it was important to ensure consistency across analysis outputs. As a global business, their strategy had to account for emergent trends and differences between countries and regions. Second, being able to identify the key drivers of customer satisfaction were critically important. Schindler recognised the importance of customer centricity and the impact customer satisfaction has on bottom line revenue. Understanding customer drivers, unlocked new discussions about what could be done different and which areas of the business required additional investment. Third, Schindler felt it was important to uncover deep and meaningful customer insights that could be turned into action. This had to also be done in a timely manner which was difficult due to manual coding.
Without actionable insights, outputs were not as valuable to decision makers. Their insight engine is now characterised by reliability, depth and timely output of actionable insights.
Insights at Schindler are..
Demonstrate depth of understanding
Reduce the time it takes to get these customer insights
Turning data into actionable insights
Case Study #2: Western Sydney University
Western Sydney University was collecting high volumes of qualitative data to measure their student experience. They wanted to use this information for driving improvements to student experience initiatives in the hope it would positively impact course completion rates. This became important to the university after the introduction of a new Performance-Based Funding Scheme in Australia which closely linked what students said about their student experience with government funding. WSU struggled to fully utilise qualitative feedback data from their student engagement surveys, even though understanding it would provide the necessary understanding required to improve student experience and ensure continued funding.
Student feedback programs at WSU include the typical end of semester feedback on teaching and unit quality, regular feedback from their significant online community of students, a rapidly maturing Voice of Student Program and various other targeted student interaction surveys. For each, both quantitative and qualitative data is obtained (which we found is fairly typical in the data sets of customer insights teams). Previously, the high volume of data was a barrier to getting the insights they needed. WSU built an insight engine by making use of technologies (including tools that integrated with Qualtrics) to assist with understanding complex data layers as well as providing insights into how to improve each step along the student experience journey. Technology is key here, since they found a suite of products that complemented each other nicely. Western Sydney University’s insight engine gives key decision makers confidence in the integrity of qualitative feedback analysis, at any volume and ensures those insights are actionable by decision makers.
Insights at WSU are..
Giving key decision makers confidence
Making sense of large volumes of qualitative data
Utilising a suite of complementary products and tools
Turning data into actionable insight
Case Study #3: American Journal Experts
American Journal Experts (Research Square) held a dominant position in their market. With over 2,000 field-specific topics and a community of 20,000 academics, Research Square has always taken pride in delivering the highest quality services in the research industry. The company, which helps researchers communicate their findings through English language editing and translation services, found word-of-mouth was sufficient to bring in new business. When new competitors entered and the competitive landscape became increasingly ‘hot’, a new approach was required. AJE believed that by embracing customer centricity, they could learn more about customers and gain an advantage over competitors.
Unlocking growth meant finding actionable insights and finding them faster than their competitors could to maintain market leadership. Their biggest challenge was finding these customer insights in an automated way after having previously relied on manual coding to get the job done. While manual coding does have some advantages in certain situations, there are clear benefits to automation, such as scalability, reducing human bias and reducing the average time to insights (key areas of focus for the company).
Technology is essential to an insight engine. After exploring their options AJE considered building a custom solution to fill that technology gap, however realised this approach had unique drawbacks. Eventually American Journal Experts settled on building a suite of complementary products and tools to make analysis and reporting easier. Dashboards made it easier for the c-suite to understand outputs from the customer insights team. It’s important to remember decision makers are not always insights savvy and will drown in data if presented with confusing spreadsheets and PowerPoint slides. One of the reasons customers appreciate Research Square is because they know the company takes their feedback seriously. Listening to customers via their NPS program has increased biweekly rolling NPS from a maximum of 43 to consistently over 65. For Research Square, an insight engine is capable of translating customer insight into customer action, in a timely and consistent manner. Communicating insights through dashboards and other reporting tools also make it easier for decision makers to understand those actionable insights.
Insights at American Journal Experts are..
