STRAT7 Bonamy Finch

Unlock the commercial value of your data

Conquer data challenges, unlock commercial value and achieve your business potential.

Table of contents

In the past, organisations gradually recognised the crucial role data played in their success, emphasising the need for systematic collection and storage.

But times have changed. Today’s organisations face mounting pressure to extract tangible commercial value from their data. Forrester reports that insights-driven businesses are growing at an average of more than 30% annually, and they’re significantly more likely to surpass and beat their revenue goals than their non-data-driven counterparts.

They’re thriving because, with the ability to get the most out of their data, they can become more customer-centric. Which is key to winning at change and staying ahead in this competitive landscape.

While 81% of organisations agree that data should be at the heart of all business decision making, many struggle to harness the full potential of their data. In fact, 40% of the insight delivered by customer intelligence teams isn’t actionable.

The data challenges businesses face​

We delve into the challenges businesses face with data, explore the true essence of being data-driven, and share our expertise on maximising the value extracted from your information.

Data dilemmas in business

Two decades ago, when the digital landscape was still in its infancy, less than one third of the UK population was online. 

But by 2005, 70% of people in the UK were online, rising to 95% by 2016. Internet adoption surged, leading to new ways for businesses to sell and engage with consumers, significantly increasing the number of touchpoints between companies and customers.

Creating opportunities but also big challenges for businesses in managing and leveraging customer data.

Data fragmentation

In today’s world, having access to data is not enough for your organisation to succeed. You also need a data strategy. Too often different teams have access to different data, and as a company it is not known where data is, who owns it and how to effectively use it.

Unfortunately, this knowledge often sits with the data scientists, but they often work in specialised roles, limiting their exposure to the data they are working with, not the data held in different teams, or the empowerment to go out and source new data. W Within data science, expertise can vary significantly, with analysis of transactional databases being very different to primary research, and very different again to online data from social media and websites. Unfortunately this acts as a barrier for the sourcing and adoption of new sources.

The real challenge is identifying valuable data sources and connecting the dots to gain a 360-degree view of customers. Overcoming these obstacles is key to unlocking the full potential of data and driving business success.

Siloed roles

There’s a trend of data, analytics, marketing and insights becoming increasingly integrated, working collaboratively to address business questions. But this isn’t always the case. There’s still instances of data requests or analysis placed into queues, with data scientists primarily responding to tasks.

A communication gap often exists between decision-makers and data scientists, as they don’t always speak the same language. Data scientists might not fully understand the intended purpose and application of their work, and their deliverables to decision-makers can sometimes tend to focus on technical details like coefficients and accuracy rather than being in a decision-ready format.

Shallow insights ​

Today, every time someone uses a device, they leave behind a digital trail: pages visited, time spent, clicks, articles read, ads seen, music listened to and so on. Now, if your company has an app or web based interface, you’re sitting on a goldmine of data. Google analytics has evolved from showing just an aggregated view of customer activity to pinpointing behaviours of individual users. Yet, many companies still haven’t made that leap from aggregated to a customer-centric view.

And, if your customers are not buying from you frequently, it’s essential to know how they’re engaging with your brand in between purchases. That way you can give them a gentle nudge towards the next transaction with you. Always look to see what data you can attach back to the individual – it’s not always possible, but when it is, it’s also important to do it the right GDPR way.

GDPR and first-party data hurdles

There’s a nervousness around navigating customer data these days, with the introduction of GDPR. Companies need explicit consent to collect and use personal information, and with big players like Apple, Google and Mozilla phasing out third-party cookies, first-party data management is crucial for delivering personalised experiences.

The truth is, users often encounter those pesky consent pop-ups focused on data collection rather than benefits. We all want relevant products, but that message gets lost. Win at consent and your company has a better chance of maintaining that valuable first-party data flow in the long run.

Having data vs being data driven

There’s a big difference. Successful companies put data and analytics at the heart of everything they do, enabling them to become more customer-centric – they can make better business decisions that improve customer experiences, driving acquisition and retention.

