How to overcome marketers challenge with data

Founder & Strategy Director
Let'sTalk Strategy
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Do you often feel you’re drowning in data? You’re not alone. The DMA’s 2017 Marketer Email Tracker report recently uncovered yet again that a ‘lack of data’, ‘data silos’ and ‘data degradation’ continues to be one of the biggest challenges for email marketers.

The lack of data is a particularly interesting challenge because every day, we create 2.5 quintillion bytes of data and 90% of the data in the world today has been created in the last two years!

Every time you have a visitor to your website, data is generated, tracking their every move. This is the same for every marketing channel, if a recipient opens and clicks your email marketing, that will also be tracked. This isn’t just limited to online marketing your customers offline journey too will be generating data. Whether a customer calls your contact centre, or visits your store, that activity will be tracked in a system. Here’s my tips on how to start providing focus to your data analysis.


Turning data into insights

As a consultant, I’ve worked with companies of all shapes and recently did so for a B2B financial organisation. As they acknowledged, they were drowning in data. They tracked every part of the conversion journey, which is gold dust to any marketer, but the analysis that followed was overwhelming and didn't provide any clear insights that inform their marketing strategy. 

To provide context the sales process was typical of many B2B brands where a prospect expressed an interest online via their website, their Sales consultant will then have a 1:1 conversation. The prospect then revisits the website to research products and gather more information. As the conversion journey was online, the new customer starts their application process. The customer is then provided with a dedicated Account Manager.

In this example, there is a mix of online and offline constantly throughout this process and the human interaction is key to keep the lead warm. As a result, some of the data gathered was manually entered as that journey progresses offline.


The challenge for the marketing team

The marketing team’s objective for their activity was to generate leads. To do this, the marketeers needed to understand the channels that were driving the most leads for both brand awareness and conversions. This may seem like an easy thing to find out, but when you have over 20 data dashboards, reports and various analytical tracking tools, it’s a challenge to glean the insights. This is where I came in, as a consultant you can take a step back, review and assess the current state of play through a fresh pair of eyes.


Where to start?

The first starting for any consultant is to understand more about the business, the sales process, average lead time to conversion, channels already being used, marketing campaigns, target market, competitors etc. This is important to be able to picture the customer and the direction of the business. 

The second focus is to determine the objectives for the insights the team need to answer the question they have. These two questions provide focus to start any data insight project:

  1. What are we trying to find out?
  2. What are the questions do we have, that currently we're unable to answer?

This may sound incredibly simple but typically it’s generally the first stage that is missed when as marketers, you’re busy dealing with the day to day.


Analysing the data

As I mentioned in this particular example, the client was drowning in data, unable to make any clear decisions from the data analysis that was already being created.

With only a few days before the marketing team had to decide where to spend their marketing budget, I started with a blank piece of paper and did the following:

  • What key analytics are needed to answer the questions?
    • This is important because it’s all too easy to measure everything. To gather real insights, you need to focus and deep dive in particular areas of activity to truly understand your marketing performance.
  • Break this analysis down into brand awareness/interest (channels driving traffic) and conversion.
    • These are two distinctive different stages of a customers’ journey. Both the mindset and information the new customer will require, will be different for these two stages, the marketing approach should therefore be different.


Statistically significant

Before you draw any conclusions from your data analysis, it’s important to ensure the data you’re analysing is statistically significant. Depending on the volume of data I’d always recommend comparing performance across 12 months as a minimum. The ideal scenario would be to compare two years' worth of data. This provides not only a year on year comparison, but also trends will become more apparent over time for example, any peak months for your business and any months were performance naturally drops will assist you when forecasting your marketing spend. In this example, a review of the months that drove the highest amount of leads against a comparison of the months that didn’t drive any leads was also created to identify any potential marketing activity that drove a higher lead volume.


Strategic insights – the untold story

In less than a day using the methods above, we could determine the channels that drove the highest volume of traffic and those that didn’t. This was also in comparison to the channels that drove the most amount of conversions. In this example, we discovered that the channels which drove high volumes of traffic didn’t provide quality traffic. The visitors from these channels stayed on the website for less 1 minute (average time spent was much higher) with a very high bounce rate. Channels that drove less traffic had been historically viewed as unnecessary to allocate marketing budget, yet the strategic insights demonstrated that those channels consistently drove the most amount of conversions.


If you’re drowning in data don’t feel alone. Take a step back, note your objectives and the questions that need an answer. This will provide focus to ensure you only focus on the data analysis you actually need, rather than trying to analyse everything.


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