Delivering competitive advantage by uncovering insights faster than competitors
Leveraging dashboards and reporting technology to communicate findings
Utilising a suite of products and tools that complement each other
Turning data into actionable insights
Step 1: Get the right people in place
A business is only as good as the people working in it. Respected customer insights teams are full of respected professionals - that’s what makes them respected. When building a world class insights engine at your business, the ‘people layer’ is foundational.
While there are different roles across typical customer insights team, the most important qualities everyone should have are;
An analytical mindset
Intellectual curiosity to learn new concepts and theories
Natural storytellers able to translate numbers into meaning
Understand the objectives of different departments within the business
Organization chart & team structure
“Business analytics as an organizational priority is expected to be on par with such critical drivers of business value as risk management, reputation management, product/service innovation, and managing growth expectations. In other words, analytics is becoming an established fact of business life and no longer the sole domain of the IT or finance department.” (Deloitte)
What’s the best structure for a world class customer insight team? There’s no one size fits all structure due to a range of factors such as insights maturity, available funding and how customer-centric the leadership team are. Forrester have presented a vision of a customer insights centre of excellence (CoE) and found world class insights departments are structured in a way that facilitates better communication and storytelling across all departments. To highlight the need for an insights centre of excellence, Forrester found 40% of marketing teams strongly agreed the output of their insights team were not actionable. 42% also strongly agreed their insights take too long to be delivered and servicing silos were not helpful. Below are three ways you could structure your customer insights team. Which one would fit the best into your business?
|💰 Salary ($USD)||$91,411 - $146,828|
|💼 Job Titles||
Head of Customer Insights, VP Insights, Director of Insights, Director Insights and Analytics, Head of Insights & Experience, General Manager of Customer Insights, Group Customer Insights Manager
We’ve looked at ways you can structure your team but regardless of structure who should you have in the customer insights team? Due to differences between job titles and salaries, we’ve taken a step back and looked at general functions. Let’s look at some typical functions - starting with the champion.
The primary responsibility of the champion is to oversee the insights department and report findings to the CEO and/or board. They’re less champion of the team and more champions of the customer insights function within the business. The end goal of every champion should be to ensure insights are taken seriously and can continue to grow with additional funding. This is a senior position within the organisation, and could even sit within the c-suite. It’s also a function that could fall under a Head of CX or Chief Customer Officer.
What does this function typically do? The champion takes charge of driving a customer-focused data-driven culture within their department and ensures Leaders under their leadership are being proactive (democratic) with the sharing of insights across the entire business. They also ensure implementation of all actionable customer insights by working closely with customer experience directors on defining the right CX initiatives and then helping those department heads measure impact. Being able to confidently connect the dots back to revenue and prove ROI is quite important for champions. In fact, measuring ROI and driving performance is one of the top reasons businesses invest in their analytical capabilities (see below).
The qualities most required for this function are varied, ranging from the soft skills necessary to build internal stakeholder relationships and strong project management experience to ensure initiatives are delivered on time and within budget. Champions are people focused leaders, capable of coaching and driving forward insight driven culture change in their organization.
LinkedIn conducted a study into the most in-demand skills for 2020 and found creativity, persuasion, collaboration, adaptability and emotional intelligence (EQ) were the most in demand soft skills. It’s clear being persuasive across departments is an essential quality for driving the insights engine. Last (and most importantly), champions must be confident storytellers, able to present actionable insights to the most senior decision-makers. Death by PowerPoint is a real thing and should be avoided at all costs. If you’re putting decision makers to sleep your insights, up your game as storytellers.
|💰 Salary ($USD)||$60,000 - $118,000|
|💼 Job Titles||
Customer Insights Manager, Senior Manager Customer Insights, Customer Advocate, Customer Insights Program Manager, Customer Experience & Insights Manager
BCG interviewed over 650 firms to learn what makes the best customer insights teams work. In their research, they found that the best customer insights leaders were:
The leader function requires good understanding of business goals and department requirements. Taking charge over an effective world class customer insights team requires forward-thinking and a strong analytical mindset. With the right person at the helm, a customer insights team can positively influence strategic decision making across different departments all the way up the c-suite. This is a function where putting on different hats is part of the job. Insights leaders will one moment have their management hat on, and then have to put on their analyst hat to uncover meaning behind a new data set of customer feedback. The key responsibility for many leaders is helping end users of their output understand what next steps to improve should be. The leader also designs and performs complex analysis against multiple data sources (often large volume) to identify actionable insights. Their educational background is typically a degree related to analytics or statistics.