What it means to be data-driven and its benefits

What does being data driven mean?

To serve your customers better, you need to understand their needs and preferences through data, so you can offer them more personalised products and services. But being data driven isn’t just about using data.

Being customer-centric

It’s essential to understand how customers interact with your category and products, and what matters to them. Enabling you to better engage them with other offerings and enhance their overall experience.

It means understanding your audience first, then designing products tailored to their needs and preferences. It’s also about monitoring their brand and competitor experiences to continue to improve and serve them better. Looking beyond just your internal data, use primary research and review sites to benchmark performance across a variety of experiences. This way, you can gain insights into both what customers are doing and the reasons behind their actions.

Treating customers as individuals

Segmentation helps you understand customer needs, demographics and behaviours so you can figure out where to focus and how to succeed. Then you need to incorporate these insights into numerous business functions and customer activities, from positioning and targeting to innovation and crafting personalised CRM messages.

It’s all too common to see the same email blast to everyone. But, by tagging segments on your database, marketing managers can show their CMOs the tangible impact of tailored communications.

Making meaningful decisions

Ditch the hunches; data gives you the confidence to make informed decisions. Which means gauging the opportunity size, evaluating past successes, assessing consumer reactions and leveraging controlgroups to measure performance. In essence, you’re gathering the evidence to keep making smart choices.

The benefits of being data driven

Remember that becoming data driven requires more than just adopting new technology and tools. It’s about creating a culture that values data and analytics, encourages data literacy among all employees and promotes a mindset of continuous improvement through data analysis.

By using data to really get why consumers do what they do, we can:

  • Develop better products and services to meet the needs of existing customers and prospects.
  • Enhance customer engagement with targeted messages and communications that trigger responses.
  • Demonstrate ROI by analysing and isolating the impact of various campaigns and activities.
  • Boost efficient decision-making with clear ROI, reducing debate and speeding up progress.
  • Gain a competitive edge over companies lagging in maturity level.
How to create a data culture to become customer-centric

Creating a data culture

Data from Deloitte indicates that 64% of companies with a customer-focused CEO are more profitable than their competitors. This is where creating a data culture comes in.

One where everyone, from the top down, understands the value of data and uses it to inform decision-making. But how do you establish this culture?

Always ask: How can we back this up?

Make data a central part of your decision-making process by always asking questions like “what data supports this direction?” and “what can we use to better target customers?” A key aspect is having a repository of easily evaluated data assets and documenting advanced models to prevent hidden knowledge. To create a data culture, question existing data sources and seek out new insights to stay ahead of the competition and drive business results.

Understanding data source strengths and weaknesses

Business data, like sales, is essential for understanding performance, but additional data is necessary to identify the drivers of growth.

Demographic data is great for understanding WHO your customers are, and transaction data is useful for understanding WHAT they’ve done with you in the past. But in categories where customers make infrequent purchases, it’s vital to supplement with engagement data (e.g. website interaction) to understand their potential behaviour. But this data alone doesn’t explain why customers behave the way they do and only provides a customer view, not a market perspective for opportunity.

Primary research is effective for understanding the attitudes and motivations behind customers and prospects – the WHY. It also provides an opportunity to gauge reactions to potential future changes. But primary research alone only captures claimed information that can’t be directly applied to individual customers on your database.

Optimising marketing mix strategy for global CSD leader​

Before the Sugar Levy was introduced in 2018, our client wanted to gauge consumer reactions to various pack, price and promotion scenarios. While plenty of historical data existed, it wasn’t enough for predicting the success of future strategies, including new pack formats.

To understand the appeal of these new strategies, we conducted a large-scale retail shelf display conjoint study with 6,000 respondents. Allowing us to calibrate future plans with real-world data. We created a predictive model for scenario planning, accounting for seasonality and competitors. We delivered several “sugar tax” bottling strategy options in terms of volume and value, fitting them into our client’s P&L metrics. Ensuring robust data sources, accurate predictions and results in a decision-making language was crucial for such a significant financial commitment. But successfully leveraging data for business decision-making isn’t just about combining data sources to build better models and forecasts.

