Why read this? : Organising your marketing data helps you make faster and better decisions. We share how to define your data needs, and identify your sources of data. Learn how to use different types of marketing data to improve decision-making. Read this to get better use out of your marketing data.
Marketing data fuels your marketing decision-making. Fill your business with enough good quality data, and your marketing “engine” will run more efficiently.
Data tells you what’s going on with customers, and how your business is going. You use it to identify opportunities and threats. It raises the questions you put into your market research process. And it helps you find the insights which drive your marketing plan and brand activation.
Marketing data - where to start?
Start with your brand goal and marketing objectives. You need to know where you’re headed to work out what marketing data you need. What is it you need to find out to hit those targets?
Then think about what marketing data sources you already have.
What reporting systems do you have to measure your performance, for example? What research have you done in the past? Are there any easy sources of data you can access?
For example, sales reports and the profit and loss gives you financial data. Brand health tracking (if you have it) gives you customer perception data. Data on specific activities like websites and CRM helps you work out how well you’re doing in those areas.
Work out if there are gaps in your marketing data. Things you need to know, but don’t.
Once you’ve done all this, you’ll have a list of :-
- marketing data you have, but don’t need. Put that to the side for now.
- marketing data you need and already have. Brilliant. Keep that.
- marketing data you need, but don’t have. You need a plan for that.
Marketing data you need, but don’t have
Not having this data is clearly a business problem. That’s the first step in the process. Market research helps you identify what data you’re missing, and what you’ll do with it when you find it.
If only we knew “data X”, then we could decide on “marketing activity Y”.
Different types of market research help you find different types of marketing data.
For example, let’s say you target people over 50. You can find data on the size and growth trends of this segment on sites like the Australian Bureau of Statistics.
Much of this data is available free if you know where to look. Occasionally, you might have to buy data in a report published by a market research company. But that’s the exception, rather than the rule.
Qualitative research is where you have a “quality” conversation with customers. You talk to them to find out why they do the things they do.
It’s helpful to identify what types of marketing data you need. But because it’s expensive to run, and based on small samples, you can’t really use it to track on-going performance.
Finally, quantitative research is where you gather data from a large group of customers. Each respondent answers a specific set of questions. The group’s big enough to statistically represent the segment you’re interested in.
The sample size gives you confidence their answers will tell you what the total segment thinks, feels and does. Quantitative research data is usually the most robust data.
Just enough data
Gathering marketing data is good. But sometimes you can’t always get what you want.
For example, you can’t get marketing data on competitor innovation until it’s in the market. You also have to factor in cost and timing. If it’s going to be too expensive or take too long, some marketing data isn’t worth paying or waiting for.
In an ideal world, you have all the marketing data you need to make decisions. But the reality is you often have to live with gaps in your marketing data. This isn’t a bad thing, as it helps you focus on what’s the most important to find out.
What you need is to have just enough marketing data to make a decision. Too much data slows you down and distracts you. Too little data won’t help you make a better marketing decision.
Decisions driven by marketing data
Marketing data drives 2 types of marketing decision.
First, there’s data to assess a new challenge or opportunity. This drives a go / no go decision on a future project. Then, there’s data to analyse an existing activity or situation. This drives a change / don’t change decision on a current project.
Marketing data for a future project
Data on future projects is hard to find.
You usually have to start with existing data. You analyse it to predict what’ll happen in the future based on what’s happened in the past.
This prediction could be informal and involve a lot of guesswork. Or it could be a full-on econometric model (see our e-Commerce forecasting article for more on this).
For example, say you’re launching an innovation. If it’s completely new to market, how do you reliably work out if customers will buy it?
You can ask customers via market research. But future purchase intent is hard for customers to answer.
It’s often better to look at data on how other similar innovations performed. Use that data to predict how well your new product will do.
It can help your business case approval if you can show examples of something that’s already worked. Approvers feel more confident in your predictions if past data “proves” there’s a future opportunity.
Marketing data for an existing project
Live projects however start to generate marketing data right away.
You use this to evaluate their performance against the project objectives.
The project brief sets out these objectives. Done properly, these will be SMART – specific, measurable, achievable, relevant and timely.
Marketing data helps you measure performance. It’s also time specific, so you can look at trends.
Of course, someone has to be responsible for gathering, analysing and reporting all this marketing data.
That might be a data manager or analyst, either your own or one at the agency. Or, if you’re smaller, you do these jobs yourself.
Gathering data means first you identify relevant data sources. Then you extract and collate the relevant marketing data when it’s needed.
Analysing data means you look at trends. You compare different data sets to look for relationships and explain performance.
Did sales go up when you spent more on advertising for example? Did you pull in more customers with that price discount? What was the impact on your loyalty rate with that special offer to your CRM list?
Finally, marketing data reporting means presenting it in a way which answers questions. It needs to highlight opportunities and challenges.
Data can be complex. Data visualisation is a real skill. It needs to be clear enough to be easy to understand and analyse.
Reporting is often done with a dashboard.
This is a short, usually one page summary. It reports performance against the project objectives, so you can track how you’re doing.
Most common types of marketing data in a dashboard
Most dashboards combine different data sources. This gives you different perspectives on the performance. What you include depends on the context and the plan. But they’d normally include sales, financial, brand and customer results.
Sales and financial summary
Most businesses already have some sort of sales and/or financial reporting.
The marketing dashboard pulls the most relevant data from those reports.
You get a marketing perspective on the numbers.
It should focus on the sales and/or financial project objectives from the brief.