|💰 Salary ($USD)||$57,000 - $80,000|
|💼 Job Titles||
Customer Insights Analyst, Customer Insights Specialist, Senior Customer Insights Analyst, User Researcher
The majority of customer insights team members will work in an analyst function, compiling data from a variety of sources and transforming that data into meaningful (and actionable) insights. These team members require strong data analysis skills. As machine learning data analysis becomes more commonplace, AI and machine learning skills will also be valuable.
Other successful qualities include a willingness to learn, adapting to change, familiarity with basic economics, and a strategic mindset.
Champions, leaders and analysts form the core of your insights engine, however there are some additional skills to consider. The technician function is without a specific title, but is nonetheless an important component to your team. Not only do world class customer insights teams require their own technology stack, but they also need to be able to integrate with existing technology used across the business. From pulling in customer conversation data stored in the help desk to working within complex CRM systems and building integrations, all customer insights teams require a high level of technical literacy to be support the work they do.
A data engineer or technical lead can help the team pull together data from across the business (or the data warehouse if you have one). Without a technology lead, the job of integrating various systems together can often fall to the Insights Manager. While they may be capable of performing such a task, it just gives the Insights Manager yet another hat to wear.
Like the technician above, there is not yet a clear job title for this type of role. Storytellers or analytics translators are an emerging role in customer insights teams. While storytelling is important for champions and leaders, it’s essential storytelling is done at all levels. When champions and leaders don’t have the time to be that storyteller to product designers, a storyteller is required. Without the ability to translate data into compelling stories, customer insights teams will always have less influence over business decisions. If you are structured to support an insights centre of excellence, this is a perfect role to have in your team that already pulls in a mix of qualitative and quantitative feedback, to make it more digestible. They blend emotions with data to articulate threats and opportunities in a way that generates buy-in. If your team does not have the capacity to incorporate a storyteller role, it’s still important to find and nurture these skills within the team. At the end of the day, everyone in your customer insights team should be ambitious enough to instinctively want to be become better storytellers.
In order to act cross-functionally across all departments, customer insights teams should report directly to someone in the c-suite. This might be the Chief Customer Officer (CCO) or even the CEO. As previously mentioned, if the team is large enough to be structured as a department, the champion might even have a seat at the head table.
A respected insight engine sits outside established functional departments (such as Operations, Marketing, Product etc.) because they are a resource used across the entire business. They’re not an explicit shared service but do work closely with all departments. An independent insights function working as a centre of excellence can also manage their own talent acquisition and development. With their own budget, the customer insights team can also invest in the technology and third-party partnerships required to be successful. The impact of a successful Insights team is further amplified when leaders uncover actionable customer insights on their own and share with relevant internal stakeholders, further building the credibility of customer insights as a function within the business.
Key reporting metrics - NPS, CSAT, CES, Revenue, Retention, Churn etc.
What metrics do you want to impact? Measuring the right things will help you fine-tune the insights process and demonstrate value.
Here are 8 metrics that should be on any team’s radar:
- Customer Lifetime Value (LTV)
- Recurring Revenue (MRR/ARR)
- Average Cart Size
- Retention & Churn
Net Promoter Score (NPS) is a simple metric used by two-thirds of the Fortune 1000. Why are so many fast-growing companies adopting NPS? It’s easy to measure and when used correctly, is used to predict customer behaviour. Did you know it’s also a useful metric to deep dive into, even if your NPS is good? NPS is a powerful metric if the business avoids the vanity metric trap and actually wants to know why rather than what. The c-suite will always want to know the why of anything sent their way.
To measure NPS, customers are asked two questions: How likely are those participants to recommend the company to friends and family? Customers respond on a scale of 1-10. A response of 6 or lower indicates the customer is a detractor and likely to churn. A response of 9 or 10 indicates the customer is a promoter and potential advocate.
