Improved data analysis is a question of re-tooling, not re-skilling

For much of the marketing community, it seems fair to say that 2016 was the year that data-led marketing truly came into its own. Rather than focusing on the creative aspects of the marketing role, the last 12 months have seen marketers transformed into data scientists; analysing reports, cleansing data sets and building in-depth profiles for each of their customers. Even so, is this new data analysis role really what those of us in marketing signed up for?

Most marketers I know entered the industry with the view to spending a career planning and executing interesting and creative campaigns, not to analyse endless reams of data. Yet, BlueVenn’s research suggests that many marketers now spend as much as 80% of their time purely working with data. This doesn’t leave long for more artistic than scientific marketing pursuits.

Given this rapid change in roles, it’s important that marketers adapt to the new data-led approach without losing the creative skills that have come to define their industry. Faced with this challenge, it’s important to understand how marketers are prepared for their new data-driven roles. To achieve this, BlueVenn surveyed more than 200 B2C marketers, helping to determine how they are coping with the onslaught of customer data, and to identify which aspects of their roles they’re struggling with the most.

Hurdles to data analysis success

72% of marketers consider data analysis more important than social media skills, BlueVenn’s research shows.

Bringing multiple data sources together to form one coherent Single Customer View (SCV) has long been regarded as one of the ‘holy grails’ of marketing, and not just for its ability to deliver an impressive ROI. Yet despite this, achieving an SCV was listed as the number one thing that kept marketers awake at night: 82% of marketers claim that they still cannot achieve a Single Customer View. Instead, they are forced to deal with customer data within individual organisational ‘silos’, analysing their various data sources (email, social, POS) in isolation. This is something that 62% of marketers claim they are worried about, making it a significant barrier to successful data-driven marketing.

It should also come as no surprise that, given the vast amounts of data at marketers’ disposal, most are still having trouble identifying what is and isn’t valuable to their organisation. Over half (54%) of marketers claim that poor quality data has damaged their ability to provide more targeted campaigns. Marketers’ principle issue is not obtaining the data, it’s knowing how to sort through it, clean it, de-duplicate and prioritise. Upcoming legislation such as the EU’s General Data Protection Regulation is also making the issue of data quality assurance more pressing.

Customer journeys can be executed seemlessly across all channels, says BlueVenn.

In addition to this, ‘Cross Journey Optimisation’ was also identified as a clear pressure point for many marketers. For the most part, this is due to the complexity of the modern sales cycle. As an example, a typical customer might browse for a product online before moving in-store to make the actual purchase. They might then compare in-store prices with those across multiple sites online, possibly even doing so using a range of different devices. Connecting these strands of activity, and allowing them to inform each other, is critical to provide insight in today’s multi-channel world. Despite this, 66% of marketers claim that they do not yet have the knowledge needed to implement such an approach.

Re-tool, don’t re-skill

So how can marketers improve their data skills for 2017? Given that 72% of marketers surveyed think that data analysis will be the most important skillset over the next two years, (more than social media, web development, graphic design or SEO), it would stand to reason that, in order to remain competitive, marketers need to re-skill into data science. This is a point that has been made quite often by marketers and by the marketing press. But is it really the right direction for marketing to take, or is the expectation placed on marketers to become data scientists misguided?

Most marketers are creatives, and the majority aren’t keen on re-training in data science in order to continue doing their day-to-day jobs. Yet despite this, marketers are frequently told to either skill up, or pass data analysis tasks on to real ‘data people’. More often than not, this simply involves either passing data analysis tasks on to the IT department (which generally lacks marketing savvy), or to expensive data agencies (who may lack context). Either way, moving data analysis outside of the marketing department is rarely an optimal solution.

Ideally, customer data analysis should remain as close to the marketing department as possible; staying in the hands of those who are closest to the customers, and understand them the best. Rather than encouraging marketers to become data scientists, the Martech community needs to step in to give them better tools. As one example, by using a Customer Data Platform (CDP), marketers can remove the need to analyse data by hand, combining and managing multiple data sources within a single interface. By implementing such tools and automating the process of data management, marketers can do the work of a data scientist without the need to drastically update their skillset. This, in turn, can free up marketers to focus their real efforts on functionality, not just data.

Read also:

The power of data – data may be the new oil, but it’s only valuable if you have a reliable engine

Intelligent ABM: Why data is your passport to success