The power of combining data sources

Each data source has strengths and weaknesses, but combining them creates something more potent. Our work with a multinational soft drinks company is a prime example of this.

The power and ROI of a 360-degree customer view

Creating a 360-degree customer view

To gain a competitive edge and be customer-centric, you should aim for a 360-degree view of your customers by gathering comprehensive data on the WHO, WHAT, WHERE, WHEN and WHY.

Combining this with broader market information leads to richer insights, improved decision-making and higher ROI. For instance, a recent database segmentation for a gambling company combined behavioural data with primary research, revealing motivations.

This allowed for a focused segmentation that identified loyal versus fickle segments with varying gambling motivations, providing effective triggers and nudges by segment.

Tailored communication based on the WHY is more likely to resonate and elicit responses, demonstrating the power of merging WHAT with WHY.

Creating unifying frameworks

Combining data sources for a 360-degree view also enables cross functional business activities. Hybrid segmentations, created from various sources, provide comprehensive consumer insights for strategic growth and are tagged onto customer databases to support segment related activities across multiple business areas.

This itself creates a unifying consumer framework from combined data. Fostering a common goal for marketing, eCRM, business intelligence, insight managers, innovation teams and service teams across multiple customer touch points.


An introduction to hybrid segmentation

Download our ebook to learn more about how this advanced approach to segmentation will elevate every customer interaction you have, fuelling business growth.

Hybrid segmentation for strong ROI

One renowned travel company’s hybrid segmentations resulted in an impressive ROI. The first was hybrid traveller segmentation. It provided insights into consumer relationships with travel, revealing different attitudes and behaviours towards holiday research and booking. Exclusively using the client’s data, the segmentation was integrated into their database for brand positioning and communication. Making it useful not just for brand positioning and high-level communication, but tailored targeting and messaging.

We also delivered a hybrid trip segmentation based on needs and experiences. It reflected that a consumer’s needs change from one trip (e.g. family beach holiday) to their next (e.g. cultural city break). The database-tagged segmentation informed innovation and opportunity identification. 

Combining primary research, transactional and geo-demographic data created segments for brand positioning, product innovation and CRM optimisation, which:

•  Helped insights, analytics and strategy teams to identify opportunities, size them and prioritise for growth.

•  Enabled the innovation team to tailor new product design based on rich needs from primary research.

•  Allowed marketing and CRM teams to target the right segments and customise messaging.

•  Demonstrated ROI through segment-based open, click and purchase analysis, with the client able to calculate many millions in additional revenue from segment related activities.

Beyond enabling a shared goal across multiple business functions, hybrid segmentation allows you to embed customer-centric activities into your operations.

Implement consumer touchpoints into everyday operations

The travel company’s segmentation showed differing holiday rep preferences among segments. By linking segments to the bookings database and reallocating reps across hotels in six cities, the company saw increased Net Promoter Score (NPS), increased Service Score, and reduced complaints. This demonstrates the power of connecting data to consumers, and there are many operational changes that can be made to  deliver a better service, offers and products:

•  Website personalisation: adapting each customer’s landing page based on their segment.

•  Digital marketing campaigns: utilising the 360-view of segments to target prospects.

•  Call centre optimisation: connecting historical transactions and segment for improved call handling.

•  Customised content: tailoring displayed content based on trip search queries and known needs and desires for that trip type.

In the short run, it’s essential to prioritise making data accessible to as many parts of the business as possible. This can set the stage for more comprehensive operationalisation in the mid-to long-term.

Data accessibility and sharing outcomes​

The data explosion led to hiring data scientists, but decision-makers often lack direct access to crucial data. Business intelligence teams share performance reports and aggregate metrics, but customer-level data is often overlooked.

For example, Google Analytics now connects web engagement with individual customers, providing valuable insights for eCommerce. Sharing data is essential, but so is sharing outcomes and findings, including the factors impacting churn and campaign success (ROI).