CSAT is a measure of customer satisfaction. It uses a simplified Likert scale (strongly agree, agree, neutral, disagree, strongly disagree) to quantify satisfaction. One of the main advantages to CSAT is you can ask more than one question and quantify answers to specific questions. In the example below, you can see how Netflix does CSAT for their customers.
CSAT produces good outputs for customer insights teams, particularly when conducted as quick exit surveys from visitors to your website, ecommerce storefront or bricks and mortar store. This upside is also a downside because CSAT is limited by predefined questions. CSAT will give you answers to the questions you want to know and as such you’ll miss out on the benefits of open ended questions.
CES or Customer Effort Score measures the difficulty website visitors have to complete certain tasks. Tasks could range from purchasing at checkout, watching a video, submitting a request for a demo or even reading a documentation article. CES is generally considered better than NPS as a measure of predicting behaviour. Just one question is asked along the lines of how easy they found the process (see examples below). You can calculate CES as a percentage of responses which were positive according to your predefined framework.
In this example, CES would be the percentage of responses which were ‘Very Easy’ or ‘Easy’.
With this question, CES is calculated as percentage of responses which answered ‘Strongly Agree’ or ‘Agree’.
Here we have a numerical range with CES calculated as the percentage of responses which gave 1-3.
When smiley faces are used to make it quick and easy for customers, quantify what each smiley face means. Angry =3, Neutral = 2 and Happy = 1. CES here would be the percentage of responses which gave a 1.
Revenue is most persuasive metric for decision makers. While NPS and CES are useful for predicting customer behaviour, revenue is a measure of overall success. Respected customer insights teams are able to connect the dots between specific CX initiatives and revenue. This is helped when a key metric (such as NPS) is quantified into a dollar value.
Customer Lifetime Value (LTV) is a measure of how much your customers spend with you over their lifetime. It’s a metric often used in SaaS companies, and is impacted by the number of purchases and value of each purchase.
Monthly/Annual Recurring Revenue (MRR/ARR) is the holy grail. Acquiring new customers, retaining existing customers, and up selling to higher plans will increase the MRR/ARR.
Average Cart Size or the amount customers purchase in one transaction, is certainly an important metric to focus on if you sell online. Ecommerce marketplaces look at this metric quite a lot to unlock new revenue streams. What products do shoppers tend to purchase together? How does free shipping impact up selling? Are detractors actually purchasing less or are they purchasing much the same as everyone else despite being unhappy? Are there clusters of concern in certain geographic areas or age demographics?
Minimising Churn and Retention should be the primary goal of your customer experience strategy. Unhappy customers probably won’t return to buy again. If your business has a monthly or annual license subscription, chances are you already look closely at churn. Churn is calculated by looking at the percentage of existing customers lost over any period of time, divided by the total customers coming into the business over that same period.
Step 2: Find your technology stream
There are three parts required to build an insights engine; data, technology, and people. Data is the fuel powering that engine. Technology is the engine itself and People ensure everything runs smoothly. We’ve already covered the Data and People layers, now let’s turn our attention to technology. Specifically, what tools and products should you have in your arsenal to understand both quantitative and qualitative data? Is there a one size fits all customer experience platform that does everything or do you need a suite of tools that brings everything together? Think of insights as a stream that flows from collection to analysis to understanding and then action. This is the stream. You start with collection tools, move to products that power analysis and then report insights with additional tools.
In this section we’ll examine the following:
How disparate data sources can be connected
How to collect customer feedback
How to understand what customer say and do
How to report actionable insights to decision makers
Data stuck in silos is a huge cause of concern for organisations. According to Gartner, 87% of business leaders believe they have low business intelligence and analytics maturity, partly driven by the problem of data silos. In their report, Gartner says that data silos “create a big obstacle for organisations 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 valuable actionable insights. To combat data silos, customer insights teams need to collaborate with other departments to understand what is being collected. Using APIs and integrations, analysts can pull then all of the data together into a useful format.
