Maximising your Data for Micro Marketing

Data driven marketing is a way of using your current marketing insights to help you make better decisions when looking to acquire new customers and keep your current customers. Micro businesses can gather marketing insights through keeping a customer database on Excel, downloading data from social media accounts and analysing website and email behaviours.

where to start with data driven marketing?

Planning is the first part in undertaking a data driven marketing approach. To identify new opportunities in your data you will have to start by understanding the different types of data you will be measuring.

• Surveys (SurveyMonkey)
• Quizzes (Qzzr)
• Focus Groups (Webinars/Google Hangouts)
• Interviews (Skype)
• Social Media Analytics
• Behavioural Metrics
• Government Reports
• Corporate Publications & Insights
• University Research
• Search Volumes (Keyword Research)

After understanding where you can start to collect data from, it’s time to start planning how you’re going to go about gathering all this data and what the objective of it will be. The idea at this stage is to turn all this information into actionable steps that improve the way you market and help direct your creative content.

what makes data driven marketing so important?

Being able to understand your data is important on two fronts; better insights of customers and higher value offered to customers. If you understand your customers better than the competition, then you’ll be best placed to add the most value. If you offer the most value then you’ve improved your marketing efficiency, lowering your customer acquisition costs and improving their lifetime value, win-win.

how can I apply data driven marketing to my business?

Applying this way of thinking to your business requires the data infrastructure to be set up (a computer with Excel is all you need, a CRM system is a plus) but most importantly it’s a mindset you’ll need to make it work. Data driven marketing is an approach that requires ongoing planning, implementation, analysis and adjustments to be successful.

Success can vary on many levels too, if you learn something new about your customers then that’s success as you’re getting closer to them, meaning an improved chance to create a deeper relationship.

To help with applying actionable steps look at the following data driven marketing roadmap:

1. design

• What are the objectives you are looking to achieve from analysing your data?
• What metrics are you currently using to gather insights and help with decision making?
• What do you want to gain from understanding your data and what will be the customer benefits?

2. diagnosis

• What are the risks of implementing a data driven marketing approach?
• Are resources available for it to be a success and is there a culture in place that isn’t afraid of change?

3. opportunities

• Are there any quick wins?
• What data can use straight away, are there any datasets you can join together?
• What metrics will you use to measure success?
• What support will be in place to help with analysis?
• How will you report on the newly analysed data?

4. tools

• What metrics will you use to measure success?
• What support will be in place to help with analysis?
• How will you report on the newly analysed data?

5. process

• When will you evaluate performance and how often?

where can I find out more about data driven marketing?

This article has only scratched the surface of data driven marketing. Micro businesses can and should be using data driven marketing to grow their business. Successful and popular examples of data driven marketing tend to be from larger companies that have marketing departments equipped with advanced CRM (customer relationship management) software and access to data scientists who can help visualise and interpret datasets.

However, for micro businesses there is way round by being creative with Microsoft Excel and the VLOOKUP function you can glean insights with ease. For those who are slightly more advanced and skills with programming, languages such as Python and R can help automate and analyse your data without the need for paying excess costs on underused software.

Originally published at bmicro.co on April 6, 2016.