How to extract customer insights to improve your business?
Have you ever wondered about the role of a data consultant or what exactly data consulting entails? For those of you who are C-level decision-makers or considering hiring a data consultancy, it’s important to know that data consulting encompasses a range of services, one of which involves expert data analysis to extract valuable insights, such as customer behaviour and preferences.
In our recent collaboration with Beam Berlin for a Customer Analytics workshop, we embarked on a journey to explore the fascinating world of leveraging customer data for smarter decision-making. This special edition of our workshop is tailored to those seeking to make smart decisions in their business endeavours. It dives deep into the realm of customer metrics, helping you differentiate between superficial vanity metrics and actionable ones, and demonstrating how you can use these insights to optimise various aspects of your business.
Actionable vs. vanity metric
Vanity metrics are like shiny baubles that may boost your ego but don’t necessarily translate into meaningful insights or drive your business forward. These metrics might make you look good on paper, but they often lack the substance needed to make informed decisions.
From a business perspective, relying on vanity metrics can be counterproductive. They might lead you down the wrong path or divert your attention from what truly matters. For instance, counting the number of likes on your social media posts may make you feel good, but it doesn’t necessarily correlate with actual business growth.
On the other hand, actionable metrics are the unsung heroes of the data world. These metrics cut through the fluff and provide genuine insights into your business’s performance. They are derived from reliable and trustworthy data sources, offering a clear picture of what’s driving your success (or failure).
Actionable metrics are the numbers that matter. They have a direct impact on the decisions you make as a business owner or manager. What’s important to remember is that what is a vanity metric in one industry could be an actionable one in another. It all depends on the context and the specific goals of your business.
How do we actually build actionable metrics?
Identify the Decision-Maker: Start by identifying the person responsible for making decisions based on the data. It’s crucial that this decision-maker is well-informed about the strengths and weaknesses of a metric. That person should be intimately familiar with the context in which the metric operates.
Deliver Insights Favourably: When presenting actionable metrics, it’s essential to deliver the insights favourably. This doesn’t mean sugar-coating the data; it means presenting it in a manner that is easy to understand and conducive to decision-making. Visualizations, clear explanations, and relevant context can all contribute to this.
The Ultimate Question: How Many Customers Do You Have?
To illustrate the difference between vanity and actionable metrics, let’s consider a simple query: „How many customers do you have?“
If your answer is „
SELECT COUNT(*) FROM customers; It is enough!“ then you might be leaning towards vanity metrics. Simply knowing the number of customers, or rather number of entries in the table, without context or deeper insights, might make you feel good, but it doesn’t provide actionable information.
However, if your response is „
SELECT COUNT(*) FROM customers; It is not enough!“ then you’re thinking in terms of actionable metrics. You recognize that the row count alone doesn’t tell the whole story. You need to dig deeper and verify the quality of the data and their actual meaning. Additionally, understand the nature of the data, customer behaviour, preferences, and retention rates to make meaningful decisions for your business.
Here are some real-life examples from our clients:
Example #1: Logistics Scale-Up
A leading logistics company believed they had complete knowledge of their client base, confidently displaying client count figures. However, a closer look at their data revealed discrepancies that raised eyebrows.
Upon analysis, we discovered that some clients had changed names and other parameters over time, resulting in duplicate accounts inflating the client count by over 10%. This impacted key performance indicators (KPIs) like average revenue. To address this issue, we proposed implementing a Slowly Changing Dimension Type 2 (SCD2) approach to preserve evolving client information accurately. This corrective action not only fixed the client count, but also set them on a path to more reliable data-driven decision-making in the future.
Example #2: Online Job Board
In the competitive realm of online job boards, one company had enjoyed a decade of success, offering a range of HR-related products. Throughout this time, they continuously updated their digital infrastructure. Yet, they faced a hidden data challenge.
Their corporate dashboards consistently displayed impressive growth figures, and no one questioned them. However, recent updates to their data warehouse, which now included legacy systems‘ data, raised a critical question: Were their 500,000 client records indicative of active users, or was this merely a cumulative count over the years (a vanity metric)? Recognising the need for clarity, the company re-evaluated their client engagement. Regional and central management collaborated to identify key performance indicators (KPIs) that truly drove the business and allowed meaningful industry comparisons.
The pivotal KPI became the count of actively engaged clients, with conditions for „active“ over the last 12 or 24 months. Clients were categorized as „new,“ „old,“ or „regained.“ Navigating their complex business landscape, with over 1,000 unique products, operations in 10+ regions, 2 selling apps, and 5 sales channels, was a challenge. To enhance data integrity, they performed data cleansing, removed duplicates, and eliminated unreliable data. They introduced a family-based approach to group-related clients into families, instead of handling them separately to reveal hidden interdependencies.
Adopting this family-based approach optimized revenue, emphasizing client families as „new,“ „old,“ or „regained.“ With data categorization in place, they implemented data versioning, retroactively re-evaluating relevant data to unveil fresh insights into their performance over time. Ultimately, this re-evaluation empowered the company to provide reliable data to their new sales channel dedicated to acquiring clients. The power of data-driven decision-making allowed them to adapt and optimize strategies, ensuring continued success in the dynamic world of online job boards.
You might ask ‚Why did the numbers not match?‘ – Because:
The significance of definitions:
Depending on which of the alternative definitions of the basic terms (like „active client“, „client age“, „client category“) we pick the related metrics will change accordingly.
In conclusion, distinguishing between vanity and actionable metrics, and knowing how to build and present actionable metrics, can be a game-changer for your organization. Remember, in the world of data-driven decision-making, the right metrics can be the key to your success.
Ready to take the next step in optimizing your data strategy?
Book an initial consultation to discover how our data consultancy services can empower your organization with data-driven insights and strategies for success.
For any immediate inquiries or to schedule a consultation, please contact us.
Thanks to Beam Berlin for providing an amazing venue for this eye-opening workshop!
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