Best practice for managing data is to find somewhere to put it all and declare that place the ultimate source of truth. Providing a single place for all departments to pull insights from means each team in the business is working from the same set of data. A data warehouse is one solution for this problem.
Architecture of a typical Data Warehouse setup
As an example, Marketing teams might be seeing a very different story when looking at social listening trends than the product team is seeing in product usage data. If these teams are making decisions separately, they aren’t moving in harmony. If however, the customer insights team is able to pull these two disparate data sources together, both Product and Marketing teams make more-informed analysis. This encourages collaboration and democratises data for everyone, including those not directly tasked with making decisions.
Here are some data warehouse solutions to consider:
Once you have a data warehouse up and running acting as an ultimate source of truth, the insights team must collect data to feed into the stream. Asking customers for their thoughts are a common place to start These are some of our favourite survey collection tools:
Jotform (Typeform alternative)
Google Forms (free)
For qualitative data, other collection inputs could be social media, YouTube comments, support chat logs and user reviews from comparison websites. You’ll need to use a web scraper tool to extract most of this data, however once acquired, it does add tremendous value to the ability of customer insights teams to become better storytellers.
These are some web scraper tools for gathering both qualitative and quantitative data:
Octoparse - a good option for teams that lack the technical expertise to scrape data
ParseHub (free) - another great tool if you want to avoid coding
Scrapy (open source)
ScraperAPI - requires technical expertise but is able to navigate around obstacles such as CAPTCHAs, proxies and browsers to extract the data you need
The data ingested by the insights engine is always a mix of qualitative and quantitative. There are tools that help to make sense of each type, as well as products which are able to do both. While there are a range of paid products, you can also play around with many of the free tools out there such as world cloud generators and free nps calculator tools.
🔢+ 💬 Both
Q Research Software - ideal for market researchers
IBM SPSS - well established and trusted tool for advanced statistical analysis
Phocas Software - suited to data analytics for manufacturing, distribution and retail industries
BirdEye - features include both sentiment and statistical analysis
MIPAR (free trial) - a versatile image analysis tool
Dedoose - ideal for understanding mixed methods as well (such as images, videos etc.)
Excel (free trial)
Google Sheets (free)
Worditout (free online word cloud generator)
MonkeyLearn Word Cloud (free)
Kapiche - handles multiple data sets (both quantitative and qualitative) and is able to make sense of your unstructured data. Other features include sentiment analysis, dashboards and measuring impact of NPS.
Collection and analysis of data must then be reported to decision makers. We like to think the role of customer insights is less collection and analysis of data and more storytelling of outputs in a manner which can be easily digested. This is where business intelligence, reporting and dashboards comes into play.
Here are some of the best tools for sharing insights to decision makers:
Google Slides (free)
Google Data Studio (free)
Sisense (previously Periscope data)
SurveyLab - if you use SurveyLab to collect survey responses, you can export into an Excel (.xls, .xlsx), SPSS (.sav, .por), PowerPoint, PDF or .csv file for any tool upload (SPSS, Statistica)
These reporting tools do a good job at reporting the facts, however on their own, they won't empower your customer insights team to tell better stories. Stories require context and narrative. Imagine reading a novel about various characters without knowing who they are, where they came from, their location (or even time period) and what their backstory is? That would be a rather dull story to read. Where these reporting and BI tools can support storytelling is by visualizing facts to help the insights team present their narrative.
Step 3: Understand current capability
Customer experience deals with strategies, programs and initiatives to delight customers. This is important because 96% of customers directly correlate their experience with loyalty (Microsoft). 73% of businesses with above-average customer experience, not only financially outperform competitors but also generate 5.7x more revenue. The role of customer insights is to understand what customers are doing and then translate that understanding into a story. This story must be understood and leads to action.
Collection of this data can come from user research, focus group sessions, support tickets, customer feedback surveys, chat bot message logs, product analytics data, the CRM, social media or anything else where customers are talking about your business. Social media and website reviews are a good source of qualitative data, however customer insights teams would need some technical expertise to scrape it.
