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.
Edit’s Director of Data Science, Marc Dallimore explains how his team helps leverage this data into actionable insight that result in a greater return on investment for our client’s marketing campaigns.
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, 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?
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. 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 – This 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.
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. It is 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.