7 Quick Tips for Better Data-Driven Marketing

Using data to drive your digital marketing strategy is very important in today’s competitive online space. Your company’s datasets and analytics should stay at the heart of your decision-making processes when planning marketing campaigns or tweaking your digital communications. That’s because an evidence-based approach, based on the real actions and behaviours of your users, takes the guesswork out of your marketing strategy and is much more likely to provide real results. We’ll explain with seven quick tips below:

Always Analyse

Analysing your data isn’t a one-time thing. You’ll want to be proactive and regularly review the data you’ve collected so you’ll be aware of any emerging trends or shifting behaviour in your users.

Identify Your Goals

Setting SMART goals – specific, achievable, relevant and time-bound – across your digital marketing strategies will give you a yardstick with which you can later analyse and measure your performance. You’ll then be able to improve and build upon your success.

Set KPIs

Your KPIs, or key performance indicators, could include return on investment, conversion rates, platform traffic or engagement, for example.

Use Split Testing

A/B testing gives you an opportunity to compare the performance of two different versions of a campaign or landing page. This will help you to fine-tune your approach and figure out exactly what’s working for your brand.

Each Channel Is Different

It’s not a one-size-fits-all approach when it comes to digital marketing. You might need to measure the performance of a Facebook campaign in a very different way to an email one, for example, and your SEO and PPC strategies will need a very different analysis approach to your social media.

Invest in Support

As trends and tools quickly change throughout the digital landscape, an expert data analysis company, such as Shepper https://shepper.com/, could help to refine your strategy.

Use AI

Artificial Intelligence can be a super helpful tool when it comes to data analysis – picking up insights you might not spot and taking some of the labour out of your analysis processes.

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