What makes customer insights as a function compelling for decision makers in your business, are the stories told when customer touch point data is combined with what they say. If customers have negative sentiment about your business they must be detractors right? Yes, but is this a negative for the business? What if you also discovered that same customer also purchased and then became a repeat customer irrespective of their negative sentiment? The story changes and the credibility of customer insights rises. Good customer insights teams are great at data collection and analysis. Exceptional customer insights teams go one step further and master the art of storytelling.
Insight maturity pyramid
When building a new customer insights team, consider how ready your business is to receive actionable customer insights. 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? Is action taken or do the insights die a slow death buried deep in a quarterly results slide deck? Knowing the capabilities of customer insights at your business makes it easier to put a plan in place to grow the maturity of the customer insights function. We’ve represented this as a pyramid.
If the level of Insights maturity is low then you’re starting out on the ground floor. This means starting small and then working hard to prove the value of insights. Do this one project at a time, looking for quick wins and low hanging fruit opportunities. With each win, build a stronger case for unlocking additional funding for investment in people, process and products. Starting on the ground might be a slow grind, however the pay off is a more mature Insights-driven and customer-centric business.
The more established your business, the more sceptical stakeholders are likely to be. Unless they are clearly falling behind and drowning in data, these stakeholders may not see the value of investing heavily in the team you are trying to put together. If those trench lines are dug deep and you’re finding it hard to make headway, communicate the impact actionable customer insights has on driving revenue goals. If you don’t know the impact because there has not been enough investment in customer insights as a function, use that lack of understanding as the basis for why the team needs additional investment.
When building from the ground floor, consider the current state of play for insights at your business. Understand the value customer insights can bring to the business and what current capabilities are. If starting from a position of low maturity, find a project to get involved with (ideally one which is a major priority for the business) and work to deliver actionable customer insights back to the team. If these insights are deep and meaningful, small wins can become big wins. If starting from a position of moderate maturity, you’re already mastering the collection, analysis and reporting side of the equation, however you need to find ways to scale up activity. Eliminate human bias with automation and use past wins to make the case for unlocking additional funding to grow the team with new talent and technology.
Also consider the following:
How do you see your insights team working in the future?
What departments will they need to interface with?
What metrics will they be responsible for?
Who is buying in and at what level? It helps to have a CEO/c-suite champion, but also consider the value of buy-in from other departmental heads or even team leaders
By clarifying long-term vision, you’ll have a clear goal to work toward when starting from scratch.
The wisdom hierarchy
The wisdom hierarchy is a useful device for visualizing the value customer insights can bring to a business.
The data layer is the foundation of customer insights yet is characterised by signals and noise and a lack of understanding about what it all means. Data collected via these sources is usually a mix of qualitative and quantitative data. When combining this data with other quantitative data such as customer age, gender, average spend per visit, number of logins per day, etc. you give yourself the best possible platform to progress rapidly up the Wisdom Hierachy.
According to HelpScout, the top 7 sources of customer feedback are:
Customer feedback surveys
Email and customer contact forms
Exploratory customer interviews
On-site activity (via analytics)
Instant feedback from your website
When data is given context it becomes information, becomes structured and useful to the right people. There is less understanding with the information layer due to the fact we’re dealing with raw knowledge absent formal analysis. Information becomes useful when human intelligence is applied and customer insights are analysed through the lens of wider business objectives. If customers are saying they are very unhappy with a company’s environmental record, this would be relevant information to the c-suite, public relations and possibly also operations. If Analysts on your customer insights team aren’t intimately familiar with your organizational strategy, they’ll likely miss these insights due to a lack of context. Rather than looking at data for the answers you want to find, it’s best practice to allow that data tell a story about what customers are doing.
Information given meaning becomes Knowledge. This where most customer insights teams with low insights maturity stop, thinking this is the value they bring to the table. Knowledge is contextual, synthesised information which offers more understanding than information. Analysis is typically done with hand coding. Automation is useful here to speed up and scale out the process of manual coding. By allowing algorithms to do the grunt, analysts are free to do what they do best - analysis. Products like Kapiche are specifically designed to help customer insights teams eliminate manual coding.