Logging and socialising past successes helps improve future strategies and outcomes. Ensuring data availability across the business and understanding customer-level insights are vital steps toward informed decision-making.

Evaluating your analytical specialisms and skill sets

Navigating analytical specialisms and skill sets

There’s a vast array of data science specialisms and skill sets:

Analytical specialisms:

  • Clustering: finding hidden patterns or groupings in a dataset (e.g. groups of people).
  • Classification: machine learning algorithms for predicting outcomes or future behaviours.
  • Market modelling: isolating where to focus across marketing mix for growth.
  • Unstructured data: Natural Language Processing (NLP) and image processing to analyse text and images.

Skill sets:

  • Statistics: analysing and interpreting data patterns.
  • Visualisation: dashboards and business intelligence.
  • Automation and data engineering: streamlining data processes.
  • Big data: handling large datasets in highly distributed file systems.

Numerous data types exist in data science, including transactional databases, web engagement, websites, call centre logs, primary research and many more. But clustering transactional databases differs from clustering survey data; while using similar algorithms, suitability, data preparation and evaluation criteria vary significantly.

Understanding the strengths, weaknesses and analytical nuances of different data types is crucial, as is connecting them for richer insights and stronger outcomes. However, all this is irrelevant without a solid understanding of the business context.

Leveraging specialisations for strategic growth and impact

It’s nearly impossible to find an individual proficient in every data science specialisation, coding, strategic thinking and communication. Focusing on a specific area, such as natural language processing with web scraping and text modelling, may leave little room for other specialisations like econometric modelling. A baseline of skills is necessary, but allowing team members to develop their own areas of expertise maximises the potential of various datasets.

One vital role in this diverse environment is the analytics translator, who bridges the gap between company strategy, data strategy and analytical activities to drive growth. They may not be hands-on with technical tasks like fine-tuning hyperparameters, but their ability to comprehend complex conversations and their implications is invaluable.

Analytics translators are skilled at simplifying intricate information for business decision-making and demonstrating return on investment (ROI) to encourage the adoption of data and analytics. By cultivating this skill set, the data science team can transition from a reactive request centre to a proactive force driving business growth and innovation.

Embracing a data-driven future

Embracing a data-driven future

In today’s complex digital world, organisations really need to embrace a holistic approach to data management. That means putting an emphasis on data-driven decision-making, being customer-centric and integrating data seamlessly for deeper insights.

Embracing the data-driven future should start with evaluating your company’s customer centricity maturity level. This assessment provides a roadmap for becoming more customer-focused, highlighting how well you understand your customers and utilises that knowledge for attracting and retaining them. It also uncovers the effectiveness of your operations in supporting customer-centric actions, including processes, vision and culture, skill set, ensuring seamless integration.

That’s why STRAT7 Path is a vital tool for maximising your data’s commercial value.

In today’s complex digital world, organisations really need to embrace a holistic approach to data management. That means putting an emphasis on data-driven decision-making, being customer-centric and integrating data seamlessly for deeper insights.

Path pinpoints your biggest maturity gaps, so you can focus on addressing the challenges that, once resolved, will greatly impact your business. It lets us understand where a company excels and where it falls short, while also demonstrating what great customer centricity looks like for organisations.

Our assessment is broken down into the four key areas, covering all your customer-facing processes. Each of the processes have several maturity dimensions, which capture the key value creating components of a customer-centric organisation.

These dimensions help us get to a granular understanding of how your company is performing within each of your processes. That way, we can identify which challenges, when solved, will make the biggest impact on your business, and chart your way forward.

For more information on STRAT7 Path, click here.


In the end, success in what is an ever-changing landscape comes down to promoting data literacy among employees and always encouraging growth through data analysis.

By nurturing a data-driven culture, businesses can truly understand their customers’ needs and act on that knowledge in a unified way, leading to improved products, experiences and targeted communication strategies.

Leveraging hybrid segmentations and a variety of data types, and focusing obsessively on customer-centricity, enables businesses to achieve strong ROI and gain a competitive edge.