This would normally include a total sales line. This shows how actual sales varies against target / forecast sales. Often, there’s a “traffic light” system used. Green is good, red is bad, and amber is watch out.
You can also include costs (especially marketing spends) and profits. Although be wary of including too much. You don’t want to duplicate the profit and loss report. For a one page marketing dashboard, a good rule of thumb no more than 3 types of sales / financial data.
Brand health summary
Most businesses also include brand health data in their marketing dashboard. This comes from continuous quantitative research with questions related to the :-
- brand choice funnel e.g. which of these products are you aware of? which products would you consider?
- brand imagery statements e.g. on a scale of one to five, how do you rate these brands for quality, value, convenience etc?
Like the sales data, you only track measures which are objectives in the brief. You use a similar traffic light system to show if you’re on track or not.
Again, keep it simple. No more than 3 numbers from the funnel and 3 brand imagery statements. Any more is confusing. Plus, there’s usually a separate brand health report if you need a deeper analysis.
How you define “customer” depends on your business context.
For retailer-led businesses, customer data relates to retailer specific measures like distribution levels and share of shelf.
For B2B businesses, customer data usually relates to service usage rates and customer satisfaction levels.
Your dashboard shouldn’t be all numbers. Leave space to add commentary to explain the numbers. Not everyone will know the context. Commentary helps you get them quickly up to speed.
Use the commentary to explain why you think changes or performance gaps happened. If you’ve already started to address those, use the commentary to explain the actions already underway.
The dashboard is usually a single page so it’s easy to understand and discuss. But often, it provokes more questions and requests for more detail.
You should therefore attach an appendix with links to the original data and data sources. This helps anyone reading the report investigate if they have questions. (it also reduces questions for the writer of the report).
Example online store dashboard
So take this e-Commerce dashboard we used for an online store, as an example.
It shows 4 different types of marketing data – campaigns, platforms, operations and sales – that’d be most relevant to track online store performance.
In platforms, the data comes from internal data sources, mainly to do with the website.
So for example, visits and conversions (the percentage of visitors who buy). In this example, we also included Net Promoter Score (NPS), a measure of customer satisfaction. It’s based on asking customers directly how likely they’d be to recommend the brand.
In this example operations applies specifically to the order to delivery system. DIFOT stands for Delivered In Full On Time. Supply chain teams use this measure to show what they class as an acceptable delivery. Anything less than 100%, and it means some deliveries were missing items or were delivered late.
Other common operations measures include the returns, complaints received by customer service and the level of fraudulent payments.
In this dashboard example, we chose to focus on sales, not costs and profits. (these were already in the profit and loss). We picked the biggest 3 products in the range for each website. The dashboard reports on the target (the KPI), the actual year to data (YTD) and the percentage variance.
Organising your marketing data plan
It’s easy to feel overwhelmed by marketing data.
So many different sources. So many updates to each of those sources. You’ve no sooner finished a report, and it feels like the next one’s due.
When it all starts too feel too much, a quick 5W session is a good way to refocus on the priorities.
The 5Ws are 5 basic questions – why, who, when, where and what – that help organise your thoughts on any complex project.
Why do you need marketing data?
Why helps you refocus on the purpose. If you don’t know why you need it, then it’s hard to stay motivated. Remember, your marketing data helps you understand customers and what’s going on in the market. You need it to make marketing decisions that drive actions.
Who is responsible for marketing data management?
Gathering, analysing and reporting data all takes time. Someone has to do those tasks. Each task requires different skills. Bigger businesses appoint an analyst with skills across all these areas to lead the process. In smaller businesses, you spread the tasks across different people, or ask your agency for help.
Most agencies will have some sort of in-house data and analysis support. For expert data and analysis jobs (like the econometric modelling we cover in our e-Commerce forecasting article), you may need to bring in a specialist agency.
When do you need your marketing data?
Another challenge with marketing data is how often you need to refresh it. Today’s data is already out of date by tomorrow.
Smaller businesses however, usually work with monthly reporting. For example, they produce a monthly marketing performance report with key marketing data.
The leadership team reviews and discusses this and comes up with actions and decisions.
The exception might be where something out of the ordinary happens. A stock recall or an availability issue, for example. When this happens, you need more recent marketing data. You set up a more regular (say weekly) reporting cycle until the “event” stops being so extraordinary.
Where do you need it?
The marketing data needs to “live” somewhere so people can access and use it. You and your team need to know where to find it when you need it.
So, think about how you store and share your data, and your dashboards. Do you use Powerpoint and Excel, for example? Do you print out copies for meetings, or are they all on email? Are they stored in secure online folders and does everyone have the same level of access?
If you can’t find it, you can’t use it.
What will you do with it?
Finally, and most importantly you need to be clear what you’ll do with your marketing data. As per our market research in the marketing plan guide, you only see the value of data when you do something with it.
Be clear what decisions you’ll make with your marketing data. What actions you’ll take to improve, change or stop doing things in your marketing plan. Be clear on the decision the marketing data will drive.
Conclusion - marketing data
Many marketers like to talk about the importance of data in marketing. But far fewer know how to gather it, analyse it and report it.
But, you need to do those marketing data skills to fuel your decision-making and actions.
That goes for both future and existing projects.
Future projects usually drive a go / no go decision. Existing projects are about tracking performance and looking for improvements.
Create a marketing dashboard for each project with the objectives from the brief. This usually includes commercial, brand health and customer data, as well as commentary to explain the context.
You can use the 5Ws model – why, who, when, where and what – to organise your thinking on marketing data. It helps you remember why you’re doing it, who’ll be responsible, when and where you need it, and most importantly what you’ll do with it.