Knowledge given insight becomes Wisdom. Once the customer insights team has achieved a critical level of understanding about what customers are saying, it should be in a strong position to produce actionable insights. Actionable insights are exactly that, an insight which is useful in some way to decision makers. It’s important to make the distinction because decision makers only care about two things; are we on track to meet our goals and if not, what can be done to unblock those obstacles?
Practical example: A company that sells tools to professional handymen will want to know customers are having difficulty at the self service checkout, but also the impact this is having on overall NPS. If impact is significant, reviewing investment in the checkout process to either streamline it or unlock extra capacity would be the key learning which gets that business cooking with gas again.
Actionable customer insights are distinct from standard customer insights because they are the findings that actually drive real change somewhere in the business (product design, marketing, support service, reliability, public relations etc.). This is where the standard Wisdom Hierarchy can be improved because it essentially stops at wisdom and fails to close the loop. A truly repeatable process for gathering, understanding and delivering actionable customer insights requires an additional layer for monitoring and tracking. Without monitoring, the team will find it difficult to give an answer on the impact of decisions made over time. Identifying an initiative is either resonating or falling flat with customers is one thing, but proving that initiative lifted revenue or retention will cement the credibility of the customer insights team.
Customer insights flywheel
Customer insights teams also need to be able to speak authoritatively on a variety of business functions including customer experience, sales, finance, and marketing. Not only does the team need to be able to run advanced data analytics from a number of different sources, but they also need to make those analytics digestible for the entire business. A respected team will aggregate data, uncover those actionable customer insights and readily disseminate voice across the business. Effective understanding of customer voice helps the business overcome challenges, take advantage of new investment opportunity, and transform data into results that matter. Respected customer insights teams deliver value every day by measuring and reporting impact of those key decisions. This allows businesses to double down on the initiatives which are working and then direct investment where it needs to go. At Kapiche, we call this the Insights Flywheel (see below), a process whereby data becomes understanding and action but also loops back to create new knowledge powering the flywheel.
Insights bring the voice of every customer into the boardroom, every design team meeting, CX strategy document and marketing campaign. The end result is that by achieving a critical level of understanding of voice of the customer (at-scale), the customer insights team guides decision-making. These actionable customer insights, in turn, drive growth in revenue, retention and customer lifetime value (to name a few).
Practical example: While monitoring social media for a Ben & Jerry’s “free cone” promotion, Unilever noticed a strong correlation between increased chatter on Twitter and an increase in sales. Not all regions showed the same trend. Real-time analysis showed stock shortages in the slower sales regions were impacting success of the promotion, which helped Unilever prevent similar issues in the future.
When building your insights engine, consider the following:
Is the voice of every customer accounted for?
What is the system/process for understanding VoC at-scale?
How are insights disseminated across the business? Are they proactively shared or are insights order takers for various departments?
When decisions or CX initiatives are executed, how is impact on VoC measured for each?
When impact is known, how much of that wisdom loops back to power the insights flywheel?
Step 4: Democratise insights across the entire business
“Executive sponsorship is vital to this level of organizational change and the best champion sits in the corner office. The CEO is the lead champion of analytics in 29 percent of companies surveyed, and these companies are 77 percent more likely to have significantly exceeded their business goals. They are also 59 percent more likely to derive actionable insights from the analytics they are tracking.” (Deloitte)
When building a customer insights team, connections made with stakeholders are extremely valuable. Analysts need to act as knowledge brokers to inform strategic decision making. This fact alone, when executed right, places the champion function, in the enviable position of acting as conduit between customers and those who need to know what customers do, (eg. CEO, CCO, CXO or even CMO). 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.
“Most executives are not comfortable accessing or using data. 67% of those surveyed (who are senior managers or higher) say they are not comfortable accessing or using data from their tools and resources. The proportion is significant even at companies with strong data-driven cultures, where 37 percent of respondents still express discomfort. This points to a major opportunity for companies to provide more education and improve the user experience if they want every employee to use insights as part of their work.” (Deloitte)
One way to think about collaboration with stakeholders is “data democratization”. The job of the customer insights team is to put the power of data into the hands of decision-makers. But they need to do it in a way which is enlightening and useful to everyone. Spreadsheets and graphs aren’t always accessible. Instead of providing raw data analysis, customer insights teams must provide context and understanding to what customers are actually doing. If not, then why are you collecting feedback? As previously mentioned, this comes in the form of storytelling. It’s not the responsibility of the customer insights team to decide next steps, however sharing out deep and meaningful insights across other departments inevitably leads to better decision-making all across the business.
