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A strategy for driving maximum value from your data

The EU’s General Data Protection Regulation (GDPR), introduced in May 2018, forced many organisations to become even more data aware and tighten up their approach to handling customer data. On the positive side, as part of the process of becoming compliant with the new laws, many businesses will have realised – if they didn’t know already – what little value they are getting from all the different datasets within their organisation.

Company executives are being told that “data is the new oil”. But, unlike oil, data is worthless as a bare resource – it only begins to become valuable once it is processed and turned into meaningful information. To get to that point, organisations have to overcome a number of significant hurdles and challenges.


Challenges In PwC’s 22nd Annual Global CEO survey, the findings showed how organisations “don’t have the capability to use the data they have to make optimised decisions”. It’s certainly not a lack of data – indeed the volume of data has expanded exponentially – or an inability to secure and protect it, and it’s not the constraints of privacy regulation. CEOs point instead to the ‘lack of analytical talent’ (54%), followed by ‘data siloing’ (51%), and ‘poor data reliability’ (50%) as the primary reasons the data they receive is inadequate1.

For businesses that do manage to unlock their data’s value, they can expect to grow their 2 bottom line by as much 25%, as per McKinsey - a figure that can easily climb depending on how effectively the data is used.

Many organisations, therefore, are looking to make significant investments in data assets. But without the appropriate strategy, it’s easy to make poor and regressive investment decisions. It’s for this very reason that we’ll see plenty of businesses who fail realise the true value and potential of their data. While GDPR might have helped organisations understand their data landscape, the issues around data management persist. Disparate systems, producing data in different formats, means that data continues to sit untapped in incompatible pots.

1 2 designing-a-data-transformation-that-delivers-value-right-from-the-start


Paragon’s approach You’ll be under no illusion that the journey to gaining business value will take some time. But with the help of a specialist data partner, each step can be taken with the assurance that the end destination will be worth it. Paragon’s industry-leading team will audit, analyse and segment your data, and will work with you on planning and strategy to make the most of the insights uncovered. We can also build and integrate the systems in your data landscape to allow you to implement communications rooted in that insight. In the following sections of this eBook, we’ll outline how we go about extracting value from our clients’ data and what you can expect to achieve from doing so.


DATA DRIVEN COMMS Communication Planning Disparate Data Sources

Data Governance & Optimisation Reporting & Evaluation Single Customer View

Creative Content Management

Relevant, Timely & Motivating Communications

@ Business Rules Engine

Communication Deployment

Data Layering (External)

Actionable Analytics

Learning & Continual Optimisation

A simplified approach at the outset It’s easy to overcomplicate what it means to garner insight from data, when a simplified approach will do. The diagram above shows the key elements in a data eco-system that will enable you to deliver ‘always on’, multi-channel communications. Ultimately, many businesses are still in the early stages of attempts to make the most of their data. For example, in NewVantage Partners’ 2019 Big Data and AI Executive

Survey 3, 69% of the c-suite executives reported that they’re yet to have created a data-driven organisation. It takes time to get to a point where you can expect to use data to its full potential. Datacentric cultures can’t be forged overnight – but that’s not to say that you can’t achieve transformational results from data-driven communication from the outset. 3


Hard data only tells half the story Ultimately, you’re not going to be able to draw all your conclusions – not accurately anyway – unless you view it in the context of soft data and human psychology.


Mining, refining and defining At Paragon our model is to mine, refine and define. Mining (What’s actually happening) – with 1 

all your data aggregated, formatted and able for visualisation in one place (a single view of the truth), it’s about spotting patterns and signals in your customers’ behaviour.

We use various analytical tools and techniques to spot how certain behaviours and patterns are impacting your business. Through this technology, we can help businesses see and understand their data. This can be through data visualisation, analytics and insights, but ultimately it gives us your roadmap we need to build a business’s data stories.  Example insight: The data shows that a once regular customer, who used to buy makeup every three months – their ‘purchase heartbeat’ – has stopped making purchases.


 efining (Why it’s happening) – trying to R get some answers as to why customers are behaving in the way they are.

Analytics enables our teams to see what is happening with certain behaviour being exhibited. But, by triangulating this with soft data – research and an understanding of human psychology and what makes people tick, our planners can start gaining some

answers around why something might be happening. Example insight: Having researched around the subject, you believe that the customer may have switched to a more sustainable ‘eco-freindly’ beauty brand.

3  Defining (What we’re going to do about

it) – now that you have a reason for your customers’ behaviour, can you create a strategy to positively impact on that?

Creating and implementing a strategy requires an understanding of customer journeys, creative messaging development and a level of automating technology to create timely, persuasive communications that connect with your audience. Tools can orchestrate highly personalised, multi-wave and multi-channel campaigns to drive actions.  Example strategy: Create a campaign that displays your eco-credentials more prominently in your messaging or demonstrates the journey you’re on to become more sustainable, with a view to ensuring that other customers don’t churn in the same way. Or win them back through that messaging combined with an appropriate offer.


Soft vs hard data “Hard data is a verifiable fact that is acquired from reliable sources according to a robust methodology. Soft data is data based on qualitative information such as a rating, survey or poll.” Simplicable

Soft data is likely a little more subjective and open to interpretation than objective hard data but with out it you can’t begin to reason why the hard data is what it is. For example, if your hard data showed that 100 of your customers disengaged last month, you might be scratching your head as to why that happened. Only when you introduce soft data into the mix – asking some of those customers to complete a survey, for instance – do you get the other half of the story, and can begin to take the required action to stop more customers churning next month.


Your data needs to be viewed in the context of:

Outside research

Customer surveys

News stories

Economic issues

Previous campaign findings

Human psychology

External factors


Data should also be understood beyond the explicit and inferred data that a business may already have within its data landscape. To fully understand how data can work for a given client, it can also be useful to layer on external data points to see how that might be driving behaviours.

Local weather data

By adding external data through trusted partners, we can start to match up both data sets to create a fully visible picture. Some of these data points include:

Identity tracing Vehicle information Household composition Home-movers Open data (such as events or crime statistics)


Communication planning Armed with your insights, it’s now about acting upon them - planning who you want to target/communicate with and what you want to achieve. The first thing with any communication, is defining your objectives. For example, you may be looking to migrate your customers who are on the cusp of VIP status. It is important to recognise the triggers which suggest they are at that point, so you can segment them and start to treat them differently. Developing customer communications that are relevant, timely and motivating, that connect with your customer is no small challenge. To ensure you’re ticking all the boxes, you need the right proposition, the right creative messaging, all individualised and matched with an omnichannel mindset for delivery. Before you deploy your data-driven communication, you need to undertake some evalution planning to set some key performance indicators (KPIs). The KPIs that you monitor will depend on your objectives for the campaign – if goals are your destination, then metrics are like guideposts. As well as planning the strategy you need to plan your evaluation ahead of time to ensure you are collecting all the information you need to prove the value of your communications and demonstrate ROMI.


An omnichannel experience builds seamless, personalised engagement across all channels and touchpoints throughout that customer journey.

A customer journey map is a visual representation of every experience your customers have with you.


For example, you can use AI to test different email treatments and switch broadcasts automatically to the winning creative route. Alternatively you can change the timing of the communication based on time of day a particular individual opens their email and is most likely to engage.

Reporting and learning You’ll want to be learning as you go, optimising and tweaking and potentially diverting resource into different channels and messages By utilising technology, we create windows into the data, in real-time when required, to understand the impact any specific campaigns or communications are having on your business and any KPIs that have been set. This can then be taken on a level, by utilising artificial intelligence (AI) to automatically optimise your communications. Once your campaign has run its course, it’s essential that you compile all the data and undertake step-back analysis to see what worked and what didn’t, so you’re able to take this into account for next time. Actionable evaluation is all about learning and making continuous improvements. But there could be a myriad of reasons why certain elements of your communication didn’t live up to expectations. Just as you did when mining, refining and defining your data pre-campaign, you need to look at all your post-campaign data in context.


Sometimes you need the wind to blow in your favour for a campaign to be a success – quite literally; if, for example, your winter campaign ties in with the cold weather, and we have a mild season, it could fall flat through no fault of your own. If the economy takes a bit of a nose dive and people are more concerned about their financial status they may not be as charitable or may not make those luxury purchases. With every campaign that you run, your marketing and sales teams should become more informed, establishing benchmarks and norms and growing the insight for the benefit of your business. As different stakeholders begin to see how data is enabling the organisation to work towards its business goals, this will drive buy-in and lead to a more joined-up approach which is conducive to maximising the value of data. It’s hard to argue against it when the bottom line is speaking for itself.

Summary What are the biggest challenges facing businesses trying to extract value from their data? A lack of analytical talent to manipulate the data, and data being stored in different places, in various formats throughout the organisation. When data is siloed, you can’t generate a full and accurate single view of truth. How many businesses are currently getting value from their data? Very few. Almost seven in 10 organisations4 have admitted that they’re not yet making strategic decisions based on data findings. Those that can generate value from their data, then, could seek a significant advantage over their competitors. What does a data-driven model look like? Mining, refining and defining their data to uncover insights which will inform their communications. Make sure you are bringing together all

your sources of hard data consistently and comprehensively into an environment where different data sources can be compared to uncover those elusive insights. Make sure you aren’t underestimating the power of soft data, research and surveys, human psychology, and the impact of external factors (weather, news, the economy etc.). Where does Paragon add value with its data services? Paragon has an experienced team of individuals who have been creating datadriven communications for the past decade. We’re one of the only agencies that have merged planning, analytics and creative together in the same team to take that insight from the data, through the strategy and planning and into the creative messaging, bringing that insight to life with communications that connect.



‘A strategy for driving maximum value from your data’ We will work with you to simplify the complexity of today’s communications landscape and deliver a brilliant experience at every stage for your customers. Whether acquiring prospects, welcoming and on-boarding new customers or in-life activities, we can provide you with end-to-end and continuous support. |

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A Strategy for Driving Maximum Value from Your Data  

Our latest eBook outlines how we go about extracting value from our clients’ data, and what you can expect to achieve from doing so.

A Strategy for Driving Maximum Value from Your Data  

Our latest eBook outlines how we go about extracting value from our clients’ data, and what you can expect to achieve from doing so.