A Data-Last Approach

Data has been the flavor of the month in the marketing world for a few years now, and strategists are increasingly expected to create campaigns with data at the core.

The increased availability of data and the expectation to create data fueled campaigns can lead to a few data-led mistakes. Here are three common ones.

Data over insight: Simply representing lots of data rather than interpreting it only demonstrates what has happened in a category. It is a recap of the past, not an insight or a strategy for the future.

Trying to represent all data available: Representing as much data as possible can be overwhelming and data points can contradict themselves, making it hard to paint a logical story. Some of the most effective campaigns revolve around one key piece of data. For Sainsbury’s to achieve their growth goal of £2.5B, they calculated that customers needed to spend an additional £1.14 per visit. This simple data point led to the award winning ‘Try Something New Today’ campaign.

Approaching data blind: Blindly searching through tonnes of data hoping to find a magic insight is almost impossible, using a filter to hone in on the data that will be valuable is far more effective.

Filter first. Data second.

One simple approach to filtering data is starting with a question. Just as we write briefs centred around a challenge for creatives to grow ideas from, you could write yourself a data-research brief with the question you are looking to solve with data. This helps focus your search on relevant data and links raw data with strategic challenges.

Another filter is the consumer journey. I used to waste time combing through piles of data to try weave together a path to purchase. With a lot of the categories we deal with you are able to personally experience the path to purchase, or know someone who has. Outline a journey based on experience and questions with the client, then comb through data to support the journey stages, or find where it is wrong and correct it. This is a much faster and more effective approach.

There are certainly other filters out there to help you work better with data, comment below to let me know what has worked for you.