Ideally, as your team grows, every strategy discussion will be supported by real-time data that continuously provides insight into KPIs and goals. To do so requires a well-equipped customer insights team that collaborates frequently with decision-makers and other departments. That doesn’t mean you need an all in one CX system, but there are products out there which make life easier.
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 organisation. Simply sharing a link to a database or even an email filled with graphs won’t influence decision-makers. The last thing you need is death by PowerPoint and the core message of your insights ending up lost on decision makers.
Data democratisation 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’ approach to CI). We argue democratising customer insights is essential for teams following the customer insights 'centre of excellence' structure. 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 democratic customer insights dashboard in Kapiche to surface key metrics
When storytelling, consider what you want to achieve. Don’t adopt a storytelling mindset just because everyone else is. Storytelling through customer insights can support any of the following goals:
Improving credibility and trust with decision makers
Empower staff outside of the insights department
Retain and spread domain knowledge before it is lost
Build the brand of customer insights at your business
Support a specific business level OKR
Also factor in who is your target audience. Up to this point, we’ve talked about storytelling through the lens of internal recipients. What about external/third party recipients of your output? Market research agencies, for example, tell customer insights related stories to their clients. By the same token, a Fortune500 company must tell a story about what customers do when communicating to shareholders. These third party recipients are not in it for the details, but they do want to walk away knowing the 20 second summary version. Stories and the art of storytelling can be powerful when built into any insights engine.
The Kapiche storyboard gives a 30,000 foot view of relationships between what customers say before you deep dive into the ‘why’ behind each surfaced keyword
Building a respected customer insights engine is not easy, however there are clear benefits to your business. Customer insights should achieve the highest level of understanding on the wisdom hierarchy. So far, we’ve already established decision makers understand the value of listening to their customers. Don’t take this as a blank cheque. Decision makers will need to see results and proving the value of your team will be a continual struggle.
The most effective way to achieve this top level buy in, is to focus on small wins. Get involved in a key project which is important to your business and work closely with internal stakeholders to understand what their needs and requirements are. Perhaps the product team want to know if a new feature is noticed or if customers are complaining about someone having ‘moved their cheese’ in the interface. Other than surfacing you have a problem, the product team won’t get lightbulb moments from a CES survey without digging into the ‘why’. This ‘why’ is important and comes from combining qualitative and quantitative data sets together. If it’s a CX initiative, the key stakeholder would want to know if they are getting a return on investment. If you can prove their initiative impacted NPS by X% and have also calculated the dollar value of 1 NPS point, then you chalk that up as a win.
Ensuring your team is proactively sharing insights out to relevant stakeholders is also important, as is a focus on public, third party reporting to external stakeholders. The glue that bonds all of this together is mastery over the art of storytelling and adapting storytelling techniques to the way your team communicates insights. If what you’re doing is just analysing and reporting facts, you’ll lose people. Death by PowerPoint is a real thing and something that should be avoided at all costs. Customer insights teams take it one step further and focus on elevating their analysis with storytelling to establish credibility across all departments, increase funding, hire new people into the team, find new technology and build respect.
Everyone wants to do customer insights right but they're setting themselves up to fail if they rely on tools that automate what humans already do manually. That's just automation and isn't true innovation. Customer insights teams today are allowing technologies to reveal the areas that need the most improvement. This video shows you how it's done. 🚀
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’
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’
Import.io, ‘8 fantastic examples of data storytelling’
⚙️ 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"’
💹 Insights ROI
Boston Consulting Group, 'Measuring the ROI of Customer Insight'
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
Journal of Information Science, ‘The wisdom hierarchy: representations of the DIKW hierarchy’