How can data science solve your marketing challenges?

The modern business collects an extensive amount of data at every stage of the customer journey, but without proper analysis, this data cannot have a meaningful impact.

In this blog post, Edit’s Director of Data Science Marc Dallimore explains how his team helps leverage this data into actionable insights that result in a greater return on investment for our client’s marketing campaigns.

How to use data science to solve your marketing challenges

What is the difference between data science and data analytics?

Before exploring how data science can solve marketing challenges, it is important to differentiate it from data analytics.

Whilst there is overlap between the two disciplines, the easiest way to summarise the difference is that data analysis on its own is primarily used to review past campaign performance, whilst data science is used to plan the future.

This is achieved by searching for compelling patterns and advising campaign marketers on the changes required to improve results.

How should data science techniques be employed?

Data science techniques are most effective when they’re being utilised to either plan new marketing strategies or optimise various aspects of an existing campaign or channel activity.

Data science is rarely a one size fits all approach. At Edit, as a specialist data science services company we employ various methods depending on each client’s specific challenges and the outcomes they are trying to achieve.

In many cases, we find that once we dissect a brief, insights can be obtained, which will make a considerable difference to campaign performance that the client may not have considered initially.

It’s therefore our role to effectively shape the work we carry out so it can have maximum impact on that all-important return on investment (ROI) figure.

What are the principal data science techniques used for campaign analysis?

There are four main categories of data science techniques that our team of Editors swear by:

Profiling and segmentation

This is the process of defining the ideal customer based on a set of unique characteristics.

Segmentation entails splitting an existing customer database into more specific subgroups, and each of these smaller groups shares unique characteristics based on information drawn from existing data.

By combining the two practices, businesses can deliver more personalised campaigns that appeal more to the needs of specific groups.

Attribution modelling

This provides a framework for analysing which channels or individual touchpoints lead to a sale or conversion.

In today’s omnichannel environment, it is rarely sufficient to credit the “last touch” a customer has had with a brand before purchase as it is likely the customer has had several prior interactions, all of which were exerted influence.

By employing attribution modelling, marketers can measure the impact of each interaction to assess their impact on the bottom line.

Predictive models

This is a technique which is employed to identify customers or prospects who are highly likely to purchase a given product given their demographic characteristics or past purchase behaviours.

Marketers can then determine where to focus ad spends and resources based on the value the customer engaging with them presents.

Market sizing and sensing

Finally, market sizing and sending is the process of estimating the capability of a market to buy a product or service in terms of the total revenue it could generate.

This helps marketers decide whether they should invest campaign resources to target it and, if so, at what level.

Using data science to overcome challenges

The above categories provide a summary of the capabilities of data science in solving marketing challenges, but often the problems we solve are much more specific.

In fact, it’s an incorrect assumption that data science can only help with the “bigger picture.” For almost every stage of the marketing planning, deployment and analysis process, there is a data science technique that can be employed to improved results.

As a handy tool, we’ve created the diagram below to specifically call out some of the specific campaign scenarios we encounter when engaging with clients at Edit, and how we use data-science techniques to help overcome them.

Download full-size version.

Data Science Marketing Challenges

Want to find out more about how data science can solve your marketing challenges?

If you want to utilise the power of your data to make informed decisions, create compelling campaigns, and ultimately boost ROI, our data science specialists can help.

As a specialist data science services company, at Edit, our intelligent data services are completely bespoke to your needs – so whatever it is you want to achieve with your data, we can help you to uncover actionable insights and make smarter decisions.

Plus, because we are experts in strategy as well as data, we can provide a truly end-to-end approach and help you to understand your customers, predict their behaviours, and create clever marketing strategies to delight them.

Basically, think of us as your one-stop-shop for all of your marketing needs! Sound good? Contact our team of Editors to discover how we can help you to utilise the power of your data.

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