by Steve Jones

What is a data driven approach and how can it create business value?

Steve Jones explains how to create value with data driven business intelligence.


The potential of data analytics to drive value is enormous. Yet too often, businesses are collecting increasingly large volumes of data but then failing to extract business insight from the information: they’re not generating outputs that are commercially understood and relevant.

That’s a missed opportunity. A data driven approach really can transform your business – and implementing and executing such a strategy is not as difficult as is widely assumed. Creating business value with analytics does not require industrial effort; smart projects often begin to pay dividends within a matter of weeks.

What is a data driven approach for your business? This is about trying to build a culture in which discussions and decisions are driven by the data. For many businesses, that will mean acquiring new tools and probably new skillsets, back-end and front-end, to enable data visualisation. But it’s easier than you think – Livingbridge has worked with many portfolio companies to help them build a data driven culture and to identify what analytical teams they need within their businesses to establish that approach.

Using data dashboard tools for visualisation

The value of business analytics will vary from business to business, depending on what they hope to achieve, but some basic ground rules are universal. The ability to identify high- and low-level drivers of performance is key to driving change; it’s crucial to be able qualify your next actions, identifying which trends are mature enough to inform your thinking; and you need a means to visualise your data for maximum impact, through timely and automated reporting.

Get these basics right and you will drive far greater engagement across the business – with marketing teams incentivised according to the impact of their actions, for example, sales teams given a reason to stay on top of CRM, and the whole organisation united behind a strategy to ensure it realises the full lifetime value of each customer.

Importantly, these ideas are business-style agnostic. Both B2B and B2C organisations can use data analytics to create business value. These cases studies on data driven business intelligence in our portfolio helps to demonstrate this.

Data driven marketing: How Stowe Family Law supercharged customer acquisition

Stowe Family Law is the largest family law specialist in the UK; it has traditionally driven the majority of traffic to its web site from a family law blog.

The challenge:

Following Livingbridge’s 2017 acquisition of the business, Stowe’s objective was to supercharge its customer acquisitions outside and beyond the blog. To do that, we needed much better visibility of Stowe’s acquisition channels. We had to identify the marketing ROI over the lifetime of the customer.

The approach:

We ran a workshop to understand what data Stowe had and how it used it. We looked at their analytics capabilities to continue to use and evolve new tools after implementation.

Problems unearthed along the way:

Much of Stowe’s data was still in physical files so there was work to be done to upload this into a usable format. It also became clear that the business would benefit from significant analytics capabilities beyond just marketing data.

The solution:

We helped Stowe build a trading and reporting insight platform to fulfil marketing, operations and finance requirements. We also hired a commercial analyst – a distinct role, additional to a database engineer – to develop data visualisations and to help Stowe drive internal adoption.

The result:

Stowe’s platform provides it with real-time ROI data by channel, providing the business with the confidence to increase spending in more expensive marketing channels where this is justified. It also has better data to manage customer enquiries, with an automated feedback loop integrating marketing and client care. Plus visibility has improved across the business, including for finance and management. Overall, we achieved a 35% increase in cases opened.

A guide to building a data driven approach: How Giacom helped identify and prioritise new customers

We invested in Giacom, the UK’s largest cloud market service provider, in 2017.

The challenge:

In order to boost customer acquisition and increase sales, Giacom needed to generate a list of the companies in its addressable market so it could target new customers more strategically.

The approach:

We used Google keywords, location searches and Google Places to help Giacom define the depth and breadth of its accessible market, as well as to design a priority metric that would help it understand which potential clients to target first.

Problems unearthed along the way:

We needed to find a way to refine the list of companies into “in” and “out” buckets so Giacom’s sales and marketing efforts would be as productive as possible.

The solution:

Livingbridge technology combines various transactional metrics such as financial results, number of employees, website traffic and online search visibility to create a ‘business size’ score on any UK company.  Using this score, we sorted Giacom’s addressable market list into priority order, including the detail of how to contact these potential new customers.

The result:

Giacom now has a clear idea of its addressable market and how to go after these businesses. While the business is at an early stage of targeting potential new clients, even a 1% conversion rate would have a massive effect on the bottom line. Moreover, Giacom now has an approach it can apply in its international markets, including the US, Germany and Italy.

A data driven approach: How Rockfish was able to understand customer sentiment:

Rockfish operates seafood restaurants on England’s “Seafood coast” in Dorset and Devon. Livingbridge invested in the business in 2019.

The challenge:

Rockfish needed customer sentiment analysis – understanding their experience on online review sites and how it changed between peak and off-peak seasons.

The approach:

We identified where customers were leaving online reviews of Rockfish’s restaurants and providing other types of feedback. These included online review sites, industry-focused sites and product review sites. We then built a sentiment algorithm to consolidate and analyse this data, using applications such as Google Chrome, Mozenda and Amazon Comprehend, as well as a Livingbridge proprietary system.

Problems unearthed along the way:

We encountered no significant problems and could successfully use Tableau to visualise the outputs from our analysis.

The solution:

We were able to provide a sentiment analysis that explicitly answered Rockfish’s original objectives, assessing overall sentiment, how the customer experience changes over the year, and key negative and positive themes. We could produce this analysis for each of Rockfish’s restaurants.

The result:

Customer sentiment has clear impacts on financial support. Twice as many customers leaving with a net promoter score (NPS) of 7-8 will return for a second visit as those giving an NPS of 1-2. Similarly, customers refunded after complaining about a bad experience are three times’ more likely to return than those who do not receive a refund. Analysis of customer acquisition costs against lifetime value identifies customers for whom automatically refunding following a complaint makes sense.

Seven key takeaways for creating a data driven culture:

1. Set a clear objective – what are the exam questions you are trying to answer? Don’t even attempt to design an analytics process until you have established what you want from it.

2. Don’t lose sight of your exam questions. Once it’s established, maintain your focus on it – it’s too easy to get distracted down rabbit holes.

3. Create a question header before you create insight. Each time you look at data, think about the question you are trying to answer and what you’re supposed to be doing with that answer.

4. Data exists everywhere and is more accessible than you think. It is almost always possible to find the data you need, or at least a good proxy for it, and it is rarely necessary to pay. There are many people who can help you find what you need.

5. Don’t aim for perfection. Start by working to get your data directionally correct rather than 100% accurate. Make a call on how accurate you need to be to get you to the next step.

6. Don’t assume you know all the answers. Data analytics should inform your thinking, not confirm your biases – keep using the outputs to challenge what you thought you knew about your business.

7. Get a data expert in. Whether you hire on a project basis or full-time, it is cheaper and easier than you might think to access data expertise. Doing so will help you bridge the gap from obtaining data to instantly analysing and actioning data.

We are always interested to meet with business owners and managers thinking about creating a data driven business culture– if this is you, we would love to talk! Please get in touch on to arrange a meeting.

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