Why you need marketing analytics
Switchboard Nov 18
Table of Contents
Did you know that 54% of companies who use marketing analytics generate higher-than-average profits? Or that highly-data-driven companies are three times more likely to see significant improvements in decision-making?
Clearly, approaching your marketing activities in a systematic way has its advantages. But what exactly is marketing analytics, and how do you use it?
What is marketing analytics?
The definition of marketing analytics is the collection and analysis of data from marketing activities to gain customer insights and improve decision-making. More specifically, this type of data analytics can be used to understand what factors affect conversions or brand awareness.
Insights are typically used to achieve greater marketing ROI, identify new or profitable customer segments, and create or modify planned marketing campaigns.
Here are the four steps to implementing marketing analytics:
- Define success – You need to establish a benchmark from which you can measure improvement. For example, if you want to increase your conversion rate, you need to determine what percentage of the audience made a purchase after viewing your ads. From here, you can measure the uplift due to your activities.
- Define your metrics – There are a multitude of different data points that can be measured, such as leads generated, conversion rate, and brand awareness. Start by defining what you are trying to achieve. This will enable you to better identify which metrics are relevant to this outcome.
- Assess your current marketing activities – To improve your marketing team’s output, you’ll need to evaluate current performance. Consider different aspects so as to identify where your company is performing well versus any potential weak spots.
- Implement marketing analytics software – Marketing analytics involves vast and ever-growing sums of data, which is impossible to manage manually, so you’ll need to think about the type of software that can help you harness it, and extract meaningful insights (we’ll talk more about this in our next post). Needless to say, it’s important to use a platform that can consolidate all marketing data into one place so you can gain a 360° view of your campaigns.
READ MORE: Delving into data analytics – the use cases
Examples of marketing analytics
There are many different applications for marketing analytics. Here are some of the ways your company can use it:
- Product development – A necessary part of designing a product is understanding what consumers think of it, and how it relates to existing products on the market. You can collect this type of data by running polls or surveys among your target audiences. Analyzing this data enables you to discover differentiating features, strengths and weaknesses. Once you understand these, your product team can modify aspects accordingly and drive higher sales.
- Customer trends – To measure attribution, it’s helpful to know as much as possible about consumers’ behavior. Collecting and analyzing this type of data can help you understand which creatives have the most impact on your audience, which products they are buying, and which touch points occurred leading up to conversion.
- Customer support – Marketing analytics can also be applied to the post-conversion customer experience. Once your customer has decided to make a purchase, are there any aspects of the checkout process which could be made easier? Is it easy to review a product?
- Future predictions – Once you’ve gained insights into existing marketing scenarios, you’ll be able to apply these lessons to future campaigns and optimize them further.
The growing size and complexity of data sets bring a number of challenges. Here are some of the main considerations when implementing marketing analytics.
- Ensure data quality – Even if you have a huge data set, it won’t be useful unless it’s high in quality and fresh. All data has a shelf life, with an estimated one fifth of media budgets being wasted due to stale information.
- Hire enough data engineers – A mere 1.9% of marketing leaders report they have the expertise they need to successfully carry out activities. You’ll need to ensure you hire both the quality and quantity of data engineers your organization requires, or outsource it to a trusted data automation partner.
- Select a suitable attribution model – When it’s time to analyze consumer behavior across multiple channels, it can be difficult to know which model to use. For example, media mix modeling produces results which are aggregated and campaign-focused, whereas multi-touch attribution produces results at the level of the individual consumer.
If you need help unifying your first or second-party data, we can help. Contact us to learn how.Schedule Demo
Catch up with the latest from Switchboard
Marketing and revenue teams can stand up analytics and AI projects 10x faster through automated data engineering platform Switchboard, the leading data engineering automation platform,…
Subscribe to our newsletter
Submit your email, and once a month we'll send you our best time-saving articles